diff --git a/.gitignore b/.gitignore index 04c7330..3c2f021 100644 --- a/.gitignore +++ b/.gitignore @@ -1,5 +1,9 @@ .env -claude.md +.claude/* +CLAUDE.md +config.json +*.safetensors +*.safetensors.index.json .DS_Store **.pyc __pycache__/* diff --git a/README.md b/README.md index 6d4fe11..5e8b1dc 100644 --- a/README.md +++ b/README.md @@ -4,8 +4,6 @@ MLX-Video is the best package for inference and finetuning of Image-Video-Audio ## Installation -Install from source: - ### Option 1: Install with pip (requires git): ```bash pip install git+https://github.com/Blaizzy/mlx-video.git @@ -16,7 +14,7 @@ pip install git+https://github.com/Blaizzy/mlx-video.git uv pip install git+https://github.com/Blaizzy/mlx-video.git ``` -Supported models: +## Supported Models - [**LTX-2**](https://huggingface.co/Lightricks/LTX-Video) — 19B parameter video generation model from Lightricks - [**Wan2.1**](https://github.com/Wan-Video/Wan2.1) — 1.3B / 14B parameter T2V models (single-model pipeline) @@ -24,36 +22,53 @@ Supported models: ## Features -- Text-to-video generation with multiple model families -- LTX-2: Two-stage pipeline with 2x spatial upscaling -- Wan2.1/2.2: Flow-matching diffusion with classifier-free guidance +**LTX-2 / LTX-2.3** +- Text-to-Video (T2V), Image-to-Video (I2V), Audio-to-Video (A2V) +- Audio-Video joint generation +- Multi-pipeline: distilled, dev, dev-two-stage, dev-two-stage-hq +- 2x spatial upscaling for images and videos +- Prompt enhancement via Gemma + +**Wan2.1 / Wan2.2** +- Text-to-Video (T2V) — 1.3B and 14B models +- Image-to-Video (I2V) — 14B model +- Flow-matching diffusion with classifier-free guidance +- LoRA support (e.g. Wan2.2-Lightning for 4-step generation) + +**General** - Optimized for Apple Silicon using MLX --- ## LTX-2 -> **ℹ️ Info:** Currently, only the distilled variant is supported. Full LTX-2 feature support is coming soon. - ### Text-to-Video Generation ```bash -uv run mlx_video.generate --prompt "Two dogs of the poodle breed wearing sunglasses, close up, cinematic, sunset" -n 100 --width 768 +# Text-to-Video (distilled, fastest) +uv run mlx_video.ltx_2.generate --prompt "Two dogs wearing sunglasses, cinematic, sunset" -n 97 --width 768 + +# Image-to-Video +uv run mlx_video.ltx_2.generate --prompt "A person dancing" --image photo.jpg + +# Audio-to-Video +uv run mlx_video.ltx_2.generate --audio-file music.wav --prompt "A band playing music" + +# Dev pipeline with CFG (higher quality) +uv run mlx_video.ltx_2.generate --pipeline dev --prompt "A cinematic scene" --cfg-scale 3.0 + +# Dev two-stage HQ (highest quality) +uv run mlx_video.ltx_2.generate --pipeline dev-two-stage-hq \ + --prompt "A cinematic scene of ocean waves at golden hour" \ + --model-repo prince-canuma/LTX-2-dev ``` Poodles demo -With custom settings: +**Converting weights:** + +Pre-converted weights are available on HuggingFace ([LTX-2-distilled](https://huggingface.co/prince-canuma/LTX-2-distilled), [LTX-2-dev](https://huggingface.co/prince-canuma/LTX-2-dev), [LTX-2.3-distilled](https://huggingface.co/prince-canuma/LTX-2.3-distilled), [LTX-2.3-dev](https://huggingface.co/prince-canuma/LTX-2.3-dev)), or convert from the original Lightricks checkpoint: -```bash -python -m mlx_video.generate \ - --prompt "Ocean waves crashing on a beach at sunset" \ - --height 768 \ - --width 768 \ - --num-frames 65 \ - --seed 123 \ - --output my_video.mp4 -``` ### LTX-2 CLI Options @@ -69,33 +84,27 @@ python -m mlx_video.generate \ | `--save-frames` | false | Save individual frames as images | | `--model-repo` | Lightricks/LTX-2 | HuggingFace model repository | -### How It Works (LTX-2) - -1. **Stage 1**: Generate at half resolution (e.g., 384×384) with 8 denoising steps -2. **Upsample**: 2× spatial upsampling via LatentUpsampler -3. **Stage 2**: Refine at full resolution (e.g., 768×768) with 3 denoising steps -4. **Decode**: VAE decoder converts latents to RGB video --- ## Wan2.1 / Wan2.2 -Both [Wan2.1](https://github.com/Wan-Video/Wan2.1) and [Wan2.2](https://github.com/Wan-Video/Wan2.2) are text-to-video diffusion models built on a DiT (Diffusion Transformer) backbone with a T5 text encoder and 3D VAE. +Both [Wan2.1](https://github.com/Wan-Video/Wan2.1) and [Wan2.2](https://github.com/Wan-Video/Wan2.2) are text-to-video diffusion models built on a DiT (Diffusion Transformer) backbone with a T5 text encoder and 3D VAE. ### Step 0: Download and Convert Weights -See the dedicated Wan2.1/Wan2.2 [README.md](mlx_video/models/wan/README.md) for details. +See the dedicated Wan2.1/Wan2.2 [README.md](mlx_video/models/wan_2/README.md) for details. ### Step 1: Generate Video ```bash # Wan2.1 — uses defaults from config (50 steps, shift=5.0, guide=5.0) -python -m mlx_video.generate_wan \ +python -m mlx_video.wan_2.generate \ --model-dir wan21_mlx \ --prompt "A cat playing piano in a cozy room" # Wan2.2 — uses defaults from config (40 steps, shift=12.0, guide=3.0,4.0) -python -m mlx_video.generate_wan \ +python -m mlx_video.wan_2.generate \ --model-dir wan22_mlx \ --prompt "A cat playing piano in a cozy room" ``` @@ -103,7 +112,7 @@ python -m mlx_video.generate_wan \ With custom settings: ```bash -python -m mlx_video.generate_wan \ +python -m mlx_video.wan_2.generate \ --model-dir wan21_mlx \ --prompt "Ocean waves at sunset, cinematic, 4K" \ --negative-prompt "blurry, low quality" \ @@ -117,13 +126,12 @@ python -m mlx_video.generate_wan \ --output-path my_video.mp4 ``` -The pipeline auto-detects the model version from `config.json` and selects the right pipeline mode (single or dual model). You can also override any parameter via CLI flags. +The pipeline auto-detects the model version from `config.json` and selects the right pipeline mode (single or dual model). -#### Image-to-Video (I2V-14B) +### Image-to-Video (I2V-14B) ```bash -# Generate video from an input image -python -m mlx_video.generate_wan \ +python -m mlx_video.wan_2.generate \ --model-dir wan22_i2v_mlx \ --prompt "The camera slowly zooms in as the subject begins to move" \ --image start.png \ @@ -131,9 +139,30 @@ python -m mlx_video.generate_wan \ --output-path my_video.mp4 ``` -The I2V-14B model encodes the input image through the Wan2.1 VAE encoder and uses channel concatenation (`y` tensor with 4 mask + 16 image latent channels) to condition generation on the first frame. +### LoRA Support -#### Generation CLI Options +LoRAs can be used with the `--lora-high` and `--lora-low` command line switches. + +For example, using the distilled [Wan2.2-Lightning](https://huggingface.co/lightx2v/Wan2.2-Lightning) LoRA for 4-step generation: + +```bash +python -m mlx_video.wan_2.generate \ + --model-dir /Volumes/SSD/Wan-AI/Wan2.2-T2V-A14B-MLX \ + --width 480 \ + --height 704 \ + --num-frames 41 \ + --prompt "Two dogs of the poodle breed sitting on a beach wearing sunglasses, nodding with their heads, close up, cinematic, sunset" \ + --steps 4 \ + --guide-scale 1 \ + --trim-first-frames 1 \ + --seed 2391784614 \ + --lora-high /Volumes/SSD/Wan-AI/lightx2v/Wan2.2-Lightning/Wan2.2-T2V-A14B-4steps-lora-rank64-Seko-V2.0/high_noise_model.safetensors 1 \ + --lora-low /Volumes/SSD/Wan-AI/lightx2v/Wan2.2-Lightning/Wan2.2-T2V-A14B-4steps-lora-rank64-Seko-V2.0/low_noise_model.safetensors 1 +``` + +![Poodles](examples/poodles-wan.gif) + +### Wan CLI Options | Option | Default | Description | |--------|---------|-------------| @@ -150,29 +179,7 @@ The I2V-14B model encodes the input image through the Wan2.1 VAE encoder and use | `--seed` | -1 (random) | Random seed for reproducibility | | `--output-path` | `output.mp4` | Output video path | -## LoRA Support - -LoRA's can be used with the `--lora-high` and `--lora-low` command line switches. - -For example, for using the the distilled [Wan2.2-Lightning](https://huggingface.co/lightx2v/Wan2.2-Lightning) LoRA, use the following command. Lightning speeds up generation by using only 4 steps and a CFG scale of 1. - -```bash -python -m mlx_video.generate_wan \ - --model-dir /Volumes/SSD/Wan-AI/Wan2.2-T2V-A14B-MLX \ - --width 480 \ - --height 704 \ - --num-frames 41 \ - --prompt "Two dogs of the poodle breed sitting on a beach wearing sunglasses, nodding with their heads, close up, cinematic, sunset" \ - --steps 4 \ - --guide-scale 1 \ - --trim-first-frames 1 \ - --seed 2391784614 \ - --lora-high /Volumes/SSD/Wan-AI/lightx2v/Wan2.2-Lightning/Wan2.2-T2V-A14B-4steps-lora-rank64-Seko-V2.0/high_noise_model.safetensors 1 \ - --lora-low /Volumes/SSD/Wan-AI/lightx2v/Wan2.2-Lightning/Wan2.2-T2V-A14B-4steps-lora-rank64-Seko-V2.0/low_noise_model.safetensors 1 - ``` - -Which results in -![Poodles](examples/poodles-wan.gif) +--- ## Requirements @@ -181,36 +188,6 @@ Which results in - MLX >= 0.22.0 - For weight conversion: PyTorch (`pip install torch`) -## Project Structure - -``` -mlx_video/ -├── generate.py # LTX-2 generation pipeline -├── generate_wan.py # Wan2.1/2.2 generation pipeline -├── convert.py # LTX-2 weight conversion -├── convert_wan.py # Wan weight conversion (PyTorch → MLX) -├── postprocess.py # Video post-processing utilities -├── utils.py # Helper functions -└── models/ - ├── ltx/ # LTX-2 model - │ ├── ltx.py # DiT transformer - │ ├── config.py # Configuration - │ ├── transformer.py # Transformer blocks - │ ├── attention.py # Multi-head attention with RoPE - │ ├── text_encoder.py # Gemma 3 text encoder - │ ├── upsampler.py # 2x spatial upsampler - │ └── video_vae/ # VAE encoder/decoder - └── wan/ # Wan2.1/2.2 model - ├── config.py # Configuration (2.1 & 2.2 presets) - ├── model.py # WanModel (DiT transformer) - ├── transformer.py # Attention blocks with 6-element modulation - ├── attention.py # Self/cross attention with QK-norm - ├── rope.py # 3-way factorized RoPE - ├── text_encoder.py # T5 UMT5-XXL encoder - ├── vae.py # 3D causal VAE decoder - └── scheduler.py # Flow-matching Euler scheduler -``` - ## License MIT diff --git a/mlx_video/__init__.py b/mlx_video/__init__.py index 9dfbdca..a04ec64 100644 --- a/mlx_video/__init__.py +++ b/mlx_video/__init__.py @@ -1,14 +1,50 @@ -from mlx_video.models.ltx import LTXModel, LTXModelConfig -from mlx_video.models.wan import WanModel, WanModelConfig -from mlx_video.convert import load_transformer_weights, load_vae_weights -import os +from mlx_video.models.ltx_2 import LTXModel, LTXModelConfig + +# Audio VAE components +from mlx_video.models.ltx_2.audio_vae import ( + AudioDecoder, + AudioEncoder, + AudioLatentShape, + AudioPatchifier, + PerChannelStatistics, + Vocoder, + decode_audio, +) + +# Conditioning +from mlx_video.models.ltx_2.conditioning import VideoConditionByLatentIndex + +# Utilities +from mlx_video.models.ltx_2.utils import ( + convert_audio_encoder, + get_model_path, + load_config, + load_safetensors, + save_weights, +) +from mlx_video.models.wan_2 import WanModel, WanModelConfig + __all__ = [ + # Models "LTXModel", "LTXModelConfig", + # Audio VAE + "AudioDecoder", + "AudioEncoder", + "Vocoder", + "decode_audio", + "AudioPatchifier", + "AudioLatentShape", + "PerChannelStatistics", + # Conditioning + "VideoConditionByLatentIndex", + # Utilities + "convert_audio_encoder", + "get_model_path", + "load_safetensors", + "load_config", + "save_weights", + # Wan Models "WanModel", "WanModelConfig", - "load_transformer_weights", - "load_vae_weights", ] - -os.environ["TRANSFORMERS_NO_ADVISORY_WARNINGS"] = "1" diff --git a/mlx_video/components/__init__.py b/mlx_video/components/__init__.py new file mode 100644 index 0000000..f70fdce --- /dev/null +++ b/mlx_video/components/__init__.py @@ -0,0 +1,3 @@ +from .smart_turn import Model, ModelConfig + +__all__ = ["Model", "ModelConfig"] diff --git a/mlx_video/conditioning/__init__.py b/mlx_video/conditioning/__init__.py deleted file mode 100644 index f976035..0000000 --- a/mlx_video/conditioning/__init__.py +++ /dev/null @@ -1,3 +0,0 @@ -"""Conditioning modules for LTX-2 video generation.""" - -from mlx_video.conditioning.latent import VideoConditionByLatentIndex, apply_conditioning diff --git a/mlx_video/convert.py b/mlx_video/convert.py deleted file mode 100644 index 11491e0..0000000 --- a/mlx_video/convert.py +++ /dev/null @@ -1,688 +0,0 @@ -import json -import shutil -from pathlib import Path -from typing import Any, Dict, Optional, Union - -import mlx.core as mx -import mlx.nn as nn -from huggingface_hub import snapshot_download - -from mlx_video.models.ltx.config import LTXModelConfig, LTXModelType -from mlx_video.models.ltx.ltx import LTXModel - - -def get_model_path( - path_or_hf_repo: str, - revision: Optional[str] = None, -) -> Path: - """Get local path to model, downloading if necessary. - - Args: - path_or_hf_repo: Local path or HuggingFace repo ID - revision: Git revision for HF repo - - Returns: - Path to model directory - """ - model_path = Path(path_or_hf_repo) - - if model_path.exists(): - return model_path - - # Download from HuggingFace - model_path = Path( - snapshot_download( - repo_id=path_or_hf_repo, - revision=revision, - allow_patterns=[ - "*.safetensors", - "*.json", - "config.json", - ], - ) - ) - - return model_path - - -def load_safetensors(path: Path) -> Dict[str, mx.array]: - """Load weights from safetensors file(s) using MLX. - - Args: - path: Path to model directory or single safetensors file - - Returns: - Dictionary of weights - """ - weights = {} - - if path.is_file(): - # Single file - use mx.load directly (handles bfloat16) - return mx.load(str(path)) - else: - # Directory - load all safetensors files - safetensor_files = list(path.glob("*.safetensors")) - for sf_path in safetensor_files: - file_weights = mx.load(str(sf_path)) - weights.update(file_weights) - - return weights - - -def load_transformer_weights(model_path: Path) -> Dict[str, mx.array]: - """Load transformer weights from LTX-2 model. - - Args: - model_path: Path to LTX-2 model directory - - Returns: - Dictionary of transformer weights - """ - # Try distilled model first, then dev - weight_files = [ - model_path / "ltx-2-19b-distilled.safetensors", - model_path / "ltx-2-19b-dev.safetensors", - ] - - for weight_file in weight_files: - if weight_file.exists(): - print(f"Loading transformer weights from {weight_file.name}...") - return mx.load(str(weight_file)) - - raise FileNotFoundError(f"No transformer weights found in {model_path}") - - -def load_vae_weights(model_path: Path) -> Dict[str, mx.array]: - """Load VAE weights from LTX-2 model. - - Args: - model_path: Path to LTX-2 model directory - - Returns: - Dictionary of VAE weights - """ - vae_path = model_path / "vae" / "diffusion_pytorch_model.safetensors" - if vae_path.exists(): - print(f"Loading VAE weights from {vae_path}...") - return mx.load(str(vae_path)) - - raise FileNotFoundError(f"VAE weights not found at {vae_path}") - - -def load_audio_vae_weights(model_path: Path) -> Dict[str, mx.array]: - """Load audio VAE weights from LTX-2 model. - - Args: - model_path: Path to LTX-2 model directory - - Returns: - Dictionary of audio VAE weights - """ - # Try different possible paths for audio VAE weights - audio_vae_paths = [ - model_path / "audio_vae" / "diffusion_pytorch_model.safetensors", - model_path / "audio_vae.safetensors", - ] - - # Also check in main model weights - main_paths = [ - model_path / "ltx-2-19b-distilled.safetensors", - model_path / "ltx-2-19b-dev.safetensors", - ] - - for audio_path in audio_vae_paths: - if audio_path.exists(): - print(f"Loading audio VAE weights from {audio_path}...") - return mx.load(str(audio_path)) - - # Check main model weights for audio_vae keys - for main_path in main_paths: - if main_path.exists(): - print(f"Loading audio VAE weights from {main_path.name}...") - all_weights = mx.load(str(main_path)) - # Filter to only audio_vae keys - audio_weights = {k: v for k, v in all_weights.items() if "audio_vae" in k} - if audio_weights: - return audio_weights - - raise FileNotFoundError(f"Audio VAE weights not found in {model_path}") - - -def load_vocoder_weights(model_path: Path) -> Dict[str, mx.array]: - """Load vocoder weights from LTX-2 model. - - Args: - model_path: Path to LTX-2 model directory - - Returns: - Dictionary of vocoder weights - """ - # Try different possible paths for vocoder weights - vocoder_paths = [ - model_path / "vocoder" / "diffusion_pytorch_model.safetensors", - model_path / "vocoder.safetensors", - ] - - # Also check in main model weights - main_paths = [ - model_path / "ltx-2-19b-distilled.safetensors", - model_path / "ltx-2-19b-dev.safetensors", - ] - - for vocoder_path in vocoder_paths: - if vocoder_path.exists(): - print(f"Loading vocoder weights from {vocoder_path}...") - return mx.load(str(vocoder_path)) - - # Check main model weights for vocoder keys - for main_path in main_paths: - if main_path.exists(): - print(f"Loading vocoder weights from {main_path.name}...") - all_weights = mx.load(str(main_path)) - # Filter to only vocoder keys - vocoder_weights = {k: v for k, v in all_weights.items() if "vocoder" in k} - if vocoder_weights: - return vocoder_weights - - raise FileNotFoundError(f"Vocoder weights not found in {model_path}") - - -def sanitize_transformer_weights(weights: Dict[str, mx.array]) -> Dict[str, mx.array]: - """Sanitize transformer weight names from PyTorch LTX-2 format to MLX format. - - Args: - weights: Dictionary of weights with PyTorch naming - - Returns: - Dictionary with MLX-compatible naming for transformer - """ - sanitized = {} - - for key, value in weights.items(): - new_key = key - - # Skip non-transformer weights (VAE, vocoder, audio_vae, connectors) - if not key.startswith("model.diffusion_model."): - continue - - # Remove 'model.diffusion_model.' prefix - new_key = key.replace("model.diffusion_model.", "") - - # Handle to_out.0 -> to_out (MLX doesn't use Sequential numbering) - new_key = new_key.replace(".to_out.0.", ".to_out.") - - # Handle feed-forward net naming - # PyTorch: ff.net.0.proj -> ff.net_0_proj (or similar) - # MLX FeedForward: uses proj_in, proj_out - new_key = new_key.replace(".ff.net.0.proj.", ".ff.proj_in.") - new_key = new_key.replace(".ff.net.2.", ".ff.proj_out.") - new_key = new_key.replace(".audio_ff.net.0.proj.", ".audio_ff.proj_in.") - new_key = new_key.replace(".audio_ff.net.2.", ".audio_ff.proj_out.") - - # Handle AdaLN naming - keep emb wrapper, just fix linear naming - # PyTorch: adaln_single.emb.timestep_embedder.linear_1 -> adaln_single.emb.timestep_embedder.linear1 - new_key = new_key.replace(".linear_1.", ".linear1.") - new_key = new_key.replace(".linear_2.", ".linear2.") - - # Handle caption projection (keep linear1/linear2 naming for compatibility) - # These are already mapped correctly in the sanitization - - sanitized[new_key] = value - - return sanitized - - -def sanitize_vae_weights(weights: Dict[str, mx.array]) -> Dict[str, mx.array]: - """Sanitize VAE weight names from PyTorch format to MLX format. - - Args: - weights: Dictionary of weights with PyTorch naming - - Returns: - Dictionary with MLX-compatible naming for VAE decoder - """ - sanitized = {} - - for key, value in weights.items(): - new_key = key - - # Skip position_ids (not needed) - if "position_ids" in key: - continue - - # Only process VAE decoder weights (skip audio_vae, etc.) - if not key.startswith("vae."): - continue - - # Handle per-channel statistics key mapping - # PyTorch: vae.per_channel_statistics.mean-of-means -> per_channel_statistics.mean - # PyTorch: vae.per_channel_statistics.std-of-means -> per_channel_statistics.std - # Be careful: mean-of-stds_over_std-of-means also ends with std-of-means - if "vae.per_channel_statistics" in key: - if key == "vae.per_channel_statistics.mean-of-means": - new_key = "per_channel_statistics.mean" - elif key == "vae.per_channel_statistics.std-of-means": - new_key = "per_channel_statistics.std" - else: - # Skip other per_channel_statistics keys (channel, mean-of-stds, etc.) - continue - elif key.startswith("vae.decoder."): - # Strip the vae.decoder. prefix for decoder weights - new_key = key.replace("vae.decoder.", "") - else: - # Skip other vae.* keys that are not decoder weights - continue - - # Handle Conv3d weight shape conversion - # PyTorch: (out_channels, in_channels, D, H, W) - # MLX: (out_channels, D, H, W, in_channels) - if "conv" in new_key.lower() and "weight" in new_key and value.ndim == 5: - # Transpose from (O, I, D, H, W) to (O, D, H, W, I) - value = mx.transpose(value, (0, 2, 3, 4, 1)) - - # Handle Conv2d weight shape conversion - # PyTorch: (out_channels, in_channels, H, W) - # MLX: (out_channels, H, W, in_channels) - if "conv" in new_key.lower() and "weight" in new_key and value.ndim == 4: - value = mx.transpose(value, (0, 2, 3, 1)) - - sanitized[new_key] = value - - return sanitized - - -def sanitize_vae_encoder_weights(weights: Dict[str, mx.array]) -> Dict[str, mx.array]: - """Sanitize VAE encoder weight names from PyTorch format to MLX format. - - Args: - weights: Dictionary of weights with PyTorch naming - - Returns: - Dictionary with MLX-compatible naming for VAE encoder - """ - sanitized = {} - - for key, value in weights.items(): - new_key = key - - # Skip position_ids (not needed) - if "position_ids" in key: - continue - - # Only process VAE encoder weights - if not key.startswith("vae."): - continue - - # Handle per-channel statistics key mapping - if "vae.per_channel_statistics" in key: - if key == "vae.per_channel_statistics.mean-of-means": - new_key = "per_channel_statistics._mean_of_means" - elif key == "vae.per_channel_statistics.std-of-means": - new_key = "per_channel_statistics._std_of_means" - else: - # Skip other per_channel_statistics keys - continue - elif key.startswith("vae.encoder."): - # Strip the vae.encoder. prefix for encoder weights - new_key = key.replace("vae.encoder.", "") - else: - # Skip other vae.* keys that are not encoder weights - continue - - # Handle Conv3d weight shape conversion - # PyTorch: (out_channels, in_channels, D, H, W) - # MLX: (out_channels, D, H, W, in_channels) - if "conv" in new_key.lower() and "weight" in new_key and value.ndim == 5: - value = mx.transpose(value, (0, 2, 3, 4, 1)) - - # Handle Conv2d weight shape conversion - if "conv" in new_key.lower() and "weight" in new_key and value.ndim == 4: - value = mx.transpose(value, (0, 2, 3, 1)) - - sanitized[new_key] = value - - return sanitized - - -def sanitize_audio_vae_weights(weights: Dict[str, mx.array]) -> Dict[str, mx.array]: - """Sanitize audio VAE weight names from PyTorch format to MLX format. - - Args: - weights: Dictionary of weights with PyTorch naming - - Returns: - Dictionary with MLX-compatible naming for audio VAE decoder - """ - sanitized = {} - - for key, value in weights.items(): - new_key = key - - # Handle audio_vae.decoder weights - if key.startswith("audio_vae.decoder."): - new_key = key.replace("audio_vae.decoder.", "") - elif key.startswith("audio_vae.per_channel_statistics."): - # Map per-channel statistics - if "mean-of-means" in key: - new_key = "per_channel_statistics._mean_of_means" - elif "std-of-means" in key: - new_key = "per_channel_statistics._std_of_means" - else: - continue # Skip other statistics keys - else: - continue # Skip non-decoder keys - - # Handle Conv2d weight shape conversion - # PyTorch: (out_channels, in_channels, H, W) - # MLX: (out_channels, H, W, in_channels) - if "conv" in new_key.lower() and "weight" in new_key and value.ndim == 4: - value = mx.transpose(value, (0, 2, 3, 1)) - - sanitized[new_key] = value - - return sanitized - - -def sanitize_vocoder_weights(weights: Dict[str, mx.array]) -> Dict[str, mx.array]: - """Sanitize vocoder weight names from PyTorch format to MLX format. - - Args: - weights: Dictionary of weights with PyTorch naming - - Returns: - Dictionary with MLX-compatible naming for vocoder - """ - sanitized = {} - - for key, value in weights.items(): - new_key = key - - # Handle vocoder weights - if key.startswith("vocoder."): - new_key = key.replace("vocoder.", "") - - # Handle ModuleList indices -> dict keys - # PyTorch: ups.0, ups.1, ... -> ups.0, ups.1, ... - # PyTorch: resblocks.0, resblocks.1, ... -> resblocks.0, resblocks.1, ... - - # Handle Conv1d weight shape conversion - # PyTorch: (out_channels, in_channels, kernel) - # MLX: (out_channels, kernel, in_channels) - if "weight" in new_key and value.ndim == 3: - if "ups" in new_key: - # ConvTranspose1d: PyTorch (in_ch, out_ch, kernel) -> MLX (out_ch, kernel, in_ch) - value = mx.transpose(value, (1, 2, 0)) - else: - # Conv1d: PyTorch (out_ch, in_ch, kernel) -> MLX (out_ch, kernel, in_ch) - value = mx.transpose(value, (0, 2, 1)) - - sanitized[new_key] = value - - return sanitized - - -def sanitize_weights(weights: Dict[str, mx.array]) -> Dict[str, mx.array]: - """Sanitize weight names from PyTorch format to MLX format. - - Generic function that handles both transformer and VAE weights. - - Args: - weights: Dictionary of weights with PyTorch naming - - Returns: - Dictionary with MLX-compatible naming - """ - sanitized = {} - - for key, value in weights.items(): - new_key = key - - # Skip position_ids (not needed) - if "position_ids" in key: - continue - - # Handle transformer weights - if key.startswith("model.diffusion_model."): - new_key = key.replace("model.diffusion_model.", "") - new_key = new_key.replace(".to_out.0.", ".to_out.") - new_key = new_key.replace(".ff.net.0.proj.", ".ff.proj_in.") - new_key = new_key.replace(".ff.net.2.", ".ff.proj_out.") - new_key = new_key.replace(".audio_ff.net.0.proj.", ".audio_ff.proj_in.") - new_key = new_key.replace(".audio_ff.net.2.", ".audio_ff.proj_out.") - new_key = new_key.replace(".linear_1.", ".linear1.") - new_key = new_key.replace(".linear_2.", ".linear2.") - - # Handle Conv3d weight shape conversion - # PyTorch: (out_channels, in_channels, D, H, W) - # MLX: (out_channels, D, H, W, in_channels) - if "conv" in key.lower() and "weight" in key and value.ndim == 5: - value = mx.transpose(value, (0, 2, 3, 4, 1)) - - # Handle Conv2d weight shape conversion - # PyTorch: (out_channels, in_channels, H, W) - # MLX: (out_channels, H, W, in_channels) - if "conv" in key.lower() and "weight" in key and value.ndim == 4: - value = mx.transpose(value, (0, 2, 3, 1)) - - sanitized[new_key] = value - - return sanitized - - -def load_config(model_path: Path) -> Dict[str, Any]: - """Load model configuration. - - Args: - model_path: Path to model directory - - Returns: - Configuration dictionary - """ - config_path = model_path / "config.json" - - if config_path.exists(): - with open(config_path, "r") as f: - return json.load(f) - - # Return default config - return {} - - -def create_model_from_config(config: Dict[str, Any]) -> LTXModel: - """Create model instance from configuration. - - Args: - config: Configuration dictionary - - Returns: - LTXModel instance - """ - # Map config to LTXModelConfig - model_config = LTXModelConfig( - model_type=LTXModelType.AudioVideo, - num_attention_heads=config.get("num_attention_heads", 32), - attention_head_dim=config.get("attention_head_dim", 128), - in_channels=config.get("in_channels", 128), - out_channels=config.get("out_channels", 128), - num_layers=config.get("num_layers", 48), - cross_attention_dim=config.get("cross_attention_dim", 4096), - caption_channels=config.get("caption_channels", 3840), - audio_num_attention_heads=config.get("audio_num_attention_heads", 32), - audio_attention_head_dim=config.get("audio_attention_head_dim", 64), - audio_in_channels=config.get("audio_in_channels", 128), - audio_out_channels=config.get("audio_out_channels", 128), - audio_cross_attention_dim=config.get("audio_cross_attention_dim", 2048), - positional_embedding_theta=config.get("positional_embedding_theta", 10000.0), - positional_embedding_max_pos=config.get("positional_embedding_max_pos", [20, 2048, 2048]), - audio_positional_embedding_max_pos=config.get("audio_positional_embedding_max_pos", [20]), - timestep_scale_multiplier=config.get("timestep_scale_multiplier", 1000), - av_ca_timestep_scale_multiplier=config.get("av_ca_timestep_scale_multiplier", 1000), - norm_eps=config.get("norm_eps", 1e-6), - ) - - return LTXModel(model_config) - - -def convert( - hf_path: str, - mlx_path: str = "mlx_model", - dtype: Optional[str] = None, - quantize: bool = False, - q_bits: int = 4, - q_group_size: int = 64, -) -> Path: - """Convert HuggingFace model to MLX format. - - Args: - hf_path: HuggingFace model path or repo ID - mlx_path: Output path for MLX model - dtype: Target dtype (float16, float32, bfloat16) - quantize: Whether to quantize the model - q_bits: Quantization bits - q_group_size: Quantization group size - - Returns: - Path to converted model - """ - print(f"Loading model from {hf_path}...") - model_path = get_model_path(hf_path) - - # Load config - config = load_config(model_path) - - # Load weights - print("Loading weights...") - weights = load_safetensors(model_path) - - # Sanitize weights - print("Sanitizing weights...") - weights = sanitize_weights(weights) - - # Convert dtype if specified - if dtype is not None: - dtype_map = { - "float16": mx.float16, - "float32": mx.float32, - "bfloat16": mx.bfloat16, - } - target_dtype = dtype_map.get(dtype, mx.float16) - print(f"Converting to {dtype}...") - weights = { - k: v.astype(target_dtype) if v.dtype in [mx.float32, mx.float16, mx.bfloat16] else v - for k, v in weights.items() - } - - # Create output directory - output_path = Path(mlx_path) - output_path.mkdir(parents=True, exist_ok=True) - - # Save weights - print(f"Saving weights to {output_path}...") - save_weights(output_path, weights) - - # Save config - config_out_path = output_path / "config.json" - with open(config_out_path, "w") as f: - json.dump(config, f, indent=2) - - print(f"Model converted successfully to {output_path}") - return output_path - - -def save_weights(path: Path, weights: Dict[str, mx.array]) -> None: - """Save weights in safetensors format. - - Args: - path: Output directory - weights: Dictionary of weights - """ - from safetensors.numpy import save_file - import numpy as np - - # Convert to numpy for safetensors - np_weights = {k: np.array(v) for k, v in weights.items()} - - # Save to file - save_file(np_weights, path / "model.safetensors") - - -def load_model( - path_or_hf_repo: str, - lazy: bool = False, -) -> LTXModel: - """Load LTX model from path or HuggingFace. - - Args: - path_or_hf_repo: Path to model or HuggingFace repo ID - lazy: Whether to use lazy loading - - Returns: - Loaded LTXModel - """ - model_path = get_model_path(path_or_hf_repo) - - # Load config - config = load_config(model_path) - - # Create model - model = create_model_from_config(config) - - # Load weights - weights = load_safetensors(model_path) - - # Sanitize if needed - weights = sanitize_weights(weights) - - # Load weights into model - model.load_weights(list(weights.items())) - - if not lazy: - mx.eval(model.parameters()) - - return model - - -if __name__ == "__main__": - import argparse - - parser = argparse.ArgumentParser(description="Convert LTX-2 model to MLX format") - parser.add_argument( - "--hf-path", - type=str, - default="Lightricks/LTX-2", - help="HuggingFace model path or repo ID", - ) - parser.add_argument( - "--mlx-path", - type=str, - default="mlx_model", - help="Output path for MLX model", - ) - parser.add_argument( - "--dtype", - type=str, - choices=["float16", "float32", "bfloat16"], - default="float16", - help="Target dtype", - ) - parser.add_argument( - "--quantize", - action="store_true", - help="Quantize the model", - ) - parser.add_argument( - "--q-bits", - type=int, - default=4, - help="Quantization bits", - ) - - args = parser.parse_args() - - convert( - hf_path=args.hf_path, - mlx_path=args.mlx_path, - dtype=args.dtype, - quantize=args.quantize, - q_bits=args.q_bits, - ) diff --git a/mlx_video/generate.py b/mlx_video/generate.py deleted file mode 100644 index 9a72fe9..0000000 --- a/mlx_video/generate.py +++ /dev/null @@ -1,710 +0,0 @@ -import argparse -import time -from pathlib import Path -from typing import Optional - -import mlx.core as mx -import mlx.nn as nn -import numpy as np -from PIL import Image -from tqdm import tqdm - -# ANSI color codes -class Colors: - CYAN = "\033[96m" - BLUE = "\033[94m" - GREEN = "\033[92m" - YELLOW = "\033[93m" - RED = "\033[91m" - MAGENTA = "\033[95m" - BOLD = "\033[1m" - DIM = "\033[2m" - RESET = "\033[0m" - -from mlx_video.models.ltx.config import LTXModelConfig, LTXModelType, LTXRopeType -from mlx_video.models.ltx.ltx import LTXModel -from mlx_video.models.ltx.transformer import Modality -from mlx_video.convert import sanitize_transformer_weights, sanitize_vae_encoder_weights -from mlx_video.utils import to_denoised, load_image, prepare_image_for_encoding -from mlx_video.models.ltx.video_vae.decoder import load_vae_decoder -from mlx_video.models.ltx.video_vae.encoder import load_vae_encoder -from mlx_video.models.ltx.video_vae.tiling import TilingConfig -from mlx_video.models.ltx.upsampler import load_upsampler, upsample_latents -from mlx_video.conditioning import VideoConditionByLatentIndex, apply_conditioning -from mlx_video.conditioning.latent import LatentState, create_initial_state, apply_denoise_mask, add_noise_with_state - -from mlx_video.utils import get_model_path - - -# Distilled sigma schedules -STAGE_1_SIGMAS = [1.0, 0.99375, 0.9875, 0.98125, 0.975, 0.909375, 0.725, 0.421875, 0.0] -STAGE_2_SIGMAS = [0.909375, 0.725, 0.421875, 0.0] - - -def create_position_grid( - batch_size: int, - num_frames: int, - height: int, - width: int, - temporal_scale: int = 8, - spatial_scale: int = 32, - fps: float = 24.0, - causal_fix: bool = True, -) -> mx.array: - """Create position grid for RoPE in pixel space. - - Args: - batch_size: Batch size - num_frames: Number of frames (latent) - height: Height (latent) - width: Width (latent) - temporal_scale: VAE temporal scale factor (default 8) - spatial_scale: VAE spatial scale factor (default 32) - fps: Frames per second (default 24.0) - causal_fix: Apply causal fix for first frame (default True) - - Returns: - Position grid of shape (B, 3, num_patches, 2) in pixel space - where dim 2 is [start, end) bounds for each patch - """ - # Patch size is (1, 1, 1) for LTX-2 - no spatial patching - patch_size_t, patch_size_h, patch_size_w = 1, 1, 1 - - # Generate grid coordinates for each dimension (frame, height, width) - t_coords = np.arange(0, num_frames, patch_size_t) - h_coords = np.arange(0, height, patch_size_h) - w_coords = np.arange(0, width, patch_size_w) - - # Create meshgrid with indexing='ij' for (frame, height, width) order - t_grid, h_grid, w_grid = np.meshgrid(t_coords, h_coords, w_coords, indexing='ij') - - # Stack to get shape (3, grid_t, grid_h, grid_w) - patch_starts = np.stack([t_grid, h_grid, w_grid], axis=0) - - # Calculate end coordinates (start + patch_size) - patch_size_delta = np.array([patch_size_t, patch_size_h, patch_size_w]).reshape(3, 1, 1, 1) - patch_ends = patch_starts + patch_size_delta - - # Stack start and end: shape (3, grid_t, grid_h, grid_w, 2) - latent_coords = np.stack([patch_starts, patch_ends], axis=-1) - - # Flatten spatial/temporal dims: (3, num_patches, 2) - num_patches = num_frames * height * width - latent_coords = latent_coords.reshape(3, num_patches, 2) - - # Broadcast to batch: (batch, 3, num_patches, 2) - latent_coords = np.tile(latent_coords[np.newaxis, ...], (batch_size, 1, 1, 1)) - - # Convert latent coords to pixel coords by scaling with VAE factors - scale_factors = np.array([temporal_scale, spatial_scale, spatial_scale]).reshape(1, 3, 1, 1) - pixel_coords = (latent_coords * scale_factors).astype(np.float32) - - # Apply causal fix for first frame temporal axis - if causal_fix: - # VAE temporal stride for first frame is 1 instead of temporal_scale - pixel_coords[:, 0, :, :] = np.clip( - pixel_coords[:, 0, :, :] + 1 - temporal_scale, - a_min=0, - a_max=None - ) - - # Convert temporal to time in seconds by dividing by fps - pixel_coords[:, 0, :, :] = pixel_coords[:, 0, :, :] / fps - - # Always return float32 for RoPE precision - bfloat16 causes quality degradation - return mx.array(pixel_coords, dtype=mx.float32) - - -def denoise( - latents: mx.array, - positions: mx.array, - text_embeddings: mx.array, - transformer: LTXModel, - sigmas: list, - verbose: bool = True, - state: Optional[LatentState] = None, -) -> mx.array: - """Run denoising loop with optional conditioning. - - Args: - latents: Noisy latent tensor (B, C, F, H, W) - positions: Position embeddings - text_embeddings: Text conditioning embeddings - transformer: LTX model - sigmas: List of sigma values for denoising schedule - verbose: Whether to show progress bar - state: Optional LatentState for I2V conditioning - - Returns: - Denoised latent tensor - """ - # If state is provided, use its latent (which may have conditioning applied) - dtype = latents.dtype - if state is not None: - latents = state.latent - - for i in tqdm(range(len(sigmas) - 1), desc="Denoising", disable=not verbose): - sigma, sigma_next = sigmas[i], sigmas[i + 1] - - b, c, f, h, w = latents.shape - num_tokens = f * h * w - latents_flat = mx.transpose(mx.reshape(latents, (b, c, -1)), (0, 2, 1)) - - # Compute per-token timesteps - # For I2V: conditioned tokens get timestep=0 (mask=0), unconditioned get timestep=sigma (mask=1) - if state is not None: - # Reshape denoise_mask from (B, 1, F, 1, 1) to (B, num_tokens) - denoise_mask_flat = mx.reshape(state.denoise_mask, (b, 1, f, 1, 1)) - denoise_mask_flat = mx.broadcast_to(denoise_mask_flat, (b, 1, f, h, w)) - denoise_mask_flat = mx.reshape(denoise_mask_flat, (b, num_tokens)) - # Per-token timesteps: sigma * mask (preserve dtype) - timesteps = mx.array(sigma, dtype=dtype) * denoise_mask_flat - else: - # All tokens get the same timestep (use latent dtype) - timesteps = mx.full((b, num_tokens), sigma, dtype=dtype) - - video_modality = Modality( - latent=latents_flat, - timesteps=timesteps, - positions=positions, - context=text_embeddings, - context_mask=None, - enabled=True, - ) - - velocity, _ = transformer(video=video_modality, audio=None) - mx.eval(velocity) - - velocity = mx.reshape(mx.transpose(velocity, (0, 2, 1)), (b, c, f, h, w)) - denoised = to_denoised(latents, velocity, sigma) - - # Apply conditioning mask if state is provided - if state is not None: - denoised = apply_denoise_mask(denoised, state.clean_latent, state.denoise_mask) - - mx.eval(denoised) - - # Euler step (preserve dtype by converting Python floats to arrays) - if sigma_next > 0: - sigma_next_arr = mx.array(sigma_next, dtype=dtype) - sigma_arr = mx.array(sigma, dtype=dtype) - latents = denoised + sigma_next_arr * (latents - denoised) / sigma_arr - else: - latents = denoised - mx.eval(latents) - - return latents - - -def generate_video( - model_repo: str, - text_encoder_repo: str, - prompt: str, - height: int = 512, - width: int = 512, - num_frames: int = 33, - seed: int = 42, - fps: int = 24, - output_path: str = "output.mp4", - save_frames: bool = False, - verbose: bool = True, - enhance_prompt: bool = False, - max_tokens: int = 512, - temperature: float = 0.7, - image: Optional[str] = None, - image_strength: float = 1.0, - image_frame_idx: int = 0, - tiling: str = "auto", - stream: bool = False, -): - """Generate video from text prompt, optionally conditioned on an image. - - Args: - model_repo: Model repository ID - text_encoder_repo: Text encoder repository ID - prompt: Text description of the video to generate - height: Output video height (must be divisible by 64) - width: Output video width (must be divisible by 64) - num_frames: Number of frames (must be 1 + 8*k, e.g., 33, 65, 97) - seed: Random seed for reproducibility - fps: Frames per second for output video - output_path: Path to save the output video - save_frames: Whether to save individual frames as images - verbose: Whether to print progress - enhance_prompt: Whether to enhance prompt using Gemma - max_tokens: Max tokens for prompt enhancement - temperature: Temperature for prompt enhancement - image: Path to conditioning image for I2V (Image-to-Video) - image_strength: Conditioning strength (1.0 = full denoise, 0.0 = keep original) - image_frame_idx: Frame index to condition (0 = first frame) - tiling: Tiling mode for VAE decoding. Options: - - "auto": Automatically determine based on video size (default) - - "none": Disable tiling - - "default": 512px spatial, 64 frame temporal - - "aggressive": 256px spatial, 32 frame temporal (lowest memory) - - "conservative": 768px spatial, 96 frame temporal (faster) - - "spatial": Spatial tiling only - - "temporal": Temporal tiling only - """ - start_time = time.time() - - # Validate dimensions - assert height % 64 == 0, f"Height must be divisible by 64, got {height}" - assert width % 64 == 0, f"Width must be divisible by 64, got {width}" - - if num_frames % 8 != 1: - adjusted_num_frames = round((num_frames - 1) / 8) * 8 + 1 - print(f"{Colors.YELLOW}⚠️ Number of frames must be 1 + 8*k. Using nearest valid value: {adjusted_num_frames}{Colors.RESET}") - num_frames = adjusted_num_frames - - - is_i2v = image is not None - mode_str = "I2V" if is_i2v else "T2V" - print(f"{Colors.BOLD}{Colors.CYAN}🎬 [{mode_str}] Generating {width}x{height} video with {num_frames} frames{Colors.RESET}") - print(f"{Colors.DIM}Prompt: {prompt[:80]}{'...' if len(prompt) > 80 else ''}{Colors.RESET}") - if is_i2v: - print(f"{Colors.DIM}Image: {image} (strength={image_strength}, frame={image_frame_idx}){Colors.RESET}") - - # Get model path - model_path = get_model_path(model_repo) - text_encoder_path = model_path if text_encoder_repo is None else get_model_path(text_encoder_repo) - - # Calculate latent dimensions - stage1_h, stage1_w = height // 2 // 32, width // 2 // 32 - stage2_h, stage2_w = height // 32, width // 32 - latent_frames = 1 + (num_frames - 1) // 8 - - mx.random.seed(seed) - - # Load text encoder - print(f"{Colors.BLUE}📝 Loading text encoder...{Colors.RESET}") - from mlx_video.models.ltx.text_encoder import LTX2TextEncoder - text_encoder = LTX2TextEncoder() - text_encoder.load(model_path=model_path, text_encoder_path=text_encoder_path) - mx.eval(text_encoder.parameters()) - - # Optionally enhance the prompt - if enhance_prompt: - print(f"{Colors.MAGENTA}✨ Enhancing prompt...{Colors.RESET}") - prompt = text_encoder.enhance_t2v(prompt, max_tokens=max_tokens, temperature=temperature, seed=seed, verbose=verbose) - print(f"{Colors.DIM}Enhanced: {prompt[:150]}{'...' if len(prompt) > 150 else ''}{Colors.RESET}") - - text_embeddings, _ = text_encoder(prompt, return_audio_embeddings=False) - model_dtype = text_embeddings.dtype # bfloat16 from text encoder - mx.eval(text_embeddings) - - del text_encoder - mx.clear_cache() - - # Load transformer - print(f"{Colors.BLUE}🤖 Loading transformer...{Colors.RESET}") - raw_weights = mx.load(str(model_path / 'ltx-2-19b-distilled.safetensors')) - sanitized = sanitize_transformer_weights(raw_weights) - # Convert transformer weights to bfloat16 for memory efficiency - sanitized = {k: v.astype(mx.bfloat16) if v.dtype == mx.float32 else v for k, v in sanitized.items()} - - config = LTXModelConfig( - model_type=LTXModelType.VideoOnly, - num_attention_heads=32, - attention_head_dim=128, - in_channels=128, - out_channels=128, - num_layers=48, - cross_attention_dim=4096, - caption_channels=3840, - rope_type=LTXRopeType.SPLIT, - double_precision_rope=True, - positional_embedding_theta=10000.0, - positional_embedding_max_pos=[20, 2048, 2048], - use_middle_indices_grid=True, - timestep_scale_multiplier=1000, - ) - - transformer = LTXModel(config) - transformer.load_weights(list(sanitized.items()), strict=False) - mx.eval(transformer.parameters()) - - # Load VAE encoder and encode image for I2V conditioning - stage1_image_latent = None - stage2_image_latent = None - if is_i2v: - print(f"{Colors.BLUE}🖼️ Loading VAE encoder and encoding image...{Colors.RESET}") - vae_encoder = load_vae_encoder(str(model_path / 'ltx-2-19b-distilled.safetensors')) - mx.eval(vae_encoder.parameters()) - - # Load and prepare image for stage 1 (half resolution) - input_image = load_image(image, height=height // 2, width=width // 2, dtype=model_dtype) - stage1_image_tensor = prepare_image_for_encoding(input_image, height // 2, width // 2, dtype=model_dtype) - stage1_image_latent = vae_encoder(stage1_image_tensor) - mx.eval(stage1_image_latent) - print(f" Stage 1 image latent: {stage1_image_latent.shape}") - - # Load and prepare image for stage 2 (full resolution) - input_image = load_image(image, height=height, width=width, dtype=model_dtype) - stage2_image_tensor = prepare_image_for_encoding(input_image, height, width, dtype=model_dtype) - stage2_image_latent = vae_encoder(stage2_image_tensor) - mx.eval(stage2_image_latent) - print(f" Stage 2 image latent: {stage2_image_latent.shape}") - - del vae_encoder - mx.clear_cache() - - # Stage 1: Generate at half resolution - print(f"{Colors.YELLOW}⚡ Stage 1: Generating at {width//2}x{height//2} (8 steps)...{Colors.RESET}") - mx.random.seed(seed) - - # Position grids stay float32 for RoPE precision - positions = create_position_grid(1, latent_frames, stage1_h, stage1_w) - mx.eval(positions) - - # Apply I2V conditioning if provided - state1 = None - if is_i2v and stage1_image_latent is not None: - # PyTorch flow: create zeros -> apply conditioning -> apply noiser - # Create initial state with zeros - latent_shape = (1, 128, latent_frames, stage1_h, stage1_w) - state1 = LatentState( - latent=mx.zeros(latent_shape, dtype=model_dtype), - clean_latent=mx.zeros(latent_shape, dtype=model_dtype), - denoise_mask=mx.ones((1, 1, latent_frames, 1, 1), dtype=model_dtype), - ) - conditioning = VideoConditionByLatentIndex( - latent=stage1_image_latent, - frame_idx=image_frame_idx, - strength=image_strength, - ) - - state1 = apply_conditioning(state1, [conditioning]) - - # Apply noiser: latent = noise * (mask * noise_scale) + latent * (1 - mask * noise_scale) - # For Stage 1, noise_scale = 1.0 (first sigma) - noise = mx.random.normal(latent_shape, dtype=model_dtype) - noise_scale = mx.array(STAGE_1_SIGMAS[0], dtype=model_dtype) # 1.0 - scaled_mask = state1.denoise_mask * noise_scale - - state1 = LatentState( - latent=noise * scaled_mask + state1.latent * (mx.array(1.0, dtype=model_dtype) - scaled_mask), - clean_latent=state1.clean_latent, - denoise_mask=state1.denoise_mask, - ) - latents = state1.latent - mx.eval(latents) - else: - # T2V: just use random noise - latents = mx.random.normal((1, 128, latent_frames, stage1_h, stage1_w), dtype=model_dtype) - mx.eval(latents) - - latents = denoise(latents, positions, text_embeddings, transformer, STAGE_1_SIGMAS, verbose=verbose, state=state1) - - # Upsample latents - print(f"{Colors.MAGENTA}🔍 Upsampling latents 2x...{Colors.RESET}") - upsampler = load_upsampler(str(model_path / 'ltx-2-spatial-upscaler-x2-1.0.safetensors')) - mx.eval(upsampler.parameters()) - - vae_decoder = load_vae_decoder( - str(model_path / 'ltx-2-19b-distilled.safetensors'), - timestep_conditioning=None # Auto-detect from model metadata - ) - - latents = upsample_latents(latents, upsampler, vae_decoder.latents_mean, vae_decoder.latents_std) - mx.eval(latents) - - del upsampler - mx.clear_cache() - - # Stage 2: Refine at full resolution - print(f"{Colors.YELLOW}⚡ Stage 2: Refining at {width}x{height} (3 steps)...{Colors.RESET}") - # Position grids stay float32 for RoPE precision - positions = create_position_grid(1, latent_frames, stage2_h, stage2_w) - mx.eval(positions) - - # Apply I2V conditioning for stage 2 if provided - state2 = None - if is_i2v and stage2_image_latent is not None: - # PyTorch flow: start with upscaled latent -> apply conditioning -> apply noiser - state2 = LatentState( - latent=latents, # Start with upscaled latent - clean_latent=mx.zeros_like(latents), - denoise_mask=mx.ones((1, 1, latent_frames, 1, 1), dtype=model_dtype), - ) - conditioning = VideoConditionByLatentIndex( - latent=stage2_image_latent, - frame_idx=image_frame_idx, - strength=image_strength, - ) - state2 = apply_conditioning(state2, [conditioning]) - - # Apply noiser: latent = noise * (mask * noise_scale) + latent * (1 - mask * noise_scale) - # For Stage 2, noise_scale = stage_2_sigmas[0] - # Conditioned frames (mask=0) keep image latent, unconditioned get partial noise - noise = mx.random.normal(latents.shape).astype(model_dtype) - noise_scale = mx.array(STAGE_2_SIGMAS[0], dtype=model_dtype) - scaled_mask = state2.denoise_mask * noise_scale - state2 = LatentState( - latent=noise * scaled_mask + state2.latent * (mx.array(1.0, dtype=model_dtype) - scaled_mask), - clean_latent=state2.clean_latent, - denoise_mask=state2.denoise_mask, - ) - latents = state2.latent - mx.eval(latents) - else: - # T2V: add noise to all frames for refinement - noise_scale = mx.array(STAGE_2_SIGMAS[0], dtype=model_dtype) - one_minus_scale = mx.array(1.0 - STAGE_2_SIGMAS[0], dtype=model_dtype) - noise = mx.random.normal(latents.shape).astype(model_dtype) - latents = noise * noise_scale + latents * one_minus_scale - mx.eval(latents) - - latents = denoise(latents, positions, text_embeddings, transformer, STAGE_2_SIGMAS, verbose=verbose, state=state2) - - del transformer - mx.clear_cache() - - # Decode to video with tiling - print(f"{Colors.BLUE}🎞️ Decoding video...{Colors.RESET}") - - # Select tiling configuration - if tiling == "none": - tiling_config = None - elif tiling == "auto": - tiling_config = TilingConfig.auto(height, width, num_frames) - elif tiling == "default": - tiling_config = TilingConfig.default() - elif tiling == "aggressive": - tiling_config = TilingConfig.aggressive() - elif tiling == "conservative": - tiling_config = TilingConfig.conservative() - elif tiling == "spatial": - tiling_config = TilingConfig.spatial_only() - elif tiling == "temporal": - tiling_config = TilingConfig.temporal_only() - else: - print(f"{Colors.YELLOW} Unknown tiling mode '{tiling}', using auto{Colors.RESET}") - tiling_config = TilingConfig.auto(height, width, num_frames) - - # Save outputs - output_path = Path(output_path) - output_path.parent.mkdir(parents=True, exist_ok=True) - - # Stream mode: write frames as they're decoded - video_writer = None - stream_pbar = None - - if stream and tiling_config is not None: - import cv2 - fourcc = cv2.VideoWriter_fourcc(*'avc1') - video_writer = cv2.VideoWriter(str(output_path), fourcc, fps, (width, height)) - stream_pbar = tqdm(total=num_frames, desc="Streaming", unit="frame") - - def on_frames_ready(frames: mx.array, start_idx: int): - """Callback to write frames as they're finalized.""" - # frames: (B, 3, num_frames, H, W) - frames = mx.squeeze(frames, axis=0) # (3, num_frames, H, W) - frames = mx.transpose(frames, (1, 2, 3, 0)) # (num_frames, H, W, 3) - frames = mx.clip((frames + 1.0) / 2.0, 0.0, 1.0) - frames = (frames * 255).astype(mx.uint8) - frames_np = np.array(frames) - - for frame in frames_np: - video_writer.write(cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)) - stream_pbar.update(1) - else: - on_frames_ready = None - - if tiling_config is not None: - spatial_info = f"{tiling_config.spatial_config.tile_size_in_pixels}px" if tiling_config.spatial_config else "none" - temporal_info = f"{tiling_config.temporal_config.tile_size_in_frames}f" if tiling_config.temporal_config else "none" - print(f"{Colors.DIM} Tiling ({tiling}): spatial={spatial_info}, temporal={temporal_info}{Colors.RESET}") - video = vae_decoder.decode_tiled(latents, tiling_config=tiling_config, tiling_mode=tiling, debug=verbose, on_frames_ready=on_frames_ready) - else: - print(f"{Colors.DIM} Tiling: disabled{Colors.RESET}") - video = vae_decoder(latents) - mx.eval(video) - mx.clear_cache() - - # Close progressive video writer if used - if video_writer is not None: - video_writer.release() - if stream_pbar is not None: - stream_pbar.close() - print(f"{Colors.GREEN}✅ Streamed video to{Colors.RESET} {output_path}") - # Still need video_np for save_frames option - video = mx.squeeze(video, axis=0) - video = mx.transpose(video, (1, 2, 3, 0)) - video = mx.clip((video + 1.0) / 2.0, 0.0, 1.0) - video = (video * 255).astype(mx.uint8) - video_np = np.array(video) - else: - # Convert to uint8 frames - video = mx.squeeze(video, axis=0) # (C, F, H, W) - video = mx.transpose(video, (1, 2, 3, 0)) # (F, H, W, C) - video = mx.clip((video + 1.0) / 2.0, 0.0, 1.0) - video = (video * 255).astype(mx.uint8) - video_np = np.array(video) - - # Save video normally - try: - import cv2 - h, w = video_np.shape[1], video_np.shape[2] - fourcc = cv2.VideoWriter_fourcc(*'avc1') - out = cv2.VideoWriter(str(output_path), fourcc, fps, (w, h)) - for frame in video_np: - out.write(cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)) - out.release() - print(f"{Colors.GREEN}✅ Saved video to{Colors.RESET} {output_path}") - except Exception as e: - print(f"{Colors.RED}❌ Could not save video: {e}{Colors.RESET}") - - if save_frames: - frames_dir = output_path.parent / f"{output_path.stem}_frames" - frames_dir.mkdir(exist_ok=True) - for i, frame in enumerate(video_np): - Image.fromarray(frame).save(frames_dir / f"frame_{i:04d}.png") - print(f"{Colors.GREEN}✅ Saved {len(video_np)} frames to {frames_dir}{Colors.RESET}") - - elapsed = time.time() - start_time - print(f"{Colors.BOLD}{Colors.GREEN}🎉 Done! Generated in {elapsed:.1f}s ({elapsed/num_frames:.2f}s/frame){Colors.RESET}") - print(f"{Colors.BOLD}{Colors.GREEN}✨ Peak memory: {mx.get_peak_memory() / (1024 ** 3):.2f}GB{Colors.RESET}") - - return video_np - - -def main(): - parser = argparse.ArgumentParser( - description="Generate videos with MLX LTX-2 (T2V and I2V)", - formatter_class=argparse.RawDescriptionHelpFormatter, - epilog=""" -Examples: - # Text-to-Video (T2V) - python -m mlx_video.generate --prompt "A cat walking on grass" - python -m mlx_video.generate --prompt "Ocean waves at sunset" --height 768 --width 768 - python -m mlx_video.generate --prompt "..." --num-frames 65 --seed 123 --output my_video.mp4 - - # Image-to-Video (I2V) - python -m mlx_video.generate --prompt "A person dancing" --image photo.jpg - python -m mlx_video.generate --prompt "Waves crashing" --image beach.png --image-strength 0.8 - """ - ) - - parser.add_argument( - "--prompt", "-p", - type=str, - required=True, - help="Text description of the video to generate" - ) - parser.add_argument( - "--height", "-H", - type=int, - default=512, - help="Output video height (default: 512, must be divisible by 32)" - ) - parser.add_argument( - "--width", "-W", - type=int, - default=512, - help="Output video width (default: 512, must be divisible by 32)" - ) - parser.add_argument( - "--num-frames", "-n", - type=int, - default=100, - help="Number of frames (default: 100)" - ) - parser.add_argument( - "--seed", "-s", - type=int, - default=42, - help="Random seed for reproducibility (default: 42)" - ) - parser.add_argument( - "--fps", - type=int, - default=24, - help="Frames per second for output video (default: 24)" - ) - parser.add_argument( - "--output-path", - type=str, - default="output.mp4", - help="Output video path (default: output.mp4)" - ) - parser.add_argument( - "--save-frames", - action="store_true", - help="Save individual frames as images" - ) - parser.add_argument( - "--model-repo", - type=str, - default="Lightricks/LTX-2", - help="Model repository to use (default: Lightricks/LTX-2)" - ) - parser.add_argument( - "--text-encoder-repo", - type=str, - default=None, - help="Text encoder repository to use (default: None)" - ) - parser.add_argument( - "--verbose", - action="store_true", - help="Verbose output" - ) - parser.add_argument( - "--enhance-prompt", - action="store_true", - help="Enhance the prompt using Gemma before generation" - ) - parser.add_argument( - "--max-tokens", - type=int, - default=512, - help="Maximum number of tokens to generate (default: 512)" - ) - parser.add_argument( - "--temperature", - type=float, - default=0.7, - help="Temperature for prompt enhancement (default: 0.7)" - ) - parser.add_argument( - "--image", "-i", - type=str, - default=None, - help="Path to conditioning image for I2V (Image-to-Video) generation" - ) - parser.add_argument( - "--image-strength", - type=float, - default=1.0, - help="Conditioning strength for I2V (1.0 = full denoise, 0.0 = keep original, default: 1.0)" - ) - parser.add_argument( - "--image-frame-idx", - type=int, - default=0, - help="Frame index to condition for I2V (0 = first frame, default: 0)" - ) - parser.add_argument( - "--tiling", - type=str, - default="auto", - choices=["auto", "none", "default", "aggressive", "conservative", "spatial", "temporal"], - help="Tiling mode for VAE decoding (default: auto). " - "auto=based on video size, none=disabled, default=512px/64f, " - "aggressive=256px/32f (lowest memory), conservative=768px/96f, spatial=spatial only, temporal=temporal only" - ) - parser.add_argument( - "--stream", - action="store_true", - help="Stream frames to output file as they're decoded (requires tiling). Allows viewing partial results sooner." - ) - args = parser.parse_args() - - generate_video( - **vars(args) - ) - - -if __name__ == "__main__": - main() diff --git a/mlx_video/generate_av.py b/mlx_video/generate_av.py deleted file mode 100644 index e0fb22b..0000000 --- a/mlx_video/generate_av.py +++ /dev/null @@ -1,821 +0,0 @@ -"""Audio-Video generation pipeline for LTX-2.""" - -import argparse -import time -from pathlib import Path -from typing import Optional - -import mlx.core as mx -import numpy as np -from tqdm import tqdm - - -# ANSI color codes -class Colors: - CYAN = "\033[96m" - BLUE = "\033[94m" - GREEN = "\033[92m" - YELLOW = "\033[93m" - RED = "\033[91m" - MAGENTA = "\033[95m" - BOLD = "\033[1m" - DIM = "\033[2m" - RESET = "\033[0m" - - -from mlx_video.models.ltx.config import LTXModelConfig, LTXModelType, LTXRopeType -from mlx_video.models.ltx.ltx import LTXModel -from mlx_video.models.ltx.transformer import Modality -from mlx_video.convert import sanitize_transformer_weights, sanitize_audio_vae_weights, sanitize_vocoder_weights -from mlx_video.utils import to_denoised, get_model_path, load_image, prepare_image_for_encoding -from mlx_video.models.ltx.video_vae.decoder import load_vae_decoder -from mlx_video.models.ltx.video_vae.encoder import load_vae_encoder -from mlx_video.models.ltx.video_vae.tiling import TilingConfig -from mlx_video.models.ltx.upsampler import load_upsampler, upsample_latents -from mlx_video.conditioning import VideoConditionByLatentIndex, apply_conditioning -from mlx_video.conditioning.latent import LatentState, apply_denoise_mask - - -# Distilled sigma schedules -STAGE_1_SIGMAS = [1.0, 0.99375, 0.9875, 0.98125, 0.975, 0.909375, 0.725, 0.421875, 0.0] -STAGE_2_SIGMAS = [0.909375, 0.725, 0.421875, 0.0] - -# Audio constants -AUDIO_SAMPLE_RATE = 24000 # Output audio sample rate -AUDIO_LATENT_SAMPLE_RATE = 16000 # VAE internal sample rate -AUDIO_HOP_LENGTH = 160 -AUDIO_LATENT_DOWNSAMPLE_FACTOR = 4 -AUDIO_LATENT_CHANNELS = 8 # Latent channels before patchifying -AUDIO_MEL_BINS = 16 -AUDIO_LATENTS_PER_SECOND = AUDIO_LATENT_SAMPLE_RATE / AUDIO_HOP_LENGTH / AUDIO_LATENT_DOWNSAMPLE_FACTOR # 25 - - -def create_video_position_grid( - batch_size: int, - num_frames: int, - height: int, - width: int, - temporal_scale: int = 8, - spatial_scale: int = 32, - fps: float = 24.0, - causal_fix: bool = True, -) -> mx.array: - """Create position grid for video RoPE in pixel space.""" - patch_size_t, patch_size_h, patch_size_w = 1, 1, 1 - - t_coords = np.arange(0, num_frames, patch_size_t) - h_coords = np.arange(0, height, patch_size_h) - w_coords = np.arange(0, width, patch_size_w) - - t_grid, h_grid, w_grid = np.meshgrid(t_coords, h_coords, w_coords, indexing='ij') - patch_starts = np.stack([t_grid, h_grid, w_grid], axis=0) - - patch_size_delta = np.array([patch_size_t, patch_size_h, patch_size_w]).reshape(3, 1, 1, 1) - patch_ends = patch_starts + patch_size_delta - - latent_coords = np.stack([patch_starts, patch_ends], axis=-1) - num_patches = num_frames * height * width - latent_coords = latent_coords.reshape(3, num_patches, 2) - latent_coords = np.tile(latent_coords[np.newaxis, ...], (batch_size, 1, 1, 1)) - - scale_factors = np.array([temporal_scale, spatial_scale, spatial_scale]).reshape(1, 3, 1, 1) - pixel_coords = (latent_coords * scale_factors).astype(np.float32) - - if causal_fix: - pixel_coords[:, 0, :, :] = np.clip( - pixel_coords[:, 0, :, :] + 1 - temporal_scale, - a_min=0, - a_max=None - ) - - pixel_coords[:, 0, :, :] = pixel_coords[:, 0, :, :] / fps - - return mx.array(pixel_coords, dtype=mx.float32) - - -def create_audio_position_grid( - batch_size: int, - audio_frames: int, - sample_rate: int = AUDIO_LATENT_SAMPLE_RATE, - hop_length: int = AUDIO_HOP_LENGTH, - downsample_factor: int = AUDIO_LATENT_DOWNSAMPLE_FACTOR, - is_causal: bool = True, -) -> mx.array: - """Create temporal position grid for audio RoPE. - - Audio positions are timestamps in seconds, shape (B, 1, T, 2). - Matches PyTorch's AudioPatchifier.get_patch_grid_bounds exactly. - """ - def get_audio_latent_time_in_sec(start_idx: int, end_idx: int) -> np.ndarray: - """Convert latent indices to seconds (matching PyTorch's _get_audio_latent_time_in_sec).""" - latent_frame = np.arange(start_idx, end_idx, dtype=np.float32) - mel_frame = latent_frame * downsample_factor - if is_causal: - # Frame offset for causal alignment (PyTorch uses +1 - downsample_factor) - mel_frame = np.clip(mel_frame + 1 - downsample_factor, 0, None) - return mel_frame * hop_length / sample_rate - - # Start times: latent indices 0 to audio_frames - start_times = get_audio_latent_time_in_sec(0, audio_frames) - - # End times: latent indices 1 to audio_frames+1 (shifted by 1) - end_times = get_audio_latent_time_in_sec(1, audio_frames + 1) - - # Shape: (B, 1, T, 2) - positions = np.stack([start_times, end_times], axis=-1) - positions = positions[np.newaxis, np.newaxis, :, :] # (1, 1, T, 2) - positions = np.tile(positions, (batch_size, 1, 1, 1)) - - return mx.array(positions, dtype=mx.float32) - - -def compute_audio_frames(num_video_frames: int, fps: float) -> int: - """Compute number of audio latent frames given video duration.""" - duration = num_video_frames / fps - return round(duration * AUDIO_LATENTS_PER_SECOND) - - -def denoise_av( - video_latents: mx.array, - audio_latents: mx.array, - video_positions: mx.array, - audio_positions: mx.array, - video_embeddings: mx.array, - audio_embeddings: mx.array, - transformer: LTXModel, - sigmas: list, - verbose: bool = True, - video_state: Optional[LatentState] = None, -) -> tuple[mx.array, mx.array]: - """Run denoising loop for audio-video generation with optional I2V conditioning. - - Args: - video_latents: Video latent tensor (B, C, F, H, W) - audio_latents: Audio latent tensor (B, C, T, F) - video_positions: Video position embeddings - audio_positions: Audio position embeddings - video_embeddings: Video text embeddings - audio_embeddings: Audio text embeddings - transformer: LTX model - sigmas: List of sigma values - verbose: Whether to show progress bar - video_state: Optional LatentState for I2V conditioning - - Returns: - Tuple of (video_latents, audio_latents) - """ - dtype = video_latents.dtype - # If video state is provided, use its latent - if video_state is not None: - video_latents = video_state.latent - - for i in tqdm(range(len(sigmas) - 1), desc="Denoising A/V", disable=not verbose): - sigma, sigma_next = sigmas[i], sigmas[i + 1] - - # Flatten video latents - b, c, f, h, w = video_latents.shape - num_video_tokens = f * h * w - video_flat = mx.transpose(mx.reshape(video_latents, (b, c, -1)), (0, 2, 1)) - - # Flatten audio latents: (B, C, T, F) -> (B, T, C*F) - ab, ac, at, af = audio_latents.shape - audio_flat = mx.transpose(audio_latents, (0, 2, 1, 3)) # (B, T, C, F) - audio_flat = mx.reshape(audio_flat, (ab, at, ac * af)) - - # Compute per-token timesteps for video - # For I2V: conditioned tokens get timestep=0 (mask=0), unconditioned get timestep=sigma (mask=1) - if video_state is not None: - # Reshape denoise_mask from (B, 1, F, 1, 1) to (B, num_tokens) - denoise_mask_flat = mx.reshape(video_state.denoise_mask, (b, 1, f, 1, 1)) - denoise_mask_flat = mx.broadcast_to(denoise_mask_flat, (b, 1, f, h, w)) - denoise_mask_flat = mx.reshape(denoise_mask_flat, (b, num_video_tokens)) - # Per-token timesteps: sigma * mask - video_timesteps = mx.array(sigma, dtype=dtype) * denoise_mask_flat - else: - # All tokens get the same timestep - video_timesteps = mx.full((b, num_video_tokens), sigma, dtype=dtype) - - video_modality = Modality( - latent=video_flat, - timesteps=video_timesteps, - positions=video_positions, - context=video_embeddings, - context_mask=None, - enabled=True, - ) - - audio_modality = Modality( - latent=audio_flat, - timesteps=mx.full((ab, at), sigma, dtype=dtype), - positions=audio_positions, - context=audio_embeddings, - context_mask=None, - enabled=True, - ) - - video_velocity, audio_velocity = transformer(video=video_modality, audio=audio_modality) - mx.eval(video_velocity, audio_velocity) - - # Reshape velocities back - video_velocity = mx.reshape(mx.transpose(video_velocity, (0, 2, 1)), (b, c, f, h, w)) - audio_velocity = mx.reshape(audio_velocity, (ab, at, ac, af)) - audio_velocity = mx.transpose(audio_velocity, (0, 2, 1, 3)) # (B, C, T, F) - - # Compute denoised - video_denoised = to_denoised(video_latents, video_velocity, sigma) - audio_denoised = to_denoised(audio_latents, audio_velocity, sigma) - - # Apply conditioning mask for video if state is provided - if video_state is not None: - video_denoised = apply_denoise_mask(video_denoised, video_state.clean_latent, video_state.denoise_mask) - - mx.eval(video_denoised, audio_denoised) - - # Euler step - use dtype-preserving arrays to avoid float32 promotion - if sigma_next > 0: - sigma_next_arr = mx.array(sigma_next, dtype=dtype) - sigma_arr = mx.array(sigma, dtype=dtype) - video_latents = video_denoised + sigma_next_arr * (video_latents - video_denoised) / sigma_arr - audio_latents = audio_denoised + sigma_next_arr * (audio_latents - audio_denoised) / sigma_arr - else: - video_latents = video_denoised - audio_latents = audio_denoised - mx.eval(video_latents, audio_latents) - - return video_latents, audio_latents - - -def load_audio_decoder(model_path: Path): - """Load audio VAE decoder.""" - from mlx_video.models.ltx.audio_vae import AudioDecoder, CausalityAxis, NormType - - decoder = AudioDecoder( - ch=128, - out_ch=2, # stereo - ch_mult=(1, 2, 4), - num_res_blocks=2, - attn_resolutions={8, 16, 32}, - resolution=256, - z_channels=AUDIO_LATENT_CHANNELS, - norm_type=NormType.PIXEL, - causality_axis=CausalityAxis.HEIGHT, - mel_bins=64, # Output mel bins - ) - - # Load weights from main model file - weight_file = model_path / "ltx-2-19b-distilled.safetensors" - if weight_file.exists(): - raw_weights = mx.load(str(weight_file)) - sanitized = sanitize_audio_vae_weights(raw_weights) - if sanitized: - decoder.load_weights(list(sanitized.items()), strict=False) - - # Manually load per-channel statistics (they're plain mx.array, not tracked by load_weights) - if "per_channel_statistics._mean_of_means" in sanitized: - decoder.per_channel_statistics._mean_of_means = sanitized["per_channel_statistics._mean_of_means"] - if "per_channel_statistics._std_of_means" in sanitized: - decoder.per_channel_statistics._std_of_means = sanitized["per_channel_statistics._std_of_means"] - - return decoder - - -def load_vocoder(model_path: Path): - """Load vocoder for mel to waveform conversion.""" - from mlx_video.models.ltx.audio_vae import Vocoder - - vocoder = Vocoder( - resblock_kernel_sizes=[3, 7, 11], - upsample_rates=[6, 5, 2, 2, 2], - upsample_kernel_sizes=[16, 15, 8, 4, 4], - resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5], [1, 3, 5]], - upsample_initial_channel=1024, - stereo=True, - output_sample_rate=AUDIO_SAMPLE_RATE, - ) - - # Load weights - weight_file = model_path / "ltx-2-19b-distilled.safetensors" - if weight_file.exists(): - raw_weights = mx.load(str(weight_file)) - sanitized = sanitize_vocoder_weights(raw_weights) - if sanitized: - vocoder.load_weights(list(sanitized.items()), strict=False) - - return vocoder - - -def save_audio(audio: np.ndarray, path: Path, sample_rate: int = AUDIO_SAMPLE_RATE): - """Save audio to WAV file.""" - import wave - - # Ensure audio is in correct format (channels, samples) or (samples,) - if audio.ndim == 2: - # (channels, samples) -> (samples, channels) - audio = audio.T - - # Normalize and convert to int16 - audio = np.clip(audio, -1.0, 1.0) - audio_int16 = (audio * 32767).astype(np.int16) - - with wave.open(str(path), 'wb') as wf: - wf.setnchannels(2 if audio_int16.ndim == 2 else 1) - wf.setsampwidth(2) # 16-bit - wf.setframerate(sample_rate) - wf.writeframes(audio_int16.tobytes()) - - -def mux_video_audio(video_path: Path, audio_path: Path, output_path: Path): - """Combine video and audio into final output using ffmpeg.""" - import subprocess - - cmd = [ - "ffmpeg", "-y", - "-i", str(video_path), - "-i", str(audio_path), - "-c:v", "copy", - "-c:a", "aac", - "-shortest", - str(output_path) - ] - - try: - subprocess.run(cmd, check=True, capture_output=True) - return True - except subprocess.CalledProcessError as e: - print(f"{Colors.RED}FFmpeg error: {e.stderr.decode()}{Colors.RESET}") - return False - except FileNotFoundError: - print(f"{Colors.RED}FFmpeg not found. Please install ffmpeg.{Colors.RESET}") - return False - - -def generate_video_with_audio( - model_repo: str, - text_encoder_repo: Optional[str], - prompt: str, - height: int = 512, - width: int = 512, - num_frames: int = 33, - seed: int = 42, - fps: int = 24, - output_path: str = "output_av.mp4", - output_audio_path: Optional[str] = None, - verbose: bool = True, - enhance_prompt: bool = False, - max_tokens: int = 512, - temperature: float = 0.7, - image: Optional[str] = None, - image_strength: float = 1.0, - image_frame_idx: int = 0, - tiling: str = "auto", -): - """Generate video with synchronized audio from text prompt, optionally conditioned on an image. - - Args: - model_repo: Model repository ID - text_encoder_repo: Text encoder repository ID - prompt: Text description of the video to generate - height: Output video height (must be divisible by 64) - width: Output video width (must be divisible by 64) - num_frames: Number of frames - seed: Random seed - fps: Frames per second - output_path: Output video path - output_audio_path: Output audio path - verbose: Whether to print progress - enhance_prompt: Whether to enhance prompt using Gemma - max_tokens: Max tokens for prompt enhancement - temperature: Temperature for prompt enhancement - image: Path to conditioning image for I2V - image_strength: Conditioning strength (1.0 = full denoise) - image_frame_idx: Frame index to condition (0 = first frame) - tiling: Tiling mode for VAE decoding (auto/none/default/aggressive/conservative/spatial/temporal) - """ - start_time = time.time() - - # Validate dimensions - assert height % 64 == 0, f"Height must be divisible by 64, got {height}" - assert width % 64 == 0, f"Width must be divisible by 64, got {width}" - - if num_frames % 8 != 1: - adjusted_num_frames = round((num_frames - 1) / 8) * 8 + 1 - print(f"{Colors.YELLOW}⚠️ Adjusted frames to {adjusted_num_frames}{Colors.RESET}") - num_frames = adjusted_num_frames - - # Calculate audio frames - audio_frames = compute_audio_frames(num_frames, fps) - - is_i2v = image is not None - mode_str = "I2V+Audio" if is_i2v else "T2V+Audio" - print(f"{Colors.BOLD}{Colors.CYAN}🎬 [{mode_str}] Generating {width}x{height} video with {num_frames} frames + audio{Colors.RESET}") - print(f"{Colors.DIM}Audio: {audio_frames} latent frames @ {AUDIO_SAMPLE_RATE}Hz{Colors.RESET}") - print(f"{Colors.DIM}Prompt: {prompt[:80]}{'...' if len(prompt) > 80 else ''}{Colors.RESET}") - if is_i2v: - print(f"{Colors.DIM}Image: {image} (strength={image_strength}, frame={image_frame_idx}){Colors.RESET}") - - model_path = get_model_path(model_repo) - text_encoder_path = model_path if text_encoder_repo is None else get_model_path(text_encoder_repo) - - # Calculate latent dimensions - stage1_h, stage1_w = height // 2 // 32, width // 2 // 32 - stage2_h, stage2_w = height // 32, width // 32 - latent_frames = 1 + (num_frames - 1) // 8 - - mx.random.seed(seed) - - # Load text encoder with audio embeddings - print(f"{Colors.BLUE}📝 Loading text encoder...{Colors.RESET}") - from mlx_video.models.ltx.text_encoder import LTX2TextEncoder - text_encoder = LTX2TextEncoder() - text_encoder.load(model_path=model_path, text_encoder_path=text_encoder_path) - mx.eval(text_encoder.parameters()) - - # Optionally enhance prompt - if enhance_prompt: - print(f"{Colors.MAGENTA}✨ Enhancing prompt...{Colors.RESET}") - prompt = text_encoder.enhance_t2v(prompt, max_tokens=max_tokens, temperature=temperature, seed=seed, verbose=verbose) - print(f"{Colors.DIM}Enhanced: {prompt[:150]}{'...' if len(prompt) > 150 else ''}{Colors.RESET}") - - # Get both video and audio embeddings - video_embeddings, audio_embeddings = text_encoder(prompt) - model_dtype = video_embeddings.dtype # bfloat16 from text encoder - mx.eval(video_embeddings, audio_embeddings) - - del text_encoder - mx.clear_cache() - - # Load transformer with AudioVideo config - print(f"{Colors.BLUE}🤖 Loading transformer (A/V mode)...{Colors.RESET}") - raw_weights = mx.load(str(model_path / 'ltx-2-19b-distilled.safetensors')) - sanitized = sanitize_transformer_weights(raw_weights) - - # Convert transformer weights to bfloat16 for memory efficiency - sanitized = {k: v.astype(mx.bfloat16) if v.dtype == mx.float32 else v for k, v in sanitized.items()} - - config = LTXModelConfig( - model_type=LTXModelType.AudioVideo, - num_attention_heads=32, - attention_head_dim=128, - in_channels=128, - out_channels=128, - num_layers=48, - cross_attention_dim=4096, - caption_channels=3840, - # Audio config - audio_num_attention_heads=32, - audio_attention_head_dim=64, - audio_in_channels=AUDIO_LATENT_CHANNELS * AUDIO_MEL_BINS, # 8 * 16 = 128 - audio_out_channels=AUDIO_LATENT_CHANNELS * AUDIO_MEL_BINS, - audio_cross_attention_dim=2048, - rope_type=LTXRopeType.SPLIT, - double_precision_rope=True, - positional_embedding_theta=10000.0, - positional_embedding_max_pos=[20, 2048, 2048], - audio_positional_embedding_max_pos=[20], - use_middle_indices_grid=True, - timestep_scale_multiplier=1000, - ) - - transformer = LTXModel(config) - transformer.load_weights(list(sanitized.items()), strict=False) - mx.eval(transformer.parameters()) - - # Load VAE encoder and encode image for I2V conditioning - stage1_image_latent = None - stage2_image_latent = None - if is_i2v: - print(f"{Colors.BLUE}🖼️ Loading VAE encoder and encoding image...{Colors.RESET}") - vae_encoder = load_vae_encoder(str(model_path / 'ltx-2-19b-distilled.safetensors')) - mx.eval(vae_encoder.parameters()) - - # Load and prepare image for stage 1 (half resolution) - input_image = load_image(image, height=height // 2, width=width // 2, dtype=model_dtype) - stage1_image_tensor = prepare_image_for_encoding(input_image, height // 2, width // 2, dtype=model_dtype) - stage1_image_latent = vae_encoder(stage1_image_tensor) - mx.eval(stage1_image_latent) - - # Load and prepare image for stage 2 (full resolution) - input_image = load_image(image, height=height, width=width, dtype=model_dtype) - stage2_image_tensor = prepare_image_for_encoding(input_image, height, width, dtype=model_dtype) - stage2_image_latent = vae_encoder(stage2_image_tensor) - mx.eval(stage2_image_latent) - - del vae_encoder - mx.clear_cache() - - # Initialize latents - print(f"{Colors.YELLOW}⚡ Stage 1: Generating at {width//2}x{height//2} (8 steps)...{Colors.RESET}") - mx.random.seed(seed) - - # Create position grids - MUST stay float32 for RoPE precision - # bfloat16 positions cause quality degradation due to precision loss in sin/cos calculations - video_positions = create_video_position_grid(1, latent_frames, stage1_h, stage1_w) # float32 - audio_positions = create_audio_position_grid(1, audio_frames) # float32 - mx.eval(video_positions, audio_positions) - - # Apply I2V conditioning for stage 1 if provided - video_state1 = None - video_latent_shape = (1, 128, latent_frames, stage1_h, stage1_w) - if is_i2v and stage1_image_latent is not None: - # PyTorch flow: create zeros -> apply conditioning -> apply noiser - video_state1 = LatentState( - latent=mx.zeros(video_latent_shape, dtype=model_dtype), - clean_latent=mx.zeros(video_latent_shape, dtype=model_dtype), - denoise_mask=mx.ones((1, 1, latent_frames, 1, 1), dtype=model_dtype), - ) - conditioning = VideoConditionByLatentIndex( - latent=stage1_image_latent, - frame_idx=image_frame_idx, - strength=image_strength, - ) - video_state1 = apply_conditioning(video_state1, [conditioning]) - - # Apply noiser: latent = noise * (mask * noise_scale) + latent * (1 - mask * noise_scale) - noise = mx.random.normal(video_latent_shape).astype(model_dtype) - noise_scale = mx.array(STAGE_1_SIGMAS[0], dtype=model_dtype) # 1.0 - scaled_mask = video_state1.denoise_mask * noise_scale - video_state1 = LatentState( - latent=noise * scaled_mask + video_state1.latent * (mx.array(1.0, dtype=model_dtype) - scaled_mask), - clean_latent=video_state1.clean_latent, - denoise_mask=video_state1.denoise_mask, - ) - video_latents = video_state1.latent - mx.eval(video_latents) - else: - # T2V: just use random noise - video_latents = mx.random.normal(video_latent_shape).astype(model_dtype) - mx.eval(video_latents) - - # Audio always uses pure noise (no I2V for audio) - audio_latents = mx.random.normal((1, AUDIO_LATENT_CHANNELS, audio_frames, AUDIO_MEL_BINS)).astype(model_dtype) - mx.eval(audio_latents) - - # Stage 1 denoising - video_latents, audio_latents = denoise_av( - video_latents, audio_latents, - video_positions, audio_positions, - video_embeddings, audio_embeddings, - transformer, STAGE_1_SIGMAS, verbose=verbose, - video_state=video_state1 - ) - - # Upsample video latents - print(f"{Colors.MAGENTA}🔍 Upsampling video latents 2x...{Colors.RESET}") - upsampler = load_upsampler(str(model_path / 'ltx-2-spatial-upscaler-x2-1.0.safetensors')) - mx.eval(upsampler.parameters()) - - vae_decoder = load_vae_decoder( - str(model_path / 'ltx-2-19b-distilled.safetensors'), - timestep_conditioning=None # Auto-detect from model metadata - ) - - video_latents = upsample_latents(video_latents, upsampler, vae_decoder.latents_mean, vae_decoder.latents_std) - mx.eval(video_latents) - - del upsampler - mx.clear_cache() - - # Stage 2: Refine at full resolution - print(f"{Colors.YELLOW}⚡ Stage 2: Refining at {width}x{height} (3 steps)...{Colors.RESET}") - # Position grids stay float32 for RoPE precision - video_positions = create_video_position_grid(1, latent_frames, stage2_h, stage2_w) # float32 - mx.eval(video_positions) - - # Apply I2V conditioning for stage 2 if provided - video_state2 = None - if is_i2v and stage2_image_latent is not None: - # PyTorch flow: start with upscaled latent -> apply conditioning -> apply noiser - video_state2 = LatentState( - latent=video_latents, # Start with upscaled latent - clean_latent=mx.zeros_like(video_latents), - denoise_mask=mx.ones((1, 1, latent_frames, 1, 1), dtype=model_dtype), - ) - conditioning = VideoConditionByLatentIndex( - latent=stage2_image_latent, - frame_idx=image_frame_idx, - strength=image_strength, - ) - video_state2 = apply_conditioning(video_state2, [conditioning]) - - # Apply noiser: conditioned frames (mask=0) keep image latent, unconditioned get partial noise - video_noise = mx.random.normal(video_latents.shape).astype(model_dtype) - noise_scale = mx.array(STAGE_2_SIGMAS[0], dtype=model_dtype) - scaled_mask = video_state2.denoise_mask * noise_scale - video_state2 = LatentState( - latent=video_noise * scaled_mask + video_state2.latent * (mx.array(1.0, dtype=model_dtype) - scaled_mask), - clean_latent=video_state2.clean_latent, - denoise_mask=video_state2.denoise_mask, - ) - video_latents = video_state2.latent - mx.eval(video_latents) - - # Audio still gets noise (no I2V for audio) - audio_noise = mx.random.normal(audio_latents.shape).astype(model_dtype) - one_minus_scale = mx.array(1.0, dtype=model_dtype) - noise_scale - audio_latents = audio_noise * noise_scale + audio_latents * one_minus_scale - mx.eval(audio_latents) - else: - # T2V: add noise to all frames for refinement - noise_scale = mx.array(STAGE_2_SIGMAS[0], dtype=model_dtype) - one_minus_scale = mx.array(1.0, dtype=model_dtype) - noise_scale - video_noise = mx.random.normal(video_latents.shape).astype(model_dtype) - audio_noise = mx.random.normal(audio_latents.shape).astype(model_dtype) - video_latents = video_noise * noise_scale + video_latents * one_minus_scale - audio_latents = audio_noise * noise_scale + audio_latents * one_minus_scale - mx.eval(video_latents, audio_latents) - - video_latents, audio_latents = denoise_av( - video_latents, audio_latents, - video_positions, audio_positions, - video_embeddings, audio_embeddings, - transformer, STAGE_2_SIGMAS, verbose=verbose, - video_state=video_state2 - ) - - del transformer - mx.clear_cache() - - # Decode video with tiling - print(f"{Colors.BLUE}🎞️ Decoding video...{Colors.RESET}") - - # Select tiling configuration - if tiling == "none": - tiling_config = None - elif tiling == "auto": - tiling_config = TilingConfig.auto(height, width, num_frames) - elif tiling == "default": - tiling_config = TilingConfig.default() - elif tiling == "aggressive": - tiling_config = TilingConfig.aggressive() - elif tiling == "conservative": - tiling_config = TilingConfig.conservative() - elif tiling == "spatial": - tiling_config = TilingConfig.spatial_only() - elif tiling == "temporal": - tiling_config = TilingConfig.temporal_only() - else: - print(f"{Colors.YELLOW} Unknown tiling mode '{tiling}', using auto{Colors.RESET}") - tiling_config = TilingConfig.auto(height, width, num_frames) - - if tiling_config is not None: - spatial_info = f"{tiling_config.spatial_config.tile_size_in_pixels}px" if tiling_config.spatial_config else "none" - temporal_info = f"{tiling_config.temporal_config.tile_size_in_frames}f" if tiling_config.temporal_config else "none" - print(f"{Colors.DIM} Tiling ({tiling}): spatial={spatial_info}, temporal={temporal_info}{Colors.RESET}") - video = vae_decoder.decode_tiled(video_latents, tiling_config=tiling_config, debug=verbose) - else: - print(f"{Colors.DIM} Tiling: disabled{Colors.RESET}") - video = vae_decoder(video_latents) - mx.eval(video) - - # Convert video to uint8 frames - video = mx.squeeze(video, axis=0) - video = mx.transpose(video, (1, 2, 3, 0)) - video = mx.clip((video + 1.0) / 2.0, 0.0, 1.0) - video = (video * 255).astype(mx.uint8) - video_np = np.array(video) - - # Decode audio - print(f"{Colors.BLUE}🔊 Decoding audio...{Colors.RESET}") - audio_decoder = load_audio_decoder(model_path) - vocoder = load_vocoder(model_path) - mx.eval(audio_decoder.parameters(), vocoder.parameters()) - - mel_spectrogram = audio_decoder(audio_latents) - mx.eval(mel_spectrogram) - - # Audio decoder output is already in vocoder format (B, C, T, F) - audio_waveform = vocoder(mel_spectrogram) - mx.eval(audio_waveform) - - audio_np = np.array(audio_waveform) - if audio_np.ndim == 3: - audio_np = audio_np[0] # Remove batch dim - - del audio_decoder, vocoder, vae_decoder - mx.clear_cache() - - # Save outputs - output_path = Path(output_path) - output_path.parent.mkdir(parents=True, exist_ok=True) - - # Save video (temporary without audio) - temp_video_path = output_path.with_suffix('.temp.mp4') - - try: - import cv2 - h, w = video_np.shape[1], video_np.shape[2] - fourcc = cv2.VideoWriter_fourcc(*'avc1') - out = cv2.VideoWriter(str(temp_video_path), fourcc, fps, (w, h)) - for frame in video_np: - out.write(cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)) - out.release() - print(f"{Colors.GREEN}✅ Video encoded{Colors.RESET}") - except Exception as e: - print(f"{Colors.RED}❌ Video encoding failed: {e}{Colors.RESET}") - return None, None - - # Save audio - audio_path = output_path.with_suffix('.wav') if output_audio_path is None else Path(output_audio_path) - save_audio(audio_np, audio_path, AUDIO_SAMPLE_RATE) - print(f"{Colors.GREEN}✅ Saved audio to{Colors.RESET} {audio_path}") - - # Mux video and audio - print(f"{Colors.BLUE}🎬 Combining video and audio...{Colors.RESET}") - if mux_video_audio(temp_video_path, audio_path, output_path): - print(f"{Colors.GREEN}✅ Saved video with audio to{Colors.RESET} {output_path}") - temp_video_path.unlink() # Remove temp file - else: - # Fallback: keep video without audio - temp_video_path.rename(output_path) - print(f"{Colors.YELLOW}⚠️ Saved video without audio to{Colors.RESET} {output_path}") - - elapsed = time.time() - start_time - print(f"{Colors.BOLD}{Colors.GREEN}🎉 Done! Generated in {elapsed:.1f}s{Colors.RESET}") - print(f"{Colors.BOLD}{Colors.GREEN}✨ Peak memory: {mx.get_peak_memory() / (1024 ** 3):.2f}GB{Colors.RESET}") - - return video_np, audio_np - - -def main(): - parser = argparse.ArgumentParser( - description="Generate videos with synchronized audio using MLX LTX-2 (T2V and I2V)", - formatter_class=argparse.RawDescriptionHelpFormatter, - epilog=""" -Examples: - # Text-to-Video with Audio (T2V+Audio) - python -m mlx_video.generate_av --prompt "Ocean waves crashing on a beach" - python -m mlx_video.generate_av --prompt "A jazz band playing" --enhance-prompt - python -m mlx_video.generate_av --prompt "..." --output my_video.mp4 --output-audio my_audio.wav - - # Image-to-Video with Audio (I2V+Audio) - python -m mlx_video.generate_av --prompt "A person dancing" --image photo.jpg - python -m mlx_video.generate_av --prompt "Waves crashing" --image beach.png --image-strength 0.8 - """ - ) - - parser.add_argument("--prompt", "-p", type=str, required=True, - help="Text description of the video/audio to generate") - parser.add_argument("--height", "-H", type=int, default=512, - help="Output video height (default: 512)") - parser.add_argument("--width", "-W", type=int, default=512, - help="Output video width (default: 512)") - parser.add_argument("--num-frames", "-n", type=int, default=65, - help="Number of frames (default: 65)") - parser.add_argument("--seed", "-s", type=int, default=42, - help="Random seed (default: 42)") - parser.add_argument("--fps", type=int, default=24, - help="Frames per second (default: 24)") - parser.add_argument("--output-path", type=str, default="output_av.mp4", - help="Output video path (default: output_av.mp4)") - parser.add_argument("--output-audio", type=str, default=None, - help="Output audio path (default: same as video with .wav)") - parser.add_argument("--model-repo", type=str, default="Lightricks/LTX-2", - help="Model repository (default: Lightricks/LTX-2)") - parser.add_argument("--text-encoder-repo", type=str, default=None, - help="Text encoder repository") - parser.add_argument("--verbose", action="store_true", - help="Verbose output") - parser.add_argument("--enhance-prompt", action="store_true", - help="Enhance prompt using Gemma") - parser.add_argument("--max-tokens", type=int, default=512, - help="Max tokens for prompt enhancement") - parser.add_argument("--temperature", type=float, default=0.7, - help="Temperature for prompt enhancement") - parser.add_argument("--image", "-i", type=str, default=None, - help="Path to conditioning image for I2V (Image-to-Video) generation") - parser.add_argument("--image-strength", type=float, default=1.0, - help="Conditioning strength for I2V (1.0 = full denoise, 0.0 = keep original, default: 1.0)") - parser.add_argument("--image-frame-idx", type=int, default=0, - help="Frame index to condition for I2V (0 = first frame, default: 0)") - parser.add_argument("--tiling", type=str, default="auto", - choices=["auto", "none", "default", "aggressive", "conservative", "spatial", "temporal"], - help="Tiling mode for VAE decoding (default: auto). " - "auto=based on size, none=disabled, default=512px/64f, " - "aggressive=256px/32f (lowest memory), conservative=768px/96f") - - args = parser.parse_args() - - generate_video_with_audio( - model_repo=args.model_repo, - text_encoder_repo=args.text_encoder_repo, - prompt=args.prompt, - height=args.height, - width=args.width, - num_frames=args.num_frames, - seed=args.seed, - fps=args.fps, - output_path=args.output_path, - output_audio_path=args.output_audio, - verbose=args.verbose, - enhance_prompt=args.enhance_prompt, - max_tokens=args.max_tokens, - temperature=args.temperature, - image=args.image, - image_strength=args.image_strength, - image_frame_idx=args.image_frame_idx, - tiling=args.tiling, - ) - - -if __name__ == "__main__": - main() diff --git a/mlx_video/lora/__init__.py b/mlx_video/lora/__init__.py index 4c0d81b..c4398e3 100644 --- a/mlx_video/lora/__init__.py +++ b/mlx_video/lora/__init__.py @@ -6,10 +6,7 @@ from mlx_video.lora.apply import ( apply_loras_to_model, apply_loras_to_weights, ) -from mlx_video.lora.loader import ( - load_lora_weights, - load_multiple_loras, -) +from mlx_video.lora.loader import load_lora_weights, load_multiple_loras from mlx_video.lora.types import AppliedLoRA, LoRAConfig, LoRAWeights __all__ = [ diff --git a/mlx_video/lora/apply.py b/mlx_video/lora/apply.py index 97b694e..dadb62d 100644 --- a/mlx_video/lora/apply.py +++ b/mlx_video/lora/apply.py @@ -66,7 +66,7 @@ def _normalize_wan_lora_key(lora_key: str, model_keys: set) -> str: candidates = [lora_key] for prefix in prefixes_to_strip: if lora_key.startswith(prefix): - candidates.append(lora_key[len(prefix):]) + candidates.append(lora_key[len(prefix) :]) for candidate in candidates: # Try as-is @@ -80,33 +80,36 @@ def _normalize_wan_lora_key(lora_key: str, model_keys: set) -> str: transformed = transformed.replace(".ffn.0.", ".ffn.fc1.") transformed = transformed.replace(".ffn.2.", ".ffn.fc2.") if transformed.endswith(".ffn.0"): - transformed = transformed[:-len(".ffn.0")] + ".ffn.fc1" + transformed = transformed[: -len(".ffn.0")] + ".ffn.fc1" if transformed.endswith(".ffn.2"): - transformed = transformed[:-len(".ffn.2")] + ".ffn.fc2" + transformed = transformed[: -len(".ffn.2")] + ".ffn.fc2" # Text embedding: text_embedding.0 → text_embedding_0 transformed = transformed.replace("text_embedding.0.", "text_embedding_0.") transformed = transformed.replace("text_embedding.2.", "text_embedding_1.") if transformed.endswith("text_embedding.0"): - transformed = transformed[:-len("text_embedding.0")] + "text_embedding_0" + transformed = transformed[: -len("text_embedding.0")] + "text_embedding_0" if transformed.endswith("text_embedding.2"): - transformed = transformed[:-len("text_embedding.2")] + "text_embedding_1" + transformed = transformed[: -len("text_embedding.2")] + "text_embedding_1" # Time embedding: time_embedding.0 → time_embedding_0 transformed = transformed.replace("time_embedding.0.", "time_embedding_0.") transformed = transformed.replace("time_embedding.2.", "time_embedding_1.") if transformed.endswith("time_embedding.0"): - transformed = transformed[:-len("time_embedding.0")] + "time_embedding_0" + transformed = transformed[: -len("time_embedding.0")] + "time_embedding_0" if transformed.endswith("time_embedding.2"): - transformed = transformed[:-len("time_embedding.2")] + "time_embedding_1" + transformed = transformed[: -len("time_embedding.2")] + "time_embedding_1" # Time projection: time_projection.1 → time_projection transformed = transformed.replace("time_projection.1.", "time_projection.") if transformed.endswith("time_projection.1"): - transformed = transformed[:-len("time_projection.1")] + "time_projection" + transformed = transformed[: -len("time_projection.1")] + "time_projection" # Patch embedding: patch_embedding → patch_embedding_proj - if "patch_embedding" in transformed and "patch_embedding_proj" not in transformed: + if ( + "patch_embedding" in transformed + and "patch_embedding_proj" not in transformed + ): transformed = transformed.replace("patch_embedding", "patch_embedding_proj") if f"{transformed}.weight" in model_keys or transformed in model_keys: @@ -115,7 +118,7 @@ def _normalize_wan_lora_key(lora_key: str, model_keys: set) -> str: # Return best attempt with prefix stripped for prefix in prefixes_to_strip: if lora_key.startswith(prefix): - return lora_key[len(prefix):] + return lora_key[len(prefix) :] return lora_key @@ -134,21 +137,25 @@ def _normalize_ltx_lora_key(lora_key: str, model_keys: set) -> str: for prefix in prefixes_to_strip: if lora_key.startswith(prefix): - normalized = lora_key[len(prefix):] + normalized = lora_key[len(prefix) :] if f"{normalized}.weight" in model_keys or normalized in model_keys: return normalized transformed = normalized if transformed.endswith(".to_out.0"): - transformed = transformed[:-len(".to_out.0")] + ".to_out" + transformed = transformed[: -len(".to_out.0")] + ".to_out" transformed = transformed.replace(".to_out.0.", ".to_out.") transformed = transformed.replace(".ff.net.0.proj.", ".ff.proj_in.") transformed = transformed.replace(".ff.net.0.proj", ".ff.proj_in") transformed = transformed.replace(".ff.net.2.", ".ff.proj_out.") transformed = transformed.replace(".ff.net.2", ".ff.proj_out") - transformed = transformed.replace(".audio_ff.net.0.proj.", ".audio_ff.proj_in.") - transformed = transformed.replace(".audio_ff.net.0.proj", ".audio_ff.proj_in") + transformed = transformed.replace( + ".audio_ff.net.0.proj.", ".audio_ff.proj_in." + ) + transformed = transformed.replace( + ".audio_ff.net.0.proj", ".audio_ff.proj_in" + ) transformed = transformed.replace(".audio_ff.net.2.", ".audio_ff.proj_out.") transformed = transformed.replace(".audio_ff.net.2", ".audio_ff.proj_out") @@ -158,7 +165,7 @@ def _normalize_ltx_lora_key(lora_key: str, model_keys: set) -> str: # Try transformations on the original key transformed = lora_key if transformed.endswith(".to_out.0"): - transformed = transformed[:-len(".to_out.0")] + ".to_out" + transformed = transformed[: -len(".to_out.0")] + ".to_out" transformed = transformed.replace(".to_out.0.", ".to_out.") transformed = transformed.replace(".ff.net.0.proj.", ".ff.proj_in.") transformed = transformed.replace(".ff.net.0.proj", ".ff.proj_in") @@ -170,7 +177,7 @@ def _normalize_ltx_lora_key(lora_key: str, model_keys: set) -> str: for prefix in prefixes_to_strip: if lora_key.startswith(prefix): - return lora_key[len(prefix):] + return lora_key[len(prefix) :] return lora_key @@ -226,7 +233,9 @@ def apply_loras_to_weights( skipped_count += 1 skipped_modules.append(module_name) if verbose and skipped_count <= 5: - print(f" DEBUG: '{module_name}' -> '{normalized_name}' -> NOT FOUND") + print( + f" DEBUG: '{module_name}' -> '{normalized_name}' -> NOT FOUND" + ) similar = [ k for k in list(model_keys)[:1000] @@ -251,13 +260,21 @@ def apply_loras_to_weights( if is_quantized: scales = modified_weights[scales_key] biases = modified_weights[biases_key] - group_size = (original_weight.shape[-1] * 32) // (scales.shape[-1] * quantization_bits) + group_size = (original_weight.shape[-1] * 32) // ( + scales.shape[-1] * quantization_bits + ) dequantized = mx.dequantize( - original_weight, scales, biases, group_size=group_size, bits=quantization_bits + original_weight, + scales, + biases, + group_size=group_size, + bits=quantization_bits, ) modified = apply_lora_to_linear(dequantized, loras) # Re-quantize with same parameters - new_w, new_scales, new_biases = mx.quantize(modified, group_size=group_size, bits=quantization_bits) + new_w, new_scales, new_biases = mx.quantize( + modified, group_size=group_size, bits=quantization_bits + ) modified_weights[weight_key] = new_w modified_weights[scales_key] = new_scales modified_weights[biases_key] = new_biases @@ -346,9 +363,15 @@ def apply_loras_to_model( parent = model try: for part in parts[:-1]: - parent = getattr(parent, part) if not part.isdigit() else parent[int(part)] + parent = ( + getattr(parent, part) if not part.isdigit() else parent[int(part)] + ) leaf_name = parts[-1] - target = getattr(parent, leaf_name) if not leaf_name.isdigit() else parent[int(leaf_name)] + target = ( + getattr(parent, leaf_name) + if not leaf_name.isdigit() + else parent[int(leaf_name)] + ) except (AttributeError, IndexError, TypeError): skipped.append(lora_key) if verbose: @@ -358,8 +381,11 @@ def apply_loras_to_model( if isinstance(target, nn.QuantizedLinear): # Dequantize → merge LoRA → replace with bf16 Linear weight = mx.dequantize( - target.weight, target.scales, target.biases, - group_size=target.group_size, bits=target.bits, + target.weight, + target.scales, + target.biases, + group_size=target.group_size, + bits=target.bits, ) merged = apply_lora_to_linear(weight, loras) new_linear = nn.Linear(merged.shape[1], merged.shape[0]) @@ -379,7 +405,9 @@ def apply_loras_to_model( else: skipped.append(lora_key) if verbose: - print(f" DEBUG: '{module_path}' is {type(target).__name__}, not Linear") + print( + f" DEBUG: '{module_path}' is {type(target).__name__}, not Linear" + ) continue if applied_count > 0: diff --git a/mlx_video/lora/loader.py b/mlx_video/lora/loader.py index adf11b1..2a44aca 100644 --- a/mlx_video/lora/loader.py +++ b/mlx_video/lora/loader.py @@ -2,7 +2,7 @@ import re from pathlib import Path -from typing import Dict, List, Optional +from typing import Dict, List import mlx.core as mx diff --git a/mlx_video/models/__init__.py b/mlx_video/models/__init__.py index 84fb090..c730f1d 100644 --- a/mlx_video/models/__init__.py +++ b/mlx_video/models/__init__.py @@ -1,3 +1,2 @@ - -from mlx_video.models.ltx import LTXModel, LTXModelConfig -from mlx_video.models.wan import WanModel, WanModelConfig +from mlx_video.models.ltx_2 import LTXModel, LTXModelConfig +from mlx_video.models.wan_2 import WanModel, WanModelConfig diff --git a/mlx_video/models/ltx/__init__.py b/mlx_video/models/ltx/__init__.py deleted file mode 100644 index 6a817e3..0000000 --- a/mlx_video/models/ltx/__init__.py +++ /dev/null @@ -1,8 +0,0 @@ - -from mlx_video.models.ltx.config import ( - LTXModelConfig, - TransformerConfig, - LTXModelType, -) -from mlx_video.models.ltx.ltx import LTXModel, X0Model -from mlx_video.models.ltx.audio_vae import AudioDecoder, Vocoder, decode_audio diff --git a/mlx_video/models/ltx/audio_vae/audio_vae.py b/mlx_video/models/ltx/audio_vae/audio_vae.py deleted file mode 100644 index 08caec5..0000000 --- a/mlx_video/models/ltx/audio_vae/audio_vae.py +++ /dev/null @@ -1,326 +0,0 @@ -"""Audio VAE encoder and decoder for LTX-2.""" - -from typing import Set, Tuple - -import mlx.core as mx -import mlx.nn as nn - -from .attention import AttentionType, make_attn -from .causal_conv_2d import make_conv2d -from .causality_axis import CausalityAxis -from .downsample import build_downsampling_path -from .normalization import NormType, build_normalization_layer -from .ops import AudioLatentShape, AudioPatchifier, PerChannelStatistics -from .resnet import ResnetBlock -from .upsample import build_upsampling_path - -LATENT_DOWNSAMPLE_FACTOR = 4 - - -def build_mid_block( - channels: int, - temb_channels: int, - dropout: float, - norm_type: NormType, - causality_axis: CausalityAxis, - attn_type: AttentionType, - add_attention: bool, -) -> dict: - """Build the middle block with two ResNet blocks and optional attention.""" - mid = {} - mid["block_1"] = ResnetBlock( - in_channels=channels, - out_channels=channels, - temb_channels=temb_channels, - dropout=dropout, - norm_type=norm_type, - causality_axis=causality_axis, - ) - mid["attn_1"] = ( - make_attn(channels, attn_type=attn_type, norm_type=norm_type) if add_attention else None - ) - mid["block_2"] = ResnetBlock( - in_channels=channels, - out_channels=channels, - temb_channels=temb_channels, - dropout=dropout, - norm_type=norm_type, - causality_axis=causality_axis, - ) - return mid - - -def run_mid_block(mid: dict, features: mx.array) -> mx.array: - """Run features through the middle block.""" - features = mid["block_1"](features, temb=None) - if mid["attn_1"] is not None: - features = mid["attn_1"](features) - return mid["block_2"](features, temb=None) - - -class AudioDecoder(nn.Module): - """ - Symmetric decoder that reconstructs audio spectrograms from latent features. - The decoder mirrors the encoder structure with configurable channel multipliers, - attention resolutions, and causal convolutions. - """ - - def __init__( - self, - *, - ch: int = 128, - out_ch: int = 2, - ch_mult: Tuple[int, ...] = (1, 2, 4), - num_res_blocks: int = 2, - attn_resolutions: Set[int] = None, - resolution: int = 256, - z_channels: int = 8, - norm_type: NormType = NormType.PIXEL, - causality_axis: CausalityAxis = CausalityAxis.HEIGHT, - dropout: float = 0.0, - mid_block_add_attention: bool = True, - sample_rate: int = 16000, - mel_hop_length: int = 160, - is_causal: bool = True, - mel_bins: int | None = None, - ) -> None: - """ - Initialize the AudioDecoder. - Args: - ch: Base number of feature channels - out_ch: Number of output channels (2 for stereo) - ch_mult: Multiplicative factors for channels at each resolution - num_res_blocks: Number of residual blocks per resolution - attn_resolutions: Resolutions at which to apply attention - resolution: Input spatial resolution - z_channels: Number of latent channels - norm_type: Normalization type - causality_axis: Axis for causal convolutions - dropout: Dropout probability - mid_block_add_attention: Whether to add attention in middle block - sample_rate: Audio sample rate - mel_hop_length: Hop length for mel spectrogram - is_causal: Whether to use causal convolutions - mel_bins: Number of mel frequency bins - """ - super().__init__() - - if attn_resolutions is None: - attn_resolutions = {8, 16, 32} - - # Internal behavioral defaults - resamp_with_conv = True - attn_type = AttentionType.VANILLA - - # Per-channel statistics for denormalizing latents - # Uses ch (base channel count) to match the patchified latent dimension - # Input latent shape: (B, z_channels, T, latent_mel_bins) = (B, 8, T, 16) - # After patchify: (B, T, z_channels * latent_mel_bins) = (B, T, 128) - # ch=128 matches this dimension, so use ch for per_channel_statistics - self.per_channel_statistics = PerChannelStatistics(latent_channels=ch) - self.sample_rate = sample_rate - self.mel_hop_length = mel_hop_length - self.is_causal = is_causal - self.mel_bins = mel_bins - - self.patchifier = AudioPatchifier( - patch_size=1, - audio_latent_downsample_factor=LATENT_DOWNSAMPLE_FACTOR, - sample_rate=sample_rate, - hop_length=mel_hop_length, - is_causal=is_causal, - ) - - self.ch = ch - self.temb_ch = 0 - self.num_resolutions = len(ch_mult) - self.num_res_blocks = num_res_blocks - self.resolution = resolution - self.out_ch = out_ch - self.give_pre_end = False - self.tanh_out = False - self.norm_type = norm_type - self.z_channels = z_channels - self.channel_multipliers = ch_mult - self.attn_resolutions = attn_resolutions - self.causality_axis = causality_axis - self.attn_type = attn_type - - base_block_channels = ch * self.channel_multipliers[-1] - base_resolution = resolution // (2 ** (self.num_resolutions - 1)) - self.z_shape = (1, z_channels, base_resolution, base_resolution) - - self.conv_in = make_conv2d( - z_channels, base_block_channels, kernel_size=3, stride=1, causality_axis=self.causality_axis - ) - - self.mid = build_mid_block( - channels=base_block_channels, - temb_channels=self.temb_ch, - dropout=dropout, - norm_type=self.norm_type, - causality_axis=self.causality_axis, - attn_type=self.attn_type, - add_attention=mid_block_add_attention, - ) - - self.up, final_block_channels = build_upsampling_path( - ch=ch, - ch_mult=ch_mult, - num_resolutions=self.num_resolutions, - num_res_blocks=num_res_blocks, - resolution=resolution, - temb_channels=self.temb_ch, - dropout=dropout, - norm_type=self.norm_type, - causality_axis=self.causality_axis, - attn_type=self.attn_type, - attn_resolutions=attn_resolutions, - resamp_with_conv=resamp_with_conv, - initial_block_channels=base_block_channels, - ) - - self.norm_out = build_normalization_layer(final_block_channels, normtype=self.norm_type) - self.conv_out = make_conv2d( - final_block_channels, out_ch, kernel_size=3, stride=1, causality_axis=self.causality_axis - ) - - def __call__(self, sample: mx.array) -> mx.array: - """ - Decode latent features back to audio spectrograms. - Args: - sample: Encoded latent representation of shape (B, H, W, C) in MLX format - or (B, C, H, W) in PyTorch format (will be transposed) - Returns: - Reconstructed audio spectrogram - """ - # Handle input format - if channels are in dim 1, transpose to channels-last - if sample.shape[1] == self.z_channels and sample.ndim == 4: - # PyTorch format (B, C, H, W) -> MLX format (B, H, W, C) - sample = mx.transpose(sample, (0, 2, 3, 1)) - - sample, target_shape = self._denormalize_latents(sample) - - h = self.conv_in(sample) - h = run_mid_block(self.mid, h) - h = self._run_upsampling_path(h) - h = self._finalize_output(h) - - return self._adjust_output_shape(h, target_shape) - - def _denormalize_latents(self, sample: mx.array) -> tuple[mx.array, AudioLatentShape]: - """Denormalize latents using per-channel statistics.""" - # sample shape: (B, H, W, C) in MLX format - latent_shape = AudioLatentShape( - batch=sample.shape[0], - channels=sample.shape[3], # channels last - frames=sample.shape[1], # height = frames - mel_bins=sample.shape[2], # width = mel_bins - ) - - sample_patched = self.patchifier.patchify(sample) - sample_denormalized = self.per_channel_statistics.un_normalize(sample_patched) - sample = self.patchifier.unpatchify(sample_denormalized, latent_shape) - - target_frames = latent_shape.frames * LATENT_DOWNSAMPLE_FACTOR - if self.causality_axis != CausalityAxis.NONE: - target_frames = max(target_frames - (LATENT_DOWNSAMPLE_FACTOR - 1), 1) - - target_shape = AudioLatentShape( - batch=latent_shape.batch, - channels=self.out_ch, - frames=target_frames, - mel_bins=self.mel_bins if self.mel_bins is not None else latent_shape.mel_bins, - ) - - return sample, target_shape - - def _adjust_output_shape( - self, - decoded_output: mx.array, - target_shape: AudioLatentShape, - ) -> mx.array: - """ - Adjust output shape to match target dimensions for variable-length audio. - Args: - decoded_output: Tensor of shape (B, H, W, C) in MLX format - target_shape: AudioLatentShape describing target dimensions - Returns: - Tensor adjusted to match target_shape exactly - """ - # Current output shape: (batch, frames, mel_bins, channels) in MLX format - _, current_time, current_freq, _ = decoded_output.shape - target_channels = target_shape.channels - target_time = target_shape.frames - target_freq = target_shape.mel_bins - - # Step 1: Crop first to avoid exceeding target dimensions - decoded_output = decoded_output[ - :, : min(current_time, target_time), : min(current_freq, target_freq), :target_channels - ] - - # Step 2: Calculate padding needed for time and frequency dimensions - time_padding_needed = target_time - decoded_output.shape[1] - freq_padding_needed = target_freq - decoded_output.shape[2] - - # Step 3: Apply padding if needed - if time_padding_needed > 0 or freq_padding_needed > 0: - # MLX pad: [(before_0, after_0), ...] - # For (B, H, W, C): H=time, W=freq - padding = [ - (0, 0), # batch - (0, max(time_padding_needed, 0)), # time - (0, max(freq_padding_needed, 0)), # freq - (0, 0), # channels - ] - decoded_output = mx.pad(decoded_output, padding) - - # Step 4: Final safety crop to ensure exact target shape - decoded_output = decoded_output[:, :target_time, :target_freq, :target_channels] - - # Transpose back to PyTorch format (B, C, H, W) for vocoder compatibility - decoded_output = mx.transpose(decoded_output, (0, 3, 1, 2)) - - return decoded_output - - def _run_upsampling_path(self, h: mx.array) -> mx.array: - """Run through upsampling path.""" - for level in reversed(range(self.num_resolutions)): - stage = self.up[level] - for block_idx in range(len(stage["block"])): - h = stage["block"][block_idx](h, temb=None) - if block_idx in stage["attn"]: - h = stage["attn"][block_idx](h) - - if level != 0 and "upsample" in stage: - h = stage["upsample"](h) - - return h - - def _finalize_output(self, h: mx.array) -> mx.array: - """Apply final normalization and convolution.""" - if self.give_pre_end: - return h - - h = self.norm_out(h) - h = nn.silu(h) - h = self.conv_out(h) - return mx.tanh(h) if self.tanh_out else h - - -def decode_audio(latent: mx.array, audio_decoder: AudioDecoder, vocoder: "Vocoder") -> mx.array: - """ - Decode an audio latent representation using the provided audio decoder and vocoder. - Args: - latent: Input audio latent tensor - audio_decoder: Model to decode the latent to spectrogram - vocoder: Model to convert spectrogram to audio waveform - Returns: - Decoded audio as a float tensor - """ - decoded_audio = audio_decoder(latent) - decoded_audio = vocoder(decoded_audio) - # Remove batch dimension if present - if decoded_audio.shape[0] == 1: - decoded_audio = decoded_audio[0] - return decoded_audio.astype(mx.float32) diff --git a/mlx_video/models/ltx/audio_vae/causality_axis.py b/mlx_video/models/ltx/audio_vae/causality_axis.py deleted file mode 100644 index 15545b3..0000000 --- a/mlx_video/models/ltx/audio_vae/causality_axis.py +++ /dev/null @@ -1,12 +0,0 @@ -"""Causality axis enum for specifying causal convolution dimensions.""" - -from enum import Enum - - -class CausalityAxis(Enum): - """Enum for specifying the causality axis in causal convolutions.""" - - NONE = None - WIDTH = "width" - HEIGHT = "height" - WIDTH_COMPATIBILITY = "width-compatibility" diff --git a/mlx_video/models/ltx/audio_vae/vocoder.py b/mlx_video/models/ltx/audio_vae/vocoder.py deleted file mode 100644 index 02b5393..0000000 --- a/mlx_video/models/ltx/audio_vae/vocoder.py +++ /dev/null @@ -1,142 +0,0 @@ -"""Vocoder for converting mel spectrograms to audio waveforms.""" - -import math -from typing import List - -import mlx.core as mx -import mlx.nn as nn - -from .resnet import LRELU_SLOPE, ResBlock1, ResBlock2, leaky_relu - - -class Vocoder(nn.Module): - """ - Vocoder model for synthesizing audio from Mel spectrograms. - Based on HiFi-GAN architecture. - - Args: - resblock_kernel_sizes: List of kernel sizes for the residual blocks - upsample_rates: List of upsampling rates - upsample_kernel_sizes: List of kernel sizes for the upsampling layers - resblock_dilation_sizes: List of dilation sizes for the residual blocks - upsample_initial_channel: Initial number of channels for upsampling - stereo: Whether to use stereo output - resblock: Type of residual block to use ("1" or "2") - output_sample_rate: Waveform sample rate - """ - - def __init__( - self, - resblock_kernel_sizes: List[int] | None = None, - upsample_rates: List[int] | None = None, - upsample_kernel_sizes: List[int] | None = None, - resblock_dilation_sizes: List[List[int]] | None = None, - upsample_initial_channel: int = 1024, - stereo: bool = True, - resblock: str = "1", - output_sample_rate: int = 24000, - ): - super().__init__() - - # Initialize default values if not provided - if resblock_kernel_sizes is None: - resblock_kernel_sizes = [3, 7, 11] - if upsample_rates is None: - upsample_rates = [6, 5, 2, 2, 2] - if upsample_kernel_sizes is None: - upsample_kernel_sizes = [16, 15, 8, 4, 4] - if resblock_dilation_sizes is None: - resblock_dilation_sizes = [[1, 3, 5], [1, 3, 5], [1, 3, 5]] - - self.output_sample_rate = output_sample_rate - self.num_kernels = len(resblock_kernel_sizes) - self.num_upsamples = len(upsample_rates) - self.upsample_rates = upsample_rates - self.upsample_kernel_sizes = upsample_kernel_sizes - self.upsample_initial_channel = upsample_initial_channel - - in_channels = 128 if stereo else 64 - self.conv_pre = nn.Conv1d(in_channels, upsample_initial_channel, kernel_size=7, stride=1, padding=3) - - resblock_class = ResBlock1 if resblock == "1" else ResBlock2 - - # Upsampling layers using ConvTranspose1d - self.ups = {} - for i, (stride, kernel_size) in enumerate(zip(upsample_rates, upsample_kernel_sizes)): - in_ch = upsample_initial_channel // (2**i) - out_ch = upsample_initial_channel // (2 ** (i + 1)) - self.ups[i] = nn.ConvTranspose1d( - in_ch, - out_ch, - kernel_size=kernel_size, - stride=stride, - padding=(kernel_size - stride) // 2, - ) - - # Residual blocks - self.resblocks = {} - block_idx = 0 - for i in range(len(self.ups)): - ch = upsample_initial_channel // (2 ** (i + 1)) - for kernel_size, dilations in zip(resblock_kernel_sizes, resblock_dilation_sizes): - self.resblocks[block_idx] = resblock_class(ch, kernel_size, tuple(dilations)) - block_idx += 1 - - out_channels = 2 if stereo else 1 - final_channels = upsample_initial_channel // (2**self.num_upsamples) - self.conv_post = nn.Conv1d(final_channels, out_channels, kernel_size=7, stride=1, padding=3) - - self.upsample_factor = math.prod(upsample_rates) - - def __call__(self, x: mx.array) -> mx.array: - """ - Forward pass of the vocoder. - Args: - x: Input Mel spectrogram tensor. Can be either: - - 3D: (batch_size, time, mel_bins) for mono - MLX format (N, L, C) - - 4D: (batch_size, 2, time, mel_bins) for stereo - PyTorch format (N, C, H, W) - Returns: - Audio waveform tensor of shape (batch_size, out_channels, audio_length) - """ - # Input: (batch, channels, time, mel_bins) from audio decoder - # Transpose to (batch, channels, mel_bins, time) - x = mx.transpose(x, (0, 1, 3, 2)) - - if x.ndim == 4: # stereo - # x shape: (batch, 2, mel_bins, time) - # Rearrange to (batch, 2*mel_bins, time) - b, s, c, t = x.shape - x = x.reshape(b, s * c, t) - - # MLX Conv1d expects (N, L, C), so transpose - # Current: (batch, channels, time) -> (batch, time, channels) - x = mx.transpose(x, (0, 2, 1)) - - x = self.conv_pre(x) - - for i in range(self.num_upsamples): - x = leaky_relu(x, LRELU_SLOPE) - x = self.ups[i](x) - - start = i * self.num_kernels - end = start + self.num_kernels - - # Apply residual blocks and average their outputs - block_outputs = [] - for idx in range(start, end): - block_outputs.append(self.resblocks[idx](x)) - - # Stack and mean - x = mx.stack(block_outputs, axis=0) - x = mx.mean(x, axis=0) - - # IMPORTANT: Use default leaky_relu slope (0.01), NOT LRELU_SLOPE (0.1) - # PyTorch uses F.leaky_relu(x) which defaults to 0.01 - x = nn.leaky_relu(x) # Default negative_slope=0.01 - x = self.conv_post(x) - x = mx.tanh(x) - - # Transpose back to (batch, channels, time) - x = mx.transpose(x, (0, 2, 1)) - - return x diff --git a/mlx_video/models/ltx/config.py b/mlx_video/models/ltx/config.py deleted file mode 100644 index 6ac9de2..0000000 --- a/mlx_video/models/ltx/config.py +++ /dev/null @@ -1,182 +0,0 @@ - -import inspect -from dataclasses import dataclass, field -from enum import Enum -from typing import Any, List, Optional - - -class LTXModelType(Enum): - AudioVideo = "ltx av model" - VideoOnly = "ltx video only model" - AudioOnly = "ltx audio only model" - - def is_video_enabled(self) -> bool: - return self in (LTXModelType.AudioVideo, LTXModelType.VideoOnly) - - def is_audio_enabled(self) -> bool: - return self in (LTXModelType.AudioVideo, LTXModelType.AudioOnly) - - -class LTXRopeType(Enum): - INTERLEAVED = "interleaved" - SPLIT = "split" - TWO_D = "2d" - -class AttentionType(Enum): - DEFAULT = "default" - -@dataclass -class BaseModelConfig: - - @classmethod - def from_dict(cls, params: dict[str, Any]) -> "BaseModelConfig": - """Create config from dictionary, filtering only valid parameters.""" - return cls( - **{ - k: v - for k, v in params.items() - if k in inspect.signature(cls).parameters - } - ) - - def to_dict(self) -> dict[str, Any]: - """Export config to dictionary.""" - result = {} - for k, v in self.__dict__.items(): - if v is not None: - if isinstance(v, Enum): - result[k] = v.value - elif hasattr(v, 'to_dict'): - result[k] = v.to_dict() - else: - result[k] = v - return result - - -@dataclass -class TransformerConfig(BaseModelConfig): - dim: int - heads: int - d_head: int - context_dim: int - - -@dataclass -class VideoVAEConfig(BaseModelConfig): - convolution_dimensions: int = 3 - in_channels: int = 3 - out_channels: int = 128 - latent_channels: int = 128 - patch_size: int = 4 - encoder_blocks: List[tuple] = field(default_factory=lambda: [ - ("res_x", {"num_layers": 4}), - ("compress_space_res", {"multiplier": 2}), - ("res_x", {"num_layers": 6}), - ("compress_time_res", {"multiplier": 2}), - ("res_x", {"num_layers": 6}), - ("compress_all_res", {"multiplier": 2}), - ("res_x", {"num_layers": 2}), - ("compress_all_res", {"multiplier": 2}), - ("res_x", {"num_layers": 2}), - ]) - decoder_blocks: List[tuple] = field(default_factory=lambda: [ - ("res_x", {"num_layers": 5, "inject_noise": False}), - ("compress_all", {"residual": True, "multiplier": 2}), - ("res_x", {"num_layers": 5, "inject_noise": False}), - ("compress_all", {"residual": True, "multiplier": 2}), - ("res_x", {"num_layers": 5, "inject_noise": False}), - ("compress_all", {"residual": True, "multiplier": 2}), - ("res_x", {"num_layers": 5, "inject_noise": False}), - ]) - - -@dataclass -class LTXModelConfig(BaseModelConfig): - - # Model type - model_type: LTXModelType = LTXModelType.AudioVideo - - # Video transformer config - num_attention_heads: int = 32 - attention_head_dim: int = 128 - in_channels: int = 128 - out_channels: int = 128 - num_layers: int = 48 - cross_attention_dim: int = 4096 - caption_channels: int = 3840 - - # Audio transformer config - audio_num_attention_heads: int = 32 - audio_attention_head_dim: int = 64 - audio_in_channels: int = 128 - audio_out_channels: int = 128 - audio_cross_attention_dim: int = 2048 - audio_caption_channels: int = 3840 # Input dim for audio text embeddings (same as video) - - # Positional embedding config - positional_embedding_theta: float = 10000.0 - positional_embedding_max_pos: Optional[List[int]] = None - audio_positional_embedding_max_pos: Optional[List[int]] = None - use_middle_indices_grid: bool = True - rope_type: LTXRopeType = LTXRopeType.INTERLEAVED - double_precision_rope: bool = False - - # Timestep config - timestep_scale_multiplier: int = 1000 - av_ca_timestep_scale_multiplier: int = 1000 - - # Normalization - norm_eps: float = 1e-6 - - # Attention type - attention_type: AttentionType = AttentionType.DEFAULT - - # VAE config - vae_config: Optional[VideoVAEConfig] = None - - def __post_init__(self): - """Set default values after initialization.""" - if self.positional_embedding_max_pos is None: - self.positional_embedding_max_pos = [20, 2048, 2048] - if self.audio_positional_embedding_max_pos is None: - self.audio_positional_embedding_max_pos = [20] - - # Convert string enum values if loading from dict - if isinstance(self.model_type, str): - self.model_type = LTXModelType(self.model_type) - if isinstance(self.rope_type, str): - self.rope_type = LTXRopeType(self.rope_type) - if isinstance(self.attention_type, str): - self.attention_type = AttentionType(self.attention_type) - - @property - def inner_dim(self) -> int: - """Video inner dimension.""" - return self.num_attention_heads * self.attention_head_dim - - @property - def audio_inner_dim(self) -> int: - """Audio inner dimension.""" - return self.audio_num_attention_heads * self.audio_attention_head_dim - - def get_video_config(self) -> Optional[TransformerConfig]: - """Get video transformer configuration.""" - if not self.model_type.is_video_enabled(): - return None - return TransformerConfig( - dim=self.inner_dim, - heads=self.num_attention_heads, - d_head=self.attention_head_dim, - context_dim=self.cross_attention_dim, - ) - - def get_audio_config(self) -> Optional[TransformerConfig]: - """Get audio transformer configuration.""" - if not self.model_type.is_audio_enabled(): - return None - return TransformerConfig( - dim=self.audio_inner_dim, - heads=self.audio_num_attention_heads, - d_head=self.audio_attention_head_dim, - context_dim=self.audio_cross_attention_dim, - ) diff --git a/mlx_video/models/ltx/video_vae/__init__.py b/mlx_video/models/ltx/video_vae/__init__.py deleted file mode 100644 index bac1644..0000000 --- a/mlx_video/models/ltx/video_vae/__init__.py +++ /dev/null @@ -1,8 +0,0 @@ -from mlx_video.models.ltx.video_vae.video_vae import VideoEncoder, VideoDecoder -from mlx_video.models.ltx.video_vae.encoder import load_vae_encoder, encode_image -from mlx_video.models.ltx.video_vae.decoder import load_vae_decoder, LTX2VideoDecoder -from mlx_video.models.ltx.video_vae.tiling import ( - TilingConfig, - SpatialTilingConfig, - TemporalTilingConfig, -) diff --git a/mlx_video/models/ltx/video_vae/encoder.py b/mlx_video/models/ltx/video_vae/encoder.py deleted file mode 100644 index 6c90a4b..0000000 --- a/mlx_video/models/ltx/video_vae/encoder.py +++ /dev/null @@ -1,187 +0,0 @@ -"""Video VAE Encoder for LTX-2 Image-to-Video. - -The encoder compresses input images/videos to latent representations. -Used for I2V (image-to-video) conditioning by encoding the input image -to latent space, which can then be used to condition video generation. -""" - -from pathlib import Path -from typing import List, Tuple, Any, Optional -import json - -import mlx.core as mx -import mlx.nn as nn - -from mlx_video.models.ltx.video_vae.video_vae import VideoEncoder, LogVarianceType, NormLayerType, PaddingModeType - - -def load_vae_encoder(model_path: str) -> VideoEncoder: - """Load VAE encoder from safetensors file. - - Args: - model_path: Path to the model weights (safetensors file or directory) - - Returns: - Loaded VideoEncoder instance - """ - from safetensors import safe_open - - model_path = Path(model_path) - - # Try to find the weights file - if model_path.is_file() and model_path.suffix == ".safetensors": - weights_path = model_path - elif (model_path / "ltx-2-19b-distilled.safetensors").exists(): - weights_path = model_path / "ltx-2-19b-distilled.safetensors" - elif (model_path / "vae" / "diffusion_pytorch_model.safetensors").exists(): - weights_path = model_path / "vae" / "diffusion_pytorch_model.safetensors" - else: - raise FileNotFoundError(f"VAE weights not found at {model_path}") - - print(f"Loading VAE encoder from {weights_path}...") - - # Read config from safetensors metadata - encoder_blocks = [] - norm_layer = NormLayerType.PIXEL_NORM - latent_log_var = LogVarianceType.UNIFORM - patch_size = 4 - - try: - with safe_open(str(weights_path), framework="numpy") as f: - metadata = f.metadata() - if metadata and "config" in metadata: - configs = json.loads(metadata["config"]) - vae_config = configs.get("vae", {}) - - # Parse encoder blocks - raw_blocks = vae_config.get("encoder_blocks", []) - for block in raw_blocks: - if isinstance(block, list) and len(block) == 2: - name, params = block - encoder_blocks.append((name, params)) - - # Parse other config - norm_str = vae_config.get("norm_layer", "pixel_norm") - norm_layer = NormLayerType.PIXEL_NORM if norm_str == "pixel_norm" else NormLayerType.GROUP_NORM - - var_str = vae_config.get("latent_log_var", "uniform") - if var_str == "uniform": - latent_log_var = LogVarianceType.UNIFORM - elif var_str == "per_channel": - latent_log_var = LogVarianceType.PER_CHANNEL - elif var_str == "constant": - latent_log_var = LogVarianceType.CONSTANT - else: - latent_log_var = LogVarianceType.NONE - - patch_size = vae_config.get("patch_size", 4) - - print(f" Loaded config: {len(encoder_blocks)} encoder blocks, norm={norm_str}, patch_size={patch_size}") - except Exception as e: - print(f" Could not read config from metadata: {e}") - # Use default config - encoder_blocks = [ - ("res_x", {"num_layers": 4}), - ("compress_space_res", {"multiplier": 2}), - ("res_x", {"num_layers": 6}), - ("compress_time_res", {"multiplier": 2}), - ("res_x", {"num_layers": 6}), - ("compress_all_res", {"multiplier": 2}), - ("res_x", {"num_layers": 2}), - ("compress_all_res", {"multiplier": 2}), - ("res_x", {"num_layers": 2}), - ] - print(f" Using default encoder config with {len(encoder_blocks)} blocks") - - # Create encoder - encoder = VideoEncoder( - convolution_dimensions=3, - in_channels=3, - out_channels=128, - encoder_blocks=encoder_blocks, - patch_size=patch_size, - norm_layer=norm_layer, - latent_log_var=latent_log_var, - encoder_spatial_padding_mode=PaddingModeType.ZEROS, - ) - - # Load weights - weights = mx.load(str(weights_path)) - - # Determine prefix based on weight keys - has_vae_prefix = any(k.startswith("vae.") for k in weights.keys()) - - if has_vae_prefix: - prefix = "vae.encoder." - stats_prefix = "vae.per_channel_statistics." - else: - prefix = "encoder." - stats_prefix = "per_channel_statistics." - - # Load per-channel statistics for normalization - mean_key = f"{stats_prefix}mean-of-means" - std_key = f"{stats_prefix}std-of-means" - - if mean_key in weights: - encoder.per_channel_statistics.mean = weights[mean_key] - print(f" Loaded latent mean: shape {weights[mean_key].shape}") - if std_key in weights: - encoder.per_channel_statistics.std = weights[std_key] - print(f" Loaded latent std: shape {weights[std_key].shape}") - - # Build encoder weights dict with key remapping - encoder_weights = {} - for key, value in weights.items(): - if not key.startswith(prefix): - continue - - # Remove prefix - new_key = key[len(prefix):] - - # Handle Conv3d weight transpose: (O, I, D, H, W) -> (O, D, H, W, I) - if ".weight" in key and value.ndim == 5: - value = mx.transpose(value, (0, 2, 3, 4, 1)) - - encoder_weights[new_key] = value - - print(f" Found {len(encoder_weights)} encoder weights") - - # Load weights - encoder.load_weights(list(encoder_weights.items()), strict=False) - - print("VAE encoder loaded successfully") - return encoder - - -def encode_image( - image: mx.array, - encoder: VideoEncoder, -) -> mx.array: - """Encode a single image to latent space. - - Args: - image: Image tensor of shape (H, W, 3) in range [0, 1] or (B, H, W, 3) - encoder: Loaded VAE encoder - - Returns: - Latent tensor of shape (1, 128, 1, H//32, W//32) - """ - # Add batch dimension if needed - if image.ndim == 3: - image = mx.expand_dims(image, axis=0) # (1, H, W, 3) - - # Convert from (B, H, W, C) to (B, C, H, W) - image = mx.transpose(image, (0, 3, 1, 2)) # (B, 3, H, W) - - # Normalize to [-1, 1] - if image.max() > 1.0: - image = image / 255.0 - image = image * 2.0 - 1.0 - - # Add temporal dimension: (B, C, H, W) -> (B, C, 1, H, W) - image = mx.expand_dims(image, axis=2) # (B, 3, 1, H, W) - - # Encode - latent = encoder(image) - - return latent diff --git a/mlx_video/models/ltx_2/README.md b/mlx_video/models/ltx_2/README.md new file mode 100644 index 0000000..5578fac --- /dev/null +++ b/mlx_video/models/ltx_2/README.md @@ -0,0 +1,371 @@ +# LTX-2 for MLX + +MLX port of [LTX-2](https://huggingface.co/Lightricks/LTX-2), a 19B parameter video generation model from Lightricks with synchronized audio-video support. + +## Pipelines + +Four pipeline types are available via the `--pipeline` flag: + +| Pipeline | Description | CFG | Stages | Speed | +|----------|-------------|-----|--------|-------| +| `distilled` (default) | Fixed sigma schedule, no CFG | No | 2 (8+3 steps) | Fastest | +| `dev` | Dynamic sigmas, constant CFG | Yes | 1 (30 steps) | Medium | +| `dev-two-stage` | Dev + LoRA refinement | Yes (stage 1) | 2 (30+3 steps) | Slow | +| `dev-two-stage-hq` | res_2s sampler + LoRA both stages | Yes (stage 1) | 2 (15+3 steps) | Slow, highest quality | + +## Usage + +### Text-to-Video (T2V) + +```bash +# Distilled (default) - fast, two-stage +uv run mlx_video.generate --prompt "Two dogs wearing sunglasses, cinematic, sunset" -n 97 --width 768 + +# Dev - single-stage with CFG +uv run mlx_video.generate --pipeline dev --prompt "A cinematic scene" --cfg-scale 3.0 + +# Dev two-stage - dev + LoRA refinement +uv run mlx_video.generate --pipeline dev-two-stage \ + --prompt "Two dogs of the poodle breed wearing sunglasses, close up, cinematic, sunset" \ + -n 145 --width 1024 --height 768 \ + --model-repo prince-canuma/LTX-2-dev \ + --cfg-scale 3.0 --lora-strength 0.8 \ + --enhance-prompt + +# Dev two-stage HQ - res_2s sampler, LoRA both stages (highest quality) +uv run mlx_video.generate --pipeline dev-two-stage-hq \ + --prompt "A cinematic scene of ocean waves at golden hour" \ + --model-repo prince-canuma/LTX-2-dev + +# HQ with custom LoRA strengths +uv run mlx_video.generate --pipeline dev-two-stage-hq \ + --prompt "A sunset over mountains" \ + --model-repo prince-canuma/LTX-2-dev \ + --lora-strength-stage-1 0.3 --lora-strength-stage-2 0.6 +``` + +### Image-to-Video (I2V) + +```bash +# Distilled I2V +uv run mlx_video.generate --prompt "A person dancing" --image photo.jpg + +# Dev I2V +uv run mlx_video.generate --pipeline dev --prompt "Waves crashing" --image beach.png --cfg-scale 3.5 +``` + +### Audio-to-Video (A2V) + +Generate video conditioned on an input audio file. Works with all four pipelines. The audio is encoded to latent space and frozen during denoising -- the transformer's cross-attention reads the audio signal to guide video generation. + +```bash +# A2V - distilled (default, fastest) +uv run mlx_video.generate --audio-file music.wav --prompt "A band playing music" + +# A2V - dev (single-stage with CFG) +uv run mlx_video.generate --pipeline dev --audio-file ocean.wav --prompt "Ocean waves" + +# A2V - dev-two-stage (dev + LoRA refinement) +uv run mlx_video.generate --pipeline dev-two-stage --audio-file music.wav \ + --prompt "A band playing music" --model-repo prince-canuma/LTX-2-dev + +# A2V - dev-two-stage-hq (highest quality) +uv run mlx_video.generate --pipeline dev-two-stage-hq --audio-file music.wav \ + --prompt "A band playing music" --model-repo prince-canuma/LTX-2-dev + +# A2V + I2V (audio + image conditioning) +uv run mlx_video.generate --audio-file rain.wav --image forest.jpg --prompt "Rain in forest" + +# A2V with custom start time +uv run mlx_video.generate --audio-file song.mp3 --audio-start-time 30.0 --prompt "Concert" +``` + +> **Note:** `--audio-file` (A2V) and `--audio` (generate audio) are mutually exclusive. Supported formats: WAV, FLAC, MP3, OGG, and video files with audio tracks. + +### Audio-Video Generation (experimental) + +Generate synchronized audio alongside video from scratch: + +```bash +uv run mlx_video.generate --prompt "Ocean waves crashing" --audio +uv run mlx_video.generate --pipeline dev --prompt "A jazz band playing" --audio --enhance-prompt + +# With full guidance (STG + modality_scale, matches PyTorch defaults) +uv run mlx_video.generate --pipeline dev --prompt "Ocean waves crashing" --audio \ + --stg-scale 1.0 --stg-blocks 29 --modality-scale 3.0 +``` + +### LoRA + +LoRA weights can be loaded from a file, directory, or HuggingFace repo: + +```bash +# From HuggingFace repo +uv run mlx_video.generate --pipeline dev-two-stage \ + --prompt "Camera dolly out of a forest" \ + --lora-path Lightricks/LTX-2-19b-LoRA-Camera-Control-Dolly-Out \ + --lora-strength 1.0 + +# From local file +uv run mlx_video.generate --pipeline dev-two-stage \ + --prompt "A scene" \ + --lora-path ./my-lora/weights.safetensors + +# From local directory (auto-detects .safetensors file) +uv run mlx_video.generate --pipeline dev-two-stage \ + --prompt "A scene" \ + --lora-path ./LTX-2-distilled/lora +``` + +### Upscaling + +```bash +# Upscale an image 2x +uv run mlx_video.upscale --input photo.png --output upscaled.png + +# Upscale a video 2x +uv run mlx_video.upscale --input video.mp4 --output upscaled.mp4 + +# Upscale with refinement (higher quality, requires text prompt) +uv run mlx_video.upscale --input video.mp4 --output upscaled.mp4 --refine --prompt "A cinematic scene" +``` + +## CLI Options + +### General + +| Option | Default | Description | +|--------|---------|-------------| +| `--prompt`, `-p` | (required) | Text description of the video | +| `--pipeline` | `distilled` | Pipeline type: `distilled`, `dev`, `dev-two-stage`, or `dev-two-stage-hq` | +| `--height`, `-H` | 512 | Output height (divisible by 64 for two-stage, 32 for dev) | +| `--width`, `-W` | 512 | Output width (divisible by 64 for two-stage, 32 for dev) | +| `--num-frames`, `-n` | 33 | Number of frames (must be 1 + 8*k) | +| `--seed`, `-s` | 42 | Random seed for reproducibility | +| `--fps` | 24 | Frames per second | +| `--output-path`, `-o` | output.mp4 | Output video path | +| `--model-repo` | Lightricks/LTX-2 | HuggingFace model repository | +| `--text-encoder-repo` | None | Separate text encoder repo (if not in model repo) | +| `--save-frames` | false | Save individual frames as images | +| `--enhance-prompt` | false | Enhance prompt using Gemma | +| `--image`, `-i` | None | Conditioning image for I2V | +| `--image-strength` | 1.0 | Conditioning strength for I2V | +| `--audio`, `-a` | false | Enable synchronized audio generation | +| `--audio-file` | None | Path to audio file for A2V conditioning | +| `--audio-start-time` | 0.0 | Start time in seconds for audio file | +| `--tiling` | `auto` | VAE tiling mode: `auto`, `none`, `aggressive`, `conservative` | +| `--stream` | false | Stream frames as they decode | +| `--spatial-upscaler` | auto (x2) | Spatial upscaler file for two-stage pipelines (see below) | + +### Spatial Upscalers (LTX-2.3) + +LTX-2.3 ships with multiple spatial upscaler variants. Use `--spatial-upscaler` to select one: + +| Variant | Scale | Output (from 256x256) | Architecture | +|---------|-------|-----------------------|--------------| +| `ltx-2.3-spatial-upscaler-x2-1.0.safetensors` (default) | 2.0x | 512x512 | Conv2d + PixelShuffle(2) | +| `ltx-2.3-spatial-upscaler-x2-1.1.safetensors` | 2.0x | 512x512 | Same arch, newer weights | +| `ltx-2.3-spatial-upscaler-x1.5-1.0.safetensors` | 1.5x | 384x384 | Conv2d + PixelShuffle(3) + BlurDownsample | + +```bash +# Default (x2-1.0, auto-detected) +uv run mlx_video.generate --prompt "A sunset" --model-repo ./LTX-2.3-distilled + +# x2-1.1 (newer weights) +uv run mlx_video.generate --prompt "A sunset" --model-repo ./LTX-2.3-distilled \ + --spatial-upscaler ltx-2.3-spatial-upscaler-x2-1.1.safetensors + +# x1.5 (smaller output, faster) +uv run mlx_video.generate --prompt "A sunset" --model-repo ./LTX-2.3-distilled \ + --spatial-upscaler ltx-2.3-spatial-upscaler-x1.5-1.0.safetensors +``` + +> **Note:** Stage 1 always runs at half the target resolution. With x1.5, the final output is 75% of `--width`/`--height` (e.g., 512 target -> 256 stage 1 -> 384 output). With x2, the output matches the target exactly. + +### Dev / Dev-Two-Stage + +| Option | Default | Description | +|--------|---------|-------------| +| `--steps` | 30 | Number of denoising steps | +| `--cfg-scale` | 3.0 | CFG guidance scale | +| `--cfg-rescale` | 0.7 | CFG rescale factor (reduces over-saturation) | +| `--negative-prompt` | (default) | Negative prompt for CFG | +| `--apg` | false | Use Adaptive Projected Guidance (more stable for I2V) | +| `--stg-scale` | 0.0 | STG scale (PyTorch default: 1.0, requires `--audio`) | +| `--stg-blocks` | None | Transformer blocks for STG ([29] for LTX-2, [28] for LTX-2.3) | +| `--modality-scale` | 1.0 | Cross-modal guidance scale (PyTorch default: 3.0, requires `--audio`) | + +### Dev-Two-Stage LoRA + +| Option | Default | Description | +|--------|---------|-------------| +| `--lora-path` | auto-detect | Path to LoRA file, directory, or HuggingFace repo | +| `--lora-strength` | 1.0 | LoRA merge strength | + +### Dev-Two-Stage HQ + +| Option | Default | Description | +|--------|---------|-------------| +| `--lora-strength-stage-1` | 0.25 | LoRA strength for stage 1 | +| `--lora-strength-stage-2` | 0.5 | LoRA strength for stage 2 | + +HQ defaults: 15 steps (vs 30), `cfg-rescale` 0.45 (vs 0.7), STG disabled. Uses the res_2s second-order sampler (2 model evals per step) for better quality at the same compute budget. + +## How It Works + +### Distilled Pipeline (default) +1. **Stage 1**: Generate at half resolution with 8 denoising steps (fixed sigmas) +2. **Upsample**: Spatial upsampling via LatentUpsampler (x2 or x1.5, selectable via `--spatial-upscaler`) +3. **Stage 2**: Refine at upsampled resolution with 3 denoising steps +4. **Decode**: VAE decoder converts latents to RGB video + +### Dev Pipeline +1. **Generate**: Full resolution with configurable steps and constant CFG +2. **Decode**: VAE decoder converts latents to RGB video + +### Dev Two-Stage Pipeline +1. **Stage 1**: Dev denoising at half resolution with CFG +2. **Upsample**: Spatial upsampling via LatentUpsampler (x2 or x1.5) +3. **Stage 2**: Distilled refinement at upsampled resolution with LoRA weights (3 steps, no CFG) +4. **Decode**: VAE decoder converts latents to RGB video + +### Dev Two-Stage HQ Pipeline +1. **Stage 1**: res_2s denoising at half resolution with CFG + LoRA@0.25 (15 steps, 2 evals/step) +2. **Upsample**: Spatial upsampling via LatentUpsampler (x2 or x1.5) +3. **Stage 2**: res_2s refinement at upsampled resolution with LoRA@0.5 (3 steps, no CFG) +4. **Decode**: VAE decoder converts latents to RGB video + +The res_2s sampler uses an exponential Rosenbrock-type Runge-Kutta integrator with SDE noise injection, producing higher quality results than Euler at the same compute budget (~30 total model evaluations). + +### Audio-to-Video (A2V) Conditioning + +A2V works by encoding input audio into the same latent space as generated audio, then **freezing** those latents during denoising: + +1. Load audio file, resample to 16kHz, compute mel-spectrogram +2. `AudioEncoder(mel_spec)` produces audio latents `(B, 8, T, 16)` +3. Normalize via `PerChannelStatistics` +4. Freeze during denoising: `timesteps=0`, `sigma=0`, skip Euler/RK updates +5. Transformer's A2V cross-attention reads frozen audio to guide video generation +6. Output: denoised video + original input audio waveform (skip audio VAE decode) + +## Converting Models + +Convert original Lightricks/LTX-2 weights to the modular mlx-video format: + +```bash +# Convert distilled model +uv run python -m mlx_video.models.ltx_2.convert \ + --source Lightricks/LTX-2 --output ./LTX-2-distilled --variant distilled + +# Convert dev model +uv run python -m mlx_video.models.ltx_2.convert \ + --source Lightricks/LTX-2 --output ./LTX-2-dev --variant dev +``` + +This extracts 7 components from the monolithic checkpoint: + +``` +LTX-2-distilled/ +├── transformer/ # DiT transformer (19B params) +├── vae/ +│ ├── decoder/ # Video VAE decoder +│ └── encoder/ # Video VAE encoder +├── audio_vae/ +│ ├── decoder/ # Audio VAE decoder +│ └── encoder/ # Audio VAE encoder +├── vocoder/ # Mel-spectrogram to waveform +└── text_projections/ # Text embedding projections +``` + +Pre-converted weights are available on HuggingFace: +- [prince-canuma/LTX-2-distilled](https://huggingface.co/prince-canuma/LTX-2-distilled) +- [prince-canuma/LTX-2-dev](https://huggingface.co/prince-canuma/LTX-2-dev) +- [prince-canuma/LTX-2.3-distilled](https://huggingface.co/prince-canuma/LTX-2.3-distilled) +- [prince-canuma/LTX-2.3-dev](https://huggingface.co/prince-canuma/LTX-2.3-dev) + +## Model Specifications + +- **Transformer**: 48 layers, 32 attention heads, 128 dim per head (19B parameters) +- **Latent channels**: 128 +- **Patch size**: 4 (for VAE patchify/unpatchify) +- **Text encoder**: Gemma 3 with 3840-dim output +- **RoPE**: Split mode with double precision (LTX-2.3) or standard (LTX-2) +- **Audio VAE**: Encoder (~35M), Decoder (~50M), Vocoder (~13M) + +### Audio VAE Architecture + +``` +Audio Encoder: mel-spectrogram -> latents (B, 8, T, 16) + - Channel multipliers: (1, 2, 4) + - ResNet blocks with optional attention + - GroupNorm or PixelNorm normalization + - Optional causal convolutions + +Audio Decoder: latents -> mel-spectrogram + - Mirrors encoder with upsampling path + - Per-channel statistics for latent normalization + +Vocoder: mel-spectrogram -> waveform (~13M params) + - HiFi-GAN style architecture + - Upsample rates: [6, 5, 2, 2, 2] + - ResBlock1 with dilations [1, 3, 5] +``` + +## Project Structure + +``` +mlx_video/models/ltx_2/ +├── __init__.py +├── config.py # LTXModelConfig, AudioEncoderModelConfig, AudioDecoderModelConfig +├── convert.py # Weight conversion from Lightricks/LTX-2 +├── generate.py # Unified generation pipeline (T2V, I2V, A2V, +Audio) +├── postprocess.py # Video post-processing +├── samplers.py # Euler and res_2s samplers +├── utils.py # Shared utilities (get_model_path, load_safetensors, etc.) +├── ltx.py # Main LTXModel (DiT transformer with AV support) +├── transformer.py # Transformer blocks, Modality dataclass +├── attention.py # Multi-head attention with RoPE +├── feed_forward.py # Feed-forward layers +├── adaln.py # Adaptive Layer Normalization +├── rope.py # Rotary Position Embeddings (split/combined) +├── text_projection.py # Text embedding projection +├── text_encoder.py # Text encoder with AV embeddings support +├── upsampler.py # LatentUpsampler for 2-stage generation +├── conditioning/ +│ ├── keyframe.py # Image-to-video keyframe conditioning +│ └── latent.py # Video-to-video latent conditioning +├── video_vae/ +│ ├── decoder.py # VAE decoder with timestep conditioning +│ ├── encoder.py # VAE encoder for image/video encoding +│ ├── convolution.py # CausalConv3d, CausalConv2d +│ ├── ops.py # patchify, unpatchify, PerChannelStatistics +│ ├── resnet.py # ResBlock3D, ResBlockGroup +│ ├── sampling.py # DepthToSpaceUpsample, SpaceToDepthDownsample +│ └── video_vae.py # Full VAE (encoder + decoder) +└── audio_vae/ + ├── audio_vae.py # Audio encoder and decoder + ├── audio_processor.py # Mel-spectrogram computation (librosa) + ├── vocoder.py # Mel-spectrogram to waveform synthesis + ├── ops.py # AudioPatchifier, PerChannelStatistics + ├── resnet.py # ResNet blocks for audio + ├── attention.py # Attention blocks for audio VAE + ├── normalization.py # Normalization layers + ├── causal_conv_2d.py # Causal 2D convolutions + ├── downsample.py # Downsampling layers + └── upsample.py # Upsampling layers +``` + +## LTX-2 vs LTX-2.3 + +LTX-2.3 introduces prompt-conditioned adaptive layer normalization (adaln): + +| Feature | LTX-2 | LTX-2.3 | +|---------|--------|---------| +| AdaLN | Standard | Prompt-conditioned (`has_prompt_adaln=True`) | +| Attention gate | None | `2.0 * sigmoid(gate_logits)` | +| Scale-shift table | 6 params | 9 params (+ cross-attn Q) | +| Text encoder connectors | 2 blocks | 8 blocks with gate_logits | +| Feature extractor | V1 (batch-level) | V2 (per-token RMSNorm) | +| RoPE | Standard | Double precision | +| STG blocks | [29] | [28] | +| Text encoder repo | Included | Separate (`--text-encoder-repo`) | diff --git a/mlx_video/models/ltx_2/__init__.py b/mlx_video/models/ltx_2/__init__.py new file mode 100644 index 0000000..dd2b1e0 --- /dev/null +++ b/mlx_video/models/ltx_2/__init__.py @@ -0,0 +1,7 @@ +from mlx_video.models.ltx_2.audio_vae import AudioDecoder, Vocoder, decode_audio +from mlx_video.models.ltx_2.config import ( + LTXModelConfig, + LTXModelType, + TransformerConfig, +) +from mlx_video.models.ltx_2.ltx_2 import LTXModel, X0Model diff --git a/mlx_video/models/ltx/adaln.py b/mlx_video/models/ltx_2/adaln.py similarity index 90% rename from mlx_video/models/ltx/adaln.py rename to mlx_video/models/ltx_2/adaln.py index fee57c1..6d61129 100644 --- a/mlx_video/models/ltx/adaln.py +++ b/mlx_video/models/ltx_2/adaln.py @@ -8,7 +8,6 @@ from mlx_video.utils import get_timestep_embedding class AdaLayerNormSingle(nn.Module): - def __init__( self, embedding_dim: int, @@ -24,7 +23,9 @@ class AdaLayerNormSingle(nn.Module): ) self.silu = nn.SiLU() - self.linear = nn.Linear(embedding_dim, embedding_coefficient * embedding_dim, bias=True) + self.linear = nn.Linear( + embedding_dim, embedding_coefficient * embedding_dim, bias=True + ) def __call__( self, @@ -56,15 +57,19 @@ class PixArtAlphaCombinedTimestepSizeEmbeddings(nn.Module): use_additional_conditions: bool = False, timestep_proj_dim: int = 256, ): - + super().__init__() self.embedding_dim = embedding_dim self.size_emb_dim = size_emb_dim self.use_additional_conditions = use_additional_conditions - self.time_proj = Timesteps(timestep_proj_dim, flip_sin_to_cos=True, downscale_freq_shift=0) - self.timestep_embedder = TimestepEmbedding(timestep_proj_dim, embedding_dim, out_dim=embedding_dim) + self.time_proj = Timesteps( + timestep_proj_dim, flip_sin_to_cos=True, downscale_freq_shift=0 + ) + self.timestep_embedder = TimestepEmbedding( + timestep_proj_dim, embedding_dim, out_dim=embedding_dim + ) if use_additional_conditions and size_emb_dim > 0: self.additional_embedder = ConditionEmbedding(size_emb_dim, embedding_dim) @@ -87,7 +92,9 @@ class PixArtAlphaCombinedTimestepSizeEmbeddings(nn.Module): # Add additional conditions if enabled if self.use_additional_conditions and self.size_emb_dim > 0: if resolution is not None and aspect_ratio is not None: - additional_embeds = self.additional_embedder(resolution, aspect_ratio, hidden_dtype) + additional_embeds = self.additional_embedder( + resolution, aspect_ratio, hidden_dtype + ) timesteps_emb = timesteps_emb + additional_embeds return timesteps_emb diff --git a/mlx_video/models/ltx/attention.py b/mlx_video/models/ltx_2/attention.py similarity index 68% rename from mlx_video/models/ltx/attention.py rename to mlx_video/models/ltx_2/attention.py index 4024c91..8f0776c 100644 --- a/mlx_video/models/ltx/attention.py +++ b/mlx_video/models/ltx_2/attention.py @@ -6,8 +6,8 @@ from typing import Optional, Tuple import mlx.core as mx import mlx.nn as nn -from mlx_video.models.ltx.config import LTXRopeType -from mlx_video.models.ltx.rope import apply_rotary_emb +from mlx_video.models.ltx_2.config import LTXRopeType +from mlx_video.models.ltx_2.rope import apply_rotary_emb def scaled_dot_product_attention( @@ -67,17 +67,8 @@ class Attention(nn.Module): dim_head: int = 64, norm_eps: float = 1e-6, rope_type: LTXRopeType = LTXRopeType.INTERLEAVED, + has_gate_logits: bool = False, ): - """Initialize attention module. - - Args: - query_dim: Dimension of query input - context_dim: Dimension of context (key/value) input. If None, same as query_dim - heads: Number of attention heads - dim_head: Dimension per head - norm_eps: Epsilon for RMS normalization - rope_type: Type of rotary position embedding - """ super().__init__() self.rope_type = rope_type @@ -99,6 +90,10 @@ class Attention(nn.Module): # Output projection self.to_out = nn.Linear(inner_dim, query_dim, bias=True) + # Per-head gating (LTX-2.3) + if has_gate_logits: + self.to_gate_logits = nn.Linear(query_dim, heads, bias=True) + def __call__( self, x: mx.array, @@ -106,6 +101,7 @@ class Attention(nn.Module): mask: Optional[mx.array] = None, pe: Optional[Tuple[mx.array, mx.array]] = None, k_pe: Optional[Tuple[mx.array, mx.array]] = None, + skip_attention: bool = False, ) -> mx.array: """Forward pass. @@ -115,28 +111,44 @@ class Attention(nn.Module): mask: Attention mask pe: Position embeddings for query (and key if k_pe is None) k_pe: Position embeddings for key (optional, uses pe if None) + skip_attention: If True, bypass Q*K*V attention and use value projection + only (for STG perturbation). Matches PyTorch all_perturbed=True. Returns: Attention output of shape (B, seq_len, query_dim) """ - # Compute Q, K, V - q = self.to_q(x) + # Compute per-head gate early (from original input) + gate = None + if hasattr(self, "to_gate_logits"): + gate = 2.0 * mx.sigmoid(self.to_gate_logits(x)) # (B, seq, heads) + context = x if context is None else context - k = self.to_k(context) v = self.to_v(context) - # Apply normalization - q = self.q_norm(q) - k = self.k_norm(k) + if skip_attention: + # STG: bypass Q*K*V attention, use value projection only + out = v + else: + # Standard attention + q = self.to_q(x) + k = self.to_k(context) - # Apply rotary position embeddings - if pe is not None: - q = apply_rotary_emb(q, pe, self.rope_type) - k_pe_to_use = pe if k_pe is None else k_pe - k = apply_rotary_emb(k, k_pe_to_use, self.rope_type) + q = self.q_norm(q) + k = self.k_norm(k) - # Compute attention - out = scaled_dot_product_attention(q, k, v, self.heads, mask) + if pe is not None: + q = apply_rotary_emb(q, pe, self.rope_type) + k_pe_to_use = pe if k_pe is None else k_pe + k = apply_rotary_emb(k, k_pe_to_use, self.rope_type) + + out = scaled_dot_product_attention(q, k, v, self.heads, mask) + + # Apply per-head gating + if gate is not None: + b, seq_len, _ = out.shape + out = mx.reshape(out, (b, seq_len, self.heads, self.dim_head)) + out = out * gate[..., None] + out = mx.reshape(out, (b, seq_len, -1)) # Project output return self.to_out(out) diff --git a/mlx_video/models/ltx/audio_vae/__init__.py b/mlx_video/models/ltx_2/audio_vae/__init__.py similarity index 75% rename from mlx_video/models/ltx/audio_vae/__init__.py rename to mlx_video/models/ltx_2/audio_vae/__init__.py index 5907e2d..59509e0 100644 --- a/mlx_video/models/ltx/audio_vae/__init__.py +++ b/mlx_video/models/ltx_2/audio_vae/__init__.py @@ -1,21 +1,28 @@ """Audio VAE module for LTX-2 audio generation.""" +from ..config import CausalityAxis from .attention import AttentionType, AttnBlock, make_attn -from .audio_vae import AudioDecoder, decode_audio +from .audio_processor import ensure_stereo, load_audio, waveform_to_mel +from .audio_vae import AudioDecoder, AudioEncoder, decode_audio from .causal_conv_2d import CausalConv2d, make_conv2d -from .causality_axis import CausalityAxis from .downsample import Downsample, build_downsampling_path from .normalization import NormType, PixelNorm, build_normalization_layer from .ops import AudioLatentShape, AudioPatchifier, PerChannelStatistics from .resnet import LRELU_SLOPE, ResBlock1, ResBlock2, ResnetBlock from .upsample import Upsample, build_upsampling_path -from .vocoder import Vocoder +from .vocoder import Vocoder, load_vocoder __all__ = [ # Main components + "AudioEncoder", "AudioDecoder", "Vocoder", + "load_vocoder", "decode_audio", + # Audio processing + "load_audio", + "ensure_stereo", + "waveform_to_mel", # Ops "AudioLatentShape", "AudioPatchifier", diff --git a/mlx_video/models/ltx/audio_vae/attention.py b/mlx_video/models/ltx_2/audio_vae/attention.py similarity index 92% rename from mlx_video/models/ltx/audio_vae/attention.py rename to mlx_video/models/ltx_2/audio_vae/attention.py index 38c5744..a4f868f 100644 --- a/mlx_video/models/ltx/audio_vae/attention.py +++ b/mlx_video/models/ltx_2/audio_vae/attention.py @@ -32,7 +32,9 @@ class AttnBlock(nn.Module): self.q = nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) self.k = nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) self.v = nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) - self.proj_out = nn.Conv2d(in_channels, in_channels, kernel_size=1, stride=1, padding=0) + self.proj_out = nn.Conv2d( + in_channels, in_channels, kernel_size=1, stride=1, padding=0 + ) def __call__(self, x: mx.array) -> mx.array: """ @@ -103,6 +105,8 @@ def make_attn( elif attn_type == AttentionType.NONE: return Identity() elif attn_type == AttentionType.LINEAR: - raise NotImplementedError(f"Attention type {attn_type.value} is not supported yet.") + raise NotImplementedError( + f"Attention type {attn_type.value} is not supported yet." + ) else: raise ValueError(f"Unknown attention type: {attn_type}") diff --git a/mlx_video/models/ltx_2/audio_vae/audio_processor.py b/mlx_video/models/ltx_2/audio_vae/audio_processor.py new file mode 100644 index 0000000..915575f --- /dev/null +++ b/mlx_video/models/ltx_2/audio_vae/audio_processor.py @@ -0,0 +1,136 @@ +"""Audio processing utilities for loading audio files and computing mel-spectrograms. + +Matches the PyTorch AudioProcessor from LTX-2 (torchaudio.transforms.MelSpectrogram) +using librosa for macOS/MLX compatibility. +""" + + +import mlx.core as mx +import numpy as np + + +def load_audio( + path: str, + target_sr: int = 16000, + start_time: float = 0.0, + max_duration: float | None = None, + mono: bool = False, +) -> tuple[np.ndarray, int]: + """Load audio file, resample to target sample rate. + + Args: + path: Path to audio file (WAV, FLAC, MP3, OGG, or video with audio track). + target_sr: Target sample rate (default 16000 Hz). + start_time: Start time in seconds. + max_duration: Maximum duration in seconds. None = read to end. + mono: If True, convert to mono. Default False (preserve channels). + + Returns: + (waveform, sample_rate) where waveform is (channels, samples) float32 numpy array. + """ + import librosa + + # librosa.load returns mono by default; we want to preserve stereo + y, sr = librosa.load( + path, + sr=target_sr, + mono=mono, + offset=start_time, + duration=max_duration, + ) + + # Ensure 2D: (channels, samples) + if y.ndim == 1: + y = y[np.newaxis, :] # (1, samples) + + return y.astype(np.float32), sr + + +def ensure_stereo(waveform: np.ndarray) -> np.ndarray: + """Ensure waveform is stereo (2, samples). Duplicates mono if needed.""" + if waveform.ndim == 1: + waveform = waveform[np.newaxis, :] + if waveform.shape[0] == 1: + waveform = np.concatenate([waveform, waveform], axis=0) + elif waveform.shape[0] > 2: + waveform = waveform[:2] + return waveform + + +def waveform_to_mel( + waveform: np.ndarray, + sample_rate: int = 16000, + n_fft: int = 1024, + hop_length: int = 160, + win_length: int = 1024, + n_mels: int = 64, + fmin: float = 0.0, + fmax: float = 8000.0, +) -> mx.array: + """Convert waveform to log-mel spectrogram matching PyTorch MelSpectrogram. + + PyTorch reference: + MelSpectrogram(sample_rate=16000, n_fft=1024, win_length=1024, hop_length=160, + f_min=0.0, f_max=8000.0, n_mels=64, power=1.0, + mel_scale="slaney", norm="slaney", center=True, pad_mode="reflect") + + Args: + waveform: (channels, samples) float32 numpy array. + sample_rate: Sample rate of the waveform. + n_fft: FFT size. + hop_length: Hop length. + win_length: Window length. + n_mels: Number of mel bins. + fmin: Minimum frequency for mel filterbank. + fmax: Maximum frequency for mel filterbank. + + Returns: + Log-mel spectrogram as mx.array of shape (1, channels, time, n_mels). + """ + import librosa + + # Ensure 2D + if waveform.ndim == 1: + waveform = waveform[np.newaxis, :] + + channels = waveform.shape[0] + mels = [] + + for ch in range(channels): + # Magnitude spectrogram (power=1.0) + S = np.abs( + librosa.stft( + waveform[ch], + n_fft=n_fft, + hop_length=hop_length, + win_length=win_length, + center=True, + pad_mode="reflect", + ) + ) + + # Mel filterbank with slaney normalization + mel_basis = librosa.filters.mel( + sr=sample_rate, + n_fft=n_fft, + n_mels=n_mels, + fmin=fmin, + fmax=fmax, + norm="slaney", + ) + mel = mel_basis @ S + + # Log scale + mel = np.log(np.clip(mel, a_min=1e-5, a_max=None)) + + # Transpose: (n_mels, time) -> (time, n_mels) + mel = mel.T + mels.append(mel) + + # Stack channels: (channels, time, n_mels) + mel_spec = np.stack(mels, axis=0) + + # Add batch dim: (1, channels, time, n_mels) + mel_spec = mel_spec[np.newaxis, ...] + + return mx.array(mel_spec, dtype=mx.float32) diff --git a/mlx_video/models/ltx_2/audio_vae/audio_vae.py b/mlx_video/models/ltx_2/audio_vae/audio_vae.py new file mode 100644 index 0000000..415c222 --- /dev/null +++ b/mlx_video/models/ltx_2/audio_vae/audio_vae.py @@ -0,0 +1,571 @@ +"""Audio VAE encoder and decoder for LTX-2.""" + +from pathlib import Path +from typing import Dict + +import mlx.core as mx +import mlx.nn as nn +from mlx_vlm.models.base import check_array_shape + +from ..config import AudioDecoderModelConfig, AudioEncoderModelConfig, CausalityAxis +from .attention import AttentionType, make_attn +from .causal_conv_2d import make_conv2d +from .downsample import build_downsampling_path +from .normalization import NormType, build_normalization_layer +from .ops import AudioLatentShape, AudioPatchifier, PerChannelStatistics +from .resnet import ResnetBlock +from .upsample import build_upsampling_path + +LATENT_DOWNSAMPLE_FACTOR = 4 + + +def build_mid_block( + channels: int, + temb_channels: int, + dropout: float, + norm_type: NormType, + causality_axis: CausalityAxis, + attn_type: AttentionType, + add_attention: bool, +) -> dict: + """Build the middle block with two ResNet blocks and optional attention.""" + mid = {} + mid["block_1"] = ResnetBlock( + in_channels=channels, + out_channels=channels, + temb_channels=temb_channels, + dropout=dropout, + norm_type=norm_type, + causality_axis=causality_axis, + ) + mid["attn_1"] = ( + make_attn(channels, attn_type=attn_type, norm_type=norm_type) + if add_attention + else None + ) + mid["block_2"] = ResnetBlock( + in_channels=channels, + out_channels=channels, + temb_channels=temb_channels, + dropout=dropout, + norm_type=norm_type, + causality_axis=causality_axis, + ) + return mid + + +def run_mid_block(mid: dict, features: mx.array) -> mx.array: + """Run features through the middle block.""" + features = mid["block_1"](features, temb=None) + if mid["attn_1"] is not None: + features = mid["attn_1"](features) + return mid["block_2"](features, temb=None) + + +class AudioEncoder(nn.Module): + """Encoder that compresses audio spectrograms into latent representations.""" + + def __init__(self, config: AudioEncoderModelConfig) -> None: + super().__init__() + + self.per_channel_statistics = PerChannelStatistics(latent_channels=config.ch) + self.sample_rate = config.sample_rate + self.mel_hop_length = config.mel_hop_length + self.is_causal = config.is_causal + self.mel_bins = config.mel_bins + + self.patchifier = AudioPatchifier( + patch_size=1, + audio_latent_downsample_factor=LATENT_DOWNSAMPLE_FACTOR, + sample_rate=config.sample_rate, + hop_length=config.mel_hop_length, + is_causal=config.is_causal, + ) + + self.ch = config.ch + self.temb_ch = 0 + self.num_resolutions = len(config.ch_mult) + self.num_res_blocks = config.num_res_blocks + self.resolution = config.resolution + self.in_channels = config.in_channels + self.z_channels = config.z_channels + self.double_z = config.double_z + self.norm_type = config.norm_type + self.causality_axis = config.causality_axis + self.attn_type = config.attn_type + + self.conv_in = make_conv2d( + config.in_channels, + self.ch, + kernel_size=3, + stride=1, + causality_axis=self.causality_axis, + ) + + self.down, block_in = build_downsampling_path( + ch=config.ch, + ch_mult=config.ch_mult, + num_resolutions=self.num_resolutions, + num_res_blocks=config.num_res_blocks, + resolution=config.resolution, + temb_channels=self.temb_ch, + dropout=config.dropout, + norm_type=self.norm_type, + causality_axis=self.causality_axis, + attn_type=self.attn_type, + attn_resolutions=config.attn_resolutions or set(), + resamp_with_conv=config.resamp_with_conv, + ) + + self.mid = build_mid_block( + channels=block_in, + temb_channels=self.temb_ch, + dropout=config.dropout, + norm_type=self.norm_type, + causality_axis=self.causality_axis, + attn_type=self.attn_type, + add_attention=config.mid_block_add_attention, + ) + + self.norm_out = build_normalization_layer(block_in, normtype=self.norm_type) + out_channels = 2 * config.z_channels if config.double_z else config.z_channels + self.conv_out = make_conv2d( + block_in, + out_channels, + kernel_size=3, + stride=1, + causality_axis=self.causality_axis, + ) + + def sanitize(self, weights: Dict[str, mx.array]) -> Dict[str, mx.array]: + """Sanitize audio encoder weights from PyTorch format.""" + sanitized = {} + for key, value in weights.items(): + new_key = key + if key.startswith("audio_vae.encoder."): + new_key = key.replace("audio_vae.encoder.", "") + elif key.startswith("encoder."): + new_key = key.replace("encoder.", "") + elif key.startswith("audio_vae.per_channel_statistics."): + if "mean-of-means" in key: + new_key = "per_channel_statistics.mean_of_means" + elif "std-of-means" in key: + new_key = "per_channel_statistics.std_of_means" + else: + continue + elif "per_channel_statistics" in key: + if "mean-of-means" in key or "latents_mean" in key: + new_key = "per_channel_statistics.mean_of_means" + elif "std-of-means" in key or "latents_std" in key: + new_key = "per_channel_statistics.std_of_means" + else: + continue + elif key == "latents_mean": + new_key = "per_channel_statistics.mean_of_means" + elif key == "latents_std": + new_key = "per_channel_statistics.std_of_means" + else: + continue + + if "conv" in new_key.lower() and "weight" in new_key and value.ndim == 4: + value = ( + value + if check_array_shape(value) + else mx.transpose(value, (0, 2, 3, 1)) + ) + + sanitized[new_key] = value + return sanitized + + @classmethod + def from_pretrained(cls, model_path: Path) -> "AudioEncoder": + """Load audio encoder from pretrained weights.""" + import json + + from mlx_video.models.ltx_2.config import AudioEncoderModelConfig + + model_path = Path(model_path) + config = AudioEncoderModelConfig.from_dict( + json.load(open(model_path / "config.json")) + ) + encoder = cls(config) + weights = mx.load(str(model_path / "model.safetensors")) + encoder.load_weights(list(weights.items()), strict=True) + return encoder + + def __call__(self, spectrogram: mx.array) -> mx.array: + """Encode audio spectrogram into normalized latent representation. + + Args: + spectrogram: (B, C, T, F) PyTorch format or (B, T, F, C) MLX format. + Returns: + Normalized latent (B, T', F', z_channels) in MLX channels-last format. + """ + if spectrogram.ndim == 4 and spectrogram.shape[1] == self.in_channels: + spectrogram = mx.transpose(spectrogram, (0, 2, 3, 1)) + + h = self.conv_in(spectrogram) + h = self._run_downsampling_path(h) + h = run_mid_block(self.mid, h) + h = self._finalize_output(h) + return self._normalize_latents(h) + + def _run_downsampling_path(self, h: mx.array) -> mx.array: + for level in range(self.num_resolutions): + stage = self.down[level] + for block_idx in range(self.num_res_blocks): + h = stage["block"][block_idx](h, temb=None) + if block_idx in stage["attn"]: + h = stage["attn"][block_idx](h) + if level != self.num_resolutions - 1 and "downsample" in stage: + h = stage["downsample"](h) + return h + + def _finalize_output(self, h: mx.array) -> mx.array: + h = self.norm_out(h) + h = nn.silu(h) + return self.conv_out(h) + + def _normalize_latents(self, h: mx.array) -> mx.array: + """Normalize encoder output using per-channel statistics. + + Takes first half of channels (mean) when double_z=True, + then patchifies, normalizes, and unpatchifies. + """ + # h shape: (B, T', F', 2*z_channels) in MLX format + z_channels = self.z_channels + means = h[..., :z_channels] + + latent_shape = AudioLatentShape( + batch=means.shape[0], + channels=means.shape[3], + frames=means.shape[1], + mel_bins=means.shape[2], + ) + + patched = self.patchifier.patchify(means) + normalized = self.per_channel_statistics.normalize(patched) + return self.patchifier.unpatchify(normalized, latent_shape) + + +class AudioDecoder(nn.Module): + """ + Symmetric decoder that reconstructs audio spectrograms from latent features. + The decoder mirrors the encoder structure with configurable channel multipliers, + attention resolutions, and causal convolutions. + """ + + def __init__( + self, + config: AudioDecoderModelConfig, + ) -> None: + """ + Initialize the AudioDecoder. + Args: + ch: Base number of feature channels + out_ch: Number of output channels (2 for stereo) + ch_mult: Multiplicative factors for channels at each resolution + num_res_blocks: Number of residual blocks per resolution + attn_resolutions: Resolutions at which to apply attention + resolution: Input spatial resolution + z_channels: Number of latent channels + norm_type: Normalization type + causality_axis: Axis for causal convolutions + dropout: Dropout probability + mid_block_add_attention: Whether to add attention in middle block + sample_rate: Audio sample rate + mel_hop_length: Hop length for mel spectrogram + is_causal: Whether to use causal convolutions + mel_bins: Number of mel frequency bins + """ + super().__init__() + + # Per-channel statistics for denormalizing latents + # Uses ch (base channel count) to match the patchified latent dimension + # Input latent shape: (B, z_channels, T, latent_mel_bins) = (B, 8, T, 16) + # After patchify: (B, T, z_channels * latent_mel_bins) = (B, T, 128) + # ch=128 matches this dimension, so use ch for per_channel_statistics + self.per_channel_statistics = PerChannelStatistics(latent_channels=config.ch) + self.sample_rate = config.sample_rate + self.mel_hop_length = config.mel_hop_length + self.is_causal = config.is_causal + self.mel_bins = config.mel_bins + + self.patchifier = AudioPatchifier( + patch_size=1, + audio_latent_downsample_factor=LATENT_DOWNSAMPLE_FACTOR, + sample_rate=config.sample_rate, + hop_length=config.mel_hop_length, + is_causal=config.is_causal, + ) + + self.ch = config.ch + self.temb_ch = 0 + self.num_resolutions = len(config.ch_mult) + self.num_res_blocks = config.num_res_blocks + self.resolution = config.resolution + self.out_ch = config.out_ch + self.give_pre_end = config.give_pre_end + self.tanh_out = config.tanh_out + self.norm_type = config.norm_type + self.z_channels = config.z_channels + self.channel_multipliers = config.ch_mult + self.attn_resolutions = config.attn_resolutions + self.causality_axis = config.causality_axis + self.attn_type = config.attn_type + + base_block_channels = config.ch * self.channel_multipliers[-1] + base_resolution = config.resolution // (2 ** (self.num_resolutions - 1)) + self.z_shape = (1, config.z_channels, base_resolution, base_resolution) + + self.conv_in = make_conv2d( + config.z_channels, + base_block_channels, + kernel_size=3, + stride=1, + causality_axis=self.causality_axis, + ) + + self.mid = build_mid_block( + channels=base_block_channels, + temb_channels=self.temb_ch, + dropout=config.dropout, + norm_type=self.norm_type, + causality_axis=self.causality_axis, + attn_type=self.attn_type, + add_attention=config.mid_block_add_attention, + ) + + self.up, final_block_channels = build_upsampling_path( + ch=config.ch, + ch_mult=config.ch_mult, + num_resolutions=self.num_resolutions, + num_res_blocks=config.num_res_blocks, + resolution=config.resolution, + temb_channels=self.temb_ch, + dropout=config.dropout, + norm_type=self.norm_type, + causality_axis=self.causality_axis, + attn_type=self.attn_type, + attn_resolutions=config.attn_resolutions, + resamp_with_conv=config.resamp_with_conv, + initial_block_channels=base_block_channels, + ) + + self.norm_out = build_normalization_layer( + final_block_channels, normtype=self.norm_type + ) + self.conv_out = make_conv2d( + final_block_channels, + config.out_ch, + kernel_size=3, + stride=1, + causality_axis=self.causality_axis, + ) + + def sanitize(self, weights: Dict[str, mx.array]) -> Dict[str, mx.array]: + """Sanitize audio VAE weight names from PyTorch format to MLX format. + + Args: + weights: Dictionary of weights with PyTorch naming + + Returns: + Dictionary with MLX-compatible naming for audio VAE decoder + """ + sanitized = {} + + for key, value in weights.items(): + new_key = key + + # Handle audio_vae.decoder weights + if key.startswith("audio_vae.decoder."): + new_key = key.replace("audio_vae.decoder.", "") + elif key.startswith("audio_vae.per_channel_statistics."): + # Map per-channel statistics + if "mean-of-means" in key: + new_key = "per_channel_statistics.mean_of_means" + elif "std-of-means" in key: + new_key = "per_channel_statistics.std_of_means" + else: + continue # Skip other statistics keys + else: + continue # Skip non-decoder keys + + # Handle Conv2d weight shape conversion + # PyTorch: (out_channels, in_channels, H, W) + # MLX: (out_channels, H, W, in_channels) + if "conv" in new_key.lower() and "weight" in new_key and value.ndim == 4: + value = ( + value + if check_array_shape(value) + else mx.transpose(value, (0, 2, 3, 1)) + ) + + sanitized[new_key] = value + + return sanitized + + @classmethod + def from_pretrained(cls, model_path: Path) -> "AudioDecoder": + """Load audio VAE decoder from pretrained model.""" + import json + + from mlx_video.models.ltx_2.config import AudioDecoderModelConfig + + config = AudioDecoderModelConfig.from_dict( + json.load(open(model_path / "config.json")) + ) + decoder = cls(config) + weights = mx.load(str(model_path / "model.safetensors")) + # weights = decoder.sanitize(weights) + decoder.load_weights(list(weights.items()), strict=True) + return decoder + + def __call__(self, sample: mx.array) -> mx.array: + """ + Decode latent features back to audio spectrograms. + Args: + sample: Encoded latent representation of shape (B, H, W, C) in MLX format + or (B, C, H, W) in PyTorch format (will be transposed) + Returns: + Reconstructed audio spectrogram + """ + # Handle input format - if channels are in dim 1, transpose to channels-last + if sample.shape[1] == self.z_channels and sample.ndim == 4: + # PyTorch format (B, C, H, W) -> MLX format (B, H, W, C) + sample = mx.transpose(sample, (0, 2, 3, 1)) + + sample, target_shape = self._denormalize_latents(sample) + + h = self.conv_in(sample) + h = run_mid_block(self.mid, h) + h = self._run_upsampling_path(h) + h = self._finalize_output(h) + + return self._adjust_output_shape(h, target_shape) + + def _denormalize_latents( + self, sample: mx.array + ) -> tuple[mx.array, AudioLatentShape]: + """Denormalize latents using per-channel statistics.""" + # sample shape: (B, H, W, C) in MLX format + latent_shape = AudioLatentShape( + batch=sample.shape[0], + channels=sample.shape[3], # channels last + frames=sample.shape[1], # height = frames + mel_bins=sample.shape[2], # width = mel_bins + ) + + sample_patched = self.patchifier.patchify(sample) + sample_denormalized = self.per_channel_statistics.un_normalize(sample_patched) + sample = self.patchifier.unpatchify(sample_denormalized, latent_shape) + + target_frames = latent_shape.frames * LATENT_DOWNSAMPLE_FACTOR + if self.causality_axis != CausalityAxis.NONE: + target_frames = max(target_frames - (LATENT_DOWNSAMPLE_FACTOR - 1), 1) + + target_shape = AudioLatentShape( + batch=latent_shape.batch, + channels=self.out_ch, + frames=target_frames, + mel_bins=( + self.mel_bins if self.mel_bins is not None else latent_shape.mel_bins + ), + ) + + return sample, target_shape + + def _adjust_output_shape( + self, + decoded_output: mx.array, + target_shape: AudioLatentShape, + ) -> mx.array: + """ + Adjust output shape to match target dimensions for variable-length audio. + Args: + decoded_output: Tensor of shape (B, H, W, C) in MLX format + target_shape: AudioLatentShape describing target dimensions + Returns: + Tensor adjusted to match target_shape exactly + """ + # Current output shape: (batch, frames, mel_bins, channels) in MLX format + _, current_time, current_freq, _ = decoded_output.shape + target_channels = target_shape.channels + target_time = target_shape.frames + target_freq = target_shape.mel_bins + + # Step 1: Crop first to avoid exceeding target dimensions + decoded_output = decoded_output[ + :, + : min(current_time, target_time), + : min(current_freq, target_freq), + :target_channels, + ] + + # Step 2: Calculate padding needed for time and frequency dimensions + time_padding_needed = target_time - decoded_output.shape[1] + freq_padding_needed = target_freq - decoded_output.shape[2] + + # Step 3: Apply padding if needed + if time_padding_needed > 0 or freq_padding_needed > 0: + # MLX pad: [(before_0, after_0), ...] + # For (B, H, W, C): H=time, W=freq + padding = [ + (0, 0), # batch + (0, max(time_padding_needed, 0)), # time + (0, max(freq_padding_needed, 0)), # freq + (0, 0), # channels + ] + decoded_output = mx.pad(decoded_output, padding) + + # Step 4: Final safety crop to ensure exact target shape + decoded_output = decoded_output[:, :target_time, :target_freq, :target_channels] + + # Transpose back to PyTorch format (B, C, H, W) for vocoder compatibility + decoded_output = mx.transpose(decoded_output, (0, 3, 1, 2)) + + return decoded_output + + def _run_upsampling_path(self, h: mx.array) -> mx.array: + """Run through upsampling path.""" + for level in reversed(range(self.num_resolutions)): + stage = self.up[level] + for block_idx in range(len(stage["block"])): + h = stage["block"][block_idx](h, temb=None) + if block_idx in stage["attn"]: + h = stage["attn"][block_idx](h) + + if level != 0 and "upsample" in stage: + h = stage["upsample"](h) + + return h + + def _finalize_output(self, h: mx.array) -> mx.array: + """Apply final normalization and convolution.""" + if self.give_pre_end: + return h + + h = self.norm_out(h) + h = nn.silu(h) + h = self.conv_out(h) + return mx.tanh(h) if self.tanh_out else h + + +def decode_audio( + latent: mx.array, audio_decoder: AudioDecoder, vocoder: "Vocoder" +) -> mx.array: + """ + Decode an audio latent representation using the provided audio decoder and vocoder. + Args: + latent: Input audio latent tensor + audio_decoder: Model to decode the latent to spectrogram + vocoder: Model to convert spectrogram to audio waveform + Returns: + Decoded audio as a float tensor + """ + decoded_audio = audio_decoder(latent) + decoded_audio = vocoder(decoded_audio) + # Remove batch dimension if present + if decoded_audio.shape[0] == 1: + decoded_audio = decoded_audio[0] + return decoded_audio.astype(mx.float32) diff --git a/mlx_video/models/ltx/audio_vae/causal_conv_2d.py b/mlx_video/models/ltx_2/audio_vae/causal_conv_2d.py similarity index 87% rename from mlx_video/models/ltx/audio_vae/causal_conv_2d.py rename to mlx_video/models/ltx_2/audio_vae/causal_conv_2d.py index 2a38448..4cc8233 100644 --- a/mlx_video/models/ltx/audio_vae/causal_conv_2d.py +++ b/mlx_video/models/ltx_2/audio_vae/causal_conv_2d.py @@ -5,7 +5,7 @@ from typing import Tuple, Union import mlx.core as mx import mlx.nn as nn -from .causality_axis import CausalityAxis +from ..config import CausalityAxis def _pair(x: Union[int, Tuple[int, int]]) -> Tuple[int, int]: @@ -53,8 +53,16 @@ class CausalConv2d(nn.Module): # For (N, H, W, C) format: axis 1 is H (height), axis 2 is W (width) if self.causality_axis == CausalityAxis.NONE: # Non-causal: symmetric padding - self.padding = (pad_h // 2, pad_h - pad_h // 2, pad_w // 2, pad_w - pad_w // 2) - elif self.causality_axis in (CausalityAxis.WIDTH, CausalityAxis.WIDTH_COMPATIBILITY): + self.padding = ( + pad_h // 2, + pad_h - pad_h // 2, + pad_w // 2, + pad_w - pad_w // 2, + ) + elif self.causality_axis in ( + CausalityAxis.WIDTH, + CausalityAxis.WIDTH_COMPATIBILITY, + ): # Causal on width: pad left (before width axis) self.padding = (pad_h // 2, pad_h - pad_h // 2, pad_w, 0) elif self.causality_axis == CausalityAxis.HEIGHT: @@ -90,7 +98,10 @@ class CausalConv2d(nn.Module): if any(p > 0 for p in self.padding): # MLX pad expects: [(before_0, after_0), (before_1, after_1), ...] # For (N, H, W, C): axis 0=N, axis 1=H, axis 2=W, axis 3=C - x = mx.pad(x, [(0, 0), (pad_h_top, pad_h_bottom), (pad_w_left, pad_w_right), (0, 0)]) + x = mx.pad( + x, + [(0, 0), (pad_h_top, pad_h_bottom), (pad_w_left, pad_w_right), (0, 0)], + ) return self.conv(x) @@ -124,7 +135,14 @@ def make_conv2d( if causality_axis is not None: # For causal convolution, padding is handled internally by CausalConv2d return CausalConv2d( - in_channels, out_channels, kernel_size, stride, dilation, groups, bias, causality_axis + in_channels, + out_channels, + kernel_size, + stride, + dilation, + groups, + bias, + causality_axis, ) else: # For non-causal convolution, use symmetric padding if not specified diff --git a/mlx_video/models/ltx/audio_vae/downsample.py b/mlx_video/models/ltx_2/audio_vae/downsample.py similarity index 90% rename from mlx_video/models/ltx/audio_vae/downsample.py rename to mlx_video/models/ltx_2/audio_vae/downsample.py index 2f553c8..f80c18e 100644 --- a/mlx_video/models/ltx/audio_vae/downsample.py +++ b/mlx_video/models/ltx_2/audio_vae/downsample.py @@ -5,8 +5,8 @@ from typing import Set, Tuple import mlx.core as mx import mlx.nn as nn +from ..config import CausalityAxis from .attention import AttentionType, make_attn -from .causality_axis import CausalityAxis from .normalization import NormType from .resnet import ResnetBlock @@ -34,7 +34,9 @@ class Downsample(nn.Module): if self.with_conv: # Do time downsampling here # no asymmetric padding in MLX conv, must do it ourselves - self.conv = nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=2, padding=0) + self.conv = nn.Conv2d( + in_channels, in_channels, kernel_size=3, stride=2, padding=0 + ) def __call__(self, x: mx.array) -> mx.array: """ @@ -116,10 +118,14 @@ def build_downsampling_path( ) block_in = block_out if curr_res in attn_resolutions: - stage["attn"][i_block] = make_attn(block_in, attn_type=attn_type, norm_type=norm_type) + stage["attn"][i_block] = make_attn( + block_in, attn_type=attn_type, norm_type=norm_type + ) if i_level != num_resolutions - 1: - stage["downsample"] = Downsample(block_in, resamp_with_conv, causality_axis=causality_axis) + stage["downsample"] = Downsample( + block_in, resamp_with_conv, causality_axis=causality_axis + ) curr_res = curr_res // 2 down_modules[i_level] = stage diff --git a/mlx_video/models/ltx/audio_vae/normalization.py b/mlx_video/models/ltx_2/audio_vae/normalization.py similarity index 94% rename from mlx_video/models/ltx/audio_vae/normalization.py rename to mlx_video/models/ltx_2/audio_vae/normalization.py index 361c6b4..8376a21 100644 --- a/mlx_video/models/ltx/audio_vae/normalization.py +++ b/mlx_video/models/ltx_2/audio_vae/normalization.py @@ -51,7 +51,9 @@ def build_normalization_layer( A normalization layer """ if normtype == NormType.GROUP: - return nn.GroupNorm(num_groups=num_groups, dims=in_channels, eps=1e-6, affine=True) + return nn.GroupNorm( + num_groups=num_groups, dims=in_channels, eps=1e-6, affine=True + ) if normtype == NormType.PIXEL: # For MLX channels-last format (B, H, W, C), normalize along channels (dim=-1) # PyTorch uses dim=1 for channels-first format (B, C, H, W) diff --git a/mlx_video/models/ltx/audio_vae/ops.py b/mlx_video/models/ltx_2/audio_vae/ops.py similarity index 90% rename from mlx_video/models/ltx/audio_vae/ops.py rename to mlx_video/models/ltx_2/audio_vae/ops.py index bf2d111..ae3cd30 100644 --- a/mlx_video/models/ltx/audio_vae/ops.py +++ b/mlx_video/models/ltx_2/audio_vae/ops.py @@ -27,21 +27,21 @@ class PerChannelStatistics(nn.Module): self.latent_channels = latent_channels # Initialize buffers - will be loaded from weights # Using underscores for MLX compatibility with weight loading - self._std_of_means = mx.ones((latent_channels,)) - self._mean_of_means = mx.zeros((latent_channels,)) + self.std_of_means = mx.ones((latent_channels,)) + self.mean_of_means = mx.zeros((latent_channels,)) def un_normalize(self, x: mx.array) -> mx.array: """Denormalize latent representation.""" # Broadcast statistics to match x shape # x shape: (B, C, ...) or (B, ..., C) - std = self._std_of_means.astype(x.dtype) - mean = self._mean_of_means.astype(x.dtype) + std = self.std_of_means.astype(x.dtype) + mean = self.mean_of_means.astype(x.dtype) return (x * std) + mean def normalize(self, x: mx.array) -> mx.array: """Normalize latent representation.""" - std = self._std_of_means.astype(x.dtype) - mean = self._mean_of_means.astype(x.dtype) + std = self.std_of_means.astype(x.dtype) + mean = self.mean_of_means.astype(x.dtype) return (x - mean) / std diff --git a/mlx_video/models/ltx/audio_vae/resnet.py b/mlx_video/models/ltx_2/audio_vae/resnet.py similarity index 85% rename from mlx_video/models/ltx/audio_vae/resnet.py rename to mlx_video/models/ltx_2/audio_vae/resnet.py index c80d938..c1b2ee5 100644 --- a/mlx_video/models/ltx/audio_vae/resnet.py +++ b/mlx_video/models/ltx_2/audio_vae/resnet.py @@ -1,12 +1,12 @@ """ResNet blocks for audio VAE and vocoder.""" -from typing import List, Tuple +from typing import Tuple import mlx.core as mx import mlx.nn as nn +from ..config import CausalityAxis from .causal_conv_2d import make_conv2d -from .causality_axis import CausalityAxis from .normalization import NormType, build_normalization_layer LRELU_SLOPE = 0.1 @@ -125,7 +125,11 @@ class ResnetBlock(nn.Module): self.norm1 = build_normalization_layer(in_channels, normtype=norm_type) self.conv1 = make_conv2d( - in_channels, out_channels, kernel_size=3, stride=1, causality_axis=causality_axis + in_channels, + out_channels, + kernel_size=3, + stride=1, + causality_axis=causality_axis, ) if temb_channels > 0: @@ -134,17 +138,29 @@ class ResnetBlock(nn.Module): self.norm2 = build_normalization_layer(out_channels, normtype=norm_type) self.dropout_rate = dropout self.conv2 = make_conv2d( - out_channels, out_channels, kernel_size=3, stride=1, causality_axis=causality_axis + out_channels, + out_channels, + kernel_size=3, + stride=1, + causality_axis=causality_axis, ) if self.in_channels != self.out_channels: if self.use_conv_shortcut: self.conv_shortcut = make_conv2d( - in_channels, out_channels, kernel_size=3, stride=1, causality_axis=causality_axis + in_channels, + out_channels, + kernel_size=3, + stride=1, + causality_axis=causality_axis, ) else: self.nin_shortcut = make_conv2d( - in_channels, out_channels, kernel_size=1, stride=1, causality_axis=causality_axis + in_channels, + out_channels, + kernel_size=1, + stride=1, + causality_axis=causality_axis, ) def __call__( @@ -168,7 +184,9 @@ class ResnetBlock(nn.Module): if temb is not None and self.temb_channels > 0: # temb: (B, temb_channels) -> (B, out_channels) # Need to add spatial dims: (B, 1, 1, out_channels) for broadcasting - h = h + mx.expand_dims(mx.expand_dims(nn.silu(self.temb_proj(temb)), axis=1), axis=1) + h = h + mx.expand_dims( + mx.expand_dims(nn.silu(self.temb_proj(temb)), axis=1), axis=1 + ) h = self.norm2(h) h = nn.silu(h) diff --git a/mlx_video/models/ltx/audio_vae/upsample.py b/mlx_video/models/ltx_2/audio_vae/upsample.py similarity index 90% rename from mlx_video/models/ltx/audio_vae/upsample.py rename to mlx_video/models/ltx_2/audio_vae/upsample.py index 731ac85..d443049 100644 --- a/mlx_video/models/ltx/audio_vae/upsample.py +++ b/mlx_video/models/ltx_2/audio_vae/upsample.py @@ -5,9 +5,9 @@ from typing import Set, Tuple import mlx.core as mx import mlx.nn as nn +from ..config import CausalityAxis from .attention import AttentionType, make_attn from .causal_conv_2d import make_conv2d -from .causality_axis import CausalityAxis from .normalization import NormType from .resnet import ResnetBlock @@ -42,7 +42,11 @@ class Upsample(nn.Module): self.causality_axis = causality_axis if self.with_conv: self.conv = make_conv2d( - in_channels, in_channels, kernel_size=3, stride=1, causality_axis=causality_axis + in_channels, + in_channels, + kernel_size=3, + stride=1, + causality_axis=causality_axis, ) def __call__(self, x: mx.array) -> mx.array: @@ -124,10 +128,14 @@ def build_upsampling_path( ) block_in = block_out if curr_res in attn_resolutions: - stage["attn"][i_block] = make_attn(block_in, attn_type=attn_type, norm_type=norm_type) + stage["attn"][i_block] = make_attn( + block_in, attn_type=attn_type, norm_type=norm_type + ) if level != 0: - stage["upsample"] = Upsample(block_in, resamp_with_conv, causality_axis=causality_axis) + stage["upsample"] = Upsample( + block_in, resamp_with_conv, causality_axis=causality_axis + ) curr_res *= 2 up_modules[level] = stage diff --git a/mlx_video/models/ltx_2/audio_vae/vocoder.py b/mlx_video/models/ltx_2/audio_vae/vocoder.py new file mode 100644 index 0000000..70e2722 --- /dev/null +++ b/mlx_video/models/ltx_2/audio_vae/vocoder.py @@ -0,0 +1,737 @@ +"""Vocoder for converting mel spectrograms to audio waveforms. + +Supports: +- HiFi-GAN (LTX-2): ResBlock1 with LeakyReLU +- BigVGAN v2 (LTX-2.3): AMPBlock1 with Snake/SnakeBeta + anti-aliased resampling +- VocoderWithBWE (LTX-2.3): Base vocoder + bandwidth extension (16kHz -> 48kHz) +""" + +import math +from pathlib import Path +from typing import Tuple + +import mlx.core as mx +import mlx.nn as nn + +from ..config import VocoderModelConfig +from .resnet import LRELU_SLOPE, ResBlock1, ResBlock2, leaky_relu + + +def get_padding(kernel_size: int, dilation: int = 1) -> int: + return int((kernel_size * dilation - dilation) / 2) + + +# --------------------------------------------------------------------------- +# Snake / SnakeBeta activations (BigVGAN v2) +# --------------------------------------------------------------------------- + + +class Snake(nn.Module): + """Snake activation: x + (1/alpha) * sin^2(alpha * x).""" + + def __init__(self, in_features: int, alpha_logscale: bool = True) -> None: + super().__init__() + self.alpha_logscale = alpha_logscale + self.alpha = ( + mx.zeros((in_features,)) if alpha_logscale else mx.ones((in_features,)) + ) + + def __call__(self, x: mx.array) -> mx.array: + # x: (N, L, C) in MLX format + alpha = self.alpha # (C,) + if self.alpha_logscale: + alpha = mx.exp(alpha) + return x + (1.0 / (alpha + 1e-9)) * mx.power(mx.sin(x * alpha), 2) + + +class SnakeBeta(nn.Module): + """SnakeBeta activation: x + (1/beta) * sin^2(alpha * x).""" + + def __init__(self, in_features: int, alpha_logscale: bool = True) -> None: + super().__init__() + self.alpha_logscale = alpha_logscale + self.alpha = ( + mx.zeros((in_features,)) if alpha_logscale else mx.ones((in_features,)) + ) + self.beta = ( + mx.zeros((in_features,)) if alpha_logscale else mx.ones((in_features,)) + ) + + def __call__(self, x: mx.array) -> mx.array: + alpha = self.alpha + beta = self.beta + if self.alpha_logscale: + alpha = mx.exp(alpha) + beta = mx.exp(beta) + return x + (1.0 / (beta + 1e-9)) * mx.power(mx.sin(x * alpha), 2) + + +# --------------------------------------------------------------------------- +# Anti-aliased resampling (Kaiser-sinc filters) +# --------------------------------------------------------------------------- + + +def _sinc(x: mx.array) -> mx.array: + return mx.where( + x == 0, + mx.ones_like(x), + mx.sin(mx.array(math.pi) * x) / (mx.array(math.pi) * x), + ) + + +def kaiser_sinc_filter1d( + cutoff: float, half_width: float, kernel_size: int +) -> mx.array: + """Compute a Kaiser-windowed sinc filter.""" + even = kernel_size % 2 == 0 + half_size = kernel_size // 2 + delta_f = 4 * half_width + amplitude = 2.285 * (half_size - 1) * math.pi * delta_f + 7.95 + if amplitude > 50.0: + beta = 0.1102 * (amplitude - 8.7) + elif amplitude >= 21.0: + beta = 0.5842 * (amplitude - 21) ** 0.4 + 0.07886 * (amplitude - 21.0) + else: + beta = 0.0 + + # Kaiser window - compute using scipy-compatible formula + import numpy as np + + window = mx.array(np.kaiser(kernel_size, beta).astype(np.float32)) + + if even: + time = mx.arange(-half_size, half_size).astype(mx.float32) + 0.5 + else: + time = mx.arange(kernel_size).astype(mx.float32) - half_size + + if cutoff == 0: + filter_ = mx.zeros_like(time) + else: + filter_ = 2 * cutoff * window * _sinc(2 * cutoff * time) + filter_ = filter_ / mx.sum(filter_) + + return filter_.reshape(1, 1, kernel_size) + + +def hann_sinc_filter1d(ratio: int) -> Tuple[mx.array, int, int, int]: + """Compute a Hann-windowed sinc filter for upsampling (used by BWE resampler).""" + import numpy as np + + rolloff = 0.99 + lowpass_filter_width = 6 + width = math.ceil(lowpass_filter_width / rolloff) + kernel_size = 2 * width * ratio + 1 + pad = width + pad_left = 2 * width * ratio + pad_right = kernel_size - ratio + + time = (np.arange(kernel_size) / ratio - width) * rolloff + time_clamped = np.clip(time, -lowpass_filter_width, lowpass_filter_width) + window = np.cos(time_clamped * math.pi / lowpass_filter_width / 2) ** 2 + sinc_filter = np.sinc(time) * window * rolloff / ratio + + filter_ = mx.array(sinc_filter.astype(np.float32)).reshape(1, 1, kernel_size) + return filter_, pad, pad_left, pad_right + + +class LowPassFilter1d(nn.Module): + """Low-pass filter using depthwise convolution with Kaiser-sinc kernel.""" + + def __init__( + self, + cutoff: float = 0.5, + half_width: float = 0.6, + stride: int = 1, + kernel_size: int = 12, + ) -> None: + super().__init__() + self.kernel_size = kernel_size + self.even = kernel_size % 2 == 0 + self.pad_left = kernel_size // 2 - int(self.even) + self.pad_right = kernel_size // 2 + self.stride = stride + # Filter buffer - shape (1, 1, K) in PyTorch format, loaded from weights + self.filter = kaiser_sinc_filter1d(cutoff, half_width, kernel_size) + + def __call__(self, x: mx.array) -> mx.array: + # x: (N, L, C) in MLX format + n, l, c = x.shape + + # Pad with edge values: replicate first/last value + first = mx.repeat(x[:, :1, :], self.pad_left, axis=1) + last = mx.repeat(x[:, -1:, :], self.pad_right, axis=1) + x = mx.concatenate([first, x, last], axis=1) + + # Expand filter for depthwise conv: (1, 1, K) -> (C, K, 1) for groups=C + # Filter is stored in PyTorch format (1, 1, K), need (C, K, 1) MLX format + filt = self.filter.astype(x.dtype) # (1, 1, K) + filt = mx.transpose(filt, (0, 2, 1)) # (1, K, 1) + filt = mx.repeat(filt, c, axis=0) # (C, K, 1) + + # Transpose x for depthwise conv: (N, L, C) -> (N*C, L, 1) then conv + x = mx.transpose(x, (0, 2, 1)) # (N, C, L) + x = x.reshape(n * c, -1, 1) # (N*C, L, 1) + + x = mx.conv1d(x, filt[:1], stride=self.stride, groups=1) # (N*C, L', 1) + + x = x.reshape(n, c, -1) # (N, C, L') + x = mx.transpose(x, (0, 2, 1)) # (N, L', C) + return x + + +class UpSample1d(nn.Module): + """Anti-aliased upsampling using transposed convolution with sinc filter.""" + + def __init__( + self, + ratio: int = 2, + kernel_size: int = None, + window_type: str = "kaiser", + ) -> None: + super().__init__() + self.ratio = ratio + self.stride = ratio + + if window_type == "hann": + filt, self.pad, self.pad_left, self.pad_right = hann_sinc_filter1d(ratio) + self.kernel_size = filt.shape[2] + self.filter = filt + else: + self.kernel_size = ( + int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size + ) + self.pad = self.kernel_size // ratio - 1 + self.pad_left = ( + self.pad * self.stride + (self.kernel_size - self.stride) // 2 + ) + self.pad_right = ( + self.pad * self.stride + (self.kernel_size - self.stride + 1) // 2 + ) + self.filter = kaiser_sinc_filter1d( + cutoff=0.5 / ratio, + half_width=0.6 / ratio, + kernel_size=self.kernel_size, + ) + + def __call__(self, x: mx.array) -> mx.array: + # x: (N, L, C) in MLX format + n, l, c = x.shape + + # Pad with edge values + first = mx.repeat(x[:, :1, :], self.pad, axis=1) + last = mx.repeat(x[:, -1:, :], self.pad, axis=1) + x = mx.concatenate([first, x, last], axis=1) + + # Process per-channel via reshape: (N, L, C) -> (N*C, L, 1) + x = mx.transpose(x, (0, 2, 1)) # (N, C, L) + x = x.reshape(n * c, -1, 1) # (N*C, L, 1) + + # Transposed conv for upsampling + # Filter: (1, 1, K) PyTorch -> (1, K, 1) MLX format for conv_transpose1d + filt = self.filter.astype(x.dtype) # (1, 1, K) + filt = mx.transpose(filt, (0, 2, 1)) # (1, K, 1) + + x = self.ratio * mx.conv_transpose1d( + x, filt, stride=self.stride + ) # (N*C, L', 1) + + # Trim padding + x = x[:, self.pad_left : -self.pad_right, :] + + x = x.reshape(n, c, -1) # (N, C, L') + x = mx.transpose(x, (0, 2, 1)) # (N, L', C) + return x + + +class DownSample1d(nn.Module): + """Anti-aliased downsampling using low-pass filter.""" + + def __init__(self, ratio: int = 2, kernel_size: int = None) -> None: + super().__init__() + self.ratio = ratio + kernel_size = int(6 * ratio // 2) * 2 if kernel_size is None else kernel_size + self.lowpass = LowPassFilter1d( + cutoff=0.5 / ratio, + half_width=0.6 / ratio, + stride=ratio, + kernel_size=kernel_size, + ) + + def __call__(self, x: mx.array) -> mx.array: + return self.lowpass(x) + + +class Activation1d(nn.Module): + """Anti-aliased activation: upsample -> activate -> downsample.""" + + def __init__( + self, + activation: nn.Module, + up_ratio: int = 2, + down_ratio: int = 2, + up_kernel_size: int = 12, + down_kernel_size: int = 12, + ) -> None: + super().__init__() + self.act = activation + self.upsample = UpSample1d(up_ratio, up_kernel_size) + self.downsample = DownSample1d(down_ratio, down_kernel_size) + + def __call__(self, x: mx.array) -> mx.array: + x = self.upsample(x) + x = self.act(x) + return self.downsample(x) + + +# --------------------------------------------------------------------------- +# AMPBlock1 (BigVGAN v2 residual block) +# --------------------------------------------------------------------------- + + +class AMPBlock1(nn.Module): + """BigVGAN v2 residual block with anti-aliased Snake activations.""" + + def __init__( + self, + channels: int, + kernel_size: int = 3, + dilation: Tuple[int, int, int] = (1, 3, 5), + activation: str = "snakebeta", + ) -> None: + super().__init__() + act_cls = SnakeBeta if activation == "snakebeta" else Snake + + self.convs1 = { + i: nn.Conv1d( + channels, + channels, + kernel_size, + stride=1, + dilation=d, + padding=get_padding(kernel_size, d), + ) + for i, d in enumerate(dilation) + } + + self.convs2 = { + i: nn.Conv1d( + channels, + channels, + kernel_size, + stride=1, + dilation=1, + padding=get_padding(kernel_size, 1), + ) + for i in range(len(dilation)) + } + + self.acts1 = {i: Activation1d(act_cls(channels)) for i in range(len(dilation))} + self.acts2 = {i: Activation1d(act_cls(channels)) for i in range(len(dilation))} + + def __call__(self, x: mx.array) -> mx.array: + for i in range(len(self.convs1)): + xt = self.acts1[i](x) + xt = self.convs1[i](xt) + xt = self.acts2[i](xt) + xt = self.convs2[i](xt) + x = x + xt + return x + + +# --------------------------------------------------------------------------- +# STFT and MelSTFT (for BWE) +# --------------------------------------------------------------------------- + + +class STFTFn(nn.Module): + """STFT via conv1d with precomputed DFT x window bases (loaded from checkpoint).""" + + def __init__(self, filter_length: int, hop_length: int, win_length: int) -> None: + super().__init__() + self.hop_length = hop_length + self.win_length = win_length + n_freqs = filter_length // 2 + 1 + # Buffers loaded from checkpoint - PyTorch format (n_freqs*2, 1, filter_length) + self.forward_basis = mx.zeros((n_freqs * 2, 1, filter_length)) + self.inverse_basis = mx.zeros((n_freqs * 2, 1, filter_length)) + + def __call__(self, y: mx.array) -> Tuple[mx.array, mx.array]: + """Compute magnitude and phase from waveform. + + Args: + y: (B, T) waveform + + Returns: + magnitude: (B, n_freqs, T_frames) + phase: (B, n_freqs, T_frames) + """ + if y.ndim == 2: + y = mx.expand_dims(y, -1) # (B, T, 1) + + left_pad = max(0, self.win_length - self.hop_length) + if left_pad > 0: + first = mx.repeat(y[:, :1, :], left_pad, axis=1) + y = mx.concatenate([first, y], axis=1) + + # forward_basis: (514, 1, 512) PyTorch format -> (514, 512, 1) MLX + basis = mx.transpose( + self.forward_basis.astype(y.dtype), (0, 2, 1) + ) # (514, K, 1) + + # Conv1d: (B, T, 1) * (514, K, 1) -> (B, T_frames, 514) + spec = mx.conv1d(y, basis, stride=self.hop_length) + + # Split real and imaginary + n_freqs = spec.shape[-1] // 2 + real = spec[..., :n_freqs] + imag = spec[..., n_freqs:] + + magnitude = mx.sqrt(real**2 + imag**2) + phase = mx.arctan2(imag.astype(mx.float32), real.astype(mx.float32)).astype( + real.dtype + ) + + # Output: (B, T_frames, n_freqs) in MLX channels-last + return magnitude, phase + + +class MelSTFT(nn.Module): + """Causal log-mel spectrogram from precomputed STFT bases.""" + + def __init__( + self, filter_length: int, hop_length: int, win_length: int, n_mel_channels: int + ) -> None: + super().__init__() + self.stft_fn = STFTFn(filter_length, hop_length, win_length) + n_freqs = filter_length // 2 + 1 + self.mel_basis = mx.zeros((n_mel_channels, n_freqs)) + + def mel_spectrogram(self, y: mx.array) -> mx.array: + """Compute log-mel spectrogram. + + Args: + y: (B, T) waveform + + Returns: + log_mel: (B, n_mels, T_frames) in channels-first for compatibility + """ + magnitude, phase = self.stft_fn(y) + # magnitude: (B, T_frames, n_freqs) + mel = ( + magnitude @ self.mel_basis.astype(magnitude.dtype).T + ) # (B, T_frames, n_mels) + log_mel = mx.log(mx.clip(mel, 1e-5, None)) + # Transpose to (B, n_mels, T_frames) for compatibility with vocoder input format + return mx.transpose(log_mel, (0, 2, 1)) + + +# --------------------------------------------------------------------------- +# Vocoder (supports both HiFi-GAN and BigVGAN v2) +# --------------------------------------------------------------------------- + + +class Vocoder(nn.Module): + """Vocoder for mel-to-waveform synthesis. + + Supports resblock="1" (HiFi-GAN / LTX-2) and resblock="AMP1" (BigVGAN v2 / LTX-2.3). + """ + + def __init__(self, config: VocoderModelConfig) -> None: + super().__init__() + + self.output_sampling_rate = config.output_sample_rate + self.num_kernels = len(config.resblock_kernel_sizes) + self.num_upsamples = len(config.upsample_rates) + self.upsample_rates = config.upsample_rates + self.is_amp = config.resblock == "AMP1" + self.use_tanh_at_final = config.use_tanh_at_final + self.apply_final_activation = config.apply_final_activation + + in_channels = 128 if config.stereo else 64 + self.conv_pre = nn.Conv1d( + in_channels, + config.upsample_initial_channel, + kernel_size=7, + stride=1, + padding=3, + ) + + # Upsampling layers + self.ups = {} + for i, (stride, kernel_size) in enumerate( + zip(config.upsample_rates, config.upsample_kernel_sizes) + ): + in_ch = config.upsample_initial_channel // (2**i) + out_ch = config.upsample_initial_channel // (2 ** (i + 1)) + self.ups[i] = nn.ConvTranspose1d( + in_ch, + out_ch, + kernel_size=kernel_size, + stride=stride, + padding=(kernel_size - stride) // 2, + ) + + # Residual blocks + if self.is_amp: + self.resblocks = {} + block_idx = 0 + for i in range(len(self.ups)): + ch = config.upsample_initial_channel // (2 ** (i + 1)) + for kernel_size, dilations in zip( + config.resblock_kernel_sizes, config.resblock_dilation_sizes + ): + self.resblocks[block_idx] = AMPBlock1( + ch, + kernel_size, + tuple(dilations), + activation=config.activation, + ) + block_idx += 1 + else: + resblock_class = ResBlock1 if config.resblock == "1" else ResBlock2 + self.resblocks = {} + block_idx = 0 + for i in range(len(self.ups)): + ch = config.upsample_initial_channel // (2 ** (i + 1)) + for kernel_size, dilations in zip( + config.resblock_kernel_sizes, config.resblock_dilation_sizes + ): + self.resblocks[block_idx] = resblock_class( + ch, kernel_size, tuple(dilations) + ) + block_idx += 1 + + final_channels = config.upsample_initial_channel // ( + 2 ** len(config.upsample_rates) + ) + + # Post-activation + if self.is_amp: + act_cls = SnakeBeta if config.activation == "snakebeta" else Snake + self.act_post = Activation1d(act_cls(final_channels)) + + # Final conv + out_channels = 2 if config.stereo else 1 + self.conv_post = nn.Conv1d( + final_channels, + out_channels, + kernel_size=7, + stride=1, + padding=3, + bias=config.use_bias_at_final, + ) + + self.upsample_factor = math.prod(config.upsample_rates) + + def __call__(self, x: mx.array) -> mx.array: + """Forward pass. + + Args: + x: Mel spectrogram (B, C, T, mel_bins) for stereo or (B, T, mel_bins) mono. + + Returns: + Waveform (B, out_channels, T_audio) in channels-first format. + """ + # (B, C, T, mel) -> (B, C, mel, T) + x = mx.transpose(x, (0, 1, 3, 2)) + + if x.ndim == 4: # stereo: (B, 2, mel, T) -> (B, 2*mel, T) + b, s, c, t = x.shape + x = x.reshape(b, s * c, t) + + # Channels-first (B, C, T) -> channels-last (B, T, C) for MLX conv + x = mx.transpose(x, (0, 2, 1)) + + x = self.conv_pre(x) + + for i in range(self.num_upsamples): + if not self.is_amp: + x = leaky_relu(x, LRELU_SLOPE) + x = self.ups[i](x) + + start = i * self.num_kernels + end = start + self.num_kernels + + block_outputs = mx.stack( + [self.resblocks[idx](x) for idx in range(start, end)], + axis=0, + ) + x = mx.mean(block_outputs, axis=0) + + if self.is_amp: + x = self.act_post(x) + else: + x = nn.leaky_relu(x) + + x = self.conv_post(x) + + if self.apply_final_activation: + x = mx.tanh(x) if self.use_tanh_at_final else mx.clip(x, -1, 1) + + # Back to channels-first (B, T, C) -> (B, C, T) + x = mx.transpose(x, (0, 2, 1)) + return x + + +# --------------------------------------------------------------------------- +# VocoderWithBWE (Bandwidth Extension) +# --------------------------------------------------------------------------- + + +class VocoderWithBWE(nn.Module): + """Vocoder + bandwidth extension upsampling (16kHz -> 48kHz). + + Chains a base vocoder with a BWE generator that predicts a residual + added to a sinc-resampled skip connection. + """ + + def __init__( + self, + vocoder: Vocoder, + bwe_generator: Vocoder, + mel_stft: MelSTFT, + input_sampling_rate: int = 16000, + output_sampling_rate: int = 48000, + hop_length: int = 80, + ) -> None: + super().__init__() + self.vocoder = vocoder + self.bwe_generator = bwe_generator + self.mel_stft = mel_stft + self.input_sampling_rate = input_sampling_rate + self.output_sampling_rate = output_sampling_rate + self.hop_length = hop_length + # Hann-windowed sinc resampler (not stored in checkpoint) + self.resampler = UpSample1d( + ratio=output_sampling_rate // input_sampling_rate, + window_type="hann", + ) + + @property + def output_sample_rate(self) -> int: + return self.output_sampling_rate + + def _compute_mel(self, audio: mx.array) -> mx.array: + """Compute log-mel spectrogram from waveform. + + Args: + audio: (B, C, T) waveform in channels-first + + Returns: + mel: (B, C, n_mels, T_frames) + """ + batch, n_channels, _ = audio.shape + flat = audio.reshape(batch * n_channels, -1) # (B*C, T) + mel = self.mel_stft.mel_spectrogram(flat) # (B*C, n_mels, T_frames) + return mel.reshape(batch, n_channels, mel.shape[1], mel.shape[2]) + + def __call__(self, mel_spec: mx.array) -> mx.array: + """Run vocoder + BWE. + + Args: + mel_spec: Mel spectrogram, same format as Vocoder.forward input. + + Returns: + Waveform (B, out_channels, T_audio) at output_sampling_rate. + """ + x = self.vocoder(mel_spec) # (B, C, T) at input_sampling_rate + _, _, length_low_rate = x.shape + output_length = ( + length_low_rate * self.output_sampling_rate // self.input_sampling_rate + ) + + # Pad to hop_length multiple + remainder = length_low_rate % self.hop_length + if remainder != 0: + pad_amount = self.hop_length - remainder + x = mx.pad(x, [(0, 0), (0, 0), (0, pad_amount)]) + + # Compute mel from vocoder output: (B, C, n_mels, T_frames) + mel = self._compute_mel(x) + + # BWE expects (B, C, T_frames, mel_bins) -> transpose last two dims + mel_for_bwe = mx.transpose(mel, (0, 1, 3, 2)) # (B, C, T_frames, n_mels) + residual = self.bwe_generator(mel_for_bwe) # (B, C, T_high) + + # Sinc upsample skip connection + # resampler expects (N, L, C): transpose from (B, C, T) -> (B, T, C) + x_for_resample = mx.transpose(x, (0, 2, 1)) + skip = self.resampler(x_for_resample) + skip = mx.transpose(skip, (0, 2, 1)) # back to (B, C, T) + + return mx.clip(residual + skip, -1, 1)[..., :output_length] + + +# --------------------------------------------------------------------------- +# Factory / from_pretrained +# --------------------------------------------------------------------------- + + +def load_vocoder(model_path: Path) -> nn.Module: + """Load vocoder from pretrained model directory. + + Automatically detects whether to load a simple Vocoder or VocoderWithBWE. + """ + import json + + config_path = model_path / "config.json" + if not config_path.exists(): + raise FileNotFoundError(f"No config.json found in {model_path}") + + with open(config_path) as f: + config_dict = json.load(f) + + weights = mx.load(str(model_path / "model.safetensors")) + + has_bwe = config_dict.get("has_bwe_generator", False) + + if has_bwe: + return _load_vocoder_with_bwe(config_dict, weights) + else: + config = VocoderModelConfig.from_dict(config_dict) + model = Vocoder(config) + model.load_weights(list(weights.items()), strict=True) + return model + + +def _load_vocoder_with_bwe(config_dict: dict, weights: dict) -> VocoderWithBWE: + """Load VocoderWithBWE from config and weights.""" + # Build vocoder from config + vocoder_cfg = config_dict.get("vocoder", {}) + vocoder_config = VocoderModelConfig.from_dict(vocoder_cfg) + vocoder = Vocoder(vocoder_config) + + # Build BWE generator from config + bwe_cfg = config_dict.get("bwe", {}) + bwe_config = VocoderModelConfig.from_dict(bwe_cfg) + bwe_config.apply_final_activation = False + bwe_generator = Vocoder(bwe_config) + + # MelSTFT from weight shapes + stft_basis = weights.get("mel_stft.stft_fn.forward_basis") + filter_length = stft_basis.shape[2] if stft_basis is not None else 512 + mel_basis = weights.get("mel_stft.mel_basis") + n_mel_channels = mel_basis.shape[0] if mel_basis is not None else 64 + + hop_length = bwe_cfg.get("hop_length", 80) + input_sr = bwe_cfg.get("input_sampling_rate", 16000) + output_sr = bwe_cfg.get("output_sampling_rate", 48000) + + mel_stft = MelSTFT( + filter_length=filter_length, + hop_length=hop_length, + win_length=filter_length, + n_mel_channels=n_mel_channels, + ) + + model = VocoderWithBWE( + vocoder=vocoder, + bwe_generator=bwe_generator, + mel_stft=mel_stft, + input_sampling_rate=input_sr, + output_sampling_rate=output_sr, + hop_length=hop_length, + ) + + model.load_weights(list(weights.items()), strict=False) + return model diff --git a/mlx_video/models/ltx_2/conditioning/__init__.py b/mlx_video/models/ltx_2/conditioning/__init__.py new file mode 100644 index 0000000..08d7c97 --- /dev/null +++ b/mlx_video/models/ltx_2/conditioning/__init__.py @@ -0,0 +1,6 @@ +"""Conditioning modules for LTX-2 video generation.""" + +from mlx_video.models.ltx_2.conditioning.latent import ( + VideoConditionByLatentIndex, + apply_conditioning, +) diff --git a/mlx_video/conditioning/latent.py b/mlx_video/models/ltx_2/conditioning/latent.py similarity index 93% rename from mlx_video/conditioning/latent.py rename to mlx_video/models/ltx_2/conditioning/latent.py index acf3d99..4f101b2 100644 --- a/mlx_video/conditioning/latent.py +++ b/mlx_video/models/ltx_2/conditioning/latent.py @@ -5,7 +5,7 @@ the video generation process at specific frame positions. """ from dataclasses import dataclass -from typing import Optional, List, Tuple +from typing import List, Optional, Tuple import mlx.core as mx @@ -22,6 +22,7 @@ class VideoConditionByLatentIndex: frame_idx: Frame index to condition (0 = first frame) strength: Denoising strength (1.0 = full denoise, 0.0 = keep original) """ + latent: mx.array frame_idx: int = 0 strength: float = 1.0 @@ -41,6 +42,7 @@ class LatentState: denoise_mask: Per-frame denoising mask (B, 1, F, 1, 1) where 1.0 = full denoise, 0.0 = keep clean """ + latent: mx.array clean_latent: mx.array denoise_mask: mx.array @@ -130,15 +132,15 @@ def apply_conditioning( if frame_idx <= i < end_idx: # Use conditioning latent cond_idx = i - frame_idx - latent_list.append(cond_latent[:, :, cond_idx:cond_idx+1]) - clean_list.append(cond_latent[:, :, cond_idx:cond_idx+1]) + latent_list.append(cond_latent[:, :, cond_idx : cond_idx + 1]) + clean_list.append(cond_latent[:, :, cond_idx : cond_idx + 1]) # Set mask: 1.0 - strength means less denoising for conditioned frames mask_list.append(mx.full((b, 1, 1, 1, 1), 1.0 - strength, dtype=dtype)) else: # Keep original - latent_list.append(state.latent[:, :, i:i+1]) - clean_list.append(state.clean_latent[:, :, i:i+1]) - mask_list.append(state.denoise_mask[:, :, i:i+1]) + latent_list.append(state.latent[:, :, i : i + 1]) + clean_list.append(state.clean_latent[:, :, i : i + 1]) + mask_list.append(state.denoise_mask[:, :, i : i + 1]) state.latent = mx.concatenate(latent_list, axis=2) state.clean_latent = mx.concatenate(clean_list, axis=2) diff --git a/mlx_video/models/ltx_2/config.py b/mlx_video/models/ltx_2/config.py new file mode 100644 index 0000000..3d96ab7 --- /dev/null +++ b/mlx_video/models/ltx_2/config.py @@ -0,0 +1,393 @@ +import inspect +from dataclasses import dataclass, field +from enum import Enum +from typing import Any, List, Optional, Tuple + + +class LTXModelType(Enum): + AudioVideo = "ltx av model" + VideoOnly = "ltx video only model" + AudioOnly = "ltx audio only model" + + def is_video_enabled(self) -> bool: + return self in (LTXModelType.AudioVideo, LTXModelType.VideoOnly) + + def is_audio_enabled(self) -> bool: + return self in (LTXModelType.AudioVideo, LTXModelType.AudioOnly) + + +class LTXRopeType(Enum): + INTERLEAVED = "interleaved" + SPLIT = "split" + TWO_D = "2d" + + +class AttentionType(Enum): + DEFAULT = "default" + + +@dataclass +class BaseModelConfig: + + @classmethod + def from_dict(cls, params: dict[str, Any]) -> "BaseModelConfig": + """Create config from dictionary, filtering only valid parameters.""" + return cls( + **{ + k: v + for k, v in params.items() + if k in inspect.signature(cls).parameters + } + ) + + def to_dict(self) -> dict[str, Any]: + """Export config to dictionary.""" + result = {} + for k, v in self.__dict__.items(): + if v is not None: + if isinstance(v, Enum): + result[k] = v.value + elif hasattr(v, "to_dict"): + result[k] = v.to_dict() + else: + result[k] = v + return result + + +@dataclass +class TransformerConfig(BaseModelConfig): + dim: int + heads: int + d_head: int + context_dim: int + + +@dataclass +class VideoVAEConfig(BaseModelConfig): + convolution_dimensions: int = 3 + in_channels: int = 3 + out_channels: int = 128 + latent_channels: int = 128 + patch_size: int = 4 + encoder_blocks: List[tuple] = field( + default_factory=lambda: [ + ("res_x", {"num_layers": 4}), + ("compress_space_res", {"multiplier": 2}), + ("res_x", {"num_layers": 6}), + ("compress_time_res", {"multiplier": 2}), + ("res_x", {"num_layers": 6}), + ("compress_all_res", {"multiplier": 2}), + ("res_x", {"num_layers": 2}), + ("compress_all_res", {"multiplier": 2}), + ("res_x", {"num_layers": 2}), + ] + ) + decoder_blocks: List[tuple] = field( + default_factory=lambda: [ + ("res_x", {"num_layers": 5, "inject_noise": False}), + ("compress_all", {"residual": True, "multiplier": 2}), + ("res_x", {"num_layers": 5, "inject_noise": False}), + ("compress_all", {"residual": True, "multiplier": 2}), + ("res_x", {"num_layers": 5, "inject_noise": False}), + ("compress_all", {"residual": True, "multiplier": 2}), + ("res_x", {"num_layers": 5, "inject_noise": False}), + ] + ) + + +@dataclass +class LTXModelConfig(BaseModelConfig): + + # Model type + model_type: LTXModelType = LTXModelType.AudioVideo + + # Video transformer config + num_attention_heads: int = 32 + attention_head_dim: int = 128 + in_channels: int = 128 + out_channels: int = 128 + num_layers: int = 48 + cross_attention_dim: int = 4096 + caption_channels: int = 3840 + + # Audio transformer config + audio_num_attention_heads: int = 32 + audio_attention_head_dim: int = 64 + audio_in_channels: int = 128 + audio_out_channels: int = 128 + audio_cross_attention_dim: int = 2048 + audio_caption_channels: int = ( + 3840 # Input dim for audio text embeddings (same as video) + ) + + # Positional embedding config + positional_embedding_theta: float = 10000.0 + positional_embedding_max_pos: Optional[List[int]] = None + audio_positional_embedding_max_pos: Optional[List[int]] = None + use_middle_indices_grid: bool = True + rope_type: LTXRopeType = LTXRopeType.INTERLEAVED + double_precision_rope: bool = False + + # Timestep config + timestep_scale_multiplier: int = 1000 + av_ca_timestep_scale_multiplier: int = 1000 + + # Normalization + norm_eps: float = 1e-6 + + # Attention type + attention_type: AttentionType = AttentionType.DEFAULT + + # LTX-2.3: prompt-conditioned adaptive layer norm + # Controls: gate_logits in attention, 9-param scale_shift_table, + # prompt_adaln_single, per-block prompt_scale_shift_table, + # removal of caption_projection + has_prompt_adaln: bool = False + + # VAE config + vae_config: Optional[VideoVAEConfig] = None + + def __post_init__(self): + """Set default values after initialization.""" + if self.positional_embedding_max_pos is None: + self.positional_embedding_max_pos = [20, 2048, 2048] + if self.audio_positional_embedding_max_pos is None: + self.audio_positional_embedding_max_pos = [20] + + # PyTorch LTX-2 configurator reads "frequencies_precision" (not + # "double_precision_rope") from the config. For LTX-2 (no prompt adaln) + # the key is absent, so double_precision_rope = False. For LTX-2.3 + # (has_prompt_adaln=True) the safetensors config has + # frequencies_precision="float64", so double_precision_rope = True. + if not self.has_prompt_adaln: + self.double_precision_rope = False + + # Convert string enum values if loading from dict + if isinstance(self.model_type, str): + self.model_type = LTXModelType(self.model_type) + if isinstance(self.rope_type, str): + self.rope_type = LTXRopeType(self.rope_type) + if isinstance(self.attention_type, str): + self.attention_type = AttentionType(self.attention_type) + + @property + def inner_dim(self) -> int: + """Video inner dimension.""" + return self.num_attention_heads * self.attention_head_dim + + @property + def audio_inner_dim(self) -> int: + """Audio inner dimension.""" + return self.audio_num_attention_heads * self.audio_attention_head_dim + + def get_video_config(self) -> Optional[TransformerConfig]: + """Get video transformer configuration.""" + if not self.model_type.is_video_enabled(): + return None + return TransformerConfig( + dim=self.inner_dim, + heads=self.num_attention_heads, + d_head=self.attention_head_dim, + context_dim=self.cross_attention_dim, + ) + + def get_audio_config(self) -> Optional[TransformerConfig]: + """Get audio transformer configuration.""" + if not self.model_type.is_audio_enabled(): + return None + return TransformerConfig( + dim=self.audio_inner_dim, + heads=self.audio_num_attention_heads, + d_head=self.audio_attention_head_dim, + context_dim=self.audio_cross_attention_dim, + ) + + +class CausalityAxis(Enum): + """Enum for specifying the causality axis in causal convolutions.""" + + NONE = None + WIDTH = "width" + HEIGHT = "height" + WIDTH_COMPATIBILITY = "width-compatibility" + + +@dataclass +class AudioDecoderModelConfig(BaseModelConfig): + ch: int = 128 + out_ch: int = 2 + ch_mult: Tuple[int, ...] = (1, 2, 4) + num_res_blocks: int = 2 + attn_resolutions: Optional[List[int]] = None + resolution: int = 256 + z_channels: int = 8 + norm_type: Enum = None + causality_axis: Enum = None + dropout: float = 0.0 + mid_block_add_attention: bool = True + sample_rate: int = 16000 + mel_hop_length: int = 160 + is_causal: bool = True + mel_bins: int | None = None + resamp_with_conv: bool = True + attn_type: str = None + give_pre_end: bool = False + tanh_out: bool = False + + def to_dict(self) -> dict[str, Any]: + result = super().to_dict() + if self.attn_resolutions is not None: + result["attn_resolutions"] = list(self.attn_resolutions) + return result + + def __post_init__(self): + """Convert string enum values to proper enum types.""" + # Import here to avoid circular imports + from .audio_vae.attention import AttentionType + from .audio_vae.normalization import NormType + + # Convert causality_axis string to enum + if isinstance(self.causality_axis, str): + self.causality_axis = CausalityAxis(self.causality_axis) + + # Convert norm_type string to enum + if isinstance(self.norm_type, str): + self.norm_type = NormType(self.norm_type) + + # Convert attn_type string to enum + if isinstance(self.attn_type, str): + self.attn_type = AttentionType(self.attn_type) + + +@dataclass +class AudioEncoderModelConfig(BaseModelConfig): + ch: int = 128 + in_channels: int = 2 + ch_mult: Tuple[int, ...] = (1, 2, 4) + num_res_blocks: int = 2 + attn_resolutions: Optional[List[int]] = None + resolution: int = 256 + z_channels: int = 8 + double_z: bool = True + n_fft: int = 1024 + norm_type: Enum = None + causality_axis: Enum = None + dropout: float = 0.0 + mid_block_add_attention: bool = True + sample_rate: int = 16000 + mel_hop_length: int = 160 + is_causal: bool = True + mel_bins: int = 64 + resamp_with_conv: bool = True + attn_type: str = None + + def to_dict(self) -> dict[str, Any]: + result = super().to_dict() + if self.attn_resolutions is not None: + result["attn_resolutions"] = list(self.attn_resolutions) + return result + + def __post_init__(self): + """Convert string enum values to proper enum types.""" + from .audio_vae.attention import AttentionType + from .audio_vae.normalization import NormType + + if isinstance(self.causality_axis, str): + self.causality_axis = CausalityAxis(self.causality_axis) + if isinstance(self.norm_type, str): + self.norm_type = NormType(self.norm_type) + if isinstance(self.attn_type, str): + self.attn_type = AttentionType(self.attn_type) + + +@dataclass +class VocoderModelConfig(BaseModelConfig): + resblock_kernel_sizes: Optional[List[int]] = None + upsample_rates: Optional[List[int]] = None + upsample_kernel_sizes: Optional[List[int]] = None + resblock_dilation_sizes: Optional[List[List[int]]] = None + upsample_initial_channel: int = 1024 + stereo: bool = True + resblock: str = "1" + output_sample_rate: int = 24000 + activation: str = "snake" + use_tanh_at_final: bool = True + apply_final_activation: bool = True + use_bias_at_final: bool = True + + def __post_init__(self): + + if self.resblock_kernel_sizes is None: + self.resblock_kernel_sizes = [3, 7, 11] + if self.upsample_rates is None: + self.upsample_rates = [6, 5, 2, 2, 2] + if self.upsample_kernel_sizes is None: + self.upsample_kernel_sizes = [16, 15, 8, 4, 4] + if self.resblock_dilation_sizes is None: + self.resblock_dilation_sizes = [[1, 3, 5], [1, 3, 5], [1, 3, 5]] + + +@dataclass +class VideoDecoderModelConfig(BaseModelConfig): + ch: int = 128 + out_ch: int = 2 + ch_mult: Tuple[int, ...] = (1, 2, 4) + num_res_blocks: int = 2 + attn_resolutions: Optional[List[int]] = None + resolution: int = 256 + z_channels: int = 8 + norm_type: Enum = None + causality_axis: Enum = None + dropout: float = 0.0 + timestep_conditioning: bool = False + + +@dataclass +class VideoEncoderModelConfig(BaseModelConfig): + convolution_dimensions: int = 3 + in_channels: int = 3 + out_channels: int = 128 + patch_size: int = 4 + norm_layer: Enum = None + latent_log_var: Enum = None + encoder_spatial_padding_mode: Enum = None + encoder_blocks: List[tuple] = field( + default_factory=lambda: [ + ("res_x", {"num_layers": 4}), + ("compress_space_res", {"multiplier": 2}), + ("res_x", {"num_layers": 6}), + ("compress_time_res", {"multiplier": 2}), + ("res_x", {"num_layers": 6}), + ("compress_all_res", {"multiplier": 2}), + ("res_x", {"num_layers": 2}), + ("compress_all_res", {"multiplier": 2}), + ("res_x", {"num_layers": 2}), + ] + ) + + def __post_init__(self): + from mlx_video.models.ltx_2.video_vae.convolution import PaddingModeType + from mlx_video.models.ltx_2.video_vae.resnet import NormLayerType + from mlx_video.models.ltx_2.video_vae.video_vae import LogVarianceType + + if self.norm_layer is None: + self.norm_layer = NormLayerType.PIXEL_NORM + if self.latent_log_var is None: + self.latent_log_var = LogVarianceType.UNIFORM + if self.encoder_spatial_padding_mode is None: + self.encoder_spatial_padding_mode = PaddingModeType.ZEROS + + if isinstance(self.norm_layer, str): + self.norm_layer = NormLayerType(self.norm_layer) + if isinstance(self.latent_log_var, str): + self.latent_log_var = LogVarianceType(self.latent_log_var) + if isinstance(self.encoder_spatial_padding_mode, str): + self.encoder_spatial_padding_mode = PaddingModeType( + self.encoder_spatial_padding_mode + ) + + def to_dict(self) -> dict[str, Any]: + result = super().to_dict() + if self.encoder_blocks is not None: + result["encoder_blocks"] = [list(block) for block in self.encoder_blocks] + return result diff --git a/mlx_video/models/ltx_2/convert.py b/mlx_video/models/ltx_2/convert.py new file mode 100644 index 0000000..ffc2a65 --- /dev/null +++ b/mlx_video/models/ltx_2/convert.py @@ -0,0 +1,857 @@ +"""Convert LTX-2/2.3 safetensors to MLX directory layout. + +Converts from the single-file format (e.g. Lightricks/LTX-2/ltx-2-19b-distilled.safetensors +or Lightricks/LTX-2.3/ltx-2.3-22b-distilled.safetensors) to the modular directory structure: + + output/ + ├── transformer/ # DiT transformer weights (sharded) + │ ├── config.json + │ ├── model-00001-of-N.safetensors + │ └── model.safetensors.index.json + ├── vae/ + │ ├── decoder/ # Video VAE decoder + │ │ ├── config.json + │ │ └── model.safetensors + │ └── encoder/ # Video VAE encoder + │ ├── config.json + │ └── model.safetensors + ├── audio_vae/ + │ ├── decoder/ # Audio VAE decoder + │ │ ├── config.json + │ │ └── model.safetensors + │ └── encoder/ # Audio VAE encoder + │ ├── config.json + │ └── model.safetensors + ├── vocoder/ # Audio vocoder + │ ├── config.json + │ └── model.safetensors + └── text_projections/ # Text projection connectors + └── model.safetensors + +Usage: + # From HF repo ID + python -m mlx_video.models.ltx_2.convert --source Lightricks/LTX-2 --output LTX-2-distilled --variant distilled + python -m mlx_video.models.ltx_2.convert --source Lightricks/LTX-2.3 --output LTX-2.3-distilled --variant distilled + + # From local folder containing the monolithic safetensors + python -m mlx_video.models.ltx_2.convert --source ./Lightricks-LTX-2/ --output LTX-2-distilled --variant distilled + + # From a direct safetensors file path + python -m mlx_video.models.ltx_2.convert --source ./ltx-2-19b-distilled.safetensors --output LTX-2-distilled --variant distilled +""" + +import argparse +import json +import re +import shutil +from pathlib import Path +from typing import Dict + +import mlx.core as mx + +# ─── Key prefix routing ────────────────────────────────────────────────────── + +TRANSFORMER_PREFIX = "model.diffusion_model." +VAE_DECODER_PREFIX = "vae.decoder." +VAE_ENCODER_PREFIX = "vae.encoder." +VAE_STATS_PREFIX = "vae.per_channel_statistics." +AUDIO_DECODER_PREFIX = "audio_vae.decoder." +AUDIO_ENCODER_PREFIX = "audio_vae.encoder." +AUDIO_STATS_PREFIX = "audio_vae.per_channel_statistics." +VOCODER_PREFIX = "vocoder." +TEXT_PROJ_PREFIX = "text_embedding_projection." +VIDEO_CONNECTOR_PREFIX = "model.diffusion_model.video_embeddings_connector." +AUDIO_CONNECTOR_PREFIX = "model.diffusion_model.audio_embeddings_connector." + + +# ─── Sanitization functions ────────────────────────────────────────────────── + + +def sanitize_transformer(weights: Dict[str, mx.array]) -> Dict[str, mx.array]: + """Sanitize transformer keys: strip prefix, rename layers, cast to bfloat16.""" + sanitized = {} + for key, value in weights.items(): + if not key.startswith(TRANSFORMER_PREFIX): + continue + # Skip connector weights (they go to text_projections) + if "audio_embeddings_connector" in key or "video_embeddings_connector" in key: + continue + + new_key = key[len(TRANSFORMER_PREFIX) :] + new_key = new_key.replace(".to_out.0.", ".to_out.") + new_key = new_key.replace(".ff.net.0.proj.", ".ff.proj_in.") + new_key = new_key.replace(".ff.net.2.", ".ff.proj_out.") + new_key = new_key.replace(".audio_ff.net.0.proj.", ".audio_ff.proj_in.") + new_key = new_key.replace(".audio_ff.net.2.", ".audio_ff.proj_out.") + new_key = new_key.replace(".linear_1.", ".linear1.") + new_key = new_key.replace(".linear_2.", ".linear2.") + + # Cast all weights to bfloat16 (matches MLX model loading behavior) + if value.dtype != mx.bfloat16: + value = value.astype(mx.bfloat16) + + sanitized[new_key] = value + return sanitized + + +def sanitize_vae_decoder(weights: Dict[str, mx.array]) -> Dict[str, mx.array]: + """Sanitize VAE decoder keys: strip prefix, transpose Conv3d, wrap .conv.""" + sanitized = {} + for key, value in weights.items(): + new_key = None + + if key.startswith(VAE_STATS_PREFIX): + if key == "vae.per_channel_statistics.mean-of-means": + new_key = "per_channel_statistics.mean" + elif key == "vae.per_channel_statistics.std-of-means": + new_key = "per_channel_statistics.std" + else: + continue + elif key.startswith(VAE_DECODER_PREFIX): + new_key = key[len(VAE_DECODER_PREFIX) :] + else: + continue + + # Conv3d weight transpose: PyTorch (O, I, D, H, W) -> MLX (O, D, H, W, I) + if ".conv.weight" in key and value.ndim == 5: + value = mx.transpose(value, (0, 2, 3, 4, 1)) + + # Wrap .conv.weight -> .conv.conv.weight (CausalConv3d wrapper) + if ".conv.weight" in new_key or ".conv.bias" in new_key: + if ".conv.conv.weight" not in new_key and ".conv.conv.bias" not in new_key: + new_key = new_key.replace(".conv.weight", ".conv.conv.weight") + new_key = new_key.replace(".conv.bias", ".conv.conv.bias") + + sanitized[new_key] = value + return sanitized + + +def sanitize_vae_encoder(weights: Dict[str, mx.array]) -> Dict[str, mx.array]: + """Sanitize VAE encoder keys: strip prefix, transpose Conv3d/Conv2d.""" + sanitized = {} + for key, value in weights.items(): + new_key = None + + if "position_ids" in key: + continue + + if key.startswith(VAE_STATS_PREFIX): + if key == "vae.per_channel_statistics.mean-of-means": + new_key = "per_channel_statistics.mean" + elif key == "vae.per_channel_statistics.std-of-means": + new_key = "per_channel_statistics.std" + else: + continue + # Per-channel statistics must stay float32 for precision + if value.dtype != mx.float32: + value = value.astype(mx.float32) + elif key.startswith(VAE_ENCODER_PREFIX): + new_key = key[len(VAE_ENCODER_PREFIX) :] + else: + continue + + # Conv3d: PyTorch (O, I, D, H, W) -> MLX (O, D, H, W, I) + if "conv" in new_key.lower() and "weight" in new_key and value.ndim == 5: + value = mx.transpose(value, (0, 2, 3, 4, 1)) + + # Conv2d: PyTorch (O, I, H, W) -> MLX (O, H, W, I) + if "conv" in new_key.lower() and "weight" in new_key and value.ndim == 4: + value = mx.transpose(value, (0, 2, 3, 1)) + + sanitized[new_key] = value + return sanitized + + +def sanitize_audio_decoder(weights: Dict[str, mx.array]) -> Dict[str, mx.array]: + """Sanitize audio VAE decoder keys: strip prefix, transpose Conv2d.""" + sanitized = {} + for key, value in weights.items(): + new_key = None + + if key.startswith(AUDIO_DECODER_PREFIX): + new_key = key[len(AUDIO_DECODER_PREFIX) :] + elif key.startswith(AUDIO_STATS_PREFIX): + if "mean-of-means" in key: + new_key = "per_channel_statistics.mean_of_means" + elif "std-of-means" in key: + new_key = "per_channel_statistics.std_of_means" + else: + continue + else: + continue + + # Conv2d: PyTorch (O, I, H, W) -> MLX (O, H, W, I) + if "conv" in new_key.lower() and "weight" in new_key and value.ndim == 4: + value = mx.transpose(value, (0, 2, 3, 1)) + + sanitized[new_key] = value + return sanitized + + +def sanitize_audio_encoder(weights: Dict[str, mx.array]) -> Dict[str, mx.array]: + """Sanitize audio VAE encoder keys: strip prefix, transpose Conv2d.""" + sanitized = {} + for key, value in weights.items(): + new_key = None + + if key.startswith(AUDIO_ENCODER_PREFIX): + new_key = key[len(AUDIO_ENCODER_PREFIX) :] + elif key.startswith(AUDIO_STATS_PREFIX): + if "mean-of-means" in key: + new_key = "per_channel_statistics.mean_of_means" + elif "std-of-means" in key: + new_key = "per_channel_statistics.std_of_means" + else: + continue + elif key == "latents_mean": + new_key = "per_channel_statistics.mean_of_means" + elif key == "latents_std": + new_key = "per_channel_statistics.std_of_means" + else: + continue + + # Conv2d: PyTorch (O, I, H, W) -> MLX (O, H, W, I) + if "conv" in new_key.lower() and "weight" in new_key and value.ndim == 4: + value = mx.transpose(value, (0, 2, 3, 1)) + + sanitized[new_key] = value + return sanitized + + +def sanitize_vocoder(weights: Dict[str, mx.array]) -> Dict[str, mx.array]: + """Sanitize vocoder keys: strip prefix, transpose Conv1d/ConvTranspose1d.""" + sanitized = {} + for key, value in weights.items(): + if not key.startswith(VOCODER_PREFIX): + continue + + new_key = key[len(VOCODER_PREFIX) :] + + # Handle Conv1d/ConvTranspose1d weight shape conversion + if "weight" in new_key and value.ndim == 3: + if "ups" in new_key: + # ConvTranspose1d: PyTorch (in_ch, out_ch, kernel) -> MLX (out_ch, kernel, in_ch) + value = mx.transpose(value, (1, 2, 0)) + else: + # Conv1d: PyTorch (out_ch, in_ch, kernel) -> MLX (out_ch, kernel, in_ch) + value = mx.transpose(value, (0, 2, 1)) + + sanitized[new_key] = value + return sanitized + + +def sanitize_connector_key(key: str) -> str: + """Sanitize connector sub-key names.""" + key = key.replace(".ff.net.0.proj.", ".ff.proj_in.") + key = key.replace(".ff.net.2.", ".ff.proj_out.") + key = key.replace(".to_out.0.", ".to_out.") + return key + + +def extract_text_projections(weights: Dict[str, mx.array]) -> Dict[str, mx.array]: + """Extract text projection weights (aggregate_embed + connectors). + + Handles both LTX-2 (aggregate_embed.weight) and LTX-2.3 + (video_aggregate_embed.*, audio_aggregate_embed.*) formats. + """ + extracted = {} + + # aggregate_embed weights (text_embedding_projection.*) + for key, value in weights.items(): + if key.startswith(TEXT_PROJ_PREFIX): + new_key = key[len(TEXT_PROJ_PREFIX) :] + extracted[new_key] = value + + # video_embeddings_connector + for key, value in weights.items(): + if key.startswith(VIDEO_CONNECTOR_PREFIX): + suffix = key[len(VIDEO_CONNECTOR_PREFIX) :] + new_key = "video_embeddings_connector." + sanitize_connector_key(suffix) + extracted[new_key] = value + + # audio_embeddings_connector + for key, value in weights.items(): + if key.startswith(AUDIO_CONNECTOR_PREFIX): + suffix = key[len(AUDIO_CONNECTOR_PREFIX) :] + new_key = "audio_embeddings_connector." + sanitize_connector_key(suffix) + extracted[new_key] = value + + return extracted + + +# ─── Saving utilities ───────────────────────────────────────────────────────── + + +def save_sharded( + weights: Dict[str, mx.array], + output_dir: Path, + max_shard_size_bytes: int = 5 * 1024 * 1024 * 1024, # 5GB per shard +): + """Save weights as sharded safetensors with an index file.""" + output_dir.mkdir(parents=True, exist_ok=True) + + # Sort keys for deterministic output + sorted_keys = sorted(weights.keys()) + + # Calculate total size + total_size = sum(weights[k].nbytes for k in sorted_keys) + + # Determine sharding + shards = [] + current_shard = {} + current_size = 0 + + for key in sorted_keys: + tensor = weights[key] + tensor_size = tensor.nbytes + + if current_size + tensor_size > max_shard_size_bytes and current_shard: + shards.append(current_shard) + current_shard = {} + current_size = 0 + + current_shard[key] = tensor + current_size += tensor_size + + if current_shard: + shards.append(current_shard) + + num_shards = len(shards) + weight_map = {} + + for i, shard in enumerate(shards): + if num_shards == 1: + filename = "model.safetensors" + else: + filename = f"model-{i+1:05d}-of-{num_shards:05d}.safetensors" + + mx.save_safetensors(str(output_dir / filename), shard) + + for key in shard: + weight_map[key] = filename + + # Write index + index = { + "metadata": {"total_size": total_size}, + "weight_map": weight_map, + } + with open(output_dir / "model.safetensors.index.json", "w") as f: + json.dump(index, f, indent=2, sort_keys=True) + + return num_shards + + +def save_single(weights: Dict[str, mx.array], output_dir: Path): + """Save weights as a single safetensors file with an index.""" + output_dir.mkdir(parents=True, exist_ok=True) + mx.save_safetensors(str(output_dir / "model.safetensors"), weights) + + # Also write index for consistency + total_size = sum(v.nbytes for v in weights.values()) + weight_map = {k: "model.safetensors" for k in sorted(weights.keys())} + index = { + "metadata": {"total_size": total_size}, + "weight_map": weight_map, + } + with open(output_dir / "model.safetensors.index.json", "w") as f: + json.dump(index, f, indent=2, sort_keys=True) + + +def save_config(config: dict, output_dir: Path): + """Save config.json to a directory.""" + output_dir.mkdir(parents=True, exist_ok=True) + with open(output_dir / "config.json", "w") as f: + json.dump(config, f, indent=4) + f.write("\n") + + +# ─── Source resolution ───────────────────────────────────────────────────────── + +# Matches monolithic model files: ltx-2-19b-distilled.safetensors, ltx-2.3-22b-dev.safetensors, etc. +MONOLITHIC_PATTERN = re.compile( + r"^ltx-[\d.]+-\d+b-(?Pdistilled|dev)\.safetensors$" +) + +# Matches upscaler files like ltx-2-spatial-upscaler-x2-1.0.safetensors, +# ltx-2.3-spatial-upscaler-x2-1.0.safetensors, etc. +UPSCALER_PATTERN = re.compile( + r"^ltx-[\d.]+-(?:spatial|temporal)-upscaler-.+\.safetensors$" +) + + +def resolve_source(source: str, variant: str) -> Path: + """Resolve source to a monolithic safetensors file path. + + Args: + source: HF repo ID (e.g. "Lightricks/LTX-2"), local directory, or direct file path. + variant: Model variant ("distilled" or "dev") to select the right file. + + Returns: + Path to the monolithic safetensors file. + """ + source_path = Path(source) + + # Direct file path + if source_path.is_file(): + return source_path + + # Local directory — find the variant's safetensors file + if source_path.is_dir(): + matches = [] + for f in sorted(source_path.glob("ltx-*b-*.safetensors")): + m = MONOLITHIC_PATTERN.match(f.name) + if m and m.group("variant") == variant: + matches.append(f) + + if matches: + return matches[0] + + # Broader fallback + all_mono = sorted(source_path.glob("ltx-*.safetensors")) + for f in all_mono: + if variant in f.name and MONOLITHIC_PATTERN.match(f.name): + return f + + raise FileNotFoundError( + f"No monolithic *-{variant}.safetensors found in {source_path}. " + f"Files found: {[f.name for f in all_mono]}" + ) + + # HF repo ID — download via huggingface_hub + if "/" in source and not source_path.exists(): + from huggingface_hub import hf_hub_download, list_repo_files + + # Find the right file in the repo + repo_files = list_repo_files(source) + target = None + for f in repo_files: + m = MONOLITHIC_PATTERN.match(f) + if m and m.group("variant") == variant: + target = f + break + + if not target: + raise FileNotFoundError( + f"No *-{variant}.safetensors found in {source}. " + f"Available: {[f for f in repo_files if f.endswith('.safetensors')]}" + ) + + print(f"Downloading {target} from {source}...") + local_path = hf_hub_download(repo_id=source, filename=target) + return Path(local_path) + + raise FileNotFoundError( + f"Source not found: {source}. Provide an HF repo ID, local directory, or file path." + ) + + +# ─── Config inference ───────────────────────────────────────────────────────── + + +def infer_transformer_config(weights: Dict[str, mx.array]) -> dict: + """Infer transformer config from weight shapes.""" + # Count transformer layers + max_layer = -1 + for key in weights: + if "transformer_blocks." in key: + parts = key.split(".") + try: + idx = parts.index("transformer_blocks") + 1 + if idx < len(parts) and parts[idx].isdigit(): + max_layer = max(max_layer, int(parts[idx])) + except ValueError: + pass + num_layers = max_layer + 1 if max_layer >= 0 else 48 + + # Detect cross_attention_dim from attn2.to_k (cross-attention input dim) + cross_attention_dim = 4096 + for key, value in weights.items(): + if "transformer_blocks.0.attn2.to_k.weight" in key: + cross_attention_dim = value.shape[-1] + break + + # Check for prompt_adaln_single (LTX-2.3 feature) + has_prompt_adaln = any("prompt_adaln_single" in k for k in weights) + + config = { + "attention_head_dim": 128, + "attention_type": "default", + "audio_attention_head_dim": 64, + "audio_caption_channels": 3840, + "audio_cross_attention_dim": 2048, + "audio_in_channels": 128, + "audio_num_attention_heads": 32, + "audio_out_channels": 128, + "audio_positional_embedding_max_pos": [20], + "av_ca_timestep_scale_multiplier": 1000, + "caption_channels": 3840, + "cross_attention_dim": cross_attention_dim, + "double_precision_rope": True, + "in_channels": 128, + "model_type": "ltx av model", + "norm_eps": 1e-06, + "num_attention_heads": 32, + "num_layers": num_layers, + "out_channels": 128, + "positional_embedding_max_pos": [20, 2048, 2048], + "positional_embedding_theta": 10000.0, + "rope_type": "split", + "timestep_scale_multiplier": 1000, + "use_middle_indices_grid": True, + } + + if has_prompt_adaln: + config["has_prompt_adaln"] = True + + return config + + +def infer_vae_decoder_config(weights: Dict[str, mx.array], variant: str) -> dict: + """Infer VAE decoder config from weights.""" + # Check for timestep conditioning keys + has_timestep = any( + "last_time_embedder" in k or "last_scale_shift_table" in k for k in weights + ) + + # Count channel multipliers from up_blocks + max_block = -1 + for key in weights: + if "up_blocks." in key: + parts = key.split(".") + try: + idx = parts.index("up_blocks") + 1 + if idx < len(parts) and parts[idx].isdigit(): + max_block = max(max_block, int(parts[idx])) + except ValueError: + pass + + # Default config + config = { + "ch": 128, + "ch_mult": [1, 2, 4], + "dropout": 0.0, + "num_res_blocks": 2, + "out_ch": 2, + "resolution": 256, + "timestep_conditioning": has_timestep, + "z_channels": 8, + } + return config + + +def infer_vae_encoder_config(weights: Dict[str, mx.array]) -> dict: + """Return VAE encoder config (architecture is consistent across versions).""" + return { + "convolution_dimensions": 3, + "encoder_blocks": [ + ["res_x", {"num_layers": 4}], + ["compress_space_res", {"multiplier": 2}], + ["res_x", {"num_layers": 6}], + ["compress_time_res", {"multiplier": 2}], + ["res_x", {"num_layers": 6}], + ["compress_all_res", {"multiplier": 2}], + ["res_x", {"num_layers": 2}], + ["compress_all_res", {"multiplier": 2}], + ["res_x", {"num_layers": 2}], + ], + "encoder_spatial_padding_mode": "zeros", + "in_channels": 3, + "latent_log_var": "uniform", + "norm_layer": "pixel_norm", + "out_channels": 128, + "patch_size": 4, + } + + +def infer_audio_vae_config(weights: Dict[str, mx.array]) -> dict: + """Return audio VAE config.""" + return { + "attn_resolutions": [], + "attn_type": "vanilla", + "causality_axis": "height", + "ch": 128, + "ch_mult": [1, 2, 4], + "dropout": 0.0, + "give_pre_end": False, + "is_causal": True, + "mel_bins": 64, + "mel_hop_length": 160, + "mid_block_add_attention": False, + "norm_type": "pixel", + "num_res_blocks": 2, + "out_ch": 2, + "resamp_with_conv": True, + "resolution": 256, + "sample_rate": 16000, + "tanh_out": False, + "z_channels": 8, + } + + +def infer_audio_encoder_config(weights: Dict[str, mx.array]) -> dict: + """Return audio encoder config (mirrors decoder but with encoder-specific fields).""" + return { + "attn_resolutions": [], + "attn_type": "vanilla", + "causality_axis": "height", + "ch": 128, + "ch_mult": [1, 2, 4], + "dropout": 0.0, + "in_channels": 2, + "double_z": True, + "is_causal": True, + "mel_bins": 64, + "mel_hop_length": 160, + "mid_block_add_attention": False, + "n_fft": 1024, + "norm_type": "pixel", + "num_res_blocks": 2, + "resamp_with_conv": True, + "resolution": 256, + "sample_rate": 16000, + "z_channels": 8, + } + + +def infer_vocoder_config(weights: Dict[str, mx.array]) -> dict: + """Infer vocoder config from weights.""" + # Check for bwe_generator (LTX-2.3 BigVGAN vocoder) + has_bwe = any(k.startswith("bwe_generator") for k in weights) + + if has_bwe: + return { + "type": "bigvgan", + "has_bwe_generator": True, + } + + return { + "output_sample_rate": 24000, + "resblock_dilation_sizes": [[1, 3, 5], [1, 3, 5], [1, 3, 5]], + "resblock_kernel_sizes": [3, 7, 11], + "stereo": True, + "upsample_initial_channel": 1024, + "upsample_kernel_sizes": [16, 15, 8, 4, 4], + "upsample_rates": [6, 5, 2, 2, 2], + } + + +# ─── Main ───────────────────────────────────────────────────────────────────── + + +def convert(source: str, output_path: Path, variant: str = "distilled"): + """Convert monolithic safetensors to modular directory layout. + + Args: + source: HF repo ID (e.g. "Lightricks/LTX-2"), local directory, or file path. + output_path: Output directory for the modular layout. + variant: "distilled" or "dev". + """ + source_path = resolve_source(source, variant) + + print(f"Loading monolithic weights from {source_path.name}...") + all_weights = mx.load(str(source_path)) + total_keys = len(all_weights) + print(f" Loaded {total_keys} keys") + + # Route keys to components + print("\nExtracting components...") + + # 1. Transformer + print(" [1/7] Transformer...") + transformer_weights = sanitize_transformer(all_weights) + num_shards = save_sharded(transformer_weights, output_path / "transformer") + config = infer_transformer_config(transformer_weights) + save_config(config, output_path / "transformer") + t_params = sum(v.size for v in transformer_weights.values()) + print( + f" {len(transformer_weights)} keys, {t_params:,} params, {num_shards} shards" + ) + + # 2. VAE Decoder + print(" [2/7] VAE Decoder...") + vae_decoder_weights = sanitize_vae_decoder(all_weights) + save_single(vae_decoder_weights, output_path / "vae" / "decoder") + config = infer_vae_decoder_config(vae_decoder_weights, variant) + save_config(config, output_path / "vae" / "decoder") + d_params = sum(v.size for v in vae_decoder_weights.values()) + print(f" {len(vae_decoder_weights)} keys, {d_params:,} params") + + # 3. VAE Encoder + print(" [3/7] VAE Encoder...") + vae_encoder_weights = sanitize_vae_encoder(all_weights) + save_single(vae_encoder_weights, output_path / "vae" / "encoder") + config = infer_vae_encoder_config(vae_encoder_weights) + save_config(config, output_path / "vae" / "encoder") + e_params = sum(v.size for v in vae_encoder_weights.values()) + print(f" {len(vae_encoder_weights)} keys, {e_params:,} params") + + # 4. Audio VAE Decoder + print(" [4/7] Audio VAE Decoder...") + audio_decoder_weights = sanitize_audio_decoder(all_weights) + save_single(audio_decoder_weights, output_path / "audio_vae" / "decoder") + config = infer_audio_vae_config(audio_decoder_weights) + save_config(config, output_path / "audio_vae" / "decoder") + a_params = sum(v.size for v in audio_decoder_weights.values()) + print(f" {len(audio_decoder_weights)} keys, {a_params:,} params") + + # 5. Audio VAE Encoder + print(" [5/7] Audio VAE Encoder...") + audio_encoder_weights = sanitize_audio_encoder(all_weights) + save_single(audio_encoder_weights, output_path / "audio_vae" / "encoder") + config = infer_audio_encoder_config(audio_encoder_weights) + save_config(config, output_path / "audio_vae" / "encoder") + ae_params = sum(v.size for v in audio_encoder_weights.values()) + print(f" {len(audio_encoder_weights)} keys, {ae_params:,} params") + + # 6. Vocoder + print(" [6/7] Vocoder...") + vocoder_weights = sanitize_vocoder(all_weights) + save_single(vocoder_weights, output_path / "vocoder") + config = infer_vocoder_config(vocoder_weights) + save_config(config, output_path / "vocoder") + v_params = sum(v.size for v in vocoder_weights.values()) + print(f" {len(vocoder_weights)} keys, {v_params:,} params") + + # 7. Text Projections + print(" [7/7] Text Projections...") + text_proj_weights = extract_text_projections(all_weights) + tp_dir = output_path / "text_projections" + tp_dir.mkdir(parents=True, exist_ok=True) + mx.save_safetensors(str(tp_dir / "model.safetensors"), text_proj_weights) + tp_params = sum(v.size for v in text_proj_weights.values()) + print(f" {len(text_proj_weights)} keys, {tp_params:,} params") + + # Copy upscaler files + print("\nCopying upscaler files...") + source_dir = source_path.parent + is_hf_repo = "/" in source and not Path(source).exists() + upscaler_files = [] + + if is_hf_repo: + from huggingface_hub import list_repo_files + + upscaler_files = [ + f for f in list_repo_files(source) if UPSCALER_PATTERN.match(f) + ] + else: + upscaler_files = [ + f.name + for f in source_dir.iterdir() + if f.is_file() and UPSCALER_PATTERN.match(f.name) + ] + + if not upscaler_files: + print(" No upscaler files found") + + for upscaler_file in sorted(upscaler_files): + dest = output_path / upscaler_file + if dest.exists(): + print(f" {upscaler_file}: already exists, skipping") + continue + + local_candidate = source_dir / upscaler_file + if local_candidate.is_file(): + shutil.copy2(str(local_candidate), str(dest)) + print(f" {upscaler_file}: copied") + elif is_hf_repo: + from huggingface_hub import hf_hub_download + + print(f" {upscaler_file}: downloading from {source}...") + downloaded = hf_hub_download(repo_id=source, filename=upscaler_file) + shutil.copy2(downloaded, str(dest)) + print(f" {upscaler_file}: done") + else: + print(f" {upscaler_file}: not found, skipping") + + # Link text_encoder and tokenizer directories + print("\nLinking text encoder & tokenizer...") + for subdir in ["text_encoder", "tokenizer"]: + dest = output_path / subdir + if dest.exists(): + print(f" {subdir}/: already exists, skipping") + continue + + local_candidate = source_dir / subdir + if local_candidate.is_dir(): + # Resolve through symlinks to get the real directory + real_path = local_candidate.resolve() + dest.symlink_to(real_path) + print(f" {subdir}/: symlinked to {real_path}") + elif is_hf_repo: + from huggingface_hub import list_repo_files, snapshot_download + + # Only download if the subdir exists in the repo + repo_files = list_repo_files(source) + if any(f.startswith(f"{subdir}/") for f in repo_files): + print(f" {subdir}/: downloading from {source}...") + snapshot_download( + repo_id=source, + allow_patterns=f"{subdir}/*", + local_dir=str(output_path), + ) + print(f" {subdir}/: done") + else: + print(f" {subdir}/: not in repo, skipping") + else: + print(f" {subdir}/: not found in source, skipping") + + # Summary + all_converted = ( + len(transformer_weights) + + len(vae_decoder_weights) + + len(vae_encoder_weights) + + len(audio_decoder_weights) + + len(audio_encoder_weights) + + len(vocoder_weights) + + len(text_proj_weights) + ) + print(f"\nDone! Converted {all_converted}/{total_keys} keys") + if all_converted < total_keys: + known_prefixes = ( + TRANSFORMER_PREFIX, + VAE_DECODER_PREFIX, + VAE_ENCODER_PREFIX, + VAE_STATS_PREFIX, + AUDIO_DECODER_PREFIX, + AUDIO_ENCODER_PREFIX, + AUDIO_STATS_PREFIX, + VOCODER_PREFIX, + TEXT_PROJ_PREFIX, + VIDEO_CONNECTOR_PREFIX, + AUDIO_CONNECTOR_PREFIX, + ) + skipped = [ + k for k in all_weights if not any(k.startswith(p) for p in known_prefixes) + ] + if skipped: + print(f" Skipped {len(skipped)} keys:") + for k in sorted(skipped)[:20]: + print(f" {k}") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser( + description="Convert monolithic LTX-2/2.3 safetensors to modular MLX layout" + ) + parser.add_argument( + "--source", + type=str, + required=True, + help="HF repo ID (e.g. Lightricks/LTX-2, Lightricks/LTX-2.3), local directory, or direct safetensors file path", + ) + parser.add_argument( + "--output", + type=str, + required=True, + help="Output directory for modular layout", + ) + parser.add_argument( + "--variant", + type=str, + choices=["distilled", "dev"], + default="distilled", + help="Model variant (affects VAE decoder config and which file to download)", + ) + args = parser.parse_args() + + convert(args.source, Path(args.output), variant=args.variant) diff --git a/mlx_video/models/ltx/feed_forward.py b/mlx_video/models/ltx_2/feed_forward.py similarity index 100% rename from mlx_video/models/ltx/feed_forward.py rename to mlx_video/models/ltx_2/feed_forward.py diff --git a/mlx_video/models/ltx_2/generate.py b/mlx_video/models/ltx_2/generate.py new file mode 100644 index 0000000..c6c592d --- /dev/null +++ b/mlx_video/models/ltx_2/generate.py @@ -0,0 +1,3364 @@ +"""Unified video and audio-video generation pipeline for LTX-2. + +Supports both distilled (two-stage with upsampling) and dev (single-stage with CFG) pipelines. +""" + +import argparse +import math +import time +from enum import Enum +from pathlib import Path +from typing import Optional + +import mlx.core as mx +import numpy as np +from PIL import Image +from rich.console import Console +from rich.panel import Panel +from rich.progress import ( + BarColumn, + Progress, + SpinnerColumn, + TaskProgressColumn, + TextColumn, + TimeRemainingColumn, +) + +# Rich console for styled output +console = Console() + + +from mlx_video.models.ltx_2.conditioning import ( + VideoConditionByLatentIndex, + apply_conditioning, +) +from mlx_video.models.ltx_2.conditioning.latent import LatentState, apply_denoise_mask +from mlx_video.models.ltx_2.ltx_2 import LTXModel +from mlx_video.models.ltx_2.transformer import Modality +from mlx_video.models.ltx_2.upsampler import load_upsampler, upsample_latents +from mlx_video.models.ltx_2.video_vae import VideoEncoder +from mlx_video.models.ltx_2.video_vae.decoder import VideoDecoder +from mlx_video.models.ltx_2.video_vae.tiling import TilingConfig +from mlx_video.utils import ( + get_model_path, + load_image, + prepare_image_for_encoding, +) + + +class PipelineType(Enum): + """Pipeline type selector.""" + + DISTILLED = "distilled" # Two-stage with upsampling, fixed sigmas, no CFG + DEV = "dev" # Single-stage, dynamic sigmas, CFG + DEV_TWO_STAGE = ( + "dev-two-stage" # Two-stage: dev (half res, CFG) + distilled LoRA (full res) + ) + DEV_TWO_STAGE_HQ = "dev-two-stage-hq" # Two-stage: res_2s sampler, LoRA both stages + + +# Distilled model sigma schedules +STAGE_1_SIGMAS = [1.0, 0.99375, 0.9875, 0.98125, 0.975, 0.909375, 0.725, 0.421875, 0.0] +STAGE_2_SIGMAS = [0.909375, 0.725, 0.421875, 0.0] + +# Dev model scheduling constants +BASE_SHIFT_ANCHOR = 1024 +MAX_SHIFT_ANCHOR = 4096 + +# Audio constants +AUDIO_SAMPLE_RATE = 24000 # Output audio sample rate +AUDIO_LATENT_SAMPLE_RATE = 16000 # VAE internal sample rate +AUDIO_HOP_LENGTH = 160 +AUDIO_LATENT_DOWNSAMPLE_FACTOR = 4 +AUDIO_LATENT_CHANNELS = 8 # Latent channels before patchifying +AUDIO_MEL_BINS = 16 +AUDIO_LATENTS_PER_SECOND = ( + AUDIO_LATENT_SAMPLE_RATE / AUDIO_HOP_LENGTH / AUDIO_LATENT_DOWNSAMPLE_FACTOR +) # 25 + +# Default negative prompt for CFG (dev pipeline) +# Matches PyTorch LTX-2 reference DEFAULT_NEGATIVE_PROMPT from constants.py +DEFAULT_NEGATIVE_PROMPT = ( + "blurry, out of focus, overexposed, underexposed, low contrast, washed out colors, excessive noise, " + "grainy texture, poor lighting, flickering, motion blur, distorted proportions, unnatural skin tones, " + "deformed facial features, asymmetrical face, missing facial features, extra limbs, disfigured hands, " + "wrong hand count, artifacts around text, inconsistent perspective, camera shake, incorrect depth of " + "field, background too sharp, background clutter, distracting reflections, harsh shadows, inconsistent " + "lighting direction, color banding, cartoonish rendering, 3D CGI look, unrealistic materials, uncanny " + "valley effect, incorrect ethnicity, wrong gender, exaggerated expressions, wrong gaze direction, " + "mismatched lip sync, silent or muted audio, distorted voice, robotic voice, echo, background noise, " + "off-sync audio, incorrect dialogue, added dialogue, repetitive speech, jittery movement, awkward " + "pauses, incorrect timing, unnatural transitions, inconsistent framing, tilted camera, flat lighting, " + "inconsistent tone, cinematic oversaturation, stylized filters, or AI artifacts." +) + + +def load_and_merge_lora( + model: LTXModel, + lora_path: str, + strength: float = 1.0, +) -> None: + """Load LoRA weights and merge them into the transformer model in-place. + + Supports two formats: + - Raw PyTorch: keys like diffusion_model.{module}.lora_A.weight (needs sanitization) + - Pre-converted MLX: keys like {module}.lora_A.weight (already sanitized) + + Merge formula: weight += (lora_B * strength) @ lora_A + + Args: + model: The LTXModel transformer to merge into + lora_path: Path to the LoRA safetensors file or directory containing one + strength: LoRA strength/coefficient (default 1.0) + """ + # Resolve path: local file/dir or HuggingFace repo + lora_file = Path(lora_path) + if lora_file.is_file(): + pass # direct file path + elif lora_file.is_dir(): + # Local directory: find safetensors inside + candidates = sorted(lora_file.glob("*.safetensors")) + if not candidates: + raise FileNotFoundError(f"No .safetensors files found in {lora_path}") + # Prefer distilled-lora files over full model weights + lora_candidates = [c for c in candidates if "distilled-lora" in c.name] + lora_file = lora_candidates[0] if lora_candidates else candidates[0] + console.print(f"[dim]Using LoRA file: {lora_file.name}[/]") + else: + # Treat as HuggingFace repo ID + lora_dir = get_model_path(lora_path) + candidates = sorted(lora_dir.glob("*.safetensors")) + if not candidates: + raise FileNotFoundError(f"No .safetensors files found in {lora_dir}") + # Prefer distilled-lora files over full model weights + lora_candidates = [c for c in candidates if "distilled-lora" in c.name] + lora_file = lora_candidates[0] if lora_candidates else candidates[0] + console.print(f"[dim]Using LoRA from repo: {lora_path} ({lora_file.name})[/]") + + # Load LoRA weights + lora_weights = mx.load(str(lora_file)) + + # Detect format: raw PyTorch has 'diffusion_model.' prefix + has_prefix = any(k.startswith("diffusion_model.") for k in lora_weights) + + # Group into A/B pairs by module name + lora_pairs = {} + for key in lora_weights: + module_key = key + if has_prefix: + if not key.startswith("diffusion_model."): + continue + module_key = key.replace("diffusion_model.", "") + + if module_key.endswith(".lora_A.weight"): + base_key = module_key.replace(".lora_A.weight", "") + lora_pairs.setdefault(base_key, {})["A"] = lora_weights[key] + elif module_key.endswith(".lora_B.weight"): + base_key = module_key.replace(".lora_B.weight", "") + lora_pairs.setdefault(base_key, {})["B"] = lora_weights[key] + + # Apply key sanitization only for raw PyTorch format + # Replacements handle both mid-string and end-of-string positions + # since LoRA base keys end at the module name without trailing dot + _LORA_KEY_REPLACEMENTS = [ + (".to_out.0", ".to_out"), + (".ff.net.0.proj", ".ff.proj_in"), + (".ff.net.2", ".ff.proj_out"), + (".audio_ff.net.0.proj", ".audio_ff.proj_in"), + (".audio_ff.net.2", ".audio_ff.proj_out"), + (".linear_1", ".linear1"), + (".linear_2", ".linear2"), + ] + if has_prefix: + sanitized_pairs = {} + for key, pair in lora_pairs.items(): + new_key = key + for old, new in _LORA_KEY_REPLACEMENTS: + if new_key.endswith(old): + new_key = new_key[: -len(old)] + new + else: + new_key = new_key.replace(old + ".", new + ".") + sanitized_pairs[new_key] = pair + else: + sanitized_pairs = lora_pairs + + # Get current model weights as a flat dict (references, not copies) + def flatten_params(params, prefix=""): + flat = {} + for k, v in params.items(): + full_key = f"{prefix}.{k}" if prefix else k + if isinstance(v, dict): + flat.update(flatten_params(v, full_key)) + else: + flat[full_key] = v + return flat + + flat_weights = flatten_params(dict(model.parameters())) + + # Merge LoRA deltas in batches to avoid doubling memory + merged_count = 0 + batch = [] + batch_size = 100 # merge 100 weights at a time, then eval to free intermediates + + for module_key, pair in sanitized_pairs.items(): + if "A" not in pair or "B" not in pair: + continue + + weight_key = f"{module_key}.weight" + if weight_key not in flat_weights: + continue + + lora_a = pair["A"].astype(mx.float32) # (rank, in_features) + lora_b = pair["B"].astype(mx.float32) # (out_features, rank) + + # delta = (lora_B * strength) @ lora_A + delta = (lora_b * strength) @ lora_a + + base_weight = flat_weights.pop(weight_key) + merged_weight = (base_weight.astype(mx.float32) + delta).astype( + base_weight.dtype + ) + batch.append((weight_key, merged_weight)) + del base_weight + merged_count += 1 + + if len(batch) >= batch_size: + model.load_weights(batch, strict=False) + mx.eval(model.parameters()) + batch.clear() + + if batch: + model.load_weights(batch, strict=False) + mx.eval(model.parameters()) + batch.clear() + + del flat_weights, lora_weights + mx.clear_cache() + console.print(f"[green]✓[/] Merged {merged_count} LoRA pairs (strength={strength})") + + +def cfg_delta(cond: mx.array, uncond: mx.array, scale: float) -> mx.array: + """Compute CFG delta for classifier-free guidance. + + Args: + cond: Conditional prediction + uncond: Unconditional prediction + scale: CFG guidance scale + + Returns: + Delta to add to unconditional for CFG: (scale - 1) * (cond - uncond) + """ + return (scale - 1.0) * (cond - uncond) + + +def apg_delta( + cond: mx.array, + uncond: mx.array, + scale: float, + eta: float = 1.0, + norm_threshold: float = 0.0, +) -> mx.array: + """Compute APG (Adaptive Projected Guidance) delta. + + Decomposes guidance into parallel and orthogonal components relative to + the conditional prediction, providing more stable guidance for I2V. + + Based on: https://arxiv.org/abs/2407.12173 + + Args: + cond: Conditional prediction (x0_pos) + uncond: Unconditional prediction (x0_neg) + scale: Guidance strength (same as CFG scale) + eta: Weight for parallel component (1.0 = keep full parallel) + norm_threshold: Clamp guidance norm to this value (0 = no clamping) + + Returns: + Delta to add to unconditional for APG guidance + """ + guidance = cond - uncond + + # Optionally clamp guidance norm for stability + if norm_threshold > 0: + guidance_norm = mx.sqrt( + mx.sum(guidance**2, axis=(-1, -2, -3), keepdims=True) + 1e-8 + ) + scale_factor = mx.minimum( + mx.ones_like(guidance_norm), norm_threshold / guidance_norm + ) + guidance = guidance * scale_factor + + # Project guidance onto cond direction + batch_size = cond.shape[0] + cond_flat = mx.reshape(cond, (batch_size, -1)) + guidance_flat = mx.reshape(guidance, (batch_size, -1)) + + # Projection coefficient: (guidance · cond) / (cond · cond) + dot_product = mx.sum(guidance_flat * cond_flat, axis=1, keepdims=True) + squared_norm = mx.sum(cond_flat**2, axis=1, keepdims=True) + 1e-8 + proj_coeff = dot_product / squared_norm + + # Reshape back and compute parallel/orthogonal components + proj_coeff = mx.reshape(proj_coeff, (batch_size,) + (1,) * (cond.ndim - 1)) + g_parallel = proj_coeff * cond + g_orth = guidance - g_parallel + + # Combine with eta weighting parallel component + g_apg = g_parallel * eta + g_orth + + return g_apg * (scale - 1.0) + + +def ltx2_scheduler( + steps: int, + num_tokens: Optional[int] = None, + max_shift: float = 2.05, + base_shift: float = 0.95, + stretch: bool = True, + terminal: float = 0.1, +) -> mx.array: + """LTX-2 scheduler for sigma generation (dev model). + + Generates a sigma schedule with token-count-dependent shifting and optional + stretching to a terminal value. + + Args: + steps: Number of inference steps + num_tokens: Number of latent tokens (F*H*W). If None, uses MAX_SHIFT_ANCHOR + max_shift: Maximum shift factor + base_shift: Base shift factor + stretch: Whether to stretch sigmas to terminal value + terminal: Terminal sigma value for stretching + + Returns: + Array of sigma values of shape (steps + 1,) + """ + tokens = num_tokens if num_tokens is not None else MAX_SHIFT_ANCHOR + sigmas = np.linspace(1.0, 0.0, steps + 1) + + # Compute shift based on token count + x1 = BASE_SHIFT_ANCHOR + x2 = MAX_SHIFT_ANCHOR + mm = (max_shift - base_shift) / (x2 - x1) + b = base_shift - mm * x1 + sigma_shift = tokens * mm + b + + # Apply shift transformation + power = 1 + with np.errstate(divide="ignore", invalid="ignore"): + sigmas = np.where( + sigmas != 0, + math.exp(sigma_shift) / (math.exp(sigma_shift) + (1 / sigmas - 1) ** power), + 0, + ) + + # Stretch sigmas to terminal value + if stretch: + non_zero_mask = sigmas != 0 + non_zero_sigmas = sigmas[non_zero_mask] + one_minus_z = 1.0 - non_zero_sigmas + scale_factor = one_minus_z[-1] / (1.0 - terminal) + stretched = 1.0 - (one_minus_z / scale_factor) + sigmas[non_zero_mask] = stretched + + return mx.array(sigmas, dtype=mx.float32) + + +def create_position_grid( + batch_size: int, + num_frames: int, + height: int, + width: int, + temporal_scale: int = 8, + spatial_scale: int = 32, + fps: float = 24.0, + causal_fix: bool = True, +) -> mx.array: + """Create position grid for RoPE in pixel space. + + Args: + batch_size: Batch size + num_frames: Number of frames (latent) + height: Height (latent) + width: Width (latent) + temporal_scale: VAE temporal scale factor (default 8) + spatial_scale: VAE spatial scale factor (default 32) + fps: Frames per second (default 24.0) + causal_fix: Apply causal fix for first frame (default True) + + Returns: + Position grid of shape (B, 3, num_patches, 2) in pixel space + where dim 2 is [start, end) bounds for each patch + """ + patch_size_t, patch_size_h, patch_size_w = 1, 1, 1 + + t_coords = np.arange(0, num_frames, patch_size_t) + h_coords = np.arange(0, height, patch_size_h) + w_coords = np.arange(0, width, patch_size_w) + + t_grid, h_grid, w_grid = np.meshgrid(t_coords, h_coords, w_coords, indexing="ij") + patch_starts = np.stack([t_grid, h_grid, w_grid], axis=0) + + patch_size_delta = np.array([patch_size_t, patch_size_h, patch_size_w]).reshape( + 3, 1, 1, 1 + ) + patch_ends = patch_starts + patch_size_delta + + latent_coords = np.stack([patch_starts, patch_ends], axis=-1) + num_patches = num_frames * height * width + latent_coords = latent_coords.reshape(3, num_patches, 2) + latent_coords = np.tile(latent_coords[np.newaxis, ...], (batch_size, 1, 1, 1)) + + scale_factors = np.array([temporal_scale, spatial_scale, spatial_scale]).reshape( + 1, 3, 1, 1 + ) + pixel_coords = (latent_coords * scale_factors).astype(np.float32) + + if causal_fix: + pixel_coords[:, 0, :, :] = np.clip( + pixel_coords[:, 0, :, :] + 1 - temporal_scale, a_min=0, a_max=None + ) + + # Divide temporal coords by fps + pixel_coords[:, 0, :, :] = pixel_coords[:, 0, :, :] / fps + + # Cast entire position grid through bfloat16 to match PyTorch's behavior. + # PyTorch does: positions = positions.to(bfloat16) on ALL coordinates before + # passing to the transformer/RoPE. This quantization is what the model was + # trained with, so we must replicate it for numerical fidelity. + positions_bf16 = mx.array(pixel_coords, dtype=mx.bfloat16) + mx.eval(positions_bf16) + return positions_bf16.astype(mx.float32) + + +def create_audio_position_grid( + batch_size: int, + audio_frames: int, + sample_rate: int = AUDIO_LATENT_SAMPLE_RATE, + hop_length: int = AUDIO_HOP_LENGTH, + downsample_factor: int = AUDIO_LATENT_DOWNSAMPLE_FACTOR, + is_causal: bool = True, +) -> mx.array: + """Create temporal position grid for audio RoPE.""" + + def get_audio_latent_time_in_sec(start_idx: int, end_idx: int) -> np.ndarray: + latent_frame = np.arange(start_idx, end_idx, dtype=np.float32) + mel_frame = latent_frame * downsample_factor + if is_causal: + mel_frame = np.clip(mel_frame + 1 - downsample_factor, 0, None) + return mel_frame * hop_length / sample_rate + + start_times = get_audio_latent_time_in_sec(0, audio_frames) + end_times = get_audio_latent_time_in_sec(1, audio_frames + 1) + + positions = np.stack([start_times, end_times], axis=-1) + positions = positions[np.newaxis, np.newaxis, :, :] + positions = np.tile(positions, (batch_size, 1, 1, 1)) + + # Cast through bfloat16 to match PyTorch's precision behavior + positions_bf16 = mx.array(positions, dtype=mx.bfloat16) + mx.eval(positions_bf16) + return positions_bf16.astype(mx.float32) + + +def compute_audio_frames(num_video_frames: int, fps: float) -> int: + """Compute number of audio latent frames given video duration.""" + duration = num_video_frames / fps + return round(duration * AUDIO_LATENTS_PER_SECOND) + + +# ============================================================================= +# Distilled Pipeline Denoising (no CFG, fixed sigmas) +# ============================================================================= + + +def denoise_distilled( + latents: mx.array, + positions: mx.array, + text_embeddings: mx.array, + transformer: LTXModel, + sigmas: list, + verbose: bool = True, + state: Optional[LatentState] = None, + audio_latents: Optional[mx.array] = None, + audio_positions: Optional[mx.array] = None, + audio_embeddings: Optional[mx.array] = None, + audio_frozen: bool = False, +) -> tuple[mx.array, Optional[mx.array]]: + """Run denoising loop for distilled pipeline (no CFG).""" + dtype = latents.dtype + enable_audio = audio_latents is not None + + if state is not None: + latents = state.latent + + # Keep latents in float32 throughout to avoid quantization noise accumulation. + latents = latents.astype(mx.float32) + if enable_audio: + audio_latents = audio_latents.astype(mx.float32) + + desc = "[cyan]Denoising A/V[/]" if enable_audio else "[cyan]Denoising[/]" + num_steps = len(sigmas) - 1 + + with Progress( + SpinnerColumn(), + TextColumn("[progress.description]{task.description}"), + BarColumn(), + TaskProgressColumn(), + TimeRemainingColumn(), + console=console, + disable=not verbose, + ) as progress: + task = progress.add_task(desc, total=num_steps) + + for i in range(num_steps): + sigma, sigma_next = sigmas[i], sigmas[i + 1] + + b, c, f, h, w = latents.shape + num_tokens = f * h * w + # Cast to model dtype for transformer input + latents_flat = mx.transpose( + mx.reshape(latents, (b, c, -1)), (0, 2, 1) + ).astype(dtype) + + if state is not None: + denoise_mask_flat = mx.reshape(state.denoise_mask, (b, 1, f, 1, 1)) + denoise_mask_flat = mx.broadcast_to(denoise_mask_flat, (b, 1, f, h, w)) + denoise_mask_flat = mx.reshape(denoise_mask_flat, (b, num_tokens)) + timesteps = mx.array(sigma, dtype=dtype) * denoise_mask_flat + else: + timesteps = mx.full((b, num_tokens), sigma, dtype=dtype) + + video_modality = Modality( + latent=latents_flat, + timesteps=timesteps, + positions=positions, + context=text_embeddings, + context_mask=None, + enabled=True, + sigma=mx.full((b,), sigma, dtype=dtype), + ) + + audio_modality = None + if enable_audio: + ab, ac, at, af = audio_latents.shape + audio_flat = mx.transpose(audio_latents, (0, 2, 1, 3)) + audio_flat = mx.reshape(audio_flat, (ab, at, ac * af)).astype(dtype) + + # A2V: frozen audio uses timesteps=0 (tells model audio is clean) + a_ts = ( + mx.zeros((ab, at), dtype=dtype) + if audio_frozen + else mx.full((ab, at), sigma, dtype=dtype) + ) + a_sig = ( + mx.zeros((ab,), dtype=dtype) + if audio_frozen + else mx.full((ab,), sigma, dtype=dtype) + ) + audio_modality = Modality( + latent=audio_flat, + timesteps=a_ts, + positions=audio_positions, + context=audio_embeddings, + context_mask=None, + enabled=True, + sigma=a_sig, + ) + + velocity, audio_velocity = transformer( + video=video_modality, audio=audio_modality + ) + mx.eval(velocity) + if audio_velocity is not None: + mx.eval(audio_velocity) + + # Compute denoised (x0) using per-token timesteps in float32 + sigma_f32 = mx.array(sigma, dtype=mx.float32) + latents_flat_f32 = mx.transpose(mx.reshape(latents, (b, c, -1)), (0, 2, 1)) + timesteps_f32 = mx.expand_dims(timesteps.astype(mx.float32), axis=-1) + x0_f32 = latents_flat_f32 - timesteps_f32 * velocity.astype(mx.float32) + denoised = mx.reshape(mx.transpose(x0_f32, (0, 2, 1)), (b, c, f, h, w)) + + audio_denoised = None + if enable_audio and audio_velocity is not None and not audio_frozen: + ab, ac, at, af = audio_latents.shape + audio_velocity = mx.reshape(audio_velocity, (ab, at, ac, af)) + audio_velocity = mx.transpose(audio_velocity, (0, 2, 1, 3)) + audio_denoised = audio_latents - sigma_f32 * audio_velocity.astype( + mx.float32 + ) + + if state is not None: + denoised = apply_denoise_mask( + denoised, state.clean_latent.astype(mx.float32), state.denoise_mask + ) + + mx.eval(denoised) + if audio_denoised is not None: + mx.eval(audio_denoised) + + # Euler step in float32 + if sigma_next > 0: + sigma_next_f32 = mx.array(sigma_next, dtype=mx.float32) + latents = denoised + sigma_next_f32 * (latents - denoised) / sigma_f32 + if enable_audio and audio_denoised is not None and not audio_frozen: + audio_latents = ( + audio_denoised + + sigma_next_f32 * (audio_latents - audio_denoised) / sigma_f32 + ) + else: + latents = denoised + if enable_audio and audio_denoised is not None and not audio_frozen: + audio_latents = audio_denoised + + mx.eval(latents) + if enable_audio: + mx.eval(audio_latents) + + progress.advance(task) + + return latents.astype(dtype), audio_latents.astype(dtype) if enable_audio else None + + +# ============================================================================= +# Dev Pipeline Denoising (with CFG, dynamic sigmas) +# ============================================================================= + + +def denoise_dev( + latents: mx.array, + positions: mx.array, + text_embeddings_pos: mx.array, + text_embeddings_neg: mx.array, + transformer: LTXModel, + sigmas: mx.array, + cfg_scale: float = 4.0, + cfg_rescale: float = 0.0, + verbose: bool = True, + state: Optional[LatentState] = None, + use_apg: bool = False, + apg_eta: float = 1.0, + apg_norm_threshold: float = 0.0, + stg_scale: float = 0.0, + stg_blocks: Optional[list] = None, +) -> mx.array: + """Run denoising loop for dev pipeline with CFG/APG and optional STG guidance. + + Args: + cfg_rescale: Rescale factor for CFG (0.0-1.0). Normalizes guided prediction + variance relative to conditional prediction to reduce over-saturation. + PyTorch default is 0.7. Set to 0.0 to disable. + use_apg: Use Adaptive Projected Guidance instead of standard CFG. + APG decomposes guidance into parallel/orthogonal components + for more stable I2V generation. + apg_eta: APG parallel component weight (1.0 = keep full parallel) + apg_norm_threshold: APG guidance norm clamp (0 = no clamping) + stg_scale: STG (Spatiotemporal Guidance) scale. 0.0 = disabled. + stg_blocks: Transformer block indices for STG perturbation. + """ + from mlx_video.models.ltx_2.rope import precompute_freqs_cis + + dtype = latents.dtype + if state is not None: + latents = state.latent + + # Keep latents in float32 throughout the denoising loop to avoid + # quantization noise accumulation over many steps. + # Model input is cast to model dtype; all denoising math stays in float32. + latents = latents.astype(mx.float32) + + sigmas_list = sigmas.tolist() + use_cfg = cfg_scale != 1.0 + use_stg = stg_scale != 0.0 and stg_blocks is not None + num_steps = len(sigmas_list) - 1 + + # Precompute RoPE once + precomputed_rope = precompute_freqs_cis( + positions, + dim=transformer.inner_dim, + theta=transformer.positional_embedding_theta, + max_pos=transformer.positional_embedding_max_pos, + use_middle_indices_grid=transformer.use_middle_indices_grid, + num_attention_heads=transformer.num_attention_heads, + rope_type=transformer.rope_type, + double_precision=transformer.config.double_precision_rope, + ) + mx.eval(precomputed_rope) + + with Progress( + SpinnerColumn(), + TextColumn("[progress.description]{task.description}"), + BarColumn(), + TaskProgressColumn(), + TimeRemainingColumn(), + console=console, + disable=not verbose, + ) as progress: + passes = ["CFG"] if use_cfg else [] + if use_stg: + passes.append("STG") + label = "+".join(passes) if passes else "uncond" + task = progress.add_task(f"[cyan]Denoising ({label})[/]", total=num_steps) + + for i in range(num_steps): + sigma = sigmas_list[i] + sigma_next = sigmas_list[i + 1] + + b, c, f, h, w = latents.shape + num_tokens = f * h * w + # Cast to model dtype for transformer input + latents_flat = mx.transpose( + mx.reshape(latents, (b, c, -1)), (0, 2, 1) + ).astype(dtype) + + if state is not None: + denoise_mask_flat = mx.reshape(state.denoise_mask, (b, 1, f, 1, 1)) + denoise_mask_flat = mx.broadcast_to(denoise_mask_flat, (b, 1, f, h, w)) + denoise_mask_flat = mx.reshape(denoise_mask_flat, (b, num_tokens)) + timesteps = mx.array(sigma, dtype=dtype) * denoise_mask_flat + else: + timesteps = mx.full((b, num_tokens), sigma, dtype=dtype) + + sigma_array = mx.full((b,), sigma, dtype=dtype) + + # Positive conditioning pass + video_modality_pos = Modality( + latent=latents_flat, + timesteps=timesteps, + positions=positions, + context=text_embeddings_pos, + context_mask=None, + enabled=True, + positional_embeddings=precomputed_rope, + sigma=sigma_array, + ) + velocity_pos, _ = transformer(video=video_modality_pos, audio=None) + + # Convert velocity to x0 (denoised) using per-token timesteps + # Matches PyTorch's X0Model: x0 = latent - timestep * velocity + # For conditioned tokens (timestep=0): x0 = latent (correct regardless of velocity) + # For unconditioned tokens (timestep=sigma): x0 = latent - sigma * velocity + latents_flat_f32 = mx.transpose(mx.reshape(latents, (b, c, -1)), (0, 2, 1)) + timesteps_f32 = mx.expand_dims(timesteps.astype(mx.float32), axis=-1) + x0_pos_f32 = latents_flat_f32 - timesteps_f32 * velocity_pos.astype( + mx.float32 + ) + + # Start with positive prediction + x0_guided_f32 = x0_pos_f32 + + if use_cfg: + # Negative conditioning pass + video_modality_neg = Modality( + latent=latents_flat, + timesteps=timesteps, + positions=positions, + context=text_embeddings_neg, + context_mask=None, + enabled=True, + positional_embeddings=precomputed_rope, + sigma=sigma_array, + ) + velocity_neg, _ = transformer(video=video_modality_neg, audio=None) + + # Convert negative velocity to x0 using per-token timesteps + x0_neg_f32 = latents_flat_f32 - timesteps_f32 * velocity_neg.astype( + mx.float32 + ) + + # Apply guidance to x0 predictions + # For conditioned tokens: x0_pos = x0_neg = latent, so delta = 0 + if use_apg: + # APG: decompose into parallel/orthogonal components for stability + x0_guided_f32 = x0_pos_f32 + apg_delta( + x0_pos_f32, + x0_neg_f32, + cfg_scale, + eta=apg_eta, + norm_threshold=apg_norm_threshold, + ) + else: + # Standard CFG + x0_guided_f32 = x0_pos_f32 + (cfg_scale - 1.0) * ( + x0_pos_f32 - x0_neg_f32 + ) + + # STG pass: skip self-attention at specified blocks + if use_stg: + velocity_ptb, _ = transformer( + video=video_modality_pos, + audio=None, + stg_video_blocks=stg_blocks, + ) + mx.eval(velocity_ptb) + + x0_ptb_f32 = latents_flat_f32 - timesteps_f32 * velocity_ptb.astype( + mx.float32 + ) + x0_guided_f32 = x0_guided_f32 + stg_scale * (x0_pos_f32 - x0_ptb_f32) + + # Apply CFG rescale if enabled (std-ratio rescaling to reduce over-saturation) + # factor = rescale * (cond_std / pred_std) + (1 - rescale) + # pred = pred * factor + if cfg_rescale > 0.0 and (use_cfg or use_stg): + v_factor = x0_pos_f32.std() / (x0_guided_f32.std() + 1e-8) + v_factor = cfg_rescale * v_factor + (1.0 - cfg_rescale) + x0_guided_f32 = x0_guided_f32 * v_factor + + # Reshape x0 from token space (b, tokens, c) to spatial (b, c, f, h, w) + denoised = mx.reshape( + mx.transpose(x0_guided_f32, (0, 2, 1)), (b, c, f, h, w) + ) + + sigma_f32 = mx.array(sigma, dtype=mx.float32) + + if state is not None: + denoised = apply_denoise_mask( + denoised, state.clean_latent.astype(mx.float32), state.denoise_mask + ) + + # Euler step in float32 (latents stay in float32) + if sigma_next > 0: + sigma_next_f32 = mx.array(sigma_next, dtype=mx.float32) + latents = denoised + sigma_next_f32 * (latents - denoised) / sigma_f32 + else: + latents = denoised + + mx.eval(latents) + progress.advance(task) + + return latents.astype(dtype) + + +def denoise_dev_av( + video_latents: mx.array, + audio_latents: mx.array, + video_positions: mx.array, + audio_positions: mx.array, + video_embeddings_pos: mx.array, + video_embeddings_neg: mx.array, + audio_embeddings_pos: mx.array, + audio_embeddings_neg: mx.array, + transformer: LTXModel, + sigmas: mx.array, + cfg_scale: float = 4.0, + audio_cfg_scale: float = 7.0, + cfg_rescale: float = 0.0, + verbose: bool = True, + video_state: Optional[LatentState] = None, + use_apg: bool = False, + apg_eta: float = 1.0, + apg_norm_threshold: float = 0.0, + stg_scale: float = 0.0, + stg_video_blocks: Optional[list] = None, + stg_audio_blocks: Optional[list] = None, + modality_scale: float = 1.0, + audio_frozen: bool = False, +) -> tuple[mx.array, mx.array]: + """Run denoising loop for dev pipeline with CFG/APG, STG, modality guidance, and audio. + + Args: + audio_cfg_scale: Separate CFG scale for audio (PyTorch default: 7.0). + cfg_rescale: Rescale factor for CFG (0.0-1.0). Normalizes guided prediction + variance to reduce artifacts. Default 0.0 means no rescaling. + use_apg: Use Adaptive Projected Guidance instead of standard CFG for video. + apg_eta: APG parallel component weight (1.0 = keep full parallel) + apg_norm_threshold: APG guidance norm clamp (0 = no clamping) + stg_scale: STG (Spatiotemporal Guidance) scale. 0.0 = disabled. + stg_video_blocks: Transformer block indices for video STG perturbation. + stg_audio_blocks: Transformer block indices for audio STG perturbation. + modality_scale: Cross-modal guidance scale. 1.0 = disabled. + """ + from mlx_video.models.ltx_2.rope import precompute_freqs_cis + + dtype = video_latents.dtype + if video_state is not None: + video_latents = video_state.latent + + # Keep latents in float32 throughout the denoising loop for precision. + video_latents = video_latents.astype(mx.float32) + audio_latents = audio_latents.astype(mx.float32) + + sigmas_list = sigmas.tolist() + use_cfg = cfg_scale != 1.0 + use_stg = stg_scale != 0.0 and stg_video_blocks is not None + use_modality = modality_scale != 1.0 + num_steps = len(sigmas_list) - 1 + + # Precompute video RoPE + precomputed_video_rope = precompute_freqs_cis( + video_positions, + dim=transformer.inner_dim, + theta=transformer.positional_embedding_theta, + max_pos=transformer.positional_embedding_max_pos, + use_middle_indices_grid=transformer.use_middle_indices_grid, + num_attention_heads=transformer.num_attention_heads, + rope_type=transformer.rope_type, + double_precision=transformer.config.double_precision_rope, + ) + + # Precompute audio RoPE + precomputed_audio_rope = precompute_freqs_cis( + audio_positions, + dim=transformer.audio_inner_dim, + theta=transformer.positional_embedding_theta, + max_pos=transformer.audio_positional_embedding_max_pos, + use_middle_indices_grid=transformer.use_middle_indices_grid, + num_attention_heads=transformer.audio_num_attention_heads, + rope_type=transformer.rope_type, + double_precision=transformer.config.double_precision_rope, + ) + mx.eval(precomputed_video_rope, precomputed_audio_rope) + + with Progress( + SpinnerColumn(), + TextColumn("[progress.description]{task.description}"), + BarColumn(), + TaskProgressColumn(), + TimeRemainingColumn(), + console=console, + disable=not verbose, + ) as progress: + passes = ["CFG"] if use_cfg else [] + if use_stg: + passes.append("STG") + if use_modality: + passes.append("Mod") + label = "+".join(passes) if passes else "uncond" + task = progress.add_task(f"[cyan]Denoising A/V ({label})[/]", total=num_steps) + + for i in range(num_steps): + sigma = sigmas_list[i] + sigma_next = sigmas_list[i + 1] + + # Flatten video latents (cast to model dtype for transformer input) + b, c, f, h, w = video_latents.shape + num_video_tokens = f * h * w + video_flat = mx.transpose( + mx.reshape(video_latents, (b, c, -1)), (0, 2, 1) + ).astype(dtype) + + # Flatten audio latents (cast to model dtype for transformer input) + ab, ac, at, af = audio_latents.shape + audio_flat = mx.transpose(audio_latents, (0, 2, 1, 3)) + audio_flat = mx.reshape(audio_flat, (ab, at, ac * af)).astype(dtype) + + # Compute timesteps + if video_state is not None: + denoise_mask_flat = mx.reshape( + video_state.denoise_mask, (b, 1, f, 1, 1) + ) + denoise_mask_flat = mx.broadcast_to(denoise_mask_flat, (b, 1, f, h, w)) + denoise_mask_flat = mx.reshape(denoise_mask_flat, (b, num_video_tokens)) + video_timesteps = mx.array(sigma, dtype=dtype) * denoise_mask_flat + else: + video_timesteps = mx.full((b, num_video_tokens), sigma, dtype=dtype) + + # A2V: frozen audio uses timesteps=0 (tells model audio is clean) + audio_timesteps = ( + mx.zeros((ab, at), dtype=dtype) + if audio_frozen + else mx.full((ab, at), sigma, dtype=dtype) + ) + + # Positive conditioning pass + sigma_array = mx.full((b,), sigma, dtype=dtype) + audio_sigma_array = ( + mx.zeros((ab,), dtype=dtype) + if audio_frozen + else mx.full((ab,), sigma, dtype=dtype) + ) + video_modality_pos = Modality( + latent=video_flat, + timesteps=video_timesteps, + positions=video_positions, + context=video_embeddings_pos, + context_mask=None, + enabled=True, + positional_embeddings=precomputed_video_rope, + sigma=sigma_array, + ) + audio_modality_pos = Modality( + latent=audio_flat, + timesteps=audio_timesteps, + positions=audio_positions, + context=audio_embeddings_pos, + context_mask=None, + enabled=True, + positional_embeddings=precomputed_audio_rope, + sigma=audio_sigma_array, + ) + video_vel_pos, audio_vel_pos = transformer( + video=video_modality_pos, audio=audio_modality_pos + ) + mx.eval(video_vel_pos, audio_vel_pos) + + # Convert velocity to denoised (x0) using per-token timesteps + # This matches PyTorch's X0ModelWrapper: x0 = latent - timestep * velocity + # For conditioned tokens (timestep=0): x0 = latent (velocity is irrelevant) + # For unconditioned tokens (timestep=sigma): x0 = latent - sigma * velocity + video_flat_f32 = mx.transpose( + mx.reshape(video_latents, (b, c, -1)), (0, 2, 1) + ) + audio_flat_f32 = mx.reshape( + mx.transpose(audio_latents, (0, 2, 1, 3)), (ab, at, ac * af) + ) + video_timesteps_f32 = mx.expand_dims( + video_timesteps.astype(mx.float32), axis=-1 + ) + audio_timesteps_f32 = mx.expand_dims( + audio_timesteps.astype(mx.float32), axis=-1 + ) + + video_x0_pos_f32 = ( + video_flat_f32 - video_timesteps_f32 * video_vel_pos.astype(mx.float32) + ) + audio_x0_pos_f32 = ( + audio_flat_f32 - audio_timesteps_f32 * audio_vel_pos.astype(mx.float32) + ) + + # Start with positive prediction + video_x0_guided_f32 = video_x0_pos_f32 + audio_x0_guided_f32 = audio_x0_pos_f32 + + # Pass 2: CFG (negative conditioning) + if use_cfg: + video_modality_neg = Modality( + latent=video_flat, + timesteps=video_timesteps, + positions=video_positions, + context=video_embeddings_neg, + context_mask=None, + enabled=True, + positional_embeddings=precomputed_video_rope, + sigma=sigma_array, + ) + audio_modality_neg = Modality( + latent=audio_flat, + timesteps=audio_timesteps, + positions=audio_positions, + context=audio_embeddings_neg, + context_mask=None, + enabled=True, + positional_embeddings=precomputed_audio_rope, + sigma=audio_sigma_array, + ) + video_vel_neg, audio_vel_neg = transformer( + video=video_modality_neg, audio=audio_modality_neg + ) + mx.eval(video_vel_neg, audio_vel_neg) + + video_x0_neg_f32 = ( + video_flat_f32 + - video_timesteps_f32 * video_vel_neg.astype(mx.float32) + ) + audio_x0_neg_f32 = ( + audio_flat_f32 + - audio_timesteps_f32 * audio_vel_neg.astype(mx.float32) + ) + + if use_apg: + video_x0_guided_f32 = video_x0_pos_f32 + apg_delta( + video_x0_pos_f32, + video_x0_neg_f32, + cfg_scale, + eta=apg_eta, + norm_threshold=apg_norm_threshold, + ) + else: + video_x0_guided_f32 = video_x0_pos_f32 + (cfg_scale - 1.0) * ( + video_x0_pos_f32 - video_x0_neg_f32 + ) + audio_x0_guided_f32 = audio_x0_pos_f32 + (audio_cfg_scale - 1.0) * ( + audio_x0_pos_f32 - audio_x0_neg_f32 + ) + + # Pass 3: STG (self-attention perturbation at specified blocks) + if use_stg: + video_vel_ptb, audio_vel_ptb = transformer( + video=video_modality_pos, + audio=audio_modality_pos, + stg_video_blocks=stg_video_blocks, + stg_audio_blocks=stg_audio_blocks, + ) + mx.eval(video_vel_ptb, audio_vel_ptb) + + video_x0_ptb_f32 = ( + video_flat_f32 + - video_timesteps_f32 * video_vel_ptb.astype(mx.float32) + ) + audio_x0_ptb_f32 = ( + audio_flat_f32 + - audio_timesteps_f32 * audio_vel_ptb.astype(mx.float32) + ) + + video_x0_guided_f32 = video_x0_guided_f32 + stg_scale * ( + video_x0_pos_f32 - video_x0_ptb_f32 + ) + audio_x0_guided_f32 = audio_x0_guided_f32 + stg_scale * ( + audio_x0_pos_f32 - audio_x0_ptb_f32 + ) + + # Pass 4: Modality isolation (skip all cross-modal attention) + if use_modality: + video_vel_iso, audio_vel_iso = transformer( + video=video_modality_pos, + audio=audio_modality_pos, + skip_cross_modal=True, + ) + mx.eval(video_vel_iso, audio_vel_iso) + + video_x0_iso_f32 = ( + video_flat_f32 + - video_timesteps_f32 * video_vel_iso.astype(mx.float32) + ) + audio_x0_iso_f32 = ( + audio_flat_f32 + - audio_timesteps_f32 * audio_vel_iso.astype(mx.float32) + ) + + video_x0_guided_f32 = video_x0_guided_f32 + (modality_scale - 1.0) * ( + video_x0_pos_f32 - video_x0_iso_f32 + ) + audio_x0_guided_f32 = audio_x0_guided_f32 + (modality_scale - 1.0) * ( + audio_x0_pos_f32 - audio_x0_iso_f32 + ) + + # Apply CFG rescale (std-ratio rescaling to reduce over-saturation) + if cfg_rescale > 0.0 and (use_cfg or use_stg or use_modality): + v_factor = video_x0_pos_f32.std() / (video_x0_guided_f32.std() + 1e-8) + v_factor = cfg_rescale * v_factor + (1.0 - cfg_rescale) + video_x0_guided_f32 = video_x0_guided_f32 * v_factor + a_factor = audio_x0_pos_f32.std() / (audio_x0_guided_f32.std() + 1e-8) + a_factor = cfg_rescale * a_factor + (1.0 - cfg_rescale) + audio_x0_guided_f32 = audio_x0_guided_f32 * a_factor + + # Reshape x0 from token space (b, tokens, c) to spatial (b, c, f, h, w) + video_denoised_f32 = mx.reshape( + mx.transpose(video_x0_guided_f32, (0, 2, 1)), (b, c, f, h, w) + ) + audio_denoised_f32 = mx.reshape(audio_x0_guided_f32, (ab, at, ac, af)) + audio_denoised_f32 = mx.transpose(audio_denoised_f32, (0, 2, 1, 3)) + + # Post-process: blend denoised with clean latent using mask + # Matches PyTorch's post_process_latent: denoised * mask + clean * (1 - mask) + sigma_f32 = mx.array(sigma, dtype=mx.float32) + + if video_state is not None: + clean_f32 = video_state.clean_latent.astype(mx.float32) + mask_f32 = video_state.denoise_mask.astype(mx.float32) + video_denoised_f32 = video_denoised_f32 * mask_f32 + clean_f32 * ( + 1.0 - mask_f32 + ) + + mx.eval(video_denoised_f32, audio_denoised_f32) + + # Euler step: sample + velocity * dt (float32) + if sigma_next > 0: + sigma_next_f32 = mx.array(sigma_next, dtype=mx.float32) + dt_f32 = sigma_next_f32 - sigma_f32 + + video_velocity_f32 = (video_latents - video_denoised_f32) / sigma_f32 + video_latents = video_latents + video_velocity_f32 * dt_f32 + + if not audio_frozen: + audio_velocity_f32 = ( + audio_latents - audio_denoised_f32 + ) / sigma_f32 + audio_latents = audio_latents + audio_velocity_f32 * dt_f32 + else: + video_latents = video_denoised_f32 + if not audio_frozen: + audio_latents = audio_denoised_f32 + + mx.eval(video_latents, audio_latents) + progress.advance(task) + + return video_latents, audio_latents + + +def denoise_res2s_av( + video_latents: mx.array, + audio_latents: mx.array, + video_positions: mx.array, + audio_positions: mx.array, + video_embeddings_pos: mx.array, + video_embeddings_neg: mx.array, + audio_embeddings_pos: mx.array, + audio_embeddings_neg: mx.array, + transformer: LTXModel, + sigmas: mx.array, + cfg_scale: float = 3.0, + audio_cfg_scale: float = 7.0, + cfg_rescale: float = 0.45, + audio_cfg_rescale: Optional[float] = None, + verbose: bool = True, + video_state: Optional[LatentState] = None, + stg_scale: float = 0.0, + stg_video_blocks: Optional[list] = None, + stg_audio_blocks: Optional[list] = None, + modality_scale: float = 1.0, + noise_seed: int = 42, + bongmath: bool = True, + bongmath_max_iter: int = 100, + audio_frozen: bool = False, +) -> tuple[mx.array, mx.array]: + """Run res_2s second-order denoising loop with CFG/STG/modality guidance. + + Two model evaluations per step (current point + midpoint), with SDE noise + injection and optional bong iteration for anchor refinement. + + Args: + audio_cfg_rescale: Separate rescale for audio. If None, uses cfg_rescale. + noise_seed: Seed for SDE noise generators. + bongmath: Enable iterative anchor refinement. + bongmath_max_iter: Max bong iterations per step. + """ + from mlx_video.models.ltx_2.rope import precompute_freqs_cis + from mlx_video.models.ltx_2.samplers import ( + get_new_noise, + get_res2s_coefficients, + sde_noise_step, + ) + + if audio_cfg_rescale is None: + audio_cfg_rescale = cfg_rescale + + dtype = video_latents.dtype + if video_state is not None: + video_latents = video_state.latent + + video_latents = video_latents.astype(mx.float32) + audio_latents = audio_latents.astype(mx.float32) + + sigmas_list = sigmas.tolist() + use_cfg = cfg_scale != 1.0 + use_stg = stg_scale != 0.0 and stg_video_blocks is not None + use_modality = modality_scale != 1.0 + n_full_steps = len(sigmas_list) - 1 + + # Pad sigmas if last is 0 (avoid division by zero in RK steps) + if sigmas_list[-1] == 0: + sigmas_list = sigmas_list[:-1] + [0.0011, 0.0] + + # Compute step sizes in log-space for the main loop steps only. + # After padding, sigmas_list may have an extra [0.0011, 0.0] tail; + # we only need hs for the n_full_steps pairs the loop actually uses. + hs = [-math.log(sigmas_list[i + 1] / sigmas_list[i]) for i in range(n_full_steps)] + + # Precompute RoPE + precomputed_video_rope = precompute_freqs_cis( + video_positions, + dim=transformer.inner_dim, + theta=transformer.positional_embedding_theta, + max_pos=transformer.positional_embedding_max_pos, + use_middle_indices_grid=transformer.use_middle_indices_grid, + num_attention_heads=transformer.num_attention_heads, + rope_type=transformer.rope_type, + double_precision=transformer.config.double_precision_rope, + ) + precomputed_audio_rope = precompute_freqs_cis( + audio_positions, + dim=transformer.audio_inner_dim, + theta=transformer.positional_embedding_theta, + max_pos=transformer.audio_positional_embedding_max_pos, + use_middle_indices_grid=transformer.use_middle_indices_grid, + num_attention_heads=transformer.audio_num_attention_heads, + rope_type=transformer.rope_type, + double_precision=transformer.config.double_precision_rope, + ) + mx.eval(precomputed_video_rope, precomputed_audio_rope) + + phi_cache = {} + c2 = 0.5 + + # Noise key management: step noise and substep noise use different keys + step_noise_key = mx.random.key(noise_seed) + substep_noise_key = mx.random.key(noise_seed + 10000) + + def _eval_guided_denoise(v_latents, a_latents, sigma): + """Run all guidance passes and return (video_denoised, audio_denoised) in float32 spatial format.""" + b, c, f, h, w = v_latents.shape + num_video_tokens = f * h * w + video_flat = mx.transpose(mx.reshape(v_latents, (b, c, -1)), (0, 2, 1)).astype( + dtype + ) + + ab, ac, at, af = a_latents.shape + audio_flat = mx.transpose(a_latents, (0, 2, 1, 3)) + audio_flat = mx.reshape(audio_flat, (ab, at, ac * af)).astype(dtype) + + # Timesteps + if video_state is not None: + denoise_mask_flat = mx.reshape(video_state.denoise_mask, (b, 1, f, 1, 1)) + denoise_mask_flat = mx.broadcast_to(denoise_mask_flat, (b, 1, f, h, w)) + denoise_mask_flat = mx.reshape(denoise_mask_flat, (b, num_video_tokens)) + video_timesteps = mx.array(sigma, dtype=dtype) * denoise_mask_flat + else: + video_timesteps = mx.full((b, num_video_tokens), sigma, dtype=dtype) + audio_timesteps = ( + mx.zeros((ab, at), dtype=dtype) + if audio_frozen + else mx.full((ab, at), sigma, dtype=dtype) + ) + + sigma_array = mx.full((b,), sigma, dtype=dtype) + audio_sigma_array = ( + mx.zeros((ab,), dtype=dtype) + if audio_frozen + else mx.full((ab,), sigma, dtype=dtype) + ) + + # Pass 1: Positive conditioning + video_modality_pos = Modality( + latent=video_flat, + timesteps=video_timesteps, + positions=video_positions, + context=video_embeddings_pos, + context_mask=None, + enabled=True, + positional_embeddings=precomputed_video_rope, + sigma=sigma_array, + ) + audio_modality_pos = Modality( + latent=audio_flat, + timesteps=audio_timesteps, + positions=audio_positions, + context=audio_embeddings_pos, + context_mask=None, + enabled=True, + positional_embeddings=precomputed_audio_rope, + sigma=audio_sigma_array, + ) + video_vel_pos, audio_vel_pos = transformer( + video=video_modality_pos, audio=audio_modality_pos + ) + mx.eval(video_vel_pos, audio_vel_pos) + + # Convert velocity to x0 + video_flat_f32 = mx.transpose(mx.reshape(v_latents, (b, c, -1)), (0, 2, 1)) + audio_flat_f32 = mx.reshape( + mx.transpose(a_latents, (0, 2, 1, 3)), (ab, at, ac * af) + ) + video_ts_f32 = mx.expand_dims(video_timesteps.astype(mx.float32), axis=-1) + audio_ts_f32 = mx.expand_dims(audio_timesteps.astype(mx.float32), axis=-1) + + video_x0_pos = video_flat_f32 - video_ts_f32 * video_vel_pos.astype(mx.float32) + audio_x0_pos = audio_flat_f32 - audio_ts_f32 * audio_vel_pos.astype(mx.float32) + + video_x0_guided = video_x0_pos + audio_x0_guided = audio_x0_pos + + # Pass 2: CFG + if use_cfg: + video_modality_neg = Modality( + latent=video_flat, + timesteps=video_timesteps, + positions=video_positions, + context=video_embeddings_neg, + context_mask=None, + enabled=True, + positional_embeddings=precomputed_video_rope, + sigma=sigma_array, + ) + audio_modality_neg = Modality( + latent=audio_flat, + timesteps=audio_timesteps, + positions=audio_positions, + context=audio_embeddings_neg, + context_mask=None, + enabled=True, + positional_embeddings=precomputed_audio_rope, + sigma=audio_sigma_array, + ) + video_vel_neg, audio_vel_neg = transformer( + video=video_modality_neg, audio=audio_modality_neg + ) + mx.eval(video_vel_neg, audio_vel_neg) + + video_x0_neg = video_flat_f32 - video_ts_f32 * video_vel_neg.astype( + mx.float32 + ) + audio_x0_neg = audio_flat_f32 - audio_ts_f32 * audio_vel_neg.astype( + mx.float32 + ) + + video_x0_guided = video_x0_pos + (cfg_scale - 1.0) * ( + video_x0_pos - video_x0_neg + ) + audio_x0_guided = audio_x0_pos + (audio_cfg_scale - 1.0) * ( + audio_x0_pos - audio_x0_neg + ) + + # Pass 3: STG + if use_stg: + video_vel_ptb, audio_vel_ptb = transformer( + video=video_modality_pos, + audio=audio_modality_pos, + stg_video_blocks=stg_video_blocks, + stg_audio_blocks=stg_audio_blocks, + ) + mx.eval(video_vel_ptb, audio_vel_ptb) + + video_x0_ptb = video_flat_f32 - video_ts_f32 * video_vel_ptb.astype( + mx.float32 + ) + audio_x0_ptb = audio_flat_f32 - audio_ts_f32 * audio_vel_ptb.astype( + mx.float32 + ) + + video_x0_guided = video_x0_guided + stg_scale * ( + video_x0_pos - video_x0_ptb + ) + audio_x0_guided = audio_x0_guided + stg_scale * ( + audio_x0_pos - audio_x0_ptb + ) + + # Pass 4: Modality isolation + if use_modality: + video_vel_iso, audio_vel_iso = transformer( + video=video_modality_pos, + audio=audio_modality_pos, + skip_cross_modal=True, + ) + mx.eval(video_vel_iso, audio_vel_iso) + + video_x0_iso = video_flat_f32 - video_ts_f32 * video_vel_iso.astype( + mx.float32 + ) + audio_x0_iso = audio_flat_f32 - audio_ts_f32 * audio_vel_iso.astype( + mx.float32 + ) + + video_x0_guided = video_x0_guided + (modality_scale - 1.0) * ( + video_x0_pos - video_x0_iso + ) + audio_x0_guided = audio_x0_guided + (modality_scale - 1.0) * ( + audio_x0_pos - audio_x0_iso + ) + + # Rescale (separate factors for video and audio) + if cfg_rescale > 0.0 and (use_cfg or use_stg or use_modality): + v_factor = video_x0_pos.std() / (video_x0_guided.std() + 1e-8) + v_factor = cfg_rescale * v_factor + (1.0 - cfg_rescale) + video_x0_guided = video_x0_guided * v_factor + if audio_cfg_rescale > 0.0 and (use_cfg or use_stg or use_modality): + a_factor = audio_x0_pos.std() / (audio_x0_guided.std() + 1e-8) + a_factor = audio_cfg_rescale * a_factor + (1.0 - audio_cfg_rescale) + audio_x0_guided = audio_x0_guided * a_factor + + # Reshape to spatial + video_denoised = mx.reshape( + mx.transpose(video_x0_guided, (0, 2, 1)), (b, c, f, h, w) + ) + audio_denoised = mx.reshape(audio_x0_guided, (ab, at, ac, af)) + audio_denoised = mx.transpose(audio_denoised, (0, 2, 1, 3)) + + # Post-process with mask + if video_state is not None: + clean_f32 = video_state.clean_latent.astype(mx.float32) + mask_f32 = video_state.denoise_mask.astype(mx.float32) + video_denoised = video_denoised * mask_f32 + clean_f32 * (1.0 - mask_f32) + + mx.eval(video_denoised, audio_denoised) + return video_denoised, audio_denoised + + # Main res_2s loop + with Progress( + SpinnerColumn(), + TextColumn("[progress.description]{task.description}"), + BarColumn(), + TaskProgressColumn(), + TimeRemainingColumn(), + console=console, + disable=not verbose, + ) as progress: + passes = ["res2s"] + if use_cfg: + passes.append("CFG") + if use_stg: + passes.append("STG") + if use_modality: + passes.append("Mod") + label = "+".join(passes) + task = progress.add_task( + f"[cyan]Denoising A/V ({label})[/]", total=n_full_steps + ) + + for step_idx in range(n_full_steps): + sigma = sigmas_list[step_idx] + sigma_next = sigmas_list[step_idx + 1] + h = hs[step_idx] + + # Initialize anchor + x_anchor_video = video_latents + x_anchor_audio = audio_latents + + # ============================================================ + # Stage 1: Evaluate denoiser at current sigma + # ============================================================ + denoised_video_1, denoised_audio_1 = _eval_guided_denoise( + video_latents, audio_latents, sigma + ) + + # RK coefficients + a21, b1, b2 = get_res2s_coefficients(h, phi_cache, c2) + + # Substep sigma (geometric midpoint for c2=0.5) + sub_sigma = math.sqrt(sigma * sigma_next) + + # Compute midpoint + eps_1_video = denoised_video_1 - x_anchor_video + x_mid_video = x_anchor_video + h * a21 * eps_1_video + + if not audio_frozen: + eps_1_audio = denoised_audio_1 - x_anchor_audio + x_mid_audio = x_anchor_audio + h * a21 * eps_1_audio + else: + eps_1_audio = None + x_mid_audio = audio_latents # frozen: pass through unchanged + + # SDE noise injection at substep + substep_noise_key, key1, key2 = mx.random.split(substep_noise_key, 3) + substep_noise_v = get_new_noise(video_latents.shape, key1) + + x_mid_video = sde_noise_step( + x_anchor_video, x_mid_video, sigma, sub_sigma, substep_noise_v + ) + if not audio_frozen: + substep_noise_a = get_new_noise(audio_latents.shape, key2) + x_mid_audio = sde_noise_step( + x_anchor_audio, x_mid_audio, sigma, sub_sigma, substep_noise_a + ) + mx.eval(x_mid_video, x_mid_audio) + + # ============================================================ + # Bong iteration: refine anchor (pure arithmetic, no model calls) + # ============================================================ + if bongmath and h < 0.5 and sigma > 0.03: + for _ in range(bongmath_max_iter): + x_anchor_video = x_mid_video - h * a21 * eps_1_video + eps_1_video = denoised_video_1 - x_anchor_video + if not audio_frozen: + x_anchor_audio = x_mid_audio - h * a21 * eps_1_audio + eps_1_audio = denoised_audio_1 - x_anchor_audio + if audio_frozen: + mx.eval(x_anchor_video, eps_1_video) + else: + mx.eval(x_anchor_video, x_anchor_audio, eps_1_video, eps_1_audio) + + # ============================================================ + # Stage 2: Evaluate denoiser at midpoint sigma + # ============================================================ + denoised_video_2, denoised_audio_2 = _eval_guided_denoise( + x_mid_video.astype(mx.float32), + x_mid_audio.astype(mx.float32), + sub_sigma, + ) + + # ============================================================ + # Final combination with RK coefficients + # ============================================================ + eps_2_video = denoised_video_2 - x_anchor_video + x_next_video = x_anchor_video + h * (b1 * eps_1_video + b2 * eps_2_video) + + # SDE noise injection at step level + step_noise_key, key1, key2 = mx.random.split(step_noise_key, 3) + step_noise_v = get_new_noise(video_latents.shape, key1) + x_next_video = sde_noise_step( + x_anchor_video, x_next_video, sigma, sigma_next, step_noise_v + ) + + video_latents = x_next_video.astype(mx.float32) + if not audio_frozen: + eps_2_audio = denoised_audio_2 - x_anchor_audio + x_next_audio = x_anchor_audio + h * ( + b1 * eps_1_audio + b2 * eps_2_audio + ) + step_noise_a = get_new_noise(audio_latents.shape, key2) + x_next_audio = sde_noise_step( + x_anchor_audio, x_next_audio, sigma, sigma_next, step_noise_a + ) + audio_latents = x_next_audio.astype(mx.float32) + + mx.eval(video_latents, audio_latents) + progress.advance(task) + + # Final clean step if original schedule ended at 0 + if sigmas.tolist()[-1] == 0: + denoised_video, denoised_audio = _eval_guided_denoise( + video_latents, audio_latents, sigmas_list[n_full_steps] + ) + video_latents = denoised_video + if not audio_frozen: + audio_latents = denoised_audio + mx.eval(video_latents, audio_latents) + + return video_latents, audio_latents + + +# ============================================================================= +# Audio Loading and Processing +# ============================================================================= + + +def load_audio_decoder(model_path: Path, pipeline: PipelineType): + """Load audio VAE decoder.""" + from mlx_video.models.ltx_2.audio_vae import AudioDecoder + + decoder = AudioDecoder.from_pretrained(model_path / "audio_vae" / "decoder") + + return decoder + + +def load_vocoder_model(model_path: Path, pipeline: PipelineType): + """Load vocoder for mel to waveform conversion. + + Automatically detects HiFi-GAN (LTX-2) or BigVGAN+BWE (LTX-2.3). + """ + from mlx_video.models.ltx_2.audio_vae.vocoder import load_vocoder as _load_vocoder + + return _load_vocoder(model_path / "vocoder") + + +def save_audio(audio: np.ndarray, path: Path, sample_rate: int = AUDIO_SAMPLE_RATE): + """Save audio to WAV file.""" + import wave + + if audio.ndim == 2: + audio = audio.T + + audio = np.clip(audio, -1.0, 1.0) + audio_int16 = (audio * 32767).astype(np.int16) + + with wave.open(str(path), "wb") as wf: + wf.setnchannels(2 if audio_int16.ndim == 2 else 1) + wf.setsampwidth(2) + wf.setframerate(sample_rate) + wf.writeframes(audio_int16.tobytes()) + + +def mux_video_audio(video_path: Path, audio_path: Path, output_path: Path): + """Combine video and audio into final output using ffmpeg.""" + import subprocess + + cmd = [ + "ffmpeg", + "-y", + "-i", + str(video_path), + "-i", + str(audio_path), + "-c:v", + "copy", + "-c:a", + "aac", + "-shortest", + str(output_path), + ] + + try: + subprocess.run(cmd, check=True, capture_output=True) + return True + except subprocess.CalledProcessError as e: + console.print(f"[red]FFmpeg error: {e.stderr.decode()}[/]") + return False + except FileNotFoundError: + console.print("[red]FFmpeg not found. Please install ffmpeg.[/]") + return False + + +# ============================================================================= +# Unified Generate Function +# ============================================================================= + + +def generate_video( + model_repo: str, + text_encoder_repo: str, + prompt: str, + pipeline: PipelineType = PipelineType.DISTILLED, + negative_prompt: str = DEFAULT_NEGATIVE_PROMPT, + height: int = 512, + width: int = 512, + num_frames: int = 33, + num_inference_steps: int = 40, + cfg_scale: float = 4.0, + audio_cfg_scale: float = 7.0, + cfg_rescale: float = 0.0, + seed: int = 42, + fps: int = 24, + output_path: str = "output.mp4", + save_frames: bool = False, + verbose: bool = True, + enhance_prompt: bool = False, + max_tokens: int = 512, + temperature: float = 0.7, + image: Optional[str] = None, + image_strength: float = 1.0, + image_frame_idx: int = 0, + tiling: str = "auto", + stream: bool = False, + audio: bool = False, + output_audio_path: Optional[str] = None, + use_apg: bool = False, + apg_eta: float = 1.0, + apg_norm_threshold: float = 0.0, + stg_scale: float = 1.0, + stg_blocks: Optional[list] = None, + modality_scale: float = 3.0, + lora_path: Optional[str] = None, + lora_strength: float = 1.0, + lora_strength_stage_1: Optional[float] = None, + lora_strength_stage_2: Optional[float] = None, + audio_file: Optional[str] = None, + audio_start_time: float = 0.0, + spatial_upscaler: Optional[str] = None, +): + """Generate video using LTX-2 models. + + Supports four pipelines: + - DISTILLED: Two-stage generation with upsampling, fixed sigma schedules, no CFG + - DEV: Single-stage generation with dynamic sigmas and CFG + - DEV_TWO_STAGE: Stage 1 dev (half res, CFG) + upsample + stage 2 distilled with LoRA (full res, no CFG) + - DEV_TWO_STAGE_HQ: res_2s sampler, LoRA both stages (0.25/0.5), lower rescale + + Args: + model_repo: Model repository ID + text_encoder_repo: Text encoder repository ID + prompt: Text description of the video to generate + pipeline: Pipeline type (DISTILLED or DEV) + negative_prompt: Negative prompt for CFG (dev pipeline only) + height: Output video height (must be divisible by 32/64) + width: Output video width (must be divisible by 32/64) + num_frames: Number of frames (must be 1 + 8*k) + num_inference_steps: Number of denoising steps (dev pipeline only) + cfg_scale: Guidance scale for CFG (dev pipeline only) + seed: Random seed for reproducibility + fps: Frames per second for output video + output_path: Path to save the output video + save_frames: Whether to save individual frames as images + verbose: Whether to print progress + enhance_prompt: Whether to enhance prompt using Gemma + max_tokens: Max tokens for prompt enhancement + temperature: Temperature for prompt enhancement + image: Path to conditioning image for I2V + image_strength: Conditioning strength for I2V + image_frame_idx: Frame index to condition for I2V + tiling: Tiling mode for VAE decoding + stream: Stream frames to output as they're decoded + audio: Enable synchronized audio generation + output_audio_path: Path to save audio file + use_apg: Use Adaptive Projected Guidance instead of CFG (more stable for I2V) + apg_eta: APG parallel component weight (1.0 = keep full parallel) + apg_norm_threshold: APG guidance norm clamp (0 = no clamping) + """ + start_time = time.time() + + # Validate dimensions + is_two_stage = pipeline in ( + PipelineType.DISTILLED, + PipelineType.DEV_TWO_STAGE, + PipelineType.DEV_TWO_STAGE_HQ, + ) + divisor = 64 if is_two_stage else 32 + assert height % divisor == 0, f"Height must be divisible by {divisor}, got {height}" + assert width % divisor == 0, f"Width must be divisible by {divisor}, got {width}" + + if num_frames % 8 != 1: + adjusted_num_frames = round((num_frames - 1) / 8) * 8 + 1 + console.print( + f"[yellow]⚠️ Number of frames must be 1 + 8*k. Using: {adjusted_num_frames}[/]" + ) + num_frames = adjusted_num_frames + + is_i2v = image is not None + is_a2v = audio_file is not None + if is_a2v and audio: + raise ValueError( + "Cannot use both --audio-file (A2V) and --audio (generate audio). Choose one." + ) + # A2V implicitly enables audio path through the transformer + if is_a2v: + audio = True + mode_str = "I2V" if is_i2v else "T2V" + if is_a2v: + mode_str = "A2V" + ("+I2V" if is_i2v else "") + elif audio: + mode_str += "+Audio" + + pipeline_names = { + PipelineType.DISTILLED: "DISTILLED", + PipelineType.DEV: "DEV", + PipelineType.DEV_TWO_STAGE: "DEV-TWO-STAGE", + PipelineType.DEV_TWO_STAGE_HQ: "DEV-TWO-STAGE-HQ", + } + pipeline_name = pipeline_names[pipeline] + header = f"[bold cyan]🎬 [{pipeline_name}] [{mode_str}] {width}x{height} • {num_frames} frames[/]" + console.print(Panel(header, expand=False)) + console.print(f"[dim]Prompt: {prompt[:80]}{'...' if len(prompt) > 80 else ''}[/]") + + if pipeline in ( + PipelineType.DEV, + PipelineType.DEV_TWO_STAGE, + PipelineType.DEV_TWO_STAGE_HQ, + ): + audio_cfg_info = f", Audio CFG: {audio_cfg_scale}" if audio else "" + stg_info = f", STG: {stg_scale} blocks={stg_blocks}" if stg_scale != 0.0 else "" + mod_info = f", Modality: {modality_scale}" if modality_scale != 1.0 else "" + console.print( + f"[dim]Steps: {num_inference_steps}, CFG: {cfg_scale}{audio_cfg_info}, Rescale: {cfg_rescale}{stg_info}{mod_info}[/]" + ) + + if is_i2v: + console.print( + f"[dim]Image: {image} (strength={image_strength}, frame={image_frame_idx})[/]" + ) + + # Always compute audio frames - PyTorch distilled pipeline unconditionally + # generates audio alongside video (model was trained with joint audio-video). + # The --audio flag only controls whether audio is decoded and saved to output. + audio_frames = compute_audio_frames(num_frames, fps) + if audio: + console.print( + f"[dim]Audio: {audio_frames} latent frames @ {AUDIO_SAMPLE_RATE}Hz[/]" + ) + + # Get model path + model_path = get_model_path(model_repo) + text_encoder_path = ( + model_path if text_encoder_repo is None else get_model_path(text_encoder_repo) + ) + + # Resolve spatial upscaler path for two-stage pipelines + upscaler_path = None + upscaler_scale = 2.0 + if is_two_stage: + if spatial_upscaler is not None: + # User-specified upscaler file + upscaler_path = ( + model_path / spatial_upscaler + if not Path(spatial_upscaler).is_absolute() + else Path(spatial_upscaler) + ) + if not upscaler_path.exists(): + # Try as a filename within model_path + upscaler_path = model_path / spatial_upscaler + # Detect scale from filename + if "x1.5" in str(upscaler_path): + upscaler_scale = 1.5 + elif "x2" in str(upscaler_path): + upscaler_scale = 2.0 + else: + # Auto-detect: prefer x2 upscaler + upscaler_files = sorted( + model_path.glob("*spatial-upscaler-x2*.safetensors") + ) + if upscaler_files: + upscaler_path = upscaler_files[0] + upscaler_scale = 2.0 + + # Calculate latent dimensions + if is_two_stage: + # Stage 1 always at half resolution (matches PyTorch) + stage1_h, stage1_w = height // 2 // 32, width // 2 // 32 + # Stage 2 resolution = stage 1 * upscaler scale + stage2_h = int(stage1_h * upscaler_scale) + stage2_w = int(stage1_w * upscaler_scale) + else: + latent_h, latent_w = height // 32, width // 32 + latent_frames = 1 + (num_frames - 1) // 8 + + mx.random.seed(seed) + + # Read transformer config to detect model version + import json + + transformer_config_path = model_path / "transformer" / "config.json" + has_prompt_adaln = False + if transformer_config_path.exists(): + with open(transformer_config_path) as f: + has_prompt_adaln = json.load(f).get("has_prompt_adaln", False) + + # Load text encoder + with console.status("[blue]📝 Loading text encoder...[/]", spinner="dots"): + from mlx_video.models.ltx_2.text_encoder import LTX2TextEncoder + + text_encoder = LTX2TextEncoder(has_prompt_adaln=has_prompt_adaln) + text_encoder.load(model_path=model_path, text_encoder_path=text_encoder_path) + mx.eval(text_encoder.parameters()) + console.print("[green]✓[/] Text encoder loaded") + + # Optionally enhance the prompt + if enhance_prompt: + console.print("[bold magenta]✨ Enhancing prompt[/]") + prompt = text_encoder.enhance_t2v( + prompt, + max_tokens=max_tokens, + temperature=temperature, + seed=seed, + verbose=verbose, + ) + console.print( + f"[dim]Enhanced: {prompt[:150]}{'...' if len(prompt) > 150 else ''}[/]" + ) + + # Encode prompts - always get audio embeddings since the model was trained + # with joint audio-video processing (PyTorch unconditionally generates audio) + if pipeline in ( + PipelineType.DEV, + PipelineType.DEV_TWO_STAGE, + PipelineType.DEV_TWO_STAGE_HQ, + ): + # Dev/dev-two-stage pipelines need positive and negative embeddings for CFG + video_embeddings_pos, audio_embeddings_pos = text_encoder( + prompt, return_audio_embeddings=True + ) + video_embeddings_neg, audio_embeddings_neg = text_encoder( + negative_prompt, return_audio_embeddings=True + ) + model_dtype = video_embeddings_pos.dtype + mx.eval( + video_embeddings_pos, + video_embeddings_neg, + audio_embeddings_pos, + audio_embeddings_neg, + ) + # For dev-two-stage, stage 2 uses single positive embedding (no CFG) + if pipeline in (PipelineType.DEV_TWO_STAGE, PipelineType.DEV_TWO_STAGE_HQ): + text_embeddings = video_embeddings_pos + else: + # Distilled pipeline - single embedding + text_embeddings, audio_embeddings = text_encoder( + prompt, return_audio_embeddings=True + ) + mx.eval(text_embeddings, audio_embeddings) + model_dtype = text_embeddings.dtype + + del text_encoder + mx.clear_cache() + + # Load transformer + transformer_desc = f"🤖 Loading {pipeline_name.lower()} transformer{' (A/V mode)' if audio else ''}..." + with console.status(f"[blue]{transformer_desc}[/]", spinner="dots"): + transformer = LTXModel.from_pretrained( + model_path=model_path / "transformer", strict=True + ) + + console.print("[green]✓[/] Transformer loaded") + + # Auto-detect stg_blocks from transformer config if not explicitly provided. + # LTX-2.3 (has_prompt_adaln=True) uses block 28; LTX-2 uses block 29. + if stg_blocks is None and stg_scale != 0.0: + if transformer.config.has_prompt_adaln: + stg_blocks = [28] + else: + stg_blocks = [29] + console.print( + f"[dim]Auto-detected STG blocks: {stg_blocks} (model={'2.3' if transformer.config.has_prompt_adaln else '2'})[/]" + ) + + # ========================================================================== + # A2V: Encode input audio to frozen latents + # ========================================================================== + a2v_audio_latents = None + a2v_waveform = None + a2v_sr = None + if is_a2v: + from mlx_video.models.ltx_2.audio_vae import AudioEncoder + from mlx_video.models.ltx_2.audio_vae.audio_processor import ( + ensure_stereo, + load_audio, + waveform_to_mel, + ) + from mlx_video.models.ltx_2.utils import convert_audio_encoder + + with console.status( + "[blue]Loading and encoding input audio (A2V)...[/]", spinner="dots" + ): + video_duration = num_frames / fps + + # Load audio + waveform, sr = load_audio( + audio_file, + target_sr=AUDIO_LATENT_SAMPLE_RATE, + start_time=audio_start_time, + max_duration=video_duration, + ) + waveform = ensure_stereo(waveform) + a2v_waveform = waveform.copy() + a2v_sr = sr + + # Compute mel-spectrogram + mel = waveform_to_mel( + waveform, + sample_rate=sr, + n_fft=1024, + hop_length=AUDIO_HOP_LENGTH, + n_mels=64, + ) + + # Convert audio encoder weights if needed, then load + encoder_dir = convert_audio_encoder( + model_path, source_repo="Lightricks/LTX-2" + ) + audio_encoder = AudioEncoder.from_pretrained(encoder_dir) + mx.eval(audio_encoder.parameters()) + + # Encode: (1, 2, time, 64) -> normalized latents + encoded = audio_encoder(mel) + mx.eval(encoded) + + # encoded is in MLX format (B, T', mel_bins', z_channels) = (1, T', 16, 8) + # Convert to PyTorch-style format for consistency: (B, C, T, mel_bins) + a2v_audio_latents = mx.transpose(encoded, (0, 3, 1, 2)).astype(model_dtype) + + # Trim/pad to match expected audio_frames + t_encoded = a2v_audio_latents.shape[2] + if t_encoded > audio_frames: + a2v_audio_latents = a2v_audio_latents[:, :, :audio_frames, :] + elif t_encoded < audio_frames: + pad_size = audio_frames - t_encoded + padding = mx.zeros( + (1, AUDIO_LATENT_CHANNELS, pad_size, AUDIO_MEL_BINS), + dtype=model_dtype, + ) + a2v_audio_latents = mx.concatenate([a2v_audio_latents, padding], axis=2) + mx.eval(a2v_audio_latents) + + del audio_encoder + mx.clear_cache() + + console.print( + f"[green]✓[/] Audio encoded ({a2v_audio_latents.shape[2]} frames from {audio_file})" + ) + + # ========================================================================== + # Pipeline-specific generation logic + # ========================================================================== + + if pipeline == PipelineType.DISTILLED: + # ====================================================================== + # DISTILLED PIPELINE: Two-stage with upsampling + # ====================================================================== + + # Load VAE encoder for I2V + stage1_image_latent = None + stage2_image_latent = None + if is_i2v: + with console.status( + "[blue]🖼️ Loading VAE encoder and encoding image...[/]", spinner="dots" + ): + vae_encoder = VideoEncoder.from_pretrained( + model_path / "vae" / "encoder" + ) + + s1_h, s1_w = stage1_h * 32, stage1_w * 32 + input_image = load_image( + image, height=s1_h, width=s1_w, dtype=model_dtype + ) + stage1_image_tensor = prepare_image_for_encoding( + input_image, s1_h, s1_w, dtype=model_dtype + ) + stage1_image_latent = vae_encoder(stage1_image_tensor) + mx.eval(stage1_image_latent) + + s2_h, s2_w = stage2_h * 32, stage2_w * 32 + input_image = load_image( + image, height=s2_h, width=s2_w, dtype=model_dtype + ) + stage2_image_tensor = prepare_image_for_encoding( + input_image, s2_h, s2_w, dtype=model_dtype + ) + stage2_image_latent = vae_encoder(stage2_image_tensor) + mx.eval(stage2_image_latent) + + del vae_encoder + mx.clear_cache() + console.print("[green]✓[/] VAE encoder loaded and image encoded") + + # Stage 1 + console.print( + f"\n[bold yellow]⚡ Stage 1:[/] Generating at {stage1_w*32}x{stage1_h*32} (8 steps)" + ) + mx.random.seed(seed) + + positions = create_position_grid(1, latent_frames, stage1_h, stage1_w) + mx.eval(positions) + + # Init audio latents/positions: use encoded A2V latents or random + audio_positions = create_audio_position_grid(1, audio_frames) + audio_latents = ( + a2v_audio_latents + if is_a2v + else mx.random.normal( + (1, AUDIO_LATENT_CHANNELS, audio_frames, AUDIO_MEL_BINS) + ).astype(model_dtype) + ) + mx.eval(audio_positions, audio_latents) + + # Apply I2V conditioning + state1 = None + if is_i2v and stage1_image_latent is not None: + latent_shape = (1, 128, latent_frames, stage1_h, stage1_w) + state1 = LatentState( + latent=mx.zeros(latent_shape, dtype=model_dtype), + clean_latent=mx.zeros(latent_shape, dtype=model_dtype), + denoise_mask=mx.ones((1, 1, latent_frames, 1, 1), dtype=model_dtype), + ) + conditioning = VideoConditionByLatentIndex( + latent=stage1_image_latent, + frame_idx=image_frame_idx, + strength=image_strength, + ) + state1 = apply_conditioning(state1, [conditioning]) + + noise = mx.random.normal(latent_shape, dtype=model_dtype) + noise_scale = mx.array(STAGE_1_SIGMAS[0], dtype=model_dtype) + scaled_mask = state1.denoise_mask * noise_scale + state1 = LatentState( + latent=noise * scaled_mask + + state1.latent * (mx.array(1.0, dtype=model_dtype) - scaled_mask), + clean_latent=state1.clean_latent, + denoise_mask=state1.denoise_mask, + ) + latents = state1.latent + mx.eval(latents) + else: + latents = mx.random.normal( + (1, 128, latent_frames, stage1_h, stage1_w), dtype=model_dtype + ) + mx.eval(latents) + + latents, audio_latents = denoise_distilled( + latents, + positions, + text_embeddings, + transformer, + STAGE_1_SIGMAS, + verbose=verbose, + state=state1, + audio_latents=audio_latents, + audio_positions=audio_positions, + audio_embeddings=audio_embeddings, + audio_frozen=is_a2v, + ) + + # Upsample latents + with console.status( + f"[magenta]🔍 Upsampling latents {upscaler_scale}x...[/]", spinner="dots" + ): + if upscaler_path is None or not upscaler_path.exists(): + raise FileNotFoundError(f"No spatial upscaler found in {model_path}") + upsampler, upscaler_scale = load_upsampler(str(upscaler_path)) + mx.eval(upsampler.parameters()) + + vae_decoder = VideoDecoder.from_pretrained( + str(model_path / "vae" / "decoder") + ) + + latents = upsample_latents( + latents, + upsampler, + vae_decoder.per_channel_statistics.mean, + vae_decoder.per_channel_statistics.std, + ) + mx.eval(latents) + + del upsampler + mx.clear_cache() + console.print("[green]✓[/] Latents upsampled") + + # Stage 2 + console.print( + f"\n[bold yellow]⚡ Stage 2:[/] Refining at {stage2_w*32}x{stage2_h*32} (3 steps)" + ) + positions = create_position_grid(1, latent_frames, stage2_h, stage2_w) + mx.eval(positions) + + state2 = None + if is_i2v and stage2_image_latent is not None: + state2 = LatentState( + latent=latents, + clean_latent=mx.zeros_like(latents), + denoise_mask=mx.ones((1, 1, latent_frames, 1, 1), dtype=model_dtype), + ) + conditioning = VideoConditionByLatentIndex( + latent=stage2_image_latent, + frame_idx=image_frame_idx, + strength=image_strength, + ) + state2 = apply_conditioning(state2, [conditioning]) + + noise = mx.random.normal(latents.shape).astype(model_dtype) + noise_scale = mx.array(STAGE_2_SIGMAS[0], dtype=model_dtype) + scaled_mask = state2.denoise_mask * noise_scale + state2 = LatentState( + latent=noise * scaled_mask + + state2.latent * (mx.array(1.0, dtype=model_dtype) - scaled_mask), + clean_latent=state2.clean_latent, + denoise_mask=state2.denoise_mask, + ) + latents = state2.latent + mx.eval(latents) + else: + noise_scale = mx.array(STAGE_2_SIGMAS[0], dtype=model_dtype) + one_minus_scale = mx.array(1.0 - STAGE_2_SIGMAS[0], dtype=model_dtype) + noise = mx.random.normal(latents.shape).astype(model_dtype) + latents = noise * noise_scale + latents * one_minus_scale + mx.eval(latents) + + # Re-noise audio at sigma=0.909375 for joint refinement (matches PyTorch) + if audio_latents is not None and not is_a2v: + audio_noise = mx.random.normal(audio_latents.shape, dtype=model_dtype) + audio_noise_scale = mx.array(STAGE_2_SIGMAS[0], dtype=model_dtype) + audio_latents = audio_noise * audio_noise_scale + audio_latents * ( + mx.array(1.0, dtype=model_dtype) - audio_noise_scale + ) + mx.eval(audio_latents) + + # Joint video + audio refinement (no CFG, positive embeddings only) + latents, audio_latents = denoise_distilled( + latents, + positions, + text_embeddings, + transformer, + STAGE_2_SIGMAS, + verbose=verbose, + state=state2, + audio_latents=audio_latents, + audio_positions=audio_positions, + audio_embeddings=audio_embeddings, + audio_frozen=is_a2v, + ) + + elif pipeline == PipelineType.DEV: + # ====================================================================== + # DEV PIPELINE: Single-stage with CFG + # ====================================================================== + + # Load VAE encoder for I2V + image_latent = None + if is_i2v: + with console.status( + "[blue]🖼️ Loading VAE encoder and encoding image...[/]", spinner="dots" + ): + vae_encoder = VideoEncoder.from_pretrained( + model_path / "vae" / "encoder" + ) + + input_image = load_image( + image, height=height, width=width, dtype=model_dtype + ) + image_tensor = prepare_image_for_encoding( + input_image, height, width, dtype=model_dtype + ) + image_latent = vae_encoder(image_tensor) + mx.eval(image_latent) + + del vae_encoder + mx.clear_cache() + console.print("[green]✓[/] VAE encoder loaded and image encoded") + + # Generate sigma schedule with token-count-dependent shifting + sigmas = ltx2_scheduler(steps=num_inference_steps) + mx.eval(sigmas) + console.print( + f"[dim]Sigma schedule: {sigmas[0].item():.4f} → {sigmas[-2].item():.4f} → {sigmas[-1].item():.4f}[/]" + ) + + console.print( + f"\n[bold yellow]⚡ Generating:[/] {width}x{height} ({num_inference_steps} steps, CFG={cfg_scale}, rescale={cfg_rescale})" + ) + mx.random.seed(seed) + + video_positions = create_position_grid(1, latent_frames, latent_h, latent_w) + mx.eval(video_positions) + + # Always init audio latents/positions - PyTorch unconditionally generates audio + audio_positions = create_audio_position_grid(1, audio_frames) + audio_latents = ( + a2v_audio_latents + if is_a2v + else mx.random.normal( + (1, AUDIO_LATENT_CHANNELS, audio_frames, AUDIO_MEL_BINS), + dtype=model_dtype, + ) + ) + mx.eval(audio_positions, audio_latents) + + # Initialize latents with optional I2V conditioning + video_state = None + video_latent_shape = (1, 128, latent_frames, latent_h, latent_w) + if is_i2v and image_latent is not None: + video_state = LatentState( + latent=mx.zeros(video_latent_shape, dtype=model_dtype), + clean_latent=mx.zeros(video_latent_shape, dtype=model_dtype), + denoise_mask=mx.ones((1, 1, latent_frames, 1, 1), dtype=model_dtype), + ) + conditioning = VideoConditionByLatentIndex( + latent=image_latent, frame_idx=image_frame_idx, strength=image_strength + ) + video_state = apply_conditioning(video_state, [conditioning]) + + noise = mx.random.normal(video_latent_shape, dtype=model_dtype) + noise_scale = sigmas[0] + scaled_mask = video_state.denoise_mask * noise_scale + video_state = LatentState( + latent=noise * scaled_mask + + video_state.latent * (mx.array(1.0, dtype=model_dtype) - scaled_mask), + clean_latent=video_state.clean_latent, + denoise_mask=video_state.denoise_mask, + ) + latents = video_state.latent + mx.eval(latents) + else: + latents = mx.random.normal(video_latent_shape, dtype=model_dtype) + mx.eval(latents) + + # Always use A/V denoising - PyTorch always processes audio+video jointly + latents, audio_latents = denoise_dev_av( + latents, + audio_latents, + video_positions, + audio_positions, + video_embeddings_pos, + video_embeddings_neg, + audio_embeddings_pos, + audio_embeddings_neg, + transformer, + sigmas, + cfg_scale=cfg_scale, + audio_cfg_scale=audio_cfg_scale, + cfg_rescale=cfg_rescale, + verbose=verbose, + video_state=video_state, + use_apg=use_apg, + apg_eta=apg_eta, + apg_norm_threshold=apg_norm_threshold, + stg_scale=stg_scale, + stg_video_blocks=stg_blocks, + stg_audio_blocks=stg_blocks, + modality_scale=modality_scale, + audio_frozen=is_a2v, + ) + + # Load VAE decoder (for dev pipeline, loaded here instead of during upsampling) + vae_decoder = VideoDecoder.from_pretrained(str(model_path / "vae" / "decoder")) + + elif pipeline == PipelineType.DEV_TWO_STAGE: + # ====================================================================== + # DEV TWO-STAGE PIPELINE: + # Stage 1: Dev denoising at half resolution with CFG + # Upsample: 2x spatial via LatentUpsampler + # Stage 2: Distilled denoising at full resolution with LoRA, no CFG + # ====================================================================== + + # Load VAE encoder for I2V + stage1_image_latent = None + stage2_image_latent = None + if is_i2v: + with console.status( + "[blue]🖼️ Loading VAE encoder and encoding image...[/]", spinner="dots" + ): + vae_encoder = VideoEncoder.from_pretrained( + model_path / "vae" / "encoder" + ) + + s1_h, s1_w = stage1_h * 32, stage1_w * 32 + input_image = load_image( + image, height=s1_h, width=s1_w, dtype=model_dtype + ) + stage1_image_tensor = prepare_image_for_encoding( + input_image, s1_h, s1_w, dtype=model_dtype + ) + stage1_image_latent = vae_encoder(stage1_image_tensor) + mx.eval(stage1_image_latent) + + s2_h, s2_w = stage2_h * 32, stage2_w * 32 + input_image = load_image( + image, height=s2_h, width=s2_w, dtype=model_dtype + ) + stage2_image_tensor = prepare_image_for_encoding( + input_image, s2_h, s2_w, dtype=model_dtype + ) + stage2_image_latent = vae_encoder(stage2_image_tensor) + mx.eval(stage2_image_latent) + + del vae_encoder + mx.clear_cache() + console.print("[green]✓[/] VAE encoder loaded and image encoded") + + # Stage 1: Dev denoising at reduced resolution with CFG + sigmas = ltx2_scheduler(steps=num_inference_steps) + mx.eval(sigmas) + console.print( + f"[dim]Stage 1 sigma schedule: {sigmas[0].item():.4f} → {sigmas[-2].item():.4f} → {sigmas[-1].item():.4f}[/]" + ) + + console.print( + f"\n[bold yellow]⚡ Stage 1:[/] Dev generating at {stage1_w*32}x{stage1_h*32} ({num_inference_steps} steps, CFG={cfg_scale}, rescale={cfg_rescale})" + ) + mx.random.seed(seed) + + positions = create_position_grid(1, latent_frames, stage1_h, stage1_w) + mx.eval(positions) + + # Always init audio latents/positions - PyTorch unconditionally generates audio + audio_positions = create_audio_position_grid(1, audio_frames) + audio_latents = ( + a2v_audio_latents + if is_a2v + else mx.random.normal( + (1, AUDIO_LATENT_CHANNELS, audio_frames, AUDIO_MEL_BINS), + dtype=model_dtype, + ) + ) + mx.eval(audio_positions, audio_latents) + + # Apply I2V conditioning for stage 1 + state1 = None + stage1_shape = (1, 128, latent_frames, stage1_h, stage1_w) + if is_i2v and stage1_image_latent is not None: + state1 = LatentState( + latent=mx.zeros(stage1_shape, dtype=model_dtype), + clean_latent=mx.zeros(stage1_shape, dtype=model_dtype), + denoise_mask=mx.ones((1, 1, latent_frames, 1, 1), dtype=model_dtype), + ) + conditioning = VideoConditionByLatentIndex( + latent=stage1_image_latent, + frame_idx=image_frame_idx, + strength=image_strength, + ) + state1 = apply_conditioning(state1, [conditioning]) + + noise = mx.random.normal(stage1_shape, dtype=model_dtype) + noise_scale = sigmas[0] + scaled_mask = state1.denoise_mask * noise_scale + state1 = LatentState( + latent=noise * scaled_mask + + state1.latent * (mx.array(1.0, dtype=model_dtype) - scaled_mask), + clean_latent=state1.clean_latent, + denoise_mask=state1.denoise_mask, + ) + latents = state1.latent + mx.eval(latents) + else: + latents = mx.random.normal(stage1_shape, dtype=model_dtype) + mx.eval(latents) + + # Stage 1: Always use joint AV denoising (matches PyTorch) + latents, audio_latents = denoise_dev_av( + latents, + audio_latents, + positions, + audio_positions, + video_embeddings_pos, + video_embeddings_neg, + audio_embeddings_pos, + audio_embeddings_neg, + transformer, + sigmas, + cfg_scale=cfg_scale, + audio_cfg_scale=audio_cfg_scale, + cfg_rescale=cfg_rescale, + verbose=verbose, + video_state=state1, + use_apg=use_apg, + apg_eta=apg_eta, + apg_norm_threshold=apg_norm_threshold, + stg_scale=stg_scale, + stg_video_blocks=stg_blocks, + stg_audio_blocks=stg_blocks, + modality_scale=modality_scale, + audio_frozen=is_a2v, + ) + + mx.eval(audio_latents) + + # Upsample latents + with console.status( + f"[magenta]🔍 Upsampling latents {upscaler_scale}x...[/]", spinner="dots" + ): + if upscaler_path is None or not upscaler_path.exists(): + raise FileNotFoundError(f"No spatial upscaler found in {model_path}") + upsampler, upscaler_scale = load_upsampler(str(upscaler_path)) + mx.eval(upsampler.parameters()) + + vae_decoder = VideoDecoder.from_pretrained( + str(model_path / "vae" / "decoder") + ) + + latents = upsample_latents( + latents, + upsampler, + vae_decoder.per_channel_statistics.mean, + vae_decoder.per_channel_statistics.std, + ) + mx.eval(latents) + + del upsampler + mx.clear_cache() + console.print("[green]✓[/] Latents upsampled") + + # Merge LoRA weights for stage 2 (distilled refinement) + if lora_path is None: + # Auto-detect LoRA file in model directory + lora_files = sorted(model_path.glob("*distilled-lora*.safetensors")) + if lora_files: + lora_path = str(lora_files[0]) + console.print(f"[dim]Auto-detected LoRA: {Path(lora_path).name}[/]") + else: + console.print( + "[yellow]⚠️ No LoRA file found. Stage 2 will use base weights.[/]" + ) + + if lora_path is not None: + with console.status( + "[blue]🔧 Merging distilled LoRA weights...[/]", spinner="dots" + ): + load_and_merge_lora(transformer, lora_path, strength=lora_strength) + + # Stage 2: Distilled refinement at full resolution (no CFG) + # Matches PyTorch: re-noise audio at sigma=0.909375, then jointly refine + # both video and audio through the distilled schedule using the LoRA-merged model. + console.print( + f"\n[bold yellow]⚡ Stage 2:[/] Distilled refining at {width}x{height} (3 steps, no CFG)" + ) + positions = create_position_grid(1, latent_frames, stage2_h, stage2_w) + mx.eval(positions) + + state2 = None + if is_i2v and stage2_image_latent is not None: + state2 = LatentState( + latent=latents, + clean_latent=mx.zeros_like(latents), + denoise_mask=mx.ones((1, 1, latent_frames, 1, 1), dtype=model_dtype), + ) + conditioning = VideoConditionByLatentIndex( + latent=stage2_image_latent, + frame_idx=image_frame_idx, + strength=image_strength, + ) + state2 = apply_conditioning(state2, [conditioning]) + + noise = mx.random.normal(latents.shape).astype(model_dtype) + noise_scale = mx.array(STAGE_2_SIGMAS[0], dtype=model_dtype) + scaled_mask = state2.denoise_mask * noise_scale + state2 = LatentState( + latent=noise * scaled_mask + + state2.latent * (mx.array(1.0, dtype=model_dtype) - scaled_mask), + clean_latent=state2.clean_latent, + denoise_mask=state2.denoise_mask, + ) + latents = state2.latent + mx.eval(latents) + else: + noise_scale = mx.array(STAGE_2_SIGMAS[0], dtype=model_dtype) + one_minus_scale = mx.array(1.0 - STAGE_2_SIGMAS[0], dtype=model_dtype) + noise = mx.random.normal(latents.shape).astype(model_dtype) + latents = noise * noise_scale + latents * one_minus_scale + mx.eval(latents) + + # Re-noise audio at sigma=0.909375 for joint refinement (matches PyTorch) + if audio_latents is not None and not is_a2v: + audio_noise = mx.random.normal(audio_latents.shape, dtype=model_dtype) + audio_noise_scale = mx.array(STAGE_2_SIGMAS[0], dtype=model_dtype) + audio_latents = audio_noise * audio_noise_scale + audio_latents * ( + mx.array(1.0, dtype=model_dtype) - audio_noise_scale + ) + mx.eval(audio_latents) + + # Joint video + audio refinement (no CFG, positive embeddings only) + latents, audio_latents = denoise_distilled( + latents, + positions, + text_embeddings, + transformer, + STAGE_2_SIGMAS, + verbose=verbose, + state=state2, + audio_latents=audio_latents, + audio_positions=audio_positions, + audio_embeddings=audio_embeddings_pos, + audio_frozen=is_a2v, + ) + + elif pipeline == PipelineType.DEV_TWO_STAGE_HQ: + # ====================================================================== + # DEV TWO-STAGE HQ PIPELINE: + # Stage 1: res_2s denoising at half resolution with CFG + LoRA@0.25 + # Upsample: 2x spatial via LatentUpsampler + # Stage 2: res_2s refinement at full resolution with LoRA@0.5, no CFG + # ====================================================================== + + # HQ defaults: STG disabled, lower rescale, fewer steps (PyTorch LTX_2_3_HQ_PARAMS) + hq_lora_strength_s1 = ( + lora_strength_stage_1 if lora_strength_stage_1 is not None else 0.25 + ) + hq_lora_strength_s2 = ( + lora_strength_stage_2 if lora_strength_stage_2 is not None else 0.5 + ) + hq_cfg_rescale = ( + cfg_rescale if cfg_rescale != 0.7 else 0.45 + ) # Override default 0.7 → 0.45 + hq_steps = ( + num_inference_steps if num_inference_steps != 30 else 15 + ) # Override default 30 → 15 + hq_stg_scale = ( + stg_scale if stg_scale != 1.0 else 0.0 + ) # Override default 1.0 → 0.0 + + # Load VAE encoder for I2V + stage1_image_latent = None + stage2_image_latent = None + if is_i2v: + with console.status( + "[blue]Loading VAE encoder and encoding image...[/]", spinner="dots" + ): + vae_encoder = VideoEncoder.from_pretrained( + model_path / "vae" / "encoder" + ) + + s1_h, s1_w = stage1_h * 32, stage1_w * 32 + input_image = load_image( + image, height=s1_h, width=s1_w, dtype=model_dtype + ) + stage1_image_tensor = prepare_image_for_encoding( + input_image, s1_h, s1_w, dtype=model_dtype + ) + stage1_image_latent = vae_encoder(stage1_image_tensor) + mx.eval(stage1_image_latent) + + s2_h, s2_w = stage2_h * 32, stage2_w * 32 + input_image = load_image( + image, height=s2_h, width=s2_w, dtype=model_dtype + ) + stage2_image_tensor = prepare_image_for_encoding( + input_image, s2_h, s2_w, dtype=model_dtype + ) + stage2_image_latent = vae_encoder(stage2_image_tensor) + mx.eval(stage2_image_latent) + + del vae_encoder + mx.clear_cache() + console.print("[green]✓[/] VAE encoder loaded and image encoded") + + # Auto-detect and merge LoRA for stage 1 (strength 0.25) + if lora_path is None: + lora_files = sorted(model_path.glob("*distilled-lora*.safetensors")) + if lora_files: + lora_path = str(lora_files[0]) + console.print(f"[dim]Auto-detected LoRA: {Path(lora_path).name}[/]") + else: + console.print( + "[yellow]Warning: No LoRA file found. HQ pipeline works best with distilled LoRA.[/]" + ) + + if lora_path is not None: + with console.status( + f"[blue]Merging distilled LoRA (stage 1, strength={hq_lora_strength_s1})...[/]", + spinner="dots", + ): + load_and_merge_lora( + transformer, lora_path, strength=hq_lora_strength_s1 + ) + + # Stage 1: res_2s denoising at reduced resolution with CFG + # HQ passes actual token count to scheduler (unlike regular dev-two-stage) + num_tokens = latent_frames * stage1_h * stage1_w + sigmas = ltx2_scheduler(steps=hq_steps, num_tokens=num_tokens) + mx.eval(sigmas) + console.print( + f"[dim]Stage 1 sigma schedule: {sigmas[0].item():.4f} -> {sigmas[-2].item():.4f} -> {sigmas[-1].item():.4f} (tokens={num_tokens})[/]" + ) + + console.print( + f"\n[bold yellow]Stage 1:[/] res_2s at {stage1_w*32}x{stage1_h*32} ({hq_steps} steps, CFG={cfg_scale}, rescale={hq_cfg_rescale})" + ) + mx.random.seed(seed) + + positions = create_position_grid(1, latent_frames, stage1_h, stage1_w) + mx.eval(positions) + + audio_positions = create_audio_position_grid(1, audio_frames) + audio_latents = ( + a2v_audio_latents + if is_a2v + else mx.random.normal( + (1, AUDIO_LATENT_CHANNELS, audio_frames, AUDIO_MEL_BINS), + dtype=model_dtype, + ) + ) + mx.eval(audio_positions, audio_latents) + + # Apply I2V conditioning for stage 1 + state1 = None + stage1_shape = (1, 128, latent_frames, stage1_h, stage1_w) + if is_i2v and stage1_image_latent is not None: + state1 = LatentState( + latent=mx.zeros(stage1_shape, dtype=model_dtype), + clean_latent=mx.zeros(stage1_shape, dtype=model_dtype), + denoise_mask=mx.ones((1, 1, latent_frames, 1, 1), dtype=model_dtype), + ) + conditioning = VideoConditionByLatentIndex( + latent=stage1_image_latent, + frame_idx=image_frame_idx, + strength=image_strength, + ) + state1 = apply_conditioning(state1, [conditioning]) + + noise = mx.random.normal(stage1_shape, dtype=model_dtype) + noise_scale = sigmas[0] + scaled_mask = state1.denoise_mask * noise_scale + state1 = LatentState( + latent=noise * scaled_mask + + state1.latent * (mx.array(1.0, dtype=model_dtype) - scaled_mask), + clean_latent=state1.clean_latent, + denoise_mask=state1.denoise_mask, + ) + latents = state1.latent + mx.eval(latents) + else: + latents = mx.random.normal(stage1_shape, dtype=model_dtype) + mx.eval(latents) + + # Stage 1: res_2s with CFG (STG disabled for HQ by default) + latents, audio_latents = denoise_res2s_av( + latents, + audio_latents, + positions, + audio_positions, + video_embeddings_pos, + video_embeddings_neg, + audio_embeddings_pos, + audio_embeddings_neg, + transformer, + sigmas, + cfg_scale=cfg_scale, + audio_cfg_scale=audio_cfg_scale, + cfg_rescale=hq_cfg_rescale, + audio_cfg_rescale=1.0, + verbose=verbose, + video_state=state1, + stg_scale=hq_stg_scale, + stg_video_blocks=stg_blocks, + stg_audio_blocks=stg_blocks, + modality_scale=modality_scale, + noise_seed=seed, + audio_frozen=is_a2v, + ) + + mx.eval(audio_latents) + + # Upsample latents + with console.status( + f"[magenta]Upsampling latents {upscaler_scale}x...[/]", spinner="dots" + ): + if upscaler_path is None or not upscaler_path.exists(): + raise FileNotFoundError(f"No spatial upscaler found in {model_path}") + upsampler, upscaler_scale = load_upsampler(str(upscaler_path)) + mx.eval(upsampler.parameters()) + + vae_decoder = VideoDecoder.from_pretrained( + str(model_path / "vae" / "decoder") + ) + + latents = upsample_latents( + latents, + upsampler, + vae_decoder.per_channel_statistics.mean, + vae_decoder.per_channel_statistics.std, + ) + mx.eval(latents) + + del upsampler + mx.clear_cache() + console.print("[green]✓[/] Latents upsampled") + + # Merge additional LoRA for stage 2 (additive: 0.25 + 0.25 = 0.5 total) + if lora_path is not None: + additional_strength = hq_lora_strength_s2 - hq_lora_strength_s1 + if additional_strength > 0: + with console.status( + f"[blue]Adjusting LoRA (stage 2, total={hq_lora_strength_s2})...[/]", + spinner="dots", + ): + load_and_merge_lora( + transformer, lora_path, strength=additional_strength + ) + + # Stage 2: res_2s refinement at full resolution (no CFG) + console.print( + f"\n[bold yellow]Stage 2:[/] res_2s refining at {stage2_w*32}x{stage2_h*32} (3 steps, no CFG)" + ) + positions = create_position_grid(1, latent_frames, stage2_h, stage2_w) + mx.eval(positions) + + state2 = None + if is_i2v and stage2_image_latent is not None: + state2 = LatentState( + latent=latents, + clean_latent=mx.zeros_like(latents), + denoise_mask=mx.ones((1, 1, latent_frames, 1, 1), dtype=model_dtype), + ) + conditioning = VideoConditionByLatentIndex( + latent=stage2_image_latent, + frame_idx=image_frame_idx, + strength=image_strength, + ) + state2 = apply_conditioning(state2, [conditioning]) + + noise = mx.random.normal(latents.shape).astype(model_dtype) + noise_scale = mx.array(STAGE_2_SIGMAS[0], dtype=model_dtype) + scaled_mask = state2.denoise_mask * noise_scale + state2 = LatentState( + latent=noise * scaled_mask + + state2.latent * (mx.array(1.0, dtype=model_dtype) - scaled_mask), + clean_latent=state2.clean_latent, + denoise_mask=state2.denoise_mask, + ) + latents = state2.latent + mx.eval(latents) + else: + noise_scale = mx.array(STAGE_2_SIGMAS[0], dtype=model_dtype) + one_minus_scale = mx.array(1.0 - STAGE_2_SIGMAS[0], dtype=model_dtype) + noise = mx.random.normal(latents.shape).astype(model_dtype) + latents = noise * noise_scale + latents * one_minus_scale + mx.eval(latents) + + # Re-noise audio at sigma=0.909375 for joint refinement + if audio_latents is not None and not is_a2v: + audio_noise = mx.random.normal(audio_latents.shape, dtype=model_dtype) + audio_noise_scale = mx.array(STAGE_2_SIGMAS[0], dtype=model_dtype) + audio_latents = audio_noise * audio_noise_scale + audio_latents * ( + mx.array(1.0, dtype=model_dtype) - audio_noise_scale + ) + mx.eval(audio_latents) + + # Stage 2: res_2s with no CFG (positive embeddings only) + stage2_sigmas = mx.array(STAGE_2_SIGMAS, dtype=mx.float32) + latents, audio_latents = denoise_res2s_av( + latents, + audio_latents, + positions, + audio_positions, + video_embeddings_pos, + video_embeddings_pos, # both pos (no neg for stage 2) + audio_embeddings_pos, + audio_embeddings_pos, + transformer, + stage2_sigmas, + cfg_scale=1.0, # no CFG + audio_cfg_scale=1.0, + cfg_rescale=0.0, + verbose=verbose, + video_state=state2, + noise_seed=seed + 1, + audio_frozen=is_a2v, + ) + + del transformer + mx.clear_cache() + + # ========================================================================== + # Decode and save outputs (common to both pipelines) + # ========================================================================== + + console.print("\n[blue]🎞️ Decoding video...[/]") + + # Select tiling configuration + if tiling == "none": + tiling_config = None + elif tiling == "auto": + tiling_config = TilingConfig.auto(height, width, num_frames) + elif tiling == "default": + tiling_config = TilingConfig.default() + elif tiling == "aggressive": + tiling_config = TilingConfig.aggressive() + elif tiling == "conservative": + tiling_config = TilingConfig.conservative() + elif tiling == "spatial": + tiling_config = TilingConfig.spatial_only() + elif tiling == "temporal": + tiling_config = TilingConfig.temporal_only() + else: + console.print(f"[yellow] Unknown tiling mode '{tiling}', using auto[/]") + tiling_config = TilingConfig.auto(height, width, num_frames) + + output_path = Path(output_path) + output_path.parent.mkdir(parents=True, exist_ok=True) + + # Stream mode + video_writer = None + stream_progress = None + + if stream and tiling_config is not None: + import cv2 + + fourcc = cv2.VideoWriter_fourcc(*"avc1") + video_writer = cv2.VideoWriter(str(output_path), fourcc, fps, (width, height)) + stream_progress = Progress( + SpinnerColumn(), + TextColumn("[progress.description]{task.description}"), + BarColumn(), + TaskProgressColumn(), + console=console, + ) + stream_progress.start() + stream_task = stream_progress.add_task( + "[cyan]Streaming frames[/]", total=num_frames + ) + + def on_frames_ready(frames: mx.array, _start_idx: int): + frames = mx.squeeze(frames, axis=0) + frames = mx.transpose(frames, (1, 2, 3, 0)) + frames = mx.clip((frames + 1.0) / 2.0, 0.0, 1.0) + frames = (frames * 255).astype(mx.uint8) + frames_np = np.array(frames) + + for frame in frames_np: + video_writer.write(cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)) + stream_progress.advance(stream_task) + + else: + on_frames_ready = None + + if tiling_config is not None: + spatial_info = ( + f"{tiling_config.spatial_config.tile_size_in_pixels}px" + if tiling_config.spatial_config + else "none" + ) + temporal_info = ( + f"{tiling_config.temporal_config.tile_size_in_frames}f" + if tiling_config.temporal_config + else "none" + ) + console.print( + f"[dim] Tiling ({tiling}): spatial={spatial_info}, temporal={temporal_info}[/]" + ) + video = vae_decoder.decode_tiled( + latents, + tiling_config=tiling_config, + tiling_mode=tiling, + debug=verbose, + on_frames_ready=on_frames_ready, + ) + else: + console.print("[dim] Tiling: disabled[/]") + video = vae_decoder(latents) + mx.eval(video) + mx.clear_cache() + + # Close stream writer + if video_writer is not None: + video_writer.release() + if stream_progress is not None: + stream_progress.stop() + console.print(f"[green]✅ Streamed video to[/] {output_path}") + video = mx.squeeze(video, axis=0) + video = mx.transpose(video, (1, 2, 3, 0)) + video = mx.clip((video + 1.0) / 2.0, 0.0, 1.0) + video = (video * 255).astype(mx.uint8) + video_np = np.array(video) + else: + video = mx.squeeze(video, axis=0) + video = mx.transpose(video, (1, 2, 3, 0)) + video = mx.clip((video + 1.0) / 2.0, 0.0, 1.0) + video = (video * 255).astype(mx.uint8) + video_np = np.array(video) + + if audio: + temp_video_path = output_path.with_suffix(".temp.mp4") + save_path = temp_video_path + else: + save_path = output_path + + try: + import cv2 + + h, w = video_np.shape[1], video_np.shape[2] + fourcc = cv2.VideoWriter_fourcc(*"avc1") + out = cv2.VideoWriter(str(save_path), fourcc, fps, (w, h)) + for frame in video_np: + out.write(cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)) + out.release() + if not audio: + console.print(f"[green]✅ Saved video to[/] {output_path}") + except Exception as e: + console.print(f"[red]❌ Could not save video: {e}[/]") + + # Decode and save audio if enabled + audio_np = None + vocoder_sample_rate = AUDIO_SAMPLE_RATE + if audio and audio_latents is not None: + if is_a2v and a2v_waveform is not None: + # A2V: use original input audio waveform (no VAE decoding needed) + audio_np = a2v_waveform + if audio_np.ndim == 1: + audio_np = audio_np[np.newaxis, :] + vocoder_sample_rate = a2v_sr or AUDIO_LATENT_SAMPLE_RATE + console.print("[green]✓[/] Using original input audio (A2V)") + else: + with console.status("[blue]Decoding audio...[/]", spinner="dots"): + audio_decoder = load_audio_decoder(model_path, pipeline) + vocoder = load_vocoder_model(model_path, pipeline) + mx.eval(audio_decoder.parameters(), vocoder.parameters()) + + mel_spectrogram = audio_decoder(audio_latents) + mx.eval(mel_spectrogram) + console.print( + f"[dim] Mel spectrogram: shape={mel_spectrogram.shape}, std={mel_spectrogram.std().item():.4f}, mean={mel_spectrogram.mean().item():.4f}[/]" + ) + + audio_waveform = vocoder(mel_spectrogram) + mx.eval(audio_waveform) + + audio_np = np.array(audio_waveform.astype(mx.float32)) + if audio_np.ndim == 3: + audio_np = audio_np[0] + + # Get sample rate from vocoder (dynamic: 24kHz for LTX-2, 48kHz for LTX-2.3 BWE) + vocoder_sample_rate = getattr( + vocoder, "output_sampling_rate", AUDIO_SAMPLE_RATE + ) + + del audio_decoder, vocoder + mx.clear_cache() + console.print("[green]✓[/] Audio decoded") + + audio_path = ( + Path(output_audio_path) + if output_audio_path + else output_path.with_suffix(".wav") + ) + save_audio(audio_np, audio_path, vocoder_sample_rate) + console.print(f"[green]✅ Saved audio to[/] {audio_path}") + + with console.status("[blue]🎬 Combining video and audio...[/]", spinner="dots"): + temp_video_path = output_path.with_suffix(".temp.mp4") + success = mux_video_audio(temp_video_path, audio_path, output_path) + if success: + console.print(f"[green]✅ Saved video with audio to[/] {output_path}") + temp_video_path.unlink() + else: + temp_video_path.rename(output_path) + console.print(f"[yellow]⚠️ Saved video without audio to[/] {output_path}") + + del vae_decoder + mx.clear_cache() + + if save_frames: + frames_dir = output_path.parent / f"{output_path.stem}_frames" + frames_dir.mkdir(exist_ok=True) + for i, frame in enumerate(video_np): + Image.fromarray(frame).save(frames_dir / f"frame_{i:04d}.png") + console.print(f"[green]✅ Saved {len(video_np)} frames to {frames_dir}[/]") + + elapsed = time.time() - start_time + minutes, seconds = divmod(elapsed, 60) + time_str = f"{int(minutes)}m {seconds:.1f}s" if minutes >= 1 else f"{seconds:.1f}s" + console.print( + Panel( + f"[bold green]🎉 Done![/] Generated in {time_str} ({elapsed/num_frames:.2f}s/frame)\n" + f"[bold green]✨ Peak memory:[/] {mx.get_peak_memory() / (1024 ** 3):.2f}GB", + expand=False, + ) + ) + + if audio: + return video_np, audio_np + return video_np + + +def main(): + parser = argparse.ArgumentParser( + description="Generate videos with MLX LTX-2 (Distilled or Dev pipeline)", + formatter_class=argparse.RawDescriptionHelpFormatter, + epilog=""" +Examples: + # Distilled pipeline (two-stage, fast, no CFG) + python -m mlx_video.generate --prompt "A cat walking on grass" + python -m mlx_video.generate --prompt "Ocean waves" --pipeline distilled + + # Dev pipeline (single-stage, CFG, higher quality) + python -m mlx_video.generate --prompt "A cat walking" --pipeline dev --cfg-scale 3.0 + python -m mlx_video.generate --prompt "Ocean waves" --pipeline dev --steps 40 + + # Dev two-stage pipeline (dev + LoRA refinement) + python -m mlx_video.generate --prompt "A cat walking" --pipeline dev-two-stage --cfg-scale 3.0 + + # Image-to-Video (works with both pipelines) + python -m mlx_video.generate --prompt "A person dancing" --image photo.jpg + python -m mlx_video.generate --prompt "Waves crashing" --image beach.png --pipeline dev + + # With Audio (works with both pipelines) + python -m mlx_video.generate --prompt "Ocean waves crashing" --audio + python -m mlx_video.generate --prompt "A jazz band playing" --audio --pipeline dev + """, + ) + + parser.add_argument( + "--prompt", + "-p", + type=str, + required=True, + help="Text description of the video to generate", + ) + parser.add_argument( + "--pipeline", + type=str, + default="distilled", + choices=["distilled", "dev", "dev-two-stage", "dev-two-stage-hq"], + help="Pipeline type: distilled (fast), dev (CFG), dev-two-stage (dev + LoRA), dev-two-stage-hq (res_2s + LoRA both stages)", + ) + parser.add_argument( + "--negative-prompt", + type=str, + default=DEFAULT_NEGATIVE_PROMPT, + help="Negative prompt for CFG (dev pipeline only)", + ) + parser.add_argument( + "--height", "-H", type=int, default=512, help="Output video height" + ) + parser.add_argument( + "--width", "-W", type=int, default=512, help="Output video width" + ) + parser.add_argument( + "--num-frames", "-n", type=int, default=33, help="Number of frames" + ) + parser.add_argument( + "--steps", + type=int, + default=30, + help="Number of inference steps (dev pipeline only, default 30)", + ) + parser.add_argument( + "--cfg-scale", + type=float, + default=3.0, + help="CFG guidance scale for video (dev pipeline only, default 3.0)", + ) + parser.add_argument( + "--audio-cfg-scale", + type=float, + default=7.0, + help="CFG guidance scale for audio (default 7.0, PyTorch default)", + ) + parser.add_argument( + "--cfg-rescale", + type=float, + default=0.7, + help="CFG rescale factor (0.0-1.0). Normalizes guided prediction variance to reduce artifacts (dev pipeline only, default 0.7)", + ) + parser.add_argument("--seed", "-s", type=int, default=42, help="Random seed") + parser.add_argument("--fps", type=int, default=24, help="Frames per second") + parser.add_argument( + "--output-path", "-o", type=str, default="output.mp4", help="Output video path" + ) + parser.add_argument( + "--save-frames", action="store_true", help="Save individual frames as images" + ) + parser.add_argument( + "--model-repo", type=str, default="Lightricks/LTX-2", help="Model repository" + ) + parser.add_argument( + "--text-encoder-repo", type=str, default=None, help="Text encoder repository" + ) + parser.add_argument("--verbose", action="store_true", help="Verbose output") + parser.add_argument( + "--enhance-prompt", action="store_true", help="Enhance the prompt using Gemma" + ) + parser.add_argument( + "--max-tokens", type=int, default=512, help="Max tokens for prompt enhancement" + ) + parser.add_argument( + "--temperature", + type=float, + default=0.7, + help="Temperature for prompt enhancement", + ) + parser.add_argument( + "--image", + "-i", + type=str, + default=None, + help="Path to conditioning image for I2V", + ) + parser.add_argument( + "--image-strength", + type=float, + default=1.0, + help="Conditioning strength for I2V", + ) + parser.add_argument( + "--image-frame-idx", + type=int, + default=0, + help="Frame index to condition for I2V", + ) + parser.add_argument( + "--tiling", + type=str, + default="auto", + choices=[ + "auto", + "none", + "default", + "aggressive", + "conservative", + "spatial", + "temporal", + ], + help="Tiling mode for VAE decoding", + ) + parser.add_argument( + "--stream", + action="store_true", + help="Stream frames to output as they're decoded", + ) + parser.add_argument( + "--audio", + "-a", + action="store_true", + help="Enable synchronized audio generation", + ) + parser.add_argument( + "--audio-file", + type=str, + default=None, + help="Path to audio file for A2V (audio-to-video) conditioning", + ) + parser.add_argument( + "--audio-start-time", + type=float, + default=0.0, + help="Start time in seconds for audio file (default: 0.0)", + ) + parser.add_argument( + "--output-audio", type=str, default=None, help="Output audio path" + ) + parser.add_argument( + "--apg", + action="store_true", + help="Use Adaptive Projected Guidance instead of CFG (more stable for I2V)", + ) + parser.add_argument( + "--apg-eta", + type=float, + default=1.0, + help="APG parallel component weight (1.0 = keep full parallel)", + ) + parser.add_argument( + "--apg-norm-threshold", + type=float, + default=0.0, + help="APG guidance norm clamp (0 = no clamping)", + ) + parser.add_argument( + "--stg-scale", + type=float, + default=1.0, + help="STG (Spatiotemporal Guidance) scale (default 1.0, 0.0 = disabled)", + ) + parser.add_argument( + "--stg-blocks", + type=int, + nargs="+", + default=None, + help="Transformer block indices for STG perturbation (default: [29] for LTX-2, [28] for LTX-2.3)", + ) + parser.add_argument( + "--modality-scale", + type=float, + default=3.0, + help="Cross-modal guidance scale (default 3.0, 1.0 = disabled)", + ) + parser.add_argument( + "--lora-path", + type=str, + default=None, + help="Path to LoRA safetensors file (dev-two-stage pipeline)", + ) + parser.add_argument( + "--lora-strength", + type=float, + default=1.0, + help="LoRA merge strength (dev-two-stage pipeline, default 1.0)", + ) + parser.add_argument( + "--lora-strength-stage-1", + type=float, + default=0.25, + help="LoRA strength for HQ stage 1 (default 0.25)", + ) + parser.add_argument( + "--lora-strength-stage-2", + type=float, + default=0.5, + help="LoRA strength for HQ stage 2 (default 0.5)", + ) + parser.add_argument( + "--spatial-upscaler", + type=str, + default=None, + help="Spatial upscaler filename (e.g. ltx-2.3-spatial-upscaler-x1.5-1.0.safetensors). " + "Auto-detects x2 by default. Use this to select x1.5 or a specific version.", + ) + args = parser.parse_args() + + pipeline_map = { + "distilled": PipelineType.DISTILLED, + "dev": PipelineType.DEV, + "dev-two-stage": PipelineType.DEV_TWO_STAGE, + "dev-two-stage-hq": PipelineType.DEV_TWO_STAGE_HQ, + } + pipeline = pipeline_map[args.pipeline] + + generate_video( + model_repo=args.model_repo, + text_encoder_repo=args.text_encoder_repo, + prompt=args.prompt, + pipeline=pipeline, + negative_prompt=args.negative_prompt, + height=args.height, + width=args.width, + num_frames=args.num_frames, + num_inference_steps=args.steps, + cfg_scale=args.cfg_scale, + audio_cfg_scale=args.audio_cfg_scale, + cfg_rescale=args.cfg_rescale, + seed=args.seed, + fps=args.fps, + output_path=args.output_path, + save_frames=args.save_frames, + verbose=args.verbose, + enhance_prompt=args.enhance_prompt, + max_tokens=args.max_tokens, + temperature=args.temperature, + image=args.image, + image_strength=args.image_strength, + image_frame_idx=args.image_frame_idx, + tiling=args.tiling, + stream=args.stream, + audio=args.audio, + output_audio_path=args.output_audio, + use_apg=args.apg, + apg_eta=args.apg_eta, + apg_norm_threshold=args.apg_norm_threshold, + stg_scale=args.stg_scale, + stg_blocks=args.stg_blocks, + modality_scale=args.modality_scale, + lora_path=args.lora_path, + lora_strength=args.lora_strength, + lora_strength_stage_1=args.lora_strength_stage_1, + lora_strength_stage_2=args.lora_strength_stage_2, + audio_file=args.audio_file, + audio_start_time=args.audio_start_time, + spatial_upscaler=args.spatial_upscaler, + ) + + +if __name__ == "__main__": + main() diff --git a/mlx_video/models/ltx/ltx.py b/mlx_video/models/ltx_2/ltx_2.py similarity index 59% rename from mlx_video/models/ltx/ltx.py rename to mlx_video/models/ltx_2/ltx_2.py index a3eef42..ec21a6e 100644 --- a/mlx_video/models/ltx/ltx.py +++ b/mlx_video/models/ltx_2/ltx_2.py @@ -1,18 +1,17 @@ +from pathlib import Path from typing import List, Optional, Tuple import mlx.core as mx import mlx.nn as nn -from mlx_video.models.ltx.config import ( +from mlx_video.models.ltx_2.adaln import AdaLayerNormSingle +from mlx_video.models.ltx_2.config import ( LTXModelConfig, - LTXModelType, LTXRopeType, - TransformerConfig, ) -from mlx_video.models.ltx.adaln import AdaLayerNormSingle -from mlx_video.models.ltx.rope import precompute_freqs_cis -from mlx_video.models.ltx.text_projection import PixArtAlphaTextProjection -from mlx_video.models.ltx.transformer import ( +from mlx_video.models.ltx_2.rope import precompute_freqs_cis +from mlx_video.models.ltx_2.text_projection import PixArtAlphaTextProjection +from mlx_video.models.ltx_2.transformer import ( BasicAVTransformerBlock, Modality, TransformerArgs, @@ -26,7 +25,7 @@ class TransformerArgsPreprocessor: self, patchify_proj: nn.Linear, adaln: AdaLayerNormSingle, - caption_projection: PixArtAlphaTextProjection, + caption_projection: Optional[PixArtAlphaTextProjection], inner_dim: int, max_pos: List[int], num_attention_heads: int, @@ -35,10 +34,12 @@ class TransformerArgsPreprocessor: positional_embedding_theta: float, rope_type: LTXRopeType, double_precision_rope: bool = False, + prompt_adaln: Optional[AdaLayerNormSingle] = None, ): self.patchify_proj = patchify_proj self.adaln = adaln self.caption_projection = caption_projection + self.prompt_adaln = prompt_adaln self.inner_dim = inner_dim self.max_pos = max_pos self.num_attention_heads = num_attention_heads @@ -56,14 +57,39 @@ class TransformerArgsPreprocessor: ) -> Tuple[mx.array, mx.array]: timestep = timestep * self.timestep_scale_multiplier - timestep_emb, embedded_timestep = self.adaln(timestep.reshape(-1), hidden_dtype=hidden_dtype) + timestep_emb, embedded_timestep = self.adaln( + timestep.reshape(-1), hidden_dtype=hidden_dtype + ) # Reshape to (batch, tokens, dim) - timestep_emb = mx.reshape(timestep_emb, (batch_size, -1, timestep_emb.shape[-1])) - embedded_timestep = mx.reshape(embedded_timestep, (batch_size, -1, embedded_timestep.shape[-1])) + timestep_emb = mx.reshape( + timestep_emb, (batch_size, -1, timestep_emb.shape[-1]) + ) + embedded_timestep = mx.reshape( + embedded_timestep, (batch_size, -1, embedded_timestep.shape[-1]) + ) return timestep_emb, embedded_timestep + def _prepare_timestep_with_adaln( + self, + adaln: AdaLayerNormSingle, + timestep: mx.array, + batch_size: int, + hidden_dtype: mx.Dtype = None, + ) -> Tuple[mx.array, mx.array]: + timestep = timestep * self.timestep_scale_multiplier + timestep_emb, embedded_timestep = adaln( + timestep.reshape(-1), hidden_dtype=hidden_dtype + ) + timestep_emb = mx.reshape( + timestep_emb, (batch_size, -1, timestep_emb.shape[-1]) + ) + embedded_timestep = mx.reshape( + embedded_timestep, (batch_size, -1, embedded_timestep.shape[-1]) + ) + return timestep_emb, embedded_timestep + def _prepare_context( self, context: mx.array, @@ -72,9 +98,8 @@ class TransformerArgsPreprocessor: ) -> Tuple[mx.array, Optional[mx.array]]: batch_size = x.shape[0] - # Context is already processed through embeddings connector in text encoder - # Here we just apply the caption projection - context = self.caption_projection(context) + if self.caption_projection is not None: + context = self.caption_projection(context) context = mx.reshape(context, (batch_size, -1, x.shape[-1])) return context, attention_mask @@ -93,7 +118,9 @@ class TransformerArgsPreprocessor: # Convert boolean/int mask to float mask # 0 -> -inf (masked), 1 -> 0 (not masked) mask = (attention_mask.astype(x_dtype) - 1) * 1e9 - mask = mx.reshape(mask, (attention_mask.shape[0], 1, -1, attention_mask.shape[-1])) + mask = mx.reshape( + mask, (attention_mask.shape[0], 1, -1, attention_mask.shape[-1]) + ) return mask def _prepare_positional_embeddings( @@ -118,16 +145,40 @@ class TransformerArgsPreprocessor: def prepare(self, modality: Modality) -> TransformerArgs: x = self.patchify_proj(modality.latent) - timestep, embedded_timestep = self._prepare_timestep(modality.timesteps, x.shape[0], hidden_dtype=x.dtype) - context, attention_mask = self._prepare_context(modality.context, x, modality.context_mask) - attention_mask = self._prepare_attention_mask(attention_mask, modality.latent.dtype) - pe = self._prepare_positional_embeddings( - positions=modality.positions, - inner_dim=self.inner_dim, - max_pos=self.max_pos, - use_middle_indices_grid=self.use_middle_indices_grid, - num_attention_heads=self.num_attention_heads, + timestep, embedded_timestep = self._prepare_timestep( + modality.timesteps, x.shape[0], hidden_dtype=x.dtype ) + context, attention_mask = self._prepare_context( + modality.context, x, modality.context_mask + ) + attention_mask = self._prepare_attention_mask( + attention_mask, modality.latent.dtype + ) + + # Use precomputed positional embeddings if provided (avoids expensive RoPE recomputation) + if modality.positional_embeddings is not None: + pe = modality.positional_embeddings + else: + pe = self._prepare_positional_embeddings( + positions=modality.positions, + inner_dim=self.inner_dim, + max_pos=self.max_pos, + use_middle_indices_grid=self.use_middle_indices_grid, + num_attention_heads=self.num_attention_heads, + ) + + # Prompt-conditioned timestep (LTX-2.3) - uses raw sigma, not per-token timesteps + prompt_timestep = None + prompt_embedded_timestep = None + if self.prompt_adaln is not None and modality.sigma is not None: + prompt_timestep, prompt_embedded_timestep = ( + self._prepare_timestep_with_adaln( + self.prompt_adaln, + modality.sigma, + x.shape[0], + hidden_dtype=x.dtype, + ) + ) return TransformerArgs( x=x, @@ -140,6 +191,8 @@ class TransformerArgsPreprocessor: cross_scale_shift_timestep=None, cross_gate_timestep=None, enabled=modality.enabled, + prompt_timesteps=prompt_timestep, + prompt_embedded_timestep=prompt_embedded_timestep, ) @@ -149,7 +202,7 @@ class MultiModalTransformerArgsPreprocessor: self, patchify_proj: nn.Linear, adaln: AdaLayerNormSingle, - caption_projection: PixArtAlphaTextProjection, + caption_projection: Optional[PixArtAlphaTextProjection], cross_scale_shift_adaln: AdaLayerNormSingle, cross_gate_adaln: AdaLayerNormSingle, inner_dim: int, @@ -163,6 +216,7 @@ class MultiModalTransformerArgsPreprocessor: rope_type: LTXRopeType, av_ca_timestep_scale_multiplier: int, double_precision_rope: bool = False, + prompt_adaln: Optional[AdaLayerNormSingle] = None, ): self.simple_preprocessor = TransformerArgsPreprocessor( patchify_proj=patchify_proj, @@ -176,6 +230,7 @@ class MultiModalTransformerArgsPreprocessor: positional_embedding_theta=positional_embedding_theta, rope_type=rope_type, double_precision_rope=double_precision_rope, + prompt_adaln=prompt_adaln, ) self.cross_scale_shift_adaln = cross_scale_shift_adaln self.cross_gate_adaln = cross_gate_adaln @@ -198,11 +253,13 @@ class MultiModalTransformerArgsPreprocessor: ) # Prepare cross-attention timestep embeddings - cross_scale_shift_timestep, cross_gate_timestep = self._prepare_cross_attention_timestep( - timestep=modality.timesteps, - timestep_scale_multiplier=self.simple_preprocessor.timestep_scale_multiplier, - batch_size=transformer_args.x.shape[0], - hidden_dtype=transformer_args.x.dtype, + cross_scale_shift_timestep, cross_gate_timestep = ( + self._prepare_cross_attention_timestep( + timestep=modality.timesteps, + timestep_scale_multiplier=self.simple_preprocessor.timestep_scale_multiplier, + batch_size=transformer_args.x.shape[0], + hidden_dtype=transformer_args.x.dtype, + ) ) return replace( @@ -223,17 +280,25 @@ class MultiModalTransformerArgsPreprocessor: av_ca_factor = self.av_ca_timestep_scale_multiplier / timestep_scale_multiplier - scale_shift_timestep, _ = self.cross_scale_shift_adaln(timestep.reshape(-1), hidden_dtype=hidden_dtype) - scale_shift_timestep = mx.reshape(scale_shift_timestep, (batch_size, -1, scale_shift_timestep.shape[-1])) + scale_shift_timestep, _ = self.cross_scale_shift_adaln( + timestep.reshape(-1), hidden_dtype=hidden_dtype + ) + scale_shift_timestep = mx.reshape( + scale_shift_timestep, (batch_size, -1, scale_shift_timestep.shape[-1]) + ) - gate_timestep, _ = self.cross_gate_adaln(timestep.reshape(-1) * av_ca_factor, hidden_dtype=hidden_dtype) - gate_timestep = mx.reshape(gate_timestep, (batch_size, -1, gate_timestep.shape[-1])) + gate_timestep, _ = self.cross_gate_adaln( + timestep.reshape(-1) * av_ca_factor, hidden_dtype=hidden_dtype + ) + gate_timestep = mx.reshape( + gate_timestep, (batch_size, -1, gate_timestep.shape[-1]) + ) return scale_shift_timestep, gate_timestep class LTXModel(nn.Module): - + def __init__(self, config: LTXModelConfig): super().__init__() @@ -254,18 +319,25 @@ class LTXModel(nn.Module): self._init_video(config) if config.model_type.is_audio_enabled(): - self.audio_positional_embedding_max_pos = config.audio_positional_embedding_max_pos + self.audio_positional_embedding_max_pos = ( + config.audio_positional_embedding_max_pos + ) self.audio_num_attention_heads = config.audio_num_attention_heads self.audio_inner_dim = config.audio_inner_dim self._init_audio(config) # Initialize cross-modal components - if config.model_type.is_video_enabled() and config.model_type.is_audio_enabled(): + if ( + config.model_type.is_video_enabled() + and config.model_type.is_audio_enabled() + ): cross_pe_max_pos = max( config.positional_embedding_max_pos[0], config.audio_positional_embedding_max_pos[0], ) - self.av_ca_timestep_scale_multiplier = config.av_ca_timestep_scale_multiplier + self.av_ca_timestep_scale_multiplier = ( + config.av_ca_timestep_scale_multiplier + ) self.audio_cross_attention_dim = config.audio_cross_attention_dim self._init_audio_video(config) @@ -275,29 +347,51 @@ class LTXModel(nn.Module): def _init_video(self, config: LTXModelConfig) -> None: self.patchify_proj = nn.Linear(config.in_channels, self.inner_dim, bias=True) - self.adaln_single = AdaLayerNormSingle(self.inner_dim) - self.caption_projection = PixArtAlphaTextProjection( - in_features=config.caption_channels, - hidden_size=self.inner_dim, + + adaln_coefficient = 9 if config.has_prompt_adaln else 6 + self.adaln_single = AdaLayerNormSingle( + self.inner_dim, embedding_coefficient=adaln_coefficient ) + if config.has_prompt_adaln: + self.prompt_adaln_single = AdaLayerNormSingle( + self.inner_dim, embedding_coefficient=2 + ) + else: + self.caption_projection = PixArtAlphaTextProjection( + in_features=config.caption_channels, + hidden_size=self.inner_dim, + ) + self.scale_shift_table = mx.zeros((2, self.inner_dim)) self.norm_out = nn.LayerNorm(self.inner_dim, eps=config.norm_eps, affine=False) self.proj_out = nn.Linear(self.inner_dim, config.out_channels) def _init_audio(self, config: LTXModelConfig) -> None: - self.audio_patchify_proj = nn.Linear(config.audio_in_channels, self.audio_inner_dim, bias=True) - self.audio_adaln_single = AdaLayerNormSingle(self.audio_inner_dim) - - # Audio caption projection: receives pre-processed embeddings from text encoder's audio_embeddings_connector - self.audio_caption_projection = PixArtAlphaTextProjection( - in_features=config.audio_caption_channels, - hidden_size=self.audio_inner_dim, + self.audio_patchify_proj = nn.Linear( + config.audio_in_channels, self.audio_inner_dim, bias=True ) + audio_adaln_coefficient = 9 if config.has_prompt_adaln else 6 + self.audio_adaln_single = AdaLayerNormSingle( + self.audio_inner_dim, embedding_coefficient=audio_adaln_coefficient + ) + + if config.has_prompt_adaln: + self.audio_prompt_adaln_single = AdaLayerNormSingle( + self.audio_inner_dim, embedding_coefficient=2 + ) + else: + self.audio_caption_projection = PixArtAlphaTextProjection( + in_features=config.audio_caption_channels, + hidden_size=self.audio_inner_dim, + ) + # Output components self.audio_scale_shift_table = mx.zeros((2, self.audio_inner_dim)) - self.audio_norm_out = nn.LayerNorm(self.audio_inner_dim, eps=config.norm_eps, affine=False) + self.audio_norm_out = nn.LayerNorm( + self.audio_inner_dim, eps=config.norm_eps, affine=False + ) self.audio_proj_out = nn.Linear(self.audio_inner_dim, config.audio_out_channels) def _init_audio_video(self, config: LTXModelConfig) -> None: @@ -320,13 +414,18 @@ class LTXModel(nn.Module): embedding_coefficient=1, ) - def _init_preprocessors(self, config: LTXModelConfig, cross_pe_max_pos: Optional[int]) -> None: - if config.model_type.is_video_enabled() and config.model_type.is_audio_enabled(): + def _init_preprocessors( + self, config: LTXModelConfig, cross_pe_max_pos: Optional[int] + ) -> None: + if ( + config.model_type.is_video_enabled() + and config.model_type.is_audio_enabled() + ): # Multi-modal preprocessors self.video_args_preprocessor = MultiModalTransformerArgsPreprocessor( patchify_proj=self.patchify_proj, adaln=self.adaln_single, - caption_projection=self.caption_projection, + caption_projection=getattr(self, "caption_projection", None), cross_scale_shift_adaln=self.av_ca_video_scale_shift_adaln_single, cross_gate_adaln=self.av_ca_a2v_gate_adaln_single, inner_dim=self.inner_dim, @@ -340,11 +439,12 @@ class LTXModel(nn.Module): rope_type=config.rope_type, av_ca_timestep_scale_multiplier=config.av_ca_timestep_scale_multiplier, double_precision_rope=config.double_precision_rope, + prompt_adaln=getattr(self, "prompt_adaln_single", None), ) self.audio_args_preprocessor = MultiModalTransformerArgsPreprocessor( patchify_proj=self.audio_patchify_proj, adaln=self.audio_adaln_single, - caption_projection=self.audio_caption_projection, + caption_projection=getattr(self, "audio_caption_projection", None), cross_scale_shift_adaln=self.av_ca_audio_scale_shift_adaln_single, cross_gate_adaln=self.av_ca_v2a_gate_adaln_single, inner_dim=self.audio_inner_dim, @@ -358,12 +458,13 @@ class LTXModel(nn.Module): rope_type=config.rope_type, av_ca_timestep_scale_multiplier=config.av_ca_timestep_scale_multiplier, double_precision_rope=config.double_precision_rope, + prompt_adaln=getattr(self, "audio_prompt_adaln_single", None), ) elif config.model_type.is_video_enabled(): self.video_args_preprocessor = TransformerArgsPreprocessor( patchify_proj=self.patchify_proj, adaln=self.adaln_single, - caption_projection=self.caption_projection, + caption_projection=getattr(self, "caption_projection", None), inner_dim=self.inner_dim, max_pos=config.positional_embedding_max_pos, num_attention_heads=self.num_attention_heads, @@ -372,12 +473,13 @@ class LTXModel(nn.Module): positional_embedding_theta=config.positional_embedding_theta, rope_type=config.rope_type, double_precision_rope=config.double_precision_rope, + prompt_adaln=getattr(self, "prompt_adaln_single", None), ) elif config.model_type.is_audio_enabled(): self.audio_args_preprocessor = TransformerArgsPreprocessor( patchify_proj=self.audio_patchify_proj, adaln=self.audio_adaln_single, - caption_projection=self.audio_caption_projection, + caption_projection=getattr(self, "audio_caption_projection", None), inner_dim=self.audio_inner_dim, max_pos=config.audio_positional_embedding_max_pos, num_attention_heads=self.audio_num_attention_heads, @@ -386,13 +488,13 @@ class LTXModel(nn.Module): positional_embedding_theta=config.positional_embedding_theta, rope_type=config.rope_type, double_precision_rope=config.double_precision_rope, + prompt_adaln=getattr(self, "audio_prompt_adaln_single", None), ) def _init_transformer_blocks(self, config: LTXModelConfig) -> None: video_config = config.get_video_config() audio_config = config.get_audio_config() - self.transformer_blocks = { idx: BasicAVTransformerBlock( idx=idx, @@ -400,6 +502,7 @@ class LTXModel(nn.Module): audio=audio_config, rope_type=config.rope_type, norm_eps=config.norm_eps, + has_prompt_adaln=config.has_prompt_adaln, ) for idx in range(config.num_layers) } @@ -408,10 +511,27 @@ class LTXModel(nn.Module): self, video: Optional[TransformerArgs], audio: Optional[TransformerArgs], + stg_video_blocks: Optional[List[int]] = None, + stg_audio_blocks: Optional[List[int]] = None, + skip_cross_modal: bool = False, ) -> Tuple[Optional[TransformerArgs], Optional[TransformerArgs]]: - """Process through all transformer blocks.""" - for block in self.transformer_blocks.values(): - video, audio = block(video=video, audio=audio) + """Process through all transformer blocks. + + Args: + stg_video_blocks: Block indices where video self-attention is skipped (STG). + stg_audio_blocks: Block indices where audio self-attention is skipped (STG). + skip_cross_modal: Skip all A2V/V2A cross-attention (modality isolation). + """ + stg_v_set = set(stg_video_blocks) if stg_video_blocks else set() + stg_a_set = set(stg_audio_blocks) if stg_audio_blocks else set() + for idx, block in self.transformer_blocks.items(): + video, audio = block( + video=video, + audio=audio, + skip_video_self_attn=(idx in stg_v_set), + skip_audio_self_attn=(idx in stg_a_set), + skip_cross_modal=skip_cross_modal, + ) return video, audio def _process_output( @@ -422,7 +542,7 @@ class LTXModel(nn.Module): x: mx.array, embedded_timestep: mx.array, ) -> mx.array: - + # scale_shift_table: (2, dim) -> expand to (1, 1, 2, dim) # embedded_timestep: (B, 1, dim) -> expand to (B, 1, 1, dim) table_expanded = scale_shift_table[None, None, :, :] # (1, 1, 2, dim) @@ -445,8 +565,19 @@ class LTXModel(nn.Module): self, video: Optional[Modality] = None, audio: Optional[Modality] = None, + stg_video_blocks: Optional[List[int]] = None, + stg_audio_blocks: Optional[List[int]] = None, + skip_cross_modal: bool = False, ) -> Tuple[Optional[mx.array], Optional[mx.array]]: - + """Forward pass. + + Args: + video: Video modality input. + audio: Audio modality input. + stg_video_blocks: Block indices where video self-attention is skipped (STG). + stg_audio_blocks: Block indices where audio self-attention is skipped (STG). + skip_cross_modal: Skip all A2V/V2A cross-attention (modality isolation). + """ # Validate inputs if not self.model_type.is_video_enabled() and video is not None: raise ValueError("Video is not enabled for this model") @@ -454,13 +585,20 @@ class LTXModel(nn.Module): raise ValueError("Audio is not enabled for this model") # Preprocess arguments - video_args = self.video_args_preprocessor.prepare(video) if video is not None else None - audio_args = self.audio_args_preprocessor.prepare(audio) if audio is not None else None + video_args = ( + self.video_args_preprocessor.prepare(video) if video is not None else None + ) + audio_args = ( + self.audio_args_preprocessor.prepare(audio) if audio is not None else None + ) # Process transformer blocks video_out, audio_out = self._process_transformer_blocks( video=video_args, audio=audio_args, + stg_video_blocks=stg_video_blocks, + stg_audio_blocks=stg_audio_blocks, + skip_cross_modal=skip_cross_modal, ) # Process outputs @@ -492,24 +630,70 @@ class LTXModel(nn.Module): def sanitize(self, weights: dict) -> dict: sanitized = {} + + has_raw_prefix = any(k.startswith("model.diffusion_model.") for k in weights) + if not has_raw_prefix: + return weights + for key, value in weights.items(): new_key = key - # Handle common remappings - # transformer_blocks.X -> transformer_blocks[X] - if "transformer_blocks." in new_key: - # Keep as-is for now, MLX handles this - pass + if not key.startswith("model.diffusion_model."): + continue + if ( + "audio_embeddings_connector" in key + or "video_embeddings_connector" in key + ): + continue + + # Remove 'model.diffusion_model.' prefix + new_key = new_key.replace("model.diffusion_model.", "") + + new_key = new_key.replace(".to_out.0.", ".to_out.") + + new_key = new_key.replace(".ff.net.0.proj.", ".ff.proj_in.") + new_key = new_key.replace(".ff.net.2.", ".ff.proj_out.") + new_key = new_key.replace(".audio_ff.net.0.proj.", ".audio_ff.proj_in.") + new_key = new_key.replace(".audio_ff.net.2.", ".audio_ff.proj_out.") + + new_key = new_key.replace(".linear_1.", ".linear1.") + new_key = new_key.replace(".linear_2.", ".linear2.") sanitized[new_key] = value return sanitized + @classmethod + def from_pretrained(cls, model_path: Path, strict: bool = True) -> "LTXModel": + import json + + config_dict = {} + with open(model_path / "config.json", "r") as f: + config_dict = json.load(f) + config = LTXModelConfig(**config_dict) + model = cls(config) + + weights = {} + + for weight_file in model_path.glob("*.safetensors"): + weights.update(mx.load(str(weight_file))) + + sanitized = model.sanitize(weights) + sanitized = { + k: v.astype(mx.bfloat16) if v.dtype == mx.float32 else v + for k, v in sanitized.items() + } + + model.load_weights(list(sanitized.items()), strict=strict) + mx.eval(model.parameters()) + model.eval() + return model + class X0Model(nn.Module): def __init__(self, velocity_model: LTXModel): - + super().__init__() self.velocity_model = velocity_model @@ -517,11 +701,24 @@ class X0Model(nn.Module): self, video: Optional[Modality] = None, audio: Optional[Modality] = None, + stg_video_blocks: Optional[List[int]] = None, + stg_audio_blocks: Optional[List[int]] = None, + skip_cross_modal: bool = False, ) -> Tuple[Optional[mx.array], Optional[mx.array]]: - - vx, ax = self.velocity_model(video, audio) - denoised_video = to_denoised(video.latent, vx, video.timesteps) if vx is not None else None - denoised_audio = to_denoised(audio.latent, ax, audio.timesteps) if ax is not None else None + vx, ax = self.velocity_model( + video, + audio, + stg_video_blocks=stg_video_blocks, + stg_audio_blocks=stg_audio_blocks, + skip_cross_modal=skip_cross_modal, + ) + + denoised_video = ( + to_denoised(video.latent, vx, video.timesteps) if vx is not None else None + ) + denoised_audio = ( + to_denoised(audio.latent, ax, audio.timesteps) if ax is not None else None + ) return denoised_video, denoised_audio diff --git a/mlx_video/postprocess.py b/mlx_video/models/ltx_2/postprocess.py similarity index 86% rename from mlx_video/postprocess.py rename to mlx_video/models/ltx_2/postprocess.py index 03ef61d..7865975 100644 --- a/mlx_video/postprocess.py +++ b/mlx_video/models/ltx_2/postprocess.py @@ -1,9 +1,10 @@ import numpy as np -from typing import Optional -def bilateral_filter(image: np.ndarray, d: int = 5, sigma_color: float = 75, sigma_space: float = 75) -> np.ndarray: +def bilateral_filter( + image: np.ndarray, d: int = 5, sigma_color: float = 75, sigma_space: float = 75 +) -> np.ndarray: """Apply bilateral filter to reduce grid artifacts while preserving edges. Args: @@ -17,6 +18,7 @@ def bilateral_filter(image: np.ndarray, d: int = 5, sigma_color: float = 75, sig """ try: import cv2 + return cv2.bilateralFilter(image, d, sigma_color, sigma_space) except ImportError: # Fallback to simple Gaussian blur if cv2 not available @@ -35,14 +37,20 @@ def gaussian_blur(image: np.ndarray, kernel_size: int = 3) -> np.ndarray: """ try: import cv2 + return cv2.GaussianBlur(image, (kernel_size, kernel_size), 0) except ImportError: # Simple box blur fallback from scipy.ndimage import uniform_filter - return uniform_filter(image, size=(kernel_size, kernel_size, 1)).astype(np.uint8) + + return uniform_filter(image, size=(kernel_size, kernel_size, 1)).astype( + np.uint8 + ) -def unsharp_mask(image: np.ndarray, kernel_size: int = 5, sigma: float = 1.0, amount: float = 1.0) -> np.ndarray: +def unsharp_mask( + image: np.ndarray, kernel_size: int = 5, sigma: float = 1.0, amount: float = 1.0 +) -> np.ndarray: """Apply unsharp masking to enhance edges after blur. Args: @@ -56,6 +64,7 @@ def unsharp_mask(image: np.ndarray, kernel_size: int = 5, sigma: float = 1.0, am """ try: import cv2 + blurred = cv2.GaussianBlur(image, (kernel_size, kernel_size), sigma) sharpened = cv2.addWeighted(image, 1 + amount, blurred, -amount, 0) return np.clip(sharpened, 0, 255).astype(np.uint8) @@ -81,23 +90,23 @@ def reduce_grid_artifacts( if method == "bilateral": d = max(3, int(5 * strength)) sigma = 50 + 50 * strength - processed = np.stack([ - bilateral_filter(frame, d=d, sigma_color=sigma, sigma_space=sigma) - for frame in video - ]) + processed = np.stack( + [ + bilateral_filter(frame, d=d, sigma_color=sigma, sigma_space=sigma) + for frame in video + ] + ) elif method == "gaussian": kernel_size = max(3, int(3 + 4 * strength)) if kernel_size % 2 == 0: kernel_size += 1 - processed = np.stack([ - gaussian_blur(frame, kernel_size=kernel_size) - for frame in video - ]) + processed = np.stack( + [gaussian_blur(frame, kernel_size=kernel_size) for frame in video] + ) elif method == "frequency": - processed = np.stack([ - remove_grid_frequency(frame, grid_size=8) - for frame in video - ]) + processed = np.stack( + [remove_grid_frequency(frame, grid_size=8) for frame in video] + ) else: raise ValueError(f"Unknown method: {method}") @@ -160,6 +169,3 @@ def remove_grid_frequency(frame: np.ndarray, grid_size: int = 8) -> np.ndarray: result[:, :, c] = np.clip(channel_filtered, 0, 255).astype(np.uint8) return result - - - diff --git a/mlx_video/models/ltx/prompts/gemma_i2v_system_prompt.txt b/mlx_video/models/ltx_2/prompts/gemma_i2v_system_prompt.txt similarity index 100% rename from mlx_video/models/ltx/prompts/gemma_i2v_system_prompt.txt rename to mlx_video/models/ltx_2/prompts/gemma_i2v_system_prompt.txt diff --git a/mlx_video/models/ltx/prompts/gemma_t2v_system_prompt.txt b/mlx_video/models/ltx_2/prompts/gemma_t2v_system_prompt.txt similarity index 100% rename from mlx_video/models/ltx/prompts/gemma_t2v_system_prompt.txt rename to mlx_video/models/ltx_2/prompts/gemma_t2v_system_prompt.txt diff --git a/mlx_video/models/ltx/rope.py b/mlx_video/models/ltx_2/rope.py similarity index 76% rename from mlx_video/models/ltx/rope.py rename to mlx_video/models/ltx_2/rope.py index 4852942..2915b55 100644 --- a/mlx_video/models/ltx/rope.py +++ b/mlx_video/models/ltx_2/rope.py @@ -1,11 +1,9 @@ - import math -from typing import Callable, List, Optional, Tuple +from typing import List, Optional, Tuple import mlx.core as mx -import numpy as np -from mlx_video.models.ltx.config import LTXRopeType +from mlx_video.models.ltx_2.config import LTXRopeType def apply_rotary_emb( @@ -87,11 +85,12 @@ def rotate_half_interleaved(x: mx.array) -> mx.array: """ # x: (..., dim) where dim is even x_even = x[..., 0::2] # [x0, x2, x4, ...] - x_odd = x[..., 1::2] # [x1, x3, x5, ...] + x_odd = x[..., 1::2] # [x1, x3, x5, ...] # Stack: [[-x1, x0], [-x3, x2], ...] then flatten to [-x1, x0, -x3, x2, ...] rotated = mx.stack([-x_odd, x_even], axis=-1) return mx.reshape(rotated, x.shape) + def apply_rotary_emb_1d( q: mx.array, k: mx.array, @@ -229,9 +228,9 @@ def get_fractional_positions( Fractional positions in range [-1, 1] after scaling """ n_pos_dims = indices_grid.shape[1] - assert n_pos_dims == len(max_pos), ( - f"Number of position dimensions ({n_pos_dims}) must match max_pos length ({len(max_pos)})" - ) + assert n_pos_dims == len( + max_pos + ), f"Number of position dimensions ({n_pos_dims}) must match max_pos length ({len(max_pos)})" # Divide each dimension by its max position fractional_positions = [] @@ -393,13 +392,25 @@ def precompute_freqs_cis( if max_pos is None: max_pos = [20, 2048, 2048] - if double_precision: return _precompute_freqs_cis_double_precision( - indices_grid, dim, theta, max_pos, use_middle_indices_grid, - num_attention_heads, rope_type + indices_grid, + dim, + theta, + max_pos, + use_middle_indices_grid, + num_attention_heads, + rope_type, ) + # Keep positions in float32 for RoPE computation. + # Even though PyTorch nominally casts positions to model dtype (bfloat16), + # empirical comparison shows float32 positions produce RoPE values matching + # PyTorch exactly (cosine=1.000). BFloat16 loses precision in fractional + # position computation that gets amplified by high-frequency indices + # (up to 15708), causing cos/sin sign flips and cosine sim of only 0.88. + indices_grid = indices_grid.astype(mx.float32) + # Generate frequency indices indices = generate_freq_grid(theta, indices_grid.shape[1], dim) @@ -429,66 +440,77 @@ def _precompute_freqs_cis_double_precision( num_attention_heads: int, rope_type: LTXRopeType, ) -> Tuple[mx.array, mx.array]: + """Compute RoPE frequencies with higher precision using float64 for frequency grid. - # Warn if positions are bfloat16 - this causes quality degradation - if indices_grid.dtype == mx.bfloat16: - import warnings - warnings.warn( - "Position grid has dtype bfloat16, which causes precision loss in RoPE that causes quality degradation in generated videos/audio. " - "Use float32 for position grids to avoid quality degradation. " - "See tests/test_rope.py::test_bfloat16_positions_cause_precision_loss", - UserWarning, - stacklevel=2 - ) + Matches PyTorch's generate_freq_grid_np: uses NumPy float64 for the critical + frequency grid computation (log-spaced values), then converts to float32. + Position grid stays in bfloat16 to match PyTorch behavior (positions are in + model dtype throughout generate_freqs). + """ + import numpy as np - # Convert to numpy float64 (first to float32 for numpy compatibility) - # Note: If input is bfloat16, precision is already lost at this step - indices_grid_np = np.array(indices_grid.astype(mx.float32)).astype(np.float64) + # Keep positions in float32 — same reasoning as the non-double-precision path. + indices_grid_f32 = indices_grid.astype(mx.float32) - # Generate frequency indices in float64 - n_pos_dims = indices_grid_np.shape[1] + n_pos_dims = indices_grid_f32.shape[1] n_elem = 2 * n_pos_dims - # Compute log-spaced frequencies - log_start = math.log(1.0) / math.log(theta) - log_end = math.log(theta) / math.log(theta) + # Compute log-spaced frequencies in float64 (matching PyTorch's generate_freq_grid_np) + # This is the critical precision step - PyTorch uses np.float64 here + log_start = np.log(1.0) / np.log(theta) + log_end = np.log(theta) / np.log(theta) # = 1.0 num_indices = dim // n_elem if num_indices == 0: num_indices = 1 - lin_space = np.linspace(log_start, log_end, num_indices) - indices_np = np.power(theta, lin_space) * (math.pi / 2) + + # Use numpy float64 for the linspace computation (matches PyTorch) + pow_indices = np.power( + theta, + np.linspace(log_start, log_end, num_indices, dtype=np.float64), + ) + # Convert to float32 tensor (matches PyTorch: torch.tensor(..., dtype=torch.float32)) + freq_indices = mx.array(pow_indices * (math.pi / 2), dtype=mx.float32) # Handle middle indices grid # Input shape: (B, n_dims, T, 2) for middle indices or (B, n_dims, T, 1) otherwise if use_middle_indices_grid: - assert len(indices_grid_np.shape) == 4 - assert indices_grid_np.shape[-1] == 2 - indices_grid_start = indices_grid_np[..., 0] - indices_grid_end = indices_grid_np[..., 1] - indices_grid_np = (indices_grid_start + indices_grid_end) / 2.0 - elif len(indices_grid_np.shape) == 4: - indices_grid_np = indices_grid_np[..., 0] - # After handling: indices_grid_np shape is (B, n_dims, T) + assert len(indices_grid_f32.shape) == 4 + assert indices_grid_f32.shape[-1] == 2 + indices_grid_start = indices_grid_f32[..., 0] + indices_grid_end = indices_grid_f32[..., 1] + indices_grid_f32 = (indices_grid_start + indices_grid_end) / 2.0 + elif len(indices_grid_f32.shape) == 4: + indices_grid_f32 = indices_grid_f32[..., 0] + # After handling: indices_grid_f32 shape is (B, n_dims, T) # Get fractional positions: (B, n_dims, T) -> (B, T, n_dims) - batch_size = indices_grid_np.shape[0] - seq_len = indices_grid_np.shape[2] - fractional_positions = np.zeros((batch_size, seq_len, n_pos_dims), dtype=np.float64) + # Compute fractional positions for each dimension + fractional_list = [] for i in range(n_pos_dims): - # indices_grid_np[:, i, :] has shape (B, T) - fractional_positions[:, :, i] = indices_grid_np[:, i, :] / max_pos[i] + frac = indices_grid_f32[:, i, :] / max_pos[i] # (B, T) + fractional_list.append(frac) + + # Stack: (B, T, n_dims) + fractional_positions = mx.stack(fractional_list, axis=-1) # Scale to [-1, 1] scaled_positions = fractional_positions * 2 - 1 # Compute frequencies: outer product - freqs = np.expand_dims(scaled_positions, axis=-1) * indices_np.reshape(1, 1, 1, -1) - freqs = np.swapaxes(freqs, -1, -2) - freqs = freqs.reshape(freqs.shape[:-2] + (-1,)) + # scaled_positions: (B, T, n_dims) -> (B, T, n_dims, 1) + # freq_indices: (num_indices,) -> (1, 1, 1, num_indices) + freqs = mx.expand_dims(scaled_positions, axis=-1) * mx.reshape( + freq_indices, (1, 1, 1, -1) + ) + # freqs: (B, T, n_dims, num_indices) - # Compute cos/sin in float64 - cos_freq = np.cos(freqs) - sin_freq = np.sin(freqs) + # Transpose and flatten: (B, T, n_dims, num_indices) -> (B, T, num_indices, n_dims) -> (B, T, num_indices * n_dims) + freqs = mx.swapaxes(freqs, -1, -2) + freqs = mx.reshape(freqs, (freqs.shape[0], freqs.shape[1], -1)) + + # Compute cos/sin + cos_freq = mx.cos(freqs) + sin_freq = mx.sin(freqs) # Prepare based on rope type if rope_type == LTXRopeType.SPLIT: @@ -498,31 +520,27 @@ def _precompute_freqs_cis_double_precision( # Add padding if pad_size > 0: - cos_padding = np.ones((*cos_freq.shape[:-1], pad_size), dtype=np.float64) - sin_padding = np.zeros((*sin_freq.shape[:-1], pad_size), dtype=np.float64) - cos_freq = np.concatenate([cos_padding, cos_freq], axis=-1) - sin_freq = np.concatenate([sin_padding, sin_freq], axis=-1) + cos_padding = mx.ones((*cos_freq.shape[:-1], pad_size), dtype=mx.float32) + sin_padding = mx.zeros((*sin_freq.shape[:-1], pad_size), dtype=mx.float32) + cos_freq = mx.concatenate([cos_padding, cos_freq], axis=-1) + sin_freq = mx.concatenate([sin_padding, sin_freq], axis=-1) # Reshape for multi-head attention: (B, T, dim//2) -> (B, H, T, dim//2//H) b, t = cos_freq.shape[0], cos_freq.shape[1] - cos_freq = cos_freq.reshape(b, t, num_attention_heads, -1) - sin_freq = sin_freq.reshape(b, t, num_attention_heads, -1) - cos_freq = np.swapaxes(cos_freq, 1, 2) - sin_freq = np.swapaxes(sin_freq, 1, 2) + cos_freq = mx.reshape(cos_freq, (b, t, num_attention_heads, -1)) + sin_freq = mx.reshape(sin_freq, (b, t, num_attention_heads, -1)) + cos_freq = mx.swapaxes(cos_freq, 1, 2) + sin_freq = mx.swapaxes(sin_freq, 1, 2) else: # Interleaved - cos_freq = np.repeat(cos_freq, 2, axis=-1) - sin_freq = np.repeat(sin_freq, 2, axis=-1) + cos_freq = mx.repeat(cos_freq, 2, axis=-1) + sin_freq = mx.repeat(sin_freq, 2, axis=-1) pad_size = dim % n_elem if pad_size > 0: - cos_padding = np.ones((*cos_freq.shape[:-1], pad_size), dtype=np.float64) - sin_padding = np.zeros((*sin_freq.shape[:-1], pad_size), dtype=np.float64) - cos_freq = np.concatenate([cos_padding, cos_freq], axis=-1) - sin_freq = np.concatenate([sin_padding, sin_freq], axis=-1) - - # Convert back to MLX (float32 for GPU compatibility) - cos_freq = mx.array(cos_freq.astype(np.float32)) - sin_freq = mx.array(sin_freq.astype(np.float32)) + cos_padding = mx.ones((*cos_freq.shape[:-1], pad_size), dtype=mx.float32) + sin_padding = mx.zeros((*sin_freq.shape[:-1], pad_size), dtype=mx.float32) + cos_freq = mx.concatenate([cos_padding, cos_freq], axis=-1) + sin_freq = mx.concatenate([sin_padding, sin_freq], axis=-1) return cos_freq, sin_freq diff --git a/mlx_video/models/ltx_2/samplers.py b/mlx_video/models/ltx_2/samplers.py new file mode 100644 index 0000000..b97faa0 --- /dev/null +++ b/mlx_video/models/ltx_2/samplers.py @@ -0,0 +1,185 @@ +"""Second-order res_2s sampler for diffusion models. + +Implements the exponential Rosenbrock-type Runge-Kutta integrator with SDE +noise injection, ported from the LTX-2 PyTorch implementation. +""" + +import math + +import mlx.core as mx + +# --------------------------------------------------------------------------- +# Phi functions and RK coefficients (pure Python math, no MLX needed) +# --------------------------------------------------------------------------- + + +def phi(j: int, neg_h: float) -> float: + """Compute phi_j(z) where z = -h (negative step size in log-space). + + phi_1(z) = (e^z - 1) / z + phi_2(z) = (e^z - 1 - z) / z^2 + phi_j(z) = (e^z - sum_{k=0}^{j-1} z^k/k!) / z^j + """ + if abs(neg_h) < 1e-10: + return 1.0 / math.factorial(j) + + remainder = sum(neg_h**k / math.factorial(k) for k in range(j)) + return (math.exp(neg_h) - remainder) / (neg_h**j) + + +def get_res2s_coefficients( + h: float, + phi_cache: dict, + c2: float = 0.5, +) -> tuple[float, float, float]: + """Compute res_2s Runge-Kutta coefficients for a given step size. + + Args: + h: Step size in log-space = log(sigma / sigma_next) + phi_cache: Dictionary to cache phi function results. + c2: Substep position (default 0.5 = midpoint) + + Returns: + (a21, b1, b2): RK coefficients. + """ + + def get_phi(j: int, neg_h: float) -> float: + cache_key = (j, neg_h) + if cache_key in phi_cache: + return phi_cache[cache_key] + result = phi(j, neg_h) + phi_cache[cache_key] = result + return result + + neg_h_c2 = -h * c2 + phi_1_c2 = get_phi(1, neg_h_c2) + a21 = c2 * phi_1_c2 + + neg_h_full = -h + phi_2_full = get_phi(2, neg_h_full) + b2 = phi_2_full / c2 + + phi_1_full = get_phi(1, neg_h_full) + b1 = phi_1_full - b2 + + return a21, b1, b2 + + +# --------------------------------------------------------------------------- +# SDE noise injection +# --------------------------------------------------------------------------- + + +def get_sde_coeff( + sigma_next: float, +) -> tuple[float, float, float]: + """Compute SDE coefficients for variance-preserving noise injection. + + Uses sigma_up = sigma_next * 0.5 (hardcoded in PyTorch Res2sDiffusionStep). + + Returns: + (alpha_ratio, sigma_down, sigma_up) + """ + sigma_up = sigma_next * 0.5 + # Clamp sigma_up to avoid sqrt(negative) + sigma_up = min(sigma_up, sigma_next * 0.9999) + + sigma_signal = 1.0 - sigma_next # sigma_max=1 + sigma_residual = math.sqrt(max(sigma_next**2 - sigma_up**2, 0.0)) + alpha_ratio = sigma_signal + sigma_residual + + if alpha_ratio == 0: + sigma_down = sigma_next + else: + sigma_down = sigma_residual / alpha_ratio + + # Handle NaN edge cases + if math.isnan(sigma_up): + sigma_up = 0.0 + if math.isnan(sigma_down): + sigma_down = sigma_next + if math.isnan(alpha_ratio): + alpha_ratio = 1.0 + + return alpha_ratio, sigma_down, sigma_up + + +def sde_noise_step( + sample: mx.array, + denoised_sample: mx.array, + sigma: float, + sigma_next: float, + noise: mx.array, +) -> mx.array: + """Apply SDE noise injection step. + + Advances sample from sigma to sigma_next with stochastic noise injection. + + Args: + sample: Current sample (anchor point) + denoised_sample: Denoised prediction at this step + sigma: Current noise level + sigma_next: Next noise level + noise: Pre-generated noise tensor (channel-wise normalized) + + Returns: + Noised sample at sigma_next + """ + alpha_ratio, sigma_down, sigma_up = get_sde_coeff(sigma_next) + + if sigma_up == 0 or sigma_next == 0: + return denoised_sample + + # Float32 arithmetic + sample_f32 = sample.astype(mx.float32) + denoised_f32 = denoised_sample.astype(mx.float32) + noise_f32 = noise.astype(mx.float32) + + # Extract epsilon prediction + eps_next = (sample_f32 - denoised_f32) / (sigma - sigma_next) + denoised_next = sample_f32 - sigma * eps_next + + # Mix deterministic and stochastic components + x_noised = ( + alpha_ratio * (denoised_next + sigma_down * eps_next) + sigma_up * noise_f32 + ) + + return x_noised + + +# --------------------------------------------------------------------------- +# Noise generation +# --------------------------------------------------------------------------- + + +def channelwise_normalize(x: mx.array) -> mx.array: + """Normalize each channel to zero mean and unit variance over spatial dims. + + Operates on the last 2 dimensions (spatial H, W or time, freq). + """ + mean = mx.mean(x, axis=(-2, -1), keepdims=True) + x = x - mean + std = mx.sqrt(mx.mean(x * x, axis=(-2, -1), keepdims=True) + 1e-8) + x = x / std + return x + + +def get_new_noise(shape: tuple, key: mx.array) -> mx.array: + """Generate channel-wise normalized Gaussian noise. + + PyTorch uses float64; we use float32 (MLX doesn't support float64). + The channel-wise normalization is the key quality-affecting step. + + Args: + shape: Shape of the noise tensor + key: MLX random key for deterministic generation + + Returns: + Channel-wise normalized noise in float32 + """ + noise = mx.random.normal(shape, dtype=mx.float32, key=key) + # Global normalization + noise = (noise - mx.mean(noise)) / (mx.sqrt(mx.mean(noise * noise)) + 1e-8) + # Channel-wise normalization + noise = channelwise_normalize(noise) + return noise diff --git a/mlx_video/models/ltx/text_encoder.py b/mlx_video/models/ltx_2/text_encoder.py similarity index 54% rename from mlx_video/models/ltx/text_encoder.py rename to mlx_video/models/ltx_2/text_encoder.py index d6461d5..fbff7e1 100644 --- a/mlx_video/models/ltx/text_encoder.py +++ b/mlx_video/models/ltx_2/text_encoder.py @@ -1,23 +1,25 @@ - - import functools import logging import math import re -from dataclasses import dataclass from pathlib import Path from typing import Dict, List, Optional, Tuple import mlx.core as mx import mlx.nn as nn -import numpy as np - -from mlx_video.utils import rms_norm, apply_quantization -from mlx_video.models.ltx.rope import apply_interleaved_rotary_emb - -from mlx_vlm.models.gemma3.language import Gemma3Model from mlx_vlm.models.gemma3.config import TextConfig +from mlx_vlm.models.gemma3.language import Gemma3Model +from rich.console import Console +from rich.progress import ( + BarColumn, + Progress, + SpinnerColumn, + TaskProgressColumn, + TextColumn, + TimeRemainingColumn, +) +from mlx_video.utils import apply_quantization, rms_norm # Path to system prompts PROMPTS_DIR = Path(__file__).parent / "prompts" @@ -34,11 +36,10 @@ def _load_system_prompt(prompt_name: str) -> str: class LanguageModel(nn.Module): - def __init__(self, config: TextConfig): super().__init__() # Create config matching LTX-2 text encoder requirements - self.config = config + self.config = config # Create the Gemma3Model from mlx-vlm self.model = Gemma3Model(self.config) @@ -49,7 +50,7 @@ class LanguageModel(nn.Module): attention_mask: Optional[mx.array], dtype: mx.Dtype, ) -> mx.array: - + causal_mask = mx.tril(mx.ones((seq_len, seq_len), dtype=mx.bool_)) if attention_mask is not None: @@ -57,15 +58,25 @@ class LanguageModel(nn.Module): padding_mask = attention_mask.astype(mx.bool_) # (batch, seq_len) combined = causal_mask[None, :, :] & padding_mask[:, None, :] - min_val = mx.finfo(dtype).min if dtype in (mx.float16, mx.bfloat16) else -1e9 - mask = mx.where(combined, mx.zeros(combined.shape, dtype=dtype), - mx.full(combined.shape, min_val, dtype=dtype)) + min_val = ( + mx.finfo(dtype).min if dtype in (mx.float16, mx.bfloat16) else -1e9 + ) + mask = mx.where( + combined, + mx.zeros(combined.shape, dtype=dtype), + mx.full(combined.shape, min_val, dtype=dtype), + ) return mask[:, None, :, :] else: # No padding mask, just causal - min_val = mx.finfo(dtype).min if dtype in (mx.float16, mx.bfloat16) else -1e9 - mask = mx.where(causal_mask, mx.zeros((seq_len, seq_len), dtype=dtype), - mx.full((seq_len, seq_len), min_val, dtype=dtype)) + min_val = ( + mx.finfo(dtype).min if dtype in (mx.float16, mx.bfloat16) else -1e9 + ) + mask = mx.where( + causal_mask, + mx.zeros((seq_len, seq_len), dtype=dtype), + mx.full((seq_len, seq_len), min_val, dtype=dtype), + ) return mask[None, None, :, :] # (1, 1, seq, seq) def __call__( @@ -89,7 +100,11 @@ class LanguageModel(nn.Module): batch_size, seq_len = inputs.shape # Get embeddings - h = input_embeddings if input_embeddings is not None else self.model.embed_tokens(inputs) + h = ( + input_embeddings + if input_embeddings is not None + else self.model.embed_tokens(inputs) + ) # Apply Gemma scaling h *= mx.array(self.config.hidden_size**0.5, mx.bfloat16).astype(h.dtype) @@ -101,11 +116,12 @@ class LanguageModel(nn.Module): if cache is None: cache = [None] * len(self.model.layers) - full_causal_mask = self._create_causal_mask_with_padding(seq_len, attention_mask, h.dtype) + full_causal_mask = self._create_causal_mask_with_padding( + seq_len, attention_mask, h.dtype + ) sliding_mask = full_causal_mask - num_layers = len(self.model.layers) for i, layer in enumerate(self.model.layers): is_global = ( @@ -145,9 +161,9 @@ class LanguageModel(nn.Module): for key, value in weights.items(): if key.startswith(prefix): if hasattr(value, "dtype") and value.dtype == mx.float32: - sanitized[key[len(prefix):]] = value.astype(mx.bfloat16) + sanitized[key[len(prefix) :]] = value.astype(mx.bfloat16) else: - sanitized[key[len(prefix):]] = value + sanitized[key[len(prefix) :]] = value return sanitized @property @@ -156,6 +172,7 @@ class LanguageModel(nn.Module): def make_cache(self): from mlx_vlm.models.cache import KVCache, RotatingKVCache + caches = [] for i in range(len(self.layers)): if ( @@ -170,6 +187,7 @@ class LanguageModel(nn.Module): @classmethod def from_pretrained(cls, model_path: str): import json + weight_files = sorted(Path(model_path).glob("*.safetensors")) config_file = Path(model_path) / "config.json" config_dict = {} @@ -177,7 +195,9 @@ class LanguageModel(nn.Module): with open(config_file, "r") as f: config_dict = json.load(f) - language_model = cls(config=TextConfig.from_dict(config_dict["text_config"])) + language_model = cls( + config=TextConfig.from_dict(config_dict["text_config"]) + ) else: raise ValueError(f"Config file not found at {model_path}") @@ -186,19 +206,18 @@ class LanguageModel(nn.Module): for i, wf in enumerate(weight_files): weights.update(mx.load(str(wf))) - if hasattr(language_model, "sanitize"): weights = language_model.sanitize(weights=weights) - - apply_quantization(model=language_model, weights=weights, quantization=quantization) + apply_quantization( + model=language_model, weights=weights, quantization=quantization + ) language_model.load_weights(list(weights.items()), strict=False) return language_model - class ConnectorAttention(nn.Module): def __init__( @@ -206,6 +225,7 @@ class ConnectorAttention(nn.Module): dim: int = 3840, num_heads: int = 30, head_dim: int = 128, + has_gate_logits: bool = False, ): super().__init__() self.num_heads = num_heads @@ -216,13 +236,14 @@ class ConnectorAttention(nn.Module): self.to_q = nn.Linear(dim, inner_dim, bias=True) self.to_k = nn.Linear(dim, inner_dim, bias=True) self.to_v = nn.Linear(dim, inner_dim, bias=True) - # Direct attribute for MLX parameter tracking (not a list) self.to_out = nn.Linear(inner_dim, dim, bias=True) - # Standard RMSNorm (not Gemma-style) on full inner_dim self.q_norm = nn.RMSNorm(inner_dim, eps=1e-6) self.k_norm = nn.RMSNorm(inner_dim, eps=1e-6) + if has_gate_logits: + self.to_gate_logits = nn.Linear(dim, num_heads, bias=True) + def __call__( self, x: mx.array, @@ -231,30 +252,44 @@ class ConnectorAttention(nn.Module): ) -> mx.array: batch_size, seq_len, _ = x.shape + # Compute per-head gate early (from original input) + gate = None + if hasattr(self, "to_gate_logits"): + gate = 2.0 * mx.sigmoid(self.to_gate_logits(x)) # (B, seq, heads) + # Project to Q, K, V - q = self.to_q(x) # (B, seq, inner_dim) + q = self.to_q(x) k = self.to_k(x) v = self.to_v(x) - # QK normalization on full inner_dim BEFORE reshape (matches PyTorch) + # QK normalization on full inner_dim BEFORE reshape q = self.q_norm(q) k = self.k_norm(k) # Reshape to (B, H, T, D) for SPLIT RoPE - q = mx.reshape(q, (batch_size, seq_len, self.num_heads, self.head_dim)).transpose(0, 2, 1, 3) - k = mx.reshape(k, (batch_size, seq_len, self.num_heads, self.head_dim)).transpose(0, 2, 1, 3) - v = mx.reshape(v, (batch_size, seq_len, self.num_heads, self.head_dim)).transpose(0, 2, 1, 3) + q = mx.reshape( + q, (batch_size, seq_len, self.num_heads, self.head_dim) + ).transpose(0, 2, 1, 3) + k = mx.reshape( + k, (batch_size, seq_len, self.num_heads, self.head_dim) + ).transpose(0, 2, 1, 3) + v = mx.reshape( + v, (batch_size, seq_len, self.num_heads, self.head_dim) + ).transpose(0, 2, 1, 3) if pe is not None: - # pe: tuple of (cos, sin) each with shape (1, num_heads, seq_len, head_dim//2) - # Apply SPLIT RoPE: operates on first half of head dimensions q = self._apply_split_rope(q, pe[0], pe[1]) k = self._apply_split_rope(k, pe[0], pe[1]) - # No mask needed for connector - after register replacement, all positions are valid out = mx.fast.scaled_dot_product_attention(q, k, v, scale=self.scale, mask=None) out = out.transpose(0, 2, 1, 3).reshape(batch_size, seq_len, -1) + # Apply per-head gating + if gate is not None: + out = mx.reshape(out, (batch_size, seq_len, self.num_heads, self.head_dim)) + out = out * gate[..., None] + out = mx.reshape(out, (batch_size, seq_len, -1)) + return self.to_out(out) def _apply_split_rope( @@ -292,7 +327,7 @@ class ConnectorAttention(nn.Module): out2 = x2 * cos_freq + x1 * sin_freq return mx.concatenate([out1, out2], axis=-1).astype(input_dtype) - + class GEGLU(nn.Module): """GELU-gated linear unit.""" @@ -316,7 +351,7 @@ class ConnectorFeedForward(nn.Module): self.proj_out = nn.Linear(inner_dim, dim, bias=True) def __call__(self, x: mx.array) -> mx.array: - x = nn.gelu(self.proj_in(x)) + x = nn.gelu_approx(self.proj_in(x)) x = self.dropout(x) x = self.proj_out(x) return x @@ -324,9 +359,17 @@ class ConnectorFeedForward(nn.Module): class ConnectorTransformerBlock(nn.Module): - def __init__(self, dim: int = 3840, num_heads: int = 30, head_dim: int = 128): + def __init__( + self, + dim: int = 3840, + num_heads: int = 30, + head_dim: int = 128, + has_gate_logits: bool = False, + ): super().__init__() - self.attn1 = ConnectorAttention(dim, num_heads, head_dim) + self.attn1 = ConnectorAttention( + dim, num_heads, head_dim, has_gate_logits=has_gate_logits + ) self.ff = ConnectorFeedForward(dim) def __call__( @@ -365,6 +408,7 @@ class Embeddings1DConnector(nn.Module): num_learnable_registers: int = 128, positional_embedding_theta: float = 10000.0, positional_embedding_max_pos: list = None, + has_gate_logits: bool = False, ): super().__init__() self.dim = dim @@ -372,18 +416,21 @@ class Embeddings1DConnector(nn.Module): self.head_dim = head_dim self.num_learnable_registers = num_learnable_registers self.positional_embedding_theta = positional_embedding_theta - self.positional_embedding_max_pos = positional_embedding_max_pos or [4096] + self.positional_embedding_max_pos = positional_embedding_max_pos or [1] - # Use dict with int keys for MLX to track parameters (lists are not tracked) self.transformer_1d_blocks = { - i: ConnectorTransformerBlock(dim, num_heads, head_dim) + i: ConnectorTransformerBlock( + dim, num_heads, head_dim, has_gate_logits=has_gate_logits + ) for i in range(num_layers) } if num_learnable_registers > 0: self.learnable_registers = mx.zeros((num_learnable_registers, dim)) - def _precompute_freqs_cis(self, seq_len: int, dtype: mx.Dtype) -> Tuple[mx.array, mx.array]: + def _precompute_freqs_cis( + self, seq_len: int, dtype: mx.Dtype + ) -> Tuple[mx.array, mx.array]: """Compute RoPE frequencies for connector (SPLIT type matching PyTorch). Returns tuple of (cos, sin) each with shape (1, num_heads, seq_len, head_dim//2). @@ -391,50 +438,54 @@ class Embeddings1DConnector(nn.Module): import numpy as np - dim = self.num_heads * self.head_dim # inner_dim = 3840 + dim = self.num_heads * self.head_dim # inner_dim theta = self.positional_embedding_theta - max_pos = self.positional_embedding_max_pos # [4096] from PyTorch + max_pos = self.positional_embedding_max_pos # [1] = PyTorch default n_elem = 2 * len(max_pos) # = 2 start = 1.0 end = theta - num_indices = dim // n_elem # 1920 + num_indices = dim // n_elem - # Use numpy float64 for precision (double_precision_rope=True in PyTorch) + # generate_freq_grid_np: compute indices in float64 then cast to float32 + # (matches PyTorch: double_precision_rope generates in np.float64, + # but returns torch.float32) log_start = np.log(start) / np.log(theta) # = 0 log_end = np.log(end) / np.log(theta) # = 1 lin_space = np.linspace(log_start, log_end, num_indices, dtype=np.float64) - indices = (np.power(theta, lin_space) * (np.pi / 2)).astype(np.float64) + indices = (np.power(theta, lin_space) * (np.pi / 2)).astype(np.float32) - # Generate positions and compute freqs (matches generate_freqs) - positions = np.arange(seq_len, dtype=np.float64) - # Scale positions by max_pos (PyTorch uses max_pos=[4096]) + # generate_freqs: positions and freqs in float32 (matching PyTorch) + positions = np.arange(seq_len, dtype=np.float32) fractional_positions = positions / max_pos[0] scaled_positions = fractional_positions * 2 - 1 # Shape: (seq_len,) - # freqs = indices * scaled_positions (outer product) - # Shape: (seq_len, num_indices) + # freqs = scaled_positions * indices (outer product) in float32 freqs = scaled_positions[:, None] * indices[None, :] - # Compute cos/sin - cos_freq = np.cos(freqs) # (seq_len, 1920) + # split_freqs_cis: cos/sin in float32 (matching PyTorch) + expected_freqs = dim // 2 + pad_size = expected_freqs - freqs.shape[-1] + + cos_freq = np.cos(freqs) # (seq_len, num_indices) sin_freq = np.sin(freqs) - # For SPLIT RoPE: pad to head_dim//2 = 64 per head, then reshape to (1, H, T, D//2) - # Current: (T, 1920) -> need (1, 30, T, 64) - # 30 heads * 64 = 1920, so no padding needed + if pad_size > 0: + cos_padding = np.ones((seq_len, pad_size), dtype=np.float32) + sin_padding = np.zeros((seq_len, pad_size), dtype=np.float32) + cos_freq = np.concatenate([cos_padding, cos_freq], axis=-1) + sin_freq = np.concatenate([sin_padding, sin_freq], axis=-1) - # Reshape: (T, 1920) -> (T, 30, 64) -> (1, 30, T, 64) + # Reshape: (T, dim//2) -> (T, H, D//2) -> (1, H, T, D//2) cos_freq = cos_freq.reshape(seq_len, self.num_heads, self.head_dim // 2) sin_freq = sin_freq.reshape(seq_len, self.num_heads, self.head_dim // 2) - # Transpose to (1, H, T, D//2) cos_freq = np.transpose(cos_freq, (1, 0, 2))[np.newaxis, ...] sin_freq = np.transpose(sin_freq, (1, 0, 2))[np.newaxis, ...] # Convert to MLX - cos_full = mx.array(cos_freq.astype(np.float32)) - sin_full = mx.array(sin_freq.astype(np.float32)) + cos_full = mx.array(cos_freq) + sin_full = mx.array(sin_freq) return cos_full.astype(dtype), sin_full.astype(dtype) @@ -448,11 +499,15 @@ class Embeddings1DConnector(nn.Module): # Binary mask: 1 for valid tokens, 0 for padded # attention_mask is additive: 0 for valid, large negative for padded - mask_binary = (attention_mask.squeeze(1).squeeze(1) >= -9000.0).astype(mx.int32) # (batch, seq) + mask_binary = (attention_mask.squeeze(1).squeeze(1) >= -9000.0).astype( + mx.int32 + ) # (batch, seq) # Tile registers to match sequence length, cast to hidden_states dtype num_tiles = seq_len // self.num_learnable_registers - registers = mx.tile(self.learnable_registers, (num_tiles, 1)).astype(dtype) # (seq_len, dim) + registers = mx.tile(self.learnable_registers, (num_tiles, 1)).astype( + dtype + ) # (seq_len, dim) # Process each batch item (PyTorch uses advanced indexing) result_list = [] @@ -465,25 +520,33 @@ class Embeddings1DConnector(nn.Module): # Extract valid tokens (where mask is 1) # Since we have left-padded input, valid tokens are at the end - valid_tokens = hs_b[seq_len - num_valid:] # (num_valid, dim) + valid_tokens = hs_b[seq_len - num_valid :] # (num_valid, dim) # Pad with zeros on the right to get back to seq_len pad_length = seq_len - num_valid if pad_length > 0: padding = mx.zeros((pad_length, dim), dtype=dtype) - adjusted = mx.concatenate([valid_tokens, padding], axis=0) # (seq_len, dim) + adjusted = mx.concatenate( + [valid_tokens, padding], axis=0 + ) # (seq_len, dim) else: adjusted = valid_tokens # Create flipped mask: 1s at front (where valid tokens now are), 0s at back - flipped_mask = mx.concatenate([ - mx.ones((num_valid,), dtype=mx.int32), - mx.zeros((pad_length,), dtype=mx.int32) - ], axis=0) # (seq,) + flipped_mask = mx.concatenate( + [ + mx.ones((num_valid,), dtype=mx.int32), + mx.zeros((pad_length,), dtype=mx.int32), + ], + axis=0, + ) # (seq,) # Combine: valid tokens at front, registers at back flipped_mask_expanded = flipped_mask[:, None].astype(dtype) # (seq, 1) - combined = flipped_mask_expanded * adjusted + (1 - flipped_mask_expanded) * registers + combined = ( + flipped_mask_expanded * adjusted + + (1 - flipped_mask_expanded) * registers + ) result_list.append(combined) hidden_states = mx.stack(result_list, axis=0) # (batch, seq, dim) @@ -510,7 +573,9 @@ class Embeddings1DConnector(nn.Module): # Process through transformer blocks for i in range(len(self.transformer_1d_blocks)): - hidden_states = self.transformer_1d_blocks[i](hidden_states, attention_mask, freqs_cis) + hidden_states = self.transformer_1d_blocks[i]( + hidden_states, attention_mask, freqs_cis + ) # Final RMS norm hidden_states = rms_norm(hidden_states) @@ -518,7 +583,6 @@ class Embeddings1DConnector(nn.Module): return hidden_states, attention_mask - def norm_and_concat_hidden_states( hidden_states: List[mx.array], attention_mask: mx.array, @@ -551,8 +615,12 @@ def norm_and_concat_hidden_states( mean = mx.sum(masked, axis=(1, 2), keepdims=True) / (denom + eps) # Compute masked min/max per layer - x_for_min = mx.where(mask, stacked, mx.full(stacked.shape, float('inf'), dtype=dtype)) - x_for_max = mx.where(mask, stacked, mx.full(stacked.shape, float('-inf'), dtype=dtype)) + x_for_min = mx.where( + mask, stacked, mx.full(stacked.shape, float("inf"), dtype=dtype) + ) + x_for_max = mx.where( + mask, stacked, mx.full(stacked.shape, float("-inf"), dtype=dtype) + ) x_min = mx.min(x_for_min, axis=(1, 2), keepdims=True) x_max = mx.max(x_for_max, axis=(1, 2), keepdims=True) range_val = x_max - x_min @@ -570,17 +638,106 @@ def norm_and_concat_hidden_states( return normed -class GemmaFeaturesExtractor(nn.Module): +def norm_and_concat_per_token_rms( + encoded_text: mx.array, + attention_mask: mx.array, +) -> mx.array: + """Per-token RMSNorm normalization for V2 feature extraction (LTX-2.3). - def __init__(self, input_dim: int = 188160, output_dim: int = 3840): + Args: + encoded_text: (B, T, D, L) stacked hidden states + attention_mask: (B, T) binary mask (1=valid, 0=padding) + + Returns: + (B, T, D*L) normalized tensor with padding zeroed out. + """ + b, t, d, num_layers = encoded_text.shape + dtype = encoded_text.dtype + + # Per-token RMSNorm across hidden dimension: variance = mean(x^2) over dim D + variance = mx.mean( + encoded_text.astype(mx.float32) ** 2, axis=2, keepdims=True + ) # (B, T, 1, L) + normed = encoded_text.astype(mx.float32) * mx.rsqrt(variance + 1e-6) + normed = normed.astype(dtype) + + # Flatten layers: (B, T, D*L) + normed = mx.reshape(normed, (b, t, d * num_layers)) + + # Zero out padded positions + mask_3d = attention_mask[:, :, None].astype(mx.bool_) # (B, T, 1) + normed = mx.where(mask_3d, normed, mx.zeros_like(normed)) + + return normed + + +def _rescale_norm(x: mx.array, target_dim: int, source_dim: int) -> mx.array: + """Rescale normalization: x * sqrt(target_dim / source_dim).""" + return x * math.sqrt(target_dim / source_dim) + + +class GemmaFeaturesExtractor(nn.Module): + """V1 feature extractor (LTX-2): 8 * (x - mean) / range normalization.""" + + def __init__( + self, input_dim: int = 188160, output_dim: int = 3840, bias: bool = False + ): super().__init__() - self.aggregate_embed = nn.Linear(input_dim, output_dim, bias=False) + self.aggregate_embed = nn.Linear(input_dim, output_dim, bias=bias) def __call__(self, x: mx.array) -> mx.array: return self.aggregate_embed(x) +class GemmaFeaturesExtractorV2(nn.Module): + """V2 feature extractor (LTX-2.3): per-token RMSNorm + rescale normalization.""" + def __init__( + self, + flat_dim: int, + embedding_dim: int, + video_output_dim: int, + audio_output_dim: int, + bias: bool = True, + ): + super().__init__() + self.embedding_dim = embedding_dim # Gemma hidden_dim (3840), used for rescale + self.video_aggregate_embed = nn.Linear(flat_dim, video_output_dim, bias=bias) + self.audio_aggregate_embed = nn.Linear(flat_dim, audio_output_dim, bias=bias) + + def __call__( + self, + hidden_states: List[mx.array], + attention_mask: mx.array, + mode: str = "video", + ) -> mx.array: + """Extract features with per-token RMSNorm + rescale. + + Args: + hidden_states: List of hidden states from all Gemma layers + attention_mask: Binary attention mask (B, T) + mode: "video" or "audio" to select which aggregate embed to use + + Returns: + Projected features + """ + # Stack hidden states: (B, T, D, L) + encoded = mx.stack(hidden_states, axis=-1) + + # Per-token RMSNorm + flatten + normed = norm_and_concat_per_token_rms(encoded, attention_mask) + normed = normed.astype(encoded.dtype) + + if mode == "video": + target_dim = self.video_aggregate_embed.weight.shape[0] + return self.video_aggregate_embed( + _rescale_norm(normed, target_dim, self.embedding_dim) + ) + else: + target_dim = self.audio_aggregate_embed.weight.shape[0] + return self.audio_aggregate_embed( + _rescale_norm(normed, target_dim, self.embedding_dim) + ) class AudioEmbeddingsConnector(nn.Module): @@ -601,129 +758,205 @@ class LTX2TextEncoder(nn.Module): hidden_dim: int = 3840, audio_dim: int = 2048, num_layers: int = 49, # 48 transformer layers + 1 embedding + has_prompt_adaln: bool = False, ): super().__init__() self.hidden_dim = hidden_dim self.audio_dim = audio_dim self.num_layers = num_layers + self.has_prompt_adaln = has_prompt_adaln self.language_model = None - # Feature extractor: 3840*49 -> 3840 - self.feature_extractor = GemmaFeaturesExtractor( - input_dim=hidden_dim * num_layers, - output_dim=hidden_dim, - ) + feature_input_dim = hidden_dim * num_layers - # Video embeddings connector: 2-layer transformer - self.video_embeddings_connector = Embeddings1DConnector( - dim=hidden_dim, - num_heads=30, - head_dim=128, - num_layers=2, - num_learnable_registers=128, - positional_embedding_max_pos=[4096], # Match PyTorch - ) + if has_prompt_adaln: + # LTX-2.3: V2 feature extractor with per-token RMSNorm + rescale + video_output_dim = 4096 + audio_output_dim = 2048 + self.feature_extractor_v2 = GemmaFeaturesExtractorV2( + flat_dim=feature_input_dim, # 3840 * 49 = 188160 (concatenated) + embedding_dim=hidden_dim, # 3840 (Gemma hidden_dim, for rescale) + video_output_dim=video_output_dim, + audio_output_dim=audio_output_dim, + bias=True, + ) - # Audio embeddings connector: separate 2-layer transformer (same architecture as video) - # Both connectors process the feature extractor output independently - self.audio_embeddings_connector = Embeddings1DConnector( - dim=hidden_dim, - num_heads=30, - head_dim=128, - num_layers=2, - num_learnable_registers=128, - positional_embedding_max_pos=[4096], # Match PyTorch - ) + # Deeper connectors with matching dims and gate_logits + # connector_positional_embedding_max_pos=[4096] from LTX-2.3 safetensors + # config (nested under config.transformer.connector_positional_embedding_max_pos) + self.video_embeddings_connector = Embeddings1DConnector( + dim=video_output_dim, + num_heads=32, + head_dim=128, + num_layers=8, + num_learnable_registers=128, + positional_embedding_max_pos=[4096], + has_gate_logits=True, + ) + self.audio_embeddings_connector = Embeddings1DConnector( + dim=audio_output_dim, + num_heads=32, + head_dim=64, + num_layers=8, + num_learnable_registers=128, + positional_embedding_max_pos=[4096], + has_gate_logits=True, + ) + else: + # LTX-2: shared feature extractor, 3840-dim connectors + self.feature_extractor = GemmaFeaturesExtractor( + feature_input_dim, hidden_dim + ) + + self.video_embeddings_connector = Embeddings1DConnector( + dim=hidden_dim, + num_heads=30, + head_dim=128, + num_layers=2, + num_learnable_registers=128, + positional_embedding_max_pos=[1], + ) + self.audio_embeddings_connector = Embeddings1DConnector( + dim=hidden_dim, + num_heads=30, + head_dim=128, + num_layers=2, + num_learnable_registers=128, + positional_embedding_max_pos=[1], + ) self.processor = None - def load(self, model_path: Optional[str] = None, text_encoder_path: Optional[str] = "google/gemma-3-12b-it"): + def load( + self, + model_path: Optional[str] = None, + text_encoder_path: Optional[str] = "google/gemma-3-12b-it", + ): if Path(str(text_encoder_path)).joinpath("text_encoder").is_dir(): text_encoder_path = str(Path(text_encoder_path) / "text_encoder") - + self.language_model = LanguageModel.from_pretrained(text_encoder_path) - # Load transformer weights for feature extractor and connector - transformer_files = list(model_path.glob("ltx-2-19*.safetensors")) - if transformer_files: - transformer_weights = mx.load(str(transformer_files[0])) + # Load transformer weights for feature extractor and connector. + # These weights are stored differently depending on the repo format: + # 1. Monolithic (Lightricks/LTX-2): single ltx-2-19b-*.safetensors at root + # with raw PyTorch key names (model.diffusion_model.* prefix) + # 2. Reformatted (prince-canuma/LTX-2-distilled): separate text_projections/ + # directory with pre-sanitized keys (no prefix, already renamed) + transformer_weights = {} + is_reformatted = False - # Load feature extractor (aggregate_embed) - if "text_embedding_projection.aggregate_embed.weight" in transformer_weights: - self.feature_extractor.aggregate_embed.weight = transformer_weights[ - "text_embedding_projection.aggregate_embed.weight" - ] + # Try reformatted layout first: text_projections/ subdirectory + text_proj_dir = model_path / "text_projections" + if text_proj_dir.is_dir(): + is_reformatted = True + for sf in text_proj_dir.glob("*.safetensors"): + transformer_weights.update(mx.load(str(sf))) + # Fall back to monolithic layout: ltx-2-19*.safetensors at root + if not transformer_weights: + transformer_files = list(model_path.glob("ltx-2-19*.safetensors")) + if transformer_files: + transformer_weights = mx.load(str(transformer_files[0])) - # Load video_embeddings_connector weights - connector_weights = {} - for key, value in transformer_weights.items(): - if key.startswith("model.diffusion_model.video_embeddings_connector."): - new_key = key.replace("model.diffusion_model.video_embeddings_connector.", "") - connector_weights[new_key] = value - - if connector_weights: - # Map weight names to our structure - mapped_weights = {} - for key, value in connector_weights.items(): - new_key = key - # Map ff.net.0.proj -> ff.proj_in (GEGLU projection) - new_key = new_key.replace(".ff.net.0.proj.", ".ff.proj_in.") - # Map ff.net.2 -> ff.proj_out (output Linear) - new_key = new_key.replace(".ff.net.2.", ".ff.proj_out.") - # Map to_out.0 -> to_out (Sequential -> direct) - new_key = new_key.replace(".to_out.0.", ".to_out.") - mapped_weights[new_key] = value - - self.video_embeddings_connector.load_weights( - list(mapped_weights.items()), strict=False - ) - - # Manually load learnable_registers (it's a plain mx.array, not a parameter) - if "learnable_registers" in connector_weights: - self.video_embeddings_connector.learnable_registers = connector_weights["learnable_registers"] - - # Load audio_embeddings_connector weights (same structure as video connector) - audio_connector_weights = {} - for key, value in transformer_weights.items(): - if key.startswith("model.diffusion_model.audio_embeddings_connector."): - new_key = key.replace("model.diffusion_model.audio_embeddings_connector.", "") - audio_connector_weights[new_key] = value - - if audio_connector_weights: - # Map weight names to our structure (same as video connector) - mapped_audio_weights = {} - for key, value in audio_connector_weights.items(): - new_key = key - # Map ff.net.0.proj -> ff.proj_in (GEGLU projection) - new_key = new_key.replace(".ff.net.0.proj.", ".ff.proj_in.") - # Map ff.net.2 -> ff.proj_out (output Linear) - new_key = new_key.replace(".ff.net.2.", ".ff.proj_out.") - # Map to_out.0 -> to_out (Sequential -> direct) - new_key = new_key.replace(".to_out.0.", ".to_out.") - mapped_audio_weights[new_key] = value - - self.audio_embeddings_connector.load_weights( - list(mapped_audio_weights.items()), strict=False - ) - - # Manually load learnable_registers (it's a plain mx.array, not a parameter) - if "learnable_registers" in audio_connector_weights: - self.audio_embeddings_connector.learnable_registers = audio_connector_weights["learnable_registers"] + if transformer_weights: + self._load_feature_extractors(transformer_weights, is_reformatted) + self._load_connector( + "video_embeddings_connector", transformer_weights, is_reformatted + ) + self._load_connector( + "audio_embeddings_connector", transformer_weights, is_reformatted + ) + else: + print( + "WARNING: No transformer weights found for text projection connectors. " + "Text conditioning will use uninitialized weights!" + ) # Load tokenizer from transformers import AutoTokenizer + tokenizer_path = model_path / "tokenizer" if tokenizer_path.exists(): - self.processor = AutoTokenizer.from_pretrained(str(tokenizer_path), trust_remote_code=True) + self.processor = AutoTokenizer.from_pretrained( + str(tokenizer_path), trust_remote_code=True + ) else: - self.processor = AutoTokenizer.from_pretrained(text_encoder_path, trust_remote_code=True) + try: + self.processor = AutoTokenizer.from_pretrained( + text_encoder_path, trust_remote_code=True + ) + except Exception: + self.processor = AutoTokenizer.from_pretrained( + "google/gemma-3-12b-it", trust_remote_code=True + ) # Set left padding to match official LTX-2 text encoder self.processor.padding_side = "left" print("Text encoder loaded successfully") + def _load_feature_extractors(self, weights: dict, is_reformatted: bool): + """Load feature extractor weights for both LTX-2 and LTX-2.3.""" + if self.has_prompt_adaln: + # LTX-2.3: V2 feature extractor with separate video/audio aggregate embeds + for attr, prefix in [ + ("video_aggregate_embed", "video_aggregate_embed"), + ("audio_aggregate_embed", "audio_aggregate_embed"), + ]: + w_key = f"{prefix}.weight" + b_key = f"{prefix}.bias" + if w_key in weights: + submodule = getattr(self.feature_extractor_v2, attr) + submodule.weight = weights[w_key] + if b_key in weights: + submodule.bias = weights[b_key] + else: + # LTX-2: single aggregate_embed + agg_key = ( + "aggregate_embed.weight" + if is_reformatted + else "text_embedding_projection.aggregate_embed.weight" + ) + if agg_key in weights: + self.feature_extractor.aggregate_embed.weight = weights[agg_key] + + def _load_connector(self, name: str, weights: dict, is_reformatted: bool): + """Load a connector's weights (video or audio).""" + connector = getattr(self, name) + + # Extract connector-specific weights + connector_weights = {} + if is_reformatted: + prefix = f"{name}." + for key, value in weights.items(): + if key.startswith(prefix): + connector_weights[key[len(prefix) :]] = value + else: + mono_prefix = f"model.diffusion_model.{name}." + for key, value in weights.items(): + if key.startswith(mono_prefix): + connector_weights[key[len(mono_prefix) :]] = value + + if not connector_weights: + return + + # Sanitize key names (only needed for monolithic/raw PyTorch keys) + mapped = {} + for key, value in connector_weights.items(): + new_key = key + if not is_reformatted: + new_key = new_key.replace(".ff.net.0.proj.", ".ff.proj_in.") + new_key = new_key.replace(".ff.net.2.", ".ff.proj_out.") + new_key = new_key.replace(".to_out.0.", ".to_out.") + mapped[new_key] = value + + connector.load_weights(list(mapped.items()), strict=False) + + # Manually load learnable_registers (plain mx.array, not tracked as parameter) + if "learnable_registers" in connector_weights: + connector.learnable_registers = connector_weights["learnable_registers"] + def encode( self, prompt: str, @@ -755,22 +988,62 @@ class LTX2TextEncoder(nn.Module): input_ids = mx.array(inputs["input_ids"]) attention_mask = mx.array(inputs["attention_mask"]) - _, all_hidden_states = self.language_model(inputs=input_ids, input_embeddings=None, attention_mask=attention_mask, output_hidden_states=True) - concat_hidden = norm_and_concat_hidden_states( - all_hidden_states, attention_mask, padding_side="left" + _, all_hidden_states = self.language_model( + inputs=input_ids, + input_embeddings=None, + attention_mask=attention_mask, + output_hidden_states=True, ) - features = self.feature_extractor(concat_hidden) - additive_mask = (attention_mask - 1).astype(features.dtype) - additive_mask = additive_mask.reshape(attention_mask.shape[0], 1, 1, -1) * 1e9 - video_embeddings, _ = self.video_embeddings_connector(features, additive_mask) + if self.has_prompt_adaln: + # LTX-2.3: V2 feature extraction (per-token RMSNorm + rescale) + video_features = self.feature_extractor_v2( + all_hidden_states, attention_mask, mode="video" + ) + additive_mask = (attention_mask - 1).astype(video_features.dtype) + additive_mask = ( + additive_mask.reshape(attention_mask.shape[0], 1, 1, -1) * 1e9 + ) - if return_audio_embeddings: - # Process features through audio connector independently (same input as video) - audio_embeddings, _ = self.audio_embeddings_connector(features, additive_mask) - return video_embeddings, audio_embeddings + video_embeddings, _ = self.video_embeddings_connector( + video_features, additive_mask + ) + + if return_audio_embeddings: + audio_features = self.feature_extractor_v2( + all_hidden_states, attention_mask, mode="audio" + ) + audio_mask = (attention_mask - 1).astype(audio_features.dtype) + audio_mask = audio_mask.reshape(attention_mask.shape[0], 1, 1, -1) * 1e9 + audio_embeddings, _ = self.audio_embeddings_connector( + audio_features, audio_mask + ) + return video_embeddings, audio_embeddings + else: + return video_embeddings, attention_mask else: - return video_embeddings, attention_mask + # LTX-2: V1 feature extraction (8 * (x - mean) / range) + concat_hidden = norm_and_concat_hidden_states( + all_hidden_states, attention_mask, padding_side="left" + ) + + video_features = self.feature_extractor(concat_hidden) + additive_mask = (attention_mask - 1).astype(video_features.dtype) + additive_mask = ( + additive_mask.reshape(attention_mask.shape[0], 1, 1, -1) * 1e9 + ) + + video_embeddings, _ = self.video_embeddings_connector( + video_features, additive_mask + ) + + if return_audio_embeddings: + audio_embeddings, _ = self.audio_embeddings_connector( + video_features, additive_mask + ) + return video_embeddings, audio_embeddings + else: + return video_embeddings, attention_mask def __call__( self, @@ -806,7 +1079,7 @@ class LTX2TextEncoder(nn.Module): # Remove leading/trailing whitespace response = response.strip() # Remove any leading punctuation - response = re.sub(r'^[^\w\s]+', '', response) + response = re.sub(r"^[^\w\s]+", "", response) return response def _apply_chat_template( @@ -827,7 +1100,9 @@ class LTX2TextEncoder(nn.Module): elif isinstance(content, list): # Handle multimodal content (image + text) text_parts = [c["text"] for c in content if c.get("type") == "text"] - formatted += f"user\n{' '.join(text_parts)}\n" + formatted += ( + f"user\n{' '.join(text_parts)}\n" + ) elif role == "assistant": formatted += f"model\n{content}\n" # Add generation prompt @@ -854,12 +1129,13 @@ class LTX2TextEncoder(nn.Module): Returns: Enhanced prompt string """ - from tqdm import tqdm try: from mlx_lm import stream_generate from mlx_lm.sample_utils import make_logits_processors, make_sampler except ImportError: - logging.warning("mlx-lm not available for prompt enhancement. Using original prompt.") + logging.warning( + "mlx-lm not available for prompt enhancement. Using original prompt." + ) return prompt if self.processor is None: @@ -878,7 +1154,6 @@ class LTX2TextEncoder(nn.Module): # Use mlx-lm generate with temperature sampling mx.random.seed(seed) - # Tokenize inputs = self.processor( formatted, @@ -887,53 +1162,70 @@ class LTX2TextEncoder(nn.Module): ) input_ids = mx.array(inputs["input_ids"]) - sampler = make_sampler(kwargs.get("temperature", 0.7), kwargs.get("top_p", 1.0), top_k=kwargs.get("top_k", -1)) + sampler = make_sampler( + kwargs.get("temperature", 0.7), + kwargs.get("top_p", 1.0), + top_k=kwargs.get("top_k", -1), + ) logits_processors = make_logits_processors( kwargs.get("logit_bias", None), kwargs.get("repetition_penalty", 1.3), kwargs.get("repetition_context_size", 20), ) - + generated_token_count = 0 generated_tokens = [] - for i, response in enumerate( - tqdm( - stream_generate( - self.language_model, - tokenizer=self.processor, - prompt=input_ids.squeeze(0), - max_tokens=max_tokens, - sampler=sampler, - logits_processors=logits_processors, - ), - total=max_tokens, - disable=not verbose, - ) - ): - next_token = mx.array([response.token]) - input_ids = mx.concatenate([input_ids, next_token[None, :]], axis=1) - generated_tokens.append(next_token.squeeze()) - generated_token_count += 1 + console = Console() - if i % 50 == 0: - mx.clear_cache() + generator = stream_generate( + self.language_model, + tokenizer=self.processor, + prompt=input_ids.squeeze(0), + max_tokens=max_tokens, + sampler=sampler, + logits_processors=logits_processors, + ) - # Check for EOS - if response.token == 1 or response.token == 107: # EOS tokens - break + progress = Progress( + SpinnerColumn(), + TextColumn("[progress.description]{task.description}"), + BarColumn(), + TaskProgressColumn(), + TimeRemainingColumn(), + console=console, + disable=not verbose, + ) + + with progress: + task = progress.add_task("[cyan]Generating[/]", total=max_tokens) + + for i, response in enumerate(generator): + next_token = mx.array([response.token]) + input_ids = mx.concatenate([input_ids, next_token[None, :]], axis=1) + generated_tokens.append(response.token) + generated_token_count += 1 + progress.update(task, advance=1) + + if i % 50 == 0: + mx.clear_cache() + + # Check for EOS + if response.token == 1 or response.token == 107: # EOS tokens + progress.update(task, completed=max_tokens) + break mx.clear_cache() # Decode only the new tokens - - enhanced_prompt = self.processor.decode(generated_tokens, skip_special_tokens=True) + enhanced_prompt = self.processor.decode( + generated_tokens, skip_special_tokens=True + ) enhanced_prompt = self._clean_response(enhanced_prompt) logging.info(f"Enhanced prompt: {enhanced_prompt}") return enhanced_prompt - def enhance_i2v( self, prompt: str, @@ -967,4 +1259,3 @@ def load_text_encoder(model_path: str = "/tmp/ltx2") -> LTX2TextEncoder: encoder = LTX2TextEncoder() encoder.load(model_path=model_path) return encoder - diff --git a/mlx_video/models/ltx/text_projection.py b/mlx_video/models/ltx_2/text_projection.py similarity index 98% rename from mlx_video/models/ltx/text_projection.py rename to mlx_video/models/ltx_2/text_projection.py index 55e684d..29165ca 100644 --- a/mlx_video/models/ltx/text_projection.py +++ b/mlx_video/models/ltx_2/text_projection.py @@ -11,7 +11,7 @@ class PixArtAlphaTextProjection(nn.Module): out_features: int | None = None, bias: bool = True, ): - + super().__init__() out_features = out_features or hidden_size diff --git a/mlx_video/models/ltx/transformer.py b/mlx_video/models/ltx_2/transformer.py similarity index 63% rename from mlx_video/models/ltx/transformer.py rename to mlx_video/models/ltx_2/transformer.py index 60ee7ec..2f2c914 100644 --- a/mlx_video/models/ltx/transformer.py +++ b/mlx_video/models/ltx_2/transformer.py @@ -4,34 +4,41 @@ from typing import Optional, Tuple import mlx.core as mx import mlx.nn as nn -from mlx_video.models.ltx.config import LTXRopeType, TransformerConfig -from mlx_video.models.ltx.attention import Attention -from mlx_video.models.ltx.feed_forward import FeedForward +from mlx_video.models.ltx_2.attention import Attention +from mlx_video.models.ltx_2.config import LTXRopeType, TransformerConfig +from mlx_video.models.ltx_2.feed_forward import FeedForward from mlx_video.utils import rms_norm @dataclass(frozen=True) class Modality: - latent: mx.array - timesteps: mx.array - positions: mx.array - context: mx.array + latent: mx.array + timesteps: mx.array + positions: mx.array + context: mx.array enabled: bool = True context_mask: Optional[mx.array] = None + # Optional precomputed positional embeddings (RoPE) to avoid recomputation + positional_embeddings: Optional[Tuple[mx.array, mx.array]] = None + # Raw sigma value (scalar per batch) for prompt adaln (LTX-2.3) + sigma: Optional[mx.array] = None @dataclass(frozen=True) class TransformerArgs: - x: mx.array - context: mx.array - context_mask: Optional[mx.array] - timesteps: mx.array - embedded_timestep: mx.array - positional_embeddings: Tuple[mx.array, mx.array] - cross_positional_embeddings: Optional[Tuple[mx.array, mx.array]] - cross_scale_shift_timestep: Optional[mx.array] - cross_gate_timestep: Optional[mx.array] + x: mx.array + context: mx.array + context_mask: Optional[mx.array] + timesteps: mx.array + embedded_timestep: mx.array + positional_embeddings: Tuple[mx.array, mx.array] + cross_positional_embeddings: Optional[Tuple[mx.array, mx.array]] + cross_scale_shift_timestep: Optional[mx.array] + cross_gate_timestep: Optional[mx.array] enabled: bool + # LTX-2.3: prompt-conditioned timestep embeddings for cross-attention + prompt_timesteps: Optional[mx.array] = None + prompt_embedded_timestep: Optional[mx.array] = None class BasicAVTransformerBlock(nn.Module): @@ -48,20 +55,13 @@ class BasicAVTransformerBlock(nn.Module): audio: Optional[TransformerConfig] = None, rope_type: LTXRopeType = LTXRopeType.INTERLEAVED, norm_eps: float = 1e-6, + has_prompt_adaln: bool = False, ): - """Initialize transformer block. - - Args: - idx: Block index - video: Video modality configuration - audio: Audio modality configuration - rope_type: Type of rotary position embedding - norm_eps: Epsilon for normalization - """ super().__init__() self.idx = idx self.norm_eps = norm_eps + self.has_prompt_adaln = has_prompt_adaln # Video components if video is not None: @@ -72,6 +72,7 @@ class BasicAVTransformerBlock(nn.Module): context_dim=None, # Self-attention rope_type=rope_type, norm_eps=norm_eps, + has_gate_logits=has_prompt_adaln, ) self.attn2 = Attention( query_dim=video.dim, @@ -80,10 +81,15 @@ class BasicAVTransformerBlock(nn.Module): dim_head=video.d_head, rope_type=rope_type, norm_eps=norm_eps, + has_gate_logits=has_prompt_adaln, ) self.ff = FeedForward(video.dim, dim_out=video.dim) - # 6 scale-shift parameters: 3 for attention, 3 for MLP - self.scale_shift_table = mx.zeros((6, video.dim)) + # 9 params for LTX-2.3 (self-attn + cross-attn + FFN), 6 for LTX-2 + num_ada_params = 9 if has_prompt_adaln else 6 + self.scale_shift_table = mx.zeros((num_ada_params, video.dim)) + + if has_prompt_adaln: + self.prompt_scale_shift_table = mx.zeros((2, video.dim)) # Audio components if audio is not None: @@ -94,6 +100,7 @@ class BasicAVTransformerBlock(nn.Module): context_dim=None, rope_type=rope_type, norm_eps=norm_eps, + has_gate_logits=has_prompt_adaln, ) self.audio_attn2 = Attention( query_dim=audio.dim, @@ -102,9 +109,14 @@ class BasicAVTransformerBlock(nn.Module): dim_head=audio.d_head, rope_type=rope_type, norm_eps=norm_eps, + has_gate_logits=has_prompt_adaln, ) self.audio_ff = FeedForward(audio.dim, dim_out=audio.dim) - self.audio_scale_shift_table = mx.zeros((6, audio.dim)) + num_audio_ada_params = 9 if has_prompt_adaln else 6 + self.audio_scale_shift_table = mx.zeros((num_audio_ada_params, audio.dim)) + + if has_prompt_adaln: + self.audio_prompt_scale_shift_table = mx.zeros((2, audio.dim)) # Cross-modal attention (when both video and audio are enabled) if audio is not None and video is not None: @@ -116,6 +128,7 @@ class BasicAVTransformerBlock(nn.Module): dim_head=audio.d_head, rope_type=rope_type, norm_eps=norm_eps, + has_gate_logits=has_prompt_adaln, ) # Video-to-Audio: Q from audio, K/V from video self.video_to_audio_attn = Attention( @@ -125,6 +138,7 @@ class BasicAVTransformerBlock(nn.Module): dim_head=audio.d_head, rope_type=rope_type, norm_eps=norm_eps, + has_gate_logits=has_prompt_adaln, ) # Scale-shift tables for cross-attention self.scale_shift_table_a2v_ca_audio = mx.zeros((5, audio.dim)) @@ -157,8 +171,7 @@ class BasicAVTransformerBlock(nn.Module): # timestep: (B, seq, num_params * dim) -> reshape to (B, seq, num_params, dim) timestep_reshaped = mx.reshape( - timestep, - (batch_size, timestep.shape[1], num_ada_params, -1) + timestep, (batch_size, timestep.shape[1], num_ada_params, -1) ) # Extract the relevant indices @@ -211,8 +224,12 @@ class BasicAVTransformerBlock(nn.Module): ) # Squeeze the sequence dimension if it's 1 - scale_shift_squeezed = tuple(mx.squeeze(t, axis=1) if t.shape[1] == 1 else t for t in scale_shift_ada) - gate_squeezed = tuple(mx.squeeze(t, axis=1) if t.shape[1] == 1 else t for t in gate_ada) + scale_shift_squeezed = tuple( + mx.squeeze(t, axis=1) if t.shape[1] == 1 else t for t in scale_shift_ada + ) + gate_squeezed = tuple( + mx.squeeze(t, axis=1) if t.shape[1] == 1 else t for t in gate_ada + ) return (*scale_shift_squeezed, *gate_squeezed) @@ -220,12 +237,18 @@ class BasicAVTransformerBlock(nn.Module): self, video: Optional[TransformerArgs] = None, audio: Optional[TransformerArgs] = None, + skip_video_self_attn: bool = False, + skip_audio_self_attn: bool = False, + skip_cross_modal: bool = False, ) -> Tuple[Optional[TransformerArgs], Optional[TransformerArgs]]: """Forward pass through transformer block. Args: video: Video modality arguments audio: Audio modality arguments + skip_video_self_attn: Skip video self-attention (for STG perturbation) + skip_audio_self_attn: Skip audio self-attention (for STG perturbation) + skip_cross_modal: Skip all cross-modal attention (for modality isolation) Returns: Tuple of (updated_video, updated_audio) TransformerArgs @@ -238,8 +261,16 @@ class BasicAVTransformerBlock(nn.Module): # Check which modalities to run run_vx = video is not None and video.enabled and vx.size > 0 run_ax = audio is not None and audio.enabled and ax.size > 0 - run_a2v = run_vx and (audio is not None and audio.enabled and ax.size > 0) - run_v2a = run_ax and (video is not None and video.enabled and vx.size > 0) + run_a2v = ( + run_vx + and (audio is not None and audio.enabled and ax.size > 0) + and not skip_cross_modal + ) + run_v2a = ( + run_ax + and (video is not None and video.enabled and vx.size > 0) + and not skip_cross_modal + ) # Process video self-attention and cross-attention with text if run_vx: @@ -247,16 +278,49 @@ class BasicAVTransformerBlock(nn.Module): self.scale_shift_table, vx.shape[0], video.timesteps, slice(0, 3) ) - # Self-attention with RoPE + # Self-attention with RoPE (skip_attention=True for STG perturbation) norm_vx = rms_norm(vx, eps=self.norm_eps) * (1 + vscale_msa) + vshift_msa - vx = vx + self.attn1(norm_vx, pe=video.positional_embeddings) * vgate_msa + vx = ( + vx + + self.attn1( + norm_vx, + pe=video.positional_embeddings, + skip_attention=skip_video_self_attn, + ) + * vgate_msa + ) # Cross-attention with text context - vx = vx + self.attn2( - rms_norm(vx, eps=self.norm_eps), - context=video.context, - mask=video.context_mask, - ) + if self.has_prompt_adaln: + # LTX-2.3: Q modulated by timestep (indices 6-8), context modulated by prompt_adaln + vshift_q, vscale_q, vgate_q = self.get_ada_values( + self.scale_shift_table, vx.shape[0], video.timesteps, slice(6, 9) + ) + vprompt_shift_kv, vprompt_scale_kv = self.get_ada_values( + self.prompt_scale_shift_table, + vx.shape[0], + video.prompt_timesteps, + slice(0, 2), + ) + attn_input = rms_norm(vx, eps=self.norm_eps) * (1 + vscale_q) + vshift_q + encoder_hidden_states = ( + video.context * (1 + vprompt_scale_kv) + vprompt_shift_kv + ) + vx = ( + vx + + self.attn2( + attn_input, + context=encoder_hidden_states, + mask=video.context_mask, + ) + * vgate_q + ) + else: + vx = vx + self.attn2( + rms_norm(vx, eps=self.norm_eps), + context=video.context, + mask=video.context_mask, + ) # Process audio self-attention and cross-attention with text if run_ax: @@ -264,16 +328,54 @@ class BasicAVTransformerBlock(nn.Module): self.audio_scale_shift_table, ax.shape[0], audio.timesteps, slice(0, 3) ) - # Self-attention with RoPE + # Self-attention with RoPE (skip_attention=True for STG perturbation) norm_ax = rms_norm(ax, eps=self.norm_eps) * (1 + ascale_msa) + ashift_msa - ax = ax + self.audio_attn1(norm_ax, pe=audio.positional_embeddings) * agate_msa + ax = ( + ax + + self.audio_attn1( + norm_ax, + pe=audio.positional_embeddings, + skip_attention=skip_audio_self_attn, + ) + * agate_msa + ) # Cross-attention with text context - ax = ax + self.audio_attn2( - rms_norm(ax, eps=self.norm_eps), - context=audio.context, - mask=audio.context_mask, - ) + if self.has_prompt_adaln: + # LTX-2.3: Q modulated by timestep (indices 6-8), context modulated by prompt_adaln + ashift_q, ascale_q, agate_q = self.get_ada_values( + self.audio_scale_shift_table, + ax.shape[0], + audio.timesteps, + slice(6, 9), + ) + aprompt_shift_kv, aprompt_scale_kv = self.get_ada_values( + self.audio_prompt_scale_shift_table, + ax.shape[0], + audio.prompt_timesteps, + slice(0, 2), + ) + attn_input_a = ( + rms_norm(ax, eps=self.norm_eps) * (1 + ascale_q) + ashift_q + ) + encoder_hidden_states_a = ( + audio.context * (1 + aprompt_scale_kv) + aprompt_shift_kv + ) + ax = ( + ax + + self.audio_attn2( + attn_input_a, + context=encoder_hidden_states_a, + mask=audio.context_mask, + ) + * agate_q + ) + else: + ax = ax + self.audio_attn2( + rms_norm(ax, eps=self.norm_eps), + context=audio.context, + mask=audio.context_mask, + ) # Audio-Video cross-modal attention if run_a2v or run_v2a: @@ -339,7 +441,7 @@ class BasicAVTransformerBlock(nn.Module): # Process video feed-forward if run_vx: vshift_mlp, vscale_mlp, vgate_mlp = self.get_ada_values( - self.scale_shift_table, vx.shape[0], video.timesteps, slice(3, None) + self.scale_shift_table, vx.shape[0], video.timesteps, slice(3, 6) ) vx_scaled = rms_norm(vx, eps=self.norm_eps) * (1 + vscale_mlp) + vshift_mlp vx = vx + self.ff(vx_scaled) * vgate_mlp @@ -347,7 +449,7 @@ class BasicAVTransformerBlock(nn.Module): # Process audio feed-forward if run_ax: ashift_mlp, ascale_mlp, agate_mlp = self.get_ada_values( - self.audio_scale_shift_table, ax.shape[0], audio.timesteps, slice(3, None) + self.audio_scale_shift_table, ax.shape[0], audio.timesteps, slice(3, 6) ) ax_scaled = rms_norm(ax, eps=self.norm_eps) * (1 + ascale_mlp) + ashift_mlp ax = ax + self.audio_ff(ax_scaled) * agate_mlp diff --git a/mlx_video/models/ltx/upsampler.py b/mlx_video/models/ltx_2/upsampler.py similarity index 53% rename from mlx_video/models/ltx/upsampler.py rename to mlx_video/models/ltx_2/upsampler.py index 7f43536..8ea8cd1 100644 --- a/mlx_video/models/ltx/upsampler.py +++ b/mlx_video/models/ltx_2/upsampler.py @@ -1,4 +1,5 @@ from typing import Tuple, Union + import mlx.core as mx import mlx.nn as nn @@ -36,11 +37,20 @@ class Conv3d(nn.Module): self.groups = groups # Weight shape: (C_out, KD, KH, KW, C_in) - scale = 1.0 / (in_channels * kernel_size[0] * kernel_size[1] * kernel_size[2]) ** 0.5 + scale = ( + 1.0 + / (in_channels * kernel_size[0] * kernel_size[1] * kernel_size[2]) ** 0.5 + ) self.weight = mx.random.uniform( low=-scale, high=scale, - shape=(out_channels, kernel_size[0], kernel_size[1], kernel_size[2], in_channels), + shape=( + out_channels, + kernel_size[0], + kernel_size[1], + kernel_size[2], + in_channels, + ), ) if bias: @@ -87,7 +97,6 @@ class GroupNorm3d(nn.Module): n, d, h, w, c = x.shape input_dtype = x.dtype - x = x.astype(mx.float32) # Reshape to (N, D*H*W, num_groups, C//num_groups) @@ -115,65 +124,138 @@ class GroupNorm3d(nn.Module): class PixelShuffle2D(nn.Module): - """Pixel shuffle for 2D spatial upsampling.""" + """Pixel shuffle for 2D spatial upsampling with per-axis factors.""" - def __init__(self, upscale_factor: int = 2): + def __init__(self, upscale_factor_h: int = 2, upscale_factor_w: int = 2): super().__init__() - self.upscale_factor = upscale_factor + self.rh = upscale_factor_h + self.rw = upscale_factor_w def __call__(self, x: mx.array) -> mx.array: - # x: (N, H, W, C) where C = out_channels * upscale_factor^2 + # x: (N, H, W, C) where C = out_channels * rh * rw n, h, w, c = x.shape - r = self.upscale_factor - out_c = c // (r * r) + rh, rw = self.rh, self.rw + out_c = c // (rh * rw) - # Reshape: (N, H, W, out_c, r, r) - x = mx.reshape(x, (n, h, w, out_c, r, r)) + # Reshape: (N, H, W, out_c, rh, rw) + x = mx.reshape(x, (n, h, w, out_c, rh, rw)) - # Permute: (N, H, r, W, r, out_c) + # Permute: (N, H, rh, W, rw, out_c) x = mx.transpose(x, (0, 1, 4, 2, 5, 3)) - # Reshape: (N, H*r, W*r, out_c) - x = mx.reshape(x, (n, h * r, w * r, out_c)) + # Reshape: (N, H*rh, W*rw, out_c) + x = mx.reshape(x, (n, h * rh, w * rw, out_c)) return x +class BlurDownsample(nn.Module): + """Anti-aliased downsampling with a fixed 5x5 binomial blur kernel. + + PyTorch source uses a depthwise conv with the binomial kernel. + The kernel weight is stored as (1, 1, 5, 5) and loaded via safetensors. + """ + + def __init__(self, stride: int = 2): + super().__init__() + self.stride = stride + # 5x5 binomial (1,4,6,4,1) kernel, normalized + # This will be overwritten by loaded weights if available + k = mx.array([1.0, 4.0, 6.0, 4.0, 1.0]) + kernel_2d = mx.outer(k, k) + kernel_2d = kernel_2d / kernel_2d.sum() + # MLX conv2d weight: (O, H, W, I) — we use (1, 5, 5, 1) for per-channel + self.kernel = kernel_2d.reshape(1, 5, 5, 1) + + def __call__(self, x: mx.array) -> mx.array: + # x: (N, H, W, C) channels-last + n, h, w, c = x.shape + + # Pad with edge replication (2 on each side for 5x5 kernel) + x = mx.pad(x, [(0, 0), (2, 2), (2, 2), (0, 0)], mode="edge") + + # Apply blur per-channel: reshape so each channel is a separate "batch" + # (N, H+4, W+4, C) -> (N*C, H+4, W+4, 1) + x = mx.transpose(x, (0, 3, 1, 2)) # (N, C, H+4, W+4) + x = mx.reshape(x, (n * c, h + 4, w + 4, 1)) + + # Depthwise conv: (N*C, H+4, W+4, 1) * (1, 5, 5, 1) -> (N*C, H_out, W_out, 1) + x = mx.conv2d(x, self.kernel, stride=(self.stride, self.stride)) + + _, h_out, w_out, _ = x.shape + # Reshape back: (N*C, H_out, W_out, 1) -> (N, C, H_out, W_out) -> (N, H_out, W_out, C) + x = mx.reshape(x, (n, c, h_out, w_out)) + x = mx.transpose(x, (0, 2, 3, 1)) + + return x + + +class SpatialUpsampler2x(nn.Module): + """Standard 2x spatial upsampler: Conv2d + PixelShuffle(2).""" + + def __init__(self, mid_channels: int = 1024): + super().__init__() + self.scale = 2.0 + # Sequential: conv (index 0) + pixel shuffle + # Weight key: upsampler.0.weight -> mapped to upsampler.conv.weight in sanitize + self.conv = nn.Conv2d(mid_channels, 4 * mid_channels, kernel_size=3, padding=1) + self.pixel_shuffle = PixelShuffle2D(2, 2) + + def __call__(self, x: mx.array) -> mx.array: + # x: (N, D, H, W, C) + n, d, h, w, c = x.shape + x = mx.reshape(x, (n * d, h, w, c)) + x = self.conv(x) + x = self.pixel_shuffle(x) + x = mx.reshape(x, (n, d, h * 2, w * 2, c)) + return x + + class SpatialRationalResampler(nn.Module): + """Rational spatial resampler for non-integer scale factors (e.g., 1.5x). - def __init__(self, mid_channels: int = 1024, scale: float = 2.0): + For scale=1.5: upsample 3x via PixelShuffle, then downsample 2x via BlurDownsample. + Rational fraction: 1.5 = 3/2. + """ + + def __init__(self, mid_channels: int = 1024, scale: float = 1.5): super().__init__() self.scale = scale - # 2D conv: mid_channels -> 4*mid_channels for pixel shuffle - self.conv = nn.Conv2d(mid_channels, 4 * mid_channels, kernel_size=3, padding=1) + # Rational fraction for 1.5: numerator=3, denominator=2 + num, den = _rational_for_scale(scale) + self.num = num + self.den = den - # Blur kernel for antialiasing - self.blur_down_kernel = mx.ones((1, 1, 5, 5)) / 25.0 - - self.pixel_shuffle = PixelShuffle2D(2) + # Conv2d: mid_channels -> num^2 * mid_channels for PixelShuffle(num) + self.conv = nn.Conv2d( + mid_channels, num * num * mid_channels, kernel_size=3, padding=1 + ) + self.pixel_shuffle = PixelShuffle2D(num, num) + self.blur_down = BlurDownsample(stride=den) def __call__(self, x: mx.array) -> mx.array: - # x: (N, D, H, W, C) - channels last 3D format - + # x: (N, D, H, W, C) n, d, h, w, c = x.shape - - # Process frame by frame - # Reshape to (N*D, H, W, C) for 2D operations x = mx.reshape(x, (n * d, h, w, c)) - # Apply 2D conv x = self.conv(x) + x = self.pixel_shuffle(x) # H*num, W*num + x = self.blur_down(x) # H*num/den, W*num/den - # Pixel shuffle for 2x upscaling - x = self.pixel_shuffle(x) - - # Reshape back to (N, D, H*2, W*2, C) - x = mx.reshape(x, (n, d, h * 2, w * 2, c)) - + _, h_out, w_out, _ = x.shape + x = mx.reshape(x, (n, d, h_out, w_out, c)) return x +def _rational_for_scale(scale: float) -> Tuple[int, int]: + """Convert a float scale to a rational fraction (numerator, denominator).""" + from fractions import Fraction + + frac = Fraction(scale).limit_denominator(10) + return frac.numerator, frac.denominator + + class ResBlock3D(nn.Module): def __init__(self, channels: int): @@ -201,48 +283,62 @@ class ResBlock3D(nn.Module): class LatentUpsampler(nn.Module): - def __init__( self, in_channels: int = 128, mid_channels: int = 1024, num_blocks_per_stage: int = 4, + spatial_scale: float = 2.0, + rational_resampler: bool = False, ): super().__init__() self.in_channels = in_channels self.mid_channels = mid_channels + self.spatial_scale = spatial_scale # Initial projection self.initial_conv = Conv3d(in_channels, mid_channels, kernel_size=3, padding=1) self.initial_norm = GroupNorm3d(32, mid_channels) # Pre-upsample ResBlocks - use dict with int keys for MLX parameter tracking - self.res_blocks = {i: ResBlock3D(mid_channels) for i in range(num_blocks_per_stage)} + self.res_blocks = { + i: ResBlock3D(mid_channels) for i in range(num_blocks_per_stage) + } # Upsampler: 2D spatial upsampling (frame-by-frame) - self.upsampler = SpatialRationalResampler(mid_channels=mid_channels, scale=2.0) + if rational_resampler: + self.upsampler = SpatialRationalResampler( + mid_channels=mid_channels, scale=spatial_scale + ) + else: + self.upsampler = SpatialUpsampler2x(mid_channels=mid_channels) # Post-upsample ResBlocks - use dict with int keys for MLX parameter tracking - self.post_upsample_res_blocks = {i: ResBlock3D(mid_channels) for i in range(num_blocks_per_stage)} + self.post_upsample_res_blocks = { + i: ResBlock3D(mid_channels) for i in range(num_blocks_per_stage) + } # Final projection self.final_conv = Conv3d(mid_channels, in_channels, kernel_size=3, padding=1) def __call__(self, latent: mx.array, debug: bool = False) -> mx.array: - """Upsample latents by 2x spatially. + """Upsample latents spatially. Args: latent: Input tensor of shape (B, C, F, H, W) - channels first debug: If True, print intermediate values for debugging Returns: - Upsampled tensor of shape (B, C, F, H*2, W*2) - channels first + Upsampled tensor of shape (B, C, F, H*scale, W*scale) - channels first """ + def debug_stats(name, t): if debug: mx.eval(t) - print(f" {name}: shape={t.shape}, min={t.min().item():.4f}, max={t.max().item():.4f}, mean={t.mean().item():.4f}") + print( + f" {name}: shape={t.shape}, min={t.min().item():.4f}, max={t.max().item():.4f}, mean={t.mean().item():.4f}" + ) if debug: print(" [DEBUG] LatentUpsampler forward pass:") @@ -250,41 +346,27 @@ class LatentUpsampler(nn.Module): # Convert from channels first (B, C, F, H, W) to channels last (B, F, H, W, C) x = mx.transpose(latent, (0, 2, 3, 4, 1)) - if debug: - debug_stats("After transpose to channels-last", x) # Initial conv x = self.initial_conv(x) - if debug: - debug_stats("After initial_conv", x) x = self.initial_norm(x) - if debug: - debug_stats("After initial_norm", x) x = nn.silu(x) - if debug: - debug_stats("After silu", x) # Pre-upsample blocks for i in sorted(self.res_blocks.keys()): x = self.res_blocks[i](x) - if debug: - debug_stats(f"After res_blocks[{i}]", x) # Upsample (2D spatial, frame-by-frame) x = self.upsampler(x) if debug: - debug_stats("After upsampler (spatial 2x)", x) + debug_stats(f"After upsampler (spatial {self.spatial_scale}x)", x) # Post-upsample blocks for i in sorted(self.post_upsample_res_blocks.keys()): x = self.post_upsample_res_blocks[i](x) - if debug: - debug_stats(f"After post_upsample_res_blocks[{i}]", x) # Final conv x = self.final_conv(x) - if debug: - debug_stats("After final_conv", x) # Convert back to channels first (B, C, F, H, W) x = mx.transpose(x, (0, 4, 1, 2, 3)) @@ -301,48 +383,73 @@ def upsample_latents( latent_std: mx.array, debug: bool = False, ) -> mx.array: - # Un-normalize: latent * std + mean latent_mean = latent_mean.reshape(1, -1, 1, 1, 1) latent_std = latent_std.reshape(1, -1, 1, 1, 1) latent = latent * latent_std + latent_mean - + # Upsample latent = upsampler(latent, debug=debug) - + # Re-normalize: (latent - mean) / std latent = (latent - latent_mean) / latent_std return latent -def load_upsampler(weights_path: str) -> LatentUpsampler: +def load_upsampler(weights_path: str) -> Tuple[LatentUpsampler, float]: """Load upsampler from safetensors weights. + Auto-detects whether the weights are for x2 or x1.5 upscaling based on + the upsampler conv output channels: + - x2: upsampler.0.weight shape [4*mid, mid, 3, 3] (4096 out channels) + - x1.5: upsampler.conv.weight shape [9*mid, mid, 3, 3] (9216 out channels) + Args: weights_path: Path to upsampler weights file Returns: - Loaded LatentUpsampler model + Tuple of (LatentUpsampler model, spatial_scale) """ print(f"Loading spatial upsampler from {weights_path}...") raw_weights = mx.load(weights_path) - # Check weight shapes to determine mid_channels - # res_blocks.0.conv1.weight should be (mid_channels, mid_channels, 3, 3, 3) + # Detect mid_channels from res_blocks sample_key = "res_blocks.0.conv1.weight" if sample_key in raw_weights: mid_channels = raw_weights[sample_key].shape[0] else: - mid_channels = 1024 # default + mid_channels = 1024 - print(f" Detected mid_channels: {mid_channels}") + # Detect upsampler type from conv output channels + # x2: conv out = 4 * mid (2^2 * mid for PixelShuffle(2)) + # x1.5: conv out = 9 * mid (3^2 * mid for PixelShuffle(3)) + blur downsample + # Both formats may have upsampler.blur_down.kernel, so use channel count + conv_key = ( + "upsampler.conv.weight" + if "upsampler.conv.weight" in raw_weights + else "upsampler.0.weight" + ) + if conv_key in raw_weights: + out_channels = raw_weights[conv_key].shape[0] + ratio = out_channels // mid_channels + rational_resampler = ratio == 9 # 3^2 for PixelShuffle(3) + blur downsample + spatial_scale = 1.5 if rational_resampler else 2.0 + else: + rational_resampler = False + spatial_scale = 2.0 + + print( + f" Detected: mid_channels={mid_channels}, scale={spatial_scale}x, rational={rational_resampler}" + ) # Create model upsampler = LatentUpsampler( in_channels=128, mid_channels=mid_channels, num_blocks_per_stage=4, + spatial_scale=spatial_scale, + rational_resampler=rational_resampler, ) # Sanitize weights - convert from PyTorch to MLX format @@ -350,19 +457,18 @@ def load_upsampler(weights_path: str) -> LatentUpsampler: for key, value in raw_weights.items(): new_key = key + # x2 upsampler uses sequential indexing: upsampler.0.* -> upsampler.conv.* + if key.startswith("upsampler.0."): + new_key = key.replace("upsampler.0.", "upsampler.conv.") + # Conv3d weights: PyTorch (O, I, D, H, W) -> MLX (O, D, H, W, I) - if "conv" in key and "weight" in key and value.ndim == 5: + if "weight" in new_key and value.ndim == 5: value = mx.transpose(value, (0, 2, 3, 4, 1)) # Conv2d weights: PyTorch (O, I, H, W) -> MLX (O, H, W, I) - if "conv" in key and "weight" in key and value.ndim == 4: + if ("weight" in new_key or "kernel" in new_key) and value.ndim == 4: value = mx.transpose(value, (0, 2, 3, 1)) - # Map upsampler.conv to upsampler.conv (SpatialRationalResampler) - # Keys: upsampler.conv.weight, upsampler.conv.bias, upsampler.blur_down.kernel - if key.startswith("upsampler."): - new_key = key # Keep as is for SpatialRationalResampler - sanitized[new_key] = value # Load weights @@ -370,4 +476,4 @@ def load_upsampler(weights_path: str) -> LatentUpsampler: print(f" Loaded {len(sanitized)} weights") - return upsampler + return upsampler, spatial_scale diff --git a/mlx_video/models/ltx_2/utils.py b/mlx_video/models/ltx_2/utils.py new file mode 100644 index 0000000..5f70378 --- /dev/null +++ b/mlx_video/models/ltx_2/utils.py @@ -0,0 +1,162 @@ +"""Shared utilities for LTX-2 model loading and conversion.""" + +import json +from pathlib import Path +from typing import Any, Dict, Optional + +import mlx.core as mx +from huggingface_hub import snapshot_download + + +def get_model_path( + path_or_hf_repo: str, + revision: Optional[str] = None, +) -> Path: + """Get local path to model, downloading if necessary. + + Args: + path_or_hf_repo: Local path or HuggingFace repo ID + revision: Git revision for HF repo + + Returns: + Path to model directory + """ + model_path = Path(path_or_hf_repo) + + if model_path.exists(): + return model_path + + model_path = Path( + snapshot_download( + repo_id=path_or_hf_repo, + revision=revision, + allow_patterns=[ + "*.safetensors", + "*.json", + "config.json", + ], + ) + ) + + return model_path + + +def load_safetensors(path: Path) -> Dict[str, mx.array]: + """Load weights from safetensors file(s) using MLX. + + Args: + path: Path to model directory or single safetensors file + + Returns: + Dictionary of weights + """ + if path.is_file(): + return mx.load(str(path)) + + weights = {} + for sf_path in path.glob("*.safetensors"): + weights.update(mx.load(str(sf_path))) + return weights + + +def load_config(model_path: Path) -> Dict[str, Any]: + """Load model configuration from config.json. + + Args: + model_path: Path to model directory + + Returns: + Configuration dictionary + """ + config_path = model_path / "config.json" + if config_path.exists(): + with open(config_path, "r") as f: + return json.load(f) + return {} + + +def save_weights(path: Path, weights: Dict[str, mx.array]) -> None: + """Save weights in safetensors format. + + Args: + path: Output directory + weights: Dictionary of weights + """ + path.mkdir(parents=True, exist_ok=True) + mx.save_safetensors(str(path / "model.safetensors"), weights) + + +def convert_audio_encoder( + model_path, + source_repo: str = "Lightricks/LTX-2", +) -> Path: + """Convert and save audio encoder weights from original HF checkpoint. + + Extracts encoder weights from the combined audio VAE safetensors, + transposes Conv2d for MLX, and saves for AudioEncoder.from_pretrained(). + + Args: + model_path: Local model directory (output location). + source_repo: HF repo containing audio_vae/diffusion_pytorch_model.safetensors. + + Returns: + Path to the audio_vae/encoder directory. + """ + model_path = Path(model_path) + encoder_dir = model_path / "audio_vae" / "encoder" + + if (encoder_dir / "model.safetensors").exists(): + return encoder_dir + + from huggingface_hub import hf_hub_download + + vae_path = hf_hub_download( + source_repo, + "audio_vae/diffusion_pytorch_model.safetensors", + ) + + raw_weights = mx.load(vae_path) + + from mlx_video.models.ltx_2.audio_vae import AudioEncoder + from mlx_video.models.ltx_2.config import AudioEncoderModelConfig + + # Build config from the decoder config (same audio VAE architecture) + decoder_config_path = model_path / "audio_vae" / "decoder" / "config.json" + if decoder_config_path.exists(): + with open(decoder_config_path) as f: + dec_cfg = json.load(f) + enc_config = { + "ch": dec_cfg.get("ch", 128), + "in_channels": dec_cfg.get("out_ch", 2), + "ch_mult": dec_cfg.get("ch_mult", [1, 2, 4]), + "num_res_blocks": dec_cfg.get("num_res_blocks", 2), + "attn_resolutions": dec_cfg.get("attn_resolutions", []), + "resolution": dec_cfg.get("resolution", 256), + "z_channels": dec_cfg.get("z_channels", 8), + "double_z": True, + "n_fft": 1024, + "norm_type": dec_cfg.get("norm_type", "pixel"), + "causality_axis": dec_cfg.get("causality_axis", "height"), + "dropout": dec_cfg.get("dropout", 0.0), + "mid_block_add_attention": dec_cfg.get("mid_block_add_attention", False), + "sample_rate": dec_cfg.get("sample_rate", 16000), + "mel_hop_length": dec_cfg.get("mel_hop_length", 160), + "is_causal": dec_cfg.get("is_causal", True), + "mel_bins": dec_cfg.get("mel_bins", 64) or 64, + "resamp_with_conv": dec_cfg.get("resamp_with_conv", True), + "attn_type": dec_cfg.get("attn_type", "vanilla"), + } + else: + enc_config = {"in_channels": 2, "double_z": True, "n_fft": 1024, "mel_bins": 64} + + config = AudioEncoderModelConfig.from_dict(enc_config) + encoder = AudioEncoder(config) + sanitized = encoder.sanitize(raw_weights) + + encoder_dir.mkdir(parents=True, exist_ok=True) + mx.save_safetensors(str(encoder_dir / "model.safetensors"), sanitized) + with open(encoder_dir / "config.json", "w") as f: + json.dump(enc_config, f, indent=2) + + print(f"Audio encoder weights saved to {encoder_dir}") + return encoder_dir diff --git a/mlx_video/models/ltx_2/video_vae/__init__.py b/mlx_video/models/ltx_2/video_vae/__init__.py new file mode 100644 index 0000000..fa19c4b --- /dev/null +++ b/mlx_video/models/ltx_2/video_vae/__init__.py @@ -0,0 +1,8 @@ +from mlx_video.models.ltx_2.video_vae.decoder import LTX2VideoDecoder, VideoDecoder +from mlx_video.models.ltx_2.video_vae.encoder import encode_image +from mlx_video.models.ltx_2.video_vae.tiling import ( + SpatialTilingConfig, + TemporalTilingConfig, + TilingConfig, +) +from mlx_video.models.ltx_2.video_vae.video_vae import VideoEncoder diff --git a/mlx_video/models/ltx/video_vae/convolution.py b/mlx_video/models/ltx_2/video_vae/convolution.py similarity index 93% rename from mlx_video/models/ltx/video_vae/convolution.py rename to mlx_video/models/ltx_2/video_vae/convolution.py index 4fe089d..db45568 100644 --- a/mlx_video/models/ltx/video_vae/convolution.py +++ b/mlx_video/models/ltx_2/video_vae/convolution.py @@ -1,5 +1,5 @@ from enum import Enum -from typing import List, Optional, Tuple, Union +from typing import Optional, Tuple, Union import mlx.core as mx import mlx.nn as nn @@ -27,14 +27,18 @@ def reflect_pad_2d(x: mx.array, pad_h: int, pad_w: int) -> mx.array: # Height padding (axis 2) if pad_h > 0: # Get reflection indices - exclude boundary - top_pad = x[:, :, 1:pad_h+1, :, :][:, :, ::-1, :, :] # Flip top portion - bottom_pad = x[:, :, -pad_h-1:-1, :, :][:, :, ::-1, :, :] # Flip bottom portion + top_pad = x[:, :, 1 : pad_h + 1, :, :][:, :, ::-1, :, :] # Flip top portion + bottom_pad = x[:, :, -pad_h - 1 : -1, :, :][ + :, :, ::-1, :, : + ] # Flip bottom portion x = mx.concatenate([top_pad, x, bottom_pad], axis=2) # Width padding (axis 3) if pad_w > 0: - left_pad = x[:, :, :, 1:pad_w+1, :][:, :, :, ::-1, :] # Flip left portion - right_pad = x[:, :, :, -pad_w-1:-1, :][:, :, :, ::-1, :] # Flip right portion + left_pad = x[:, :, :, 1 : pad_w + 1, :][:, :, :, ::-1, :] # Flip left portion + right_pad = x[:, :, :, -pad_w - 1 : -1, :][ + :, :, :, ::-1, : + ] # Flip right portion x = mx.concatenate([left_pad, x, right_pad], axis=3) return x @@ -50,7 +54,7 @@ def make_conv_nd( causal: bool = False, spatial_padding_mode: PaddingModeType = PaddingModeType.ZEROS, ) -> nn.Module: - + if dims == 2: return CausalConv2d( in_channels=in_channels, @@ -118,15 +122,17 @@ class CausalConv3d(nn.Module): ) def __call__(self, x: mx.array, causal: Optional[bool] = None) -> mx.array: - + use_causal = causal if causal is not None else self.causal - # Apply temporal padding via frame replication + # Apply temporal padding via frame replication # Only apply if kernel_size > 1 if self.time_kernel_size > 1: if use_causal: # Causal: replicate first frame kernel_size-1 times at the beginning - first_frame_pad = mx.repeat(x[:, :, :1, :, :], self.time_kernel_size - 1, axis=2) + first_frame_pad = mx.repeat( + x[:, :, :1, :, :], self.time_kernel_size - 1, axis=2 + ) x = mx.concatenate([first_frame_pad, x], axis=2) else: # Non-causal: replicate first frame at start, last frame at end @@ -176,7 +182,6 @@ class CausalConv3d(nn.Module): """ b, d, h, w, c = x.shape - total_elements = d * h * w * c max_safe_elements = 30 * 192 * 192 * 128 # ~140M elements per chunk @@ -191,11 +196,10 @@ class CausalConv3d(nn.Module): overlap = kernel_t - 1 - expected_output_frames = d - overlap outputs = [] - out_idx = 0 + out_idx = 0 # Process chunks in_start = 0 diff --git a/mlx_video/models/ltx/video_vae/decoder.py b/mlx_video/models/ltx_2/video_vae/decoder.py similarity index 56% rename from mlx_video/models/ltx/video_vae/decoder.py rename to mlx_video/models/ltx_2/video_vae/decoder.py index 9a6cbb3..7d1b8e3 100644 --- a/mlx_video/models/ltx/video_vae/decoder.py +++ b/mlx_video/models/ltx_2/video_vae/decoder.py @@ -15,15 +15,16 @@ Architecture (from PyTorch weights): """ import math -from typing import Optional +from pathlib import Path +from typing import Dict, Optional import mlx.core as mx import mlx.nn as nn -from mlx_video.models.ltx.video_vae.convolution import CausalConv3d, PaddingModeType -from mlx_video.models.ltx.video_vae.ops import unpatchify -from mlx_video.models.ltx.video_vae.sampling import DepthToSpaceUpsample -from mlx_video.models.ltx.video_vae.tiling import TilingConfig, decode_with_tiling +from mlx_video.models.ltx_2.video_vae.convolution import CausalConv3d, PaddingModeType +from mlx_video.models.ltx_2.video_vae.ops import PerChannelStatistics, unpatchify +from mlx_video.models.ltx_2.video_vae.sampling import DepthToSpaceUpsample +from mlx_video.models.ltx_2.video_vae.tiling import TilingConfig, decode_with_tiling def get_timestep_embedding( @@ -76,16 +77,14 @@ class PixArtAlphaTimestepEmbedder(nn.Module): def __init__(self, embedding_dim: int): super().__init__() self.timestep_embedder = TimestepEmbedding( - in_channels=256, - time_embed_dim=embedding_dim + in_channels=256, time_embed_dim=embedding_dim ) - def __call__(self, timestep: mx.array, hidden_dtype: mx.Dtype = mx.float32) -> mx.array: + def __call__( + self, timestep: mx.array, hidden_dtype: mx.Dtype = mx.float32 + ) -> mx.array: timesteps_proj = get_timestep_embedding( - timestep, - embedding_dim=256, - flip_sin_to_cos=True, - downscale_freq_shift=0 + timestep, embedding_dim=256, flip_sin_to_cos=True, downscale_freq_shift=0 ) timesteps_emb = self.timestep_embedder(timesteps_proj.astype(hidden_dtype)) return timesteps_emb @@ -118,6 +117,7 @@ class ResnetBlock3DSimple(nn.Module): def _make_conv_wrapper(self, in_ch, out_ch, padding_mode): """Create a wrapper object with a 'conv' attribute to match PyTorch naming.""" + class ConvWrapper(nn.Module): def __init__(self_inner): super().__init__() @@ -129,13 +129,15 @@ class ResnetBlock3DSimple(nn.Module): padding=1, spatial_padding_mode=padding_mode, ) + def __call__(self_inner, x, causal=False): return self_inner.conv(x, causal=causal) + return ConvWrapper() def pixel_norm(self, x: mx.array, eps: float = 1e-8) -> mx.array: """Apply pixel normalization.""" - return x / mx.sqrt(mx.mean(x ** 2, axis=1, keepdims=True) + eps) + return x / mx.sqrt(mx.mean(x**2, axis=1, keepdims=True) + eps) def __call__( self, @@ -152,7 +154,9 @@ class ResnetBlock3DSimple(nn.Module): if self.timestep_conditioning and timestep_embed is not None: # scale_shift_table: (4, C), timestep_embed: (B, 4*C, 1, 1, 1) # Combine table with timestep embedding - ada_values = self.scale_shift_table[None, :, :, None, None, None] # (1, 4, C, 1, 1, 1) + ada_values = self.scale_shift_table[ + None, :, :, None, None, None + ] # (1, 4, C, 1, 1, 1) # Reshape timestep_embed from (B, 4*C, 1, 1, 1) to (B, 4, C, 1, 1, 1) channels = self.scale_shift_table.shape[1] ts_reshaped = timestep_embed.reshape(batch_size, 4, channels, 1, 1, 1) @@ -198,16 +202,14 @@ class ResBlockGroup(nn.Module): # Time embedder for this block group: embed_dim = 4 * channels if timestep_conditioning: - self.time_embedder = PixArtAlphaTimestepEmbedder( - embedding_dim=channels * 4 - ) + self.time_embedder = PixArtAlphaTimestepEmbedder(embedding_dim=channels * 4) # Use dict with int keys for MLX to track parameters properly self.res_blocks = { i: ResnetBlock3DSimple( channels, spatial_padding_mode, - timestep_conditioning=timestep_conditioning + timestep_conditioning=timestep_conditioning, ) for i in range(num_layers) } @@ -223,8 +225,7 @@ class ResBlockGroup(nn.Module): if self.timestep_conditioning and timestep is not None: batch_size = x.shape[0] timestep_embed = self.time_embedder( - timestep.flatten(), - hidden_dtype=x.dtype + timestep.flatten(), hidden_dtype=x.dtype ) # Reshape to (B, 4*C, 1, 1, 1) for broadcasting timestep_embed = timestep_embed.reshape(batch_size, -1, 1, 1, 1) @@ -249,6 +250,18 @@ class LTX2VideoDecoder(nn.Module): - conv_out: 128 -> 48 (3 * 4^2 for patch_size=4) """ + # Block definitions: ("res", channels, num_layers) or ("d2s", in_channels, reduction, stride) + # stride is (D, H, W) tuple + DEFAULT_BLOCKS = [ + ("res", 1024, 5), + ("d2s", 1024, 2, (2, 2, 2)), + ("res", 512, 5), + ("d2s", 512, 2, (2, 2, 2)), + ("res", 256, 5), + ("d2s", 256, 2, (2, 2, 2)), + ("res", 128, 5), + ] + def __init__( self, in_channels: int = 128, @@ -257,6 +270,7 @@ class LTX2VideoDecoder(nn.Module): num_layers_per_block: int = 5, spatial_padding_mode: PaddingModeType = PaddingModeType.REFLECT, timestep_conditioning: bool = True, + decoder_blocks: list = None, ): super().__init__() @@ -269,72 +283,72 @@ class LTX2VideoDecoder(nn.Module): self.decode_timestep = 0.05 # Per-channel statistics for denormalization (loaded from weights) - self.latents_mean = mx.zeros((in_channels,)) - self.latents_std = mx.ones((in_channels,)) + self.per_channel_statistics = PerChannelStatistics(latent_channels=in_channels) - # Initial conv: 128 -> 1024 + blocks = decoder_blocks or self.DEFAULT_BLOCKS + first_ch = blocks[0][1] + last_ch = blocks[-1][1] + + # Initial conv: in_channels -> first block channels class ConvInWrapper(nn.Module): def __init__(self_inner): super().__init__() self_inner.conv = CausalConv3d( in_channels=in_channels, - out_channels=1024, + out_channels=first_ch, kernel_size=3, stride=1, padding=1, spatial_padding_mode=spatial_padding_mode, ) + def __call__(self_inner, x, causal=False): return self_inner.conv(x, causal=causal) + self.conv_in = ConvInWrapper() - # Up blocks: alternating ResBlockGroup and DepthToSpaceUpsample - # Use dict with int keys for MLX to track parameters properly - self.up_blocks = { - 0: ResBlockGroup(1024, num_layers_per_block, spatial_padding_mode, timestep_conditioning), - 1: DepthToSpaceUpsample( - dims=3, - in_channels=1024, - stride=(2, 2, 2), - residual=True, - out_channels_reduction_factor=2, - spatial_padding_mode=spatial_padding_mode, - ), - 2: ResBlockGroup(512, num_layers_per_block, spatial_padding_mode, timestep_conditioning), - 3: DepthToSpaceUpsample( - dims=3, - in_channels=512, - stride=(2, 2, 2), - residual=True, - out_channels_reduction_factor=2, - spatial_padding_mode=spatial_padding_mode, - ), - 4: ResBlockGroup(256, num_layers_per_block, spatial_padding_mode, timestep_conditioning), - 5: DepthToSpaceUpsample( - dims=3, - in_channels=256, - stride=(2, 2, 2), - residual=True, - out_channels_reduction_factor=2, - spatial_padding_mode=spatial_padding_mode, - ), - 6: ResBlockGroup(128, num_layers_per_block, spatial_padding_mode, timestep_conditioning), - } + # Build up blocks from config + self.up_blocks = {} + for idx, block_def in enumerate(blocks): + block_type = block_def[0] + ch = block_def[1] + if block_type == "res": + num_layers = ( + block_def[2] if len(block_def) > 2 else num_layers_per_block + ) + self.up_blocks[idx] = ResBlockGroup( + ch, num_layers, spatial_padding_mode, timestep_conditioning + ) + elif block_type == "d2s": + reduction = block_def[2] if len(block_def) > 2 else 2 + stride = block_def[3] if len(block_def) > 3 else (2, 2, 2) + residual = block_def[4] if len(block_def) > 4 else True + self.up_blocks[idx] = DepthToSpaceUpsample( + dims=3, + in_channels=ch, + stride=stride, + residual=residual, + out_channels_reduction_factor=reduction, + spatial_padding_mode=spatial_padding_mode, + ) final_out_channels = out_channels * patch_size * patch_size + class ConvOutWrapper(nn.Module): def __init__(self_inner): super().__init__() self_inner.conv = CausalConv3d( - in_channels=128, + in_channels=last_ch, out_channels=final_out_channels, kernel_size=3, stride=1, padding=1, spatial_padding_mode=spatial_padding_mode, ) + def __call__(self_inner, x, causal=False): return self_inner.conv(x, causal=causal) + self.conv_out = ConvOutWrapper() self.act = nn.SiLU() @@ -342,21 +356,202 @@ class LTX2VideoDecoder(nn.Module): if timestep_conditioning: self.timestep_scale_multiplier = mx.array(1000.0) self.last_time_embedder = PixArtAlphaTimestepEmbedder( - embedding_dim=128 * 2 # 256, matches (2, 128) table + embedding_dim=last_ch * 2 ) - self.last_scale_shift_table = mx.zeros((2, 128)) + self.last_scale_shift_table = mx.zeros((2, last_ch)) - def denormalize(self, x: mx.array) -> mx.array: - """Denormalize latents using per-channel statistics.""" - dtype = x.dtype - # Cast to float32 for precision (statistics may be in bfloat16) - mean = self.latents_mean.astype(mx.float32).reshape(1, -1, 1, 1, 1) - std = self.latents_std.astype(mx.float32).reshape(1, -1, 1, 1, 1) - return (x * std + mean).astype(dtype) + def sanitize(self, weights: Dict[str, mx.array]) -> Dict[str, mx.array]: + # Build decoder weights dict with key remapping + sanitized = {} + if "per_channel_statistics.mean" in weights: + return weights + for key, value in weights.items(): + new_key = key + + if not key.startswith("vae.") or key.startswith("vae.encoder."): + continue + + if key.startswith("vae.per_channel_statistics."): + # Map per-channel statistics (use exact key matching) + if key == "vae.per_channel_statistics.mean-of-means": + new_key = "per_channel_statistics.mean" + elif key == "vae.per_channel_statistics.std-of-means": + new_key = "per_channel_statistics.std" + else: + continue # Skip other statistics keys + + if key.startswith("vae.decoder."): + new_key = key.replace("vae.decoder.", "") + + # Handle Conv3d weight transpose: (O, I, D, H, W) -> (O, D, H, W, I) + if ".conv.weight" in key and value.ndim == 5: + value = mx.transpose(value, (0, 2, 3, 4, 1)) + + if ".conv.bias" in key: + pass # bias doesn't need transpose + + if ".conv.weight" in new_key or ".conv.bias" in new_key: + + if ( + ".conv.conv.weight" not in new_key + and ".conv.conv.bias" not in new_key + ): + new_key = new_key.replace(".conv.weight", ".conv.conv.weight") + new_key = new_key.replace(".conv.bias", ".conv.conv.bias") + + sanitized[new_key] = value + return sanitized + + @classmethod + def from_pretrained( + cls, model_path: Path, strict: bool = True + ) -> "LTX2VideoDecoder": + """Load a pretrained decoder from a directory with config.json and weights. + + Args: + model_path: Path to directory containing config.json and safetensors files, + or path to a single safetensors file. + strict: Whether to require all weight keys to match. + + Returns: + Loaded LTX2VideoDecoder instance + """ + import json + + model_path = Path(model_path) + config_dict = {} + + # Load config from directory + config_path = model_path / "config.json" + if config_path.exists(): + with open(config_path) as f: + config_dict = json.load(f) + + # Load weights from directory + weight_files = sorted(model_path.glob("*.safetensors")) + if not weight_files: + raise FileNotFoundError(f"No safetensors files found in {model_path}") + weights = {} + for wf in weight_files: + weights.update(mx.load(str(wf))) + + # Infer block structure from weights + decoder_blocks = cls._infer_blocks(weights) + + # Determine spatial padding mode from config + spatial_padding_mode_str = config_dict.get("spatial_padding_mode", "reflect") + spatial_padding_mode = PaddingModeType(spatial_padding_mode_str) + + model = cls( + timestep_conditioning=config_dict.get("timestep_conditioning", False), + decoder_blocks=decoder_blocks, + spatial_padding_mode=spatial_padding_mode, + ) + weights = model.sanitize(weights) + model.load_weights(list(weights.items()), strict=strict) + return model + + @staticmethod + def _infer_blocks(weights: dict) -> list: + """Infer decoder block structure from weight keys.""" + block_indices = set() + for k in weights: + if "up_blocks." in k: + idx_str = k.split("up_blocks.")[1].split(".")[0] + if idx_str.isdigit(): + block_indices.add(int(idx_str)) + + if not block_indices: + return None + + # First pass: collect block info + raw_blocks = [] + for idx in sorted(block_indices): + has_conv = any(f"up_blocks.{idx}.conv." in k for k in weights) + res_indices = set() + for k in weights: + prefix = f"up_blocks.{idx}.res_blocks." + if prefix in k: + res_idx = k.split(prefix)[1].split(".")[0] + if res_idx.isdigit(): + res_indices.add(int(res_idx)) + + if has_conv and not res_indices: + # D2S block - get conv shape + for k, v in weights.items(): + if f"up_blocks.{idx}.conv." in k and "weight" in k: + in_ch = v.shape[-1] if v.ndim == 5 else v.shape[1] + conv_out_ch = v.shape[0] + raw_blocks.append(("d2s", in_ch, conv_out_ch)) + break + elif res_indices: + num_res = max(res_indices) + 1 + for k, v in weights.items(): + if f"up_blocks.{idx}.res_blocks.0.conv1" in k and "weight" in k: + ch = v.shape[0] + raw_blocks.append(("res", ch, num_res)) + break + + # Second pass: determine d2s strides using the channel progression + # For each d2s block, the next res block tells us the expected output channels + blocks = [] + d2s_strides = [] + for i, block in enumerate(raw_blocks): + if block[0] == "res": + blocks.append(block) + elif block[0] == "d2s": + in_ch, conv_out_ch = block[1], block[2] + # Find next res block's channels + next_ch = None + for j in range(i + 1, len(raw_blocks)): + if raw_blocks[j][0] == "res": + next_ch = raw_blocks[j][1] + break + + if next_ch is None: + next_ch = in_ch // 2 # fallback + + # out_ch = in_ch // reduction + reduction = in_ch // next_ch if next_ch > 0 else 2 + + # conv_out = next_ch * multiplier → multiplier = conv_out / next_ch + multiplier = conv_out_ch // next_ch if next_ch > 0 else 8 + + # Determine stride from multiplier + if multiplier == 8: + stride = (2, 2, 2) + elif multiplier == 4: + stride = (1, 2, 2) + elif multiplier == 2: + stride = (2, 1, 1) + else: + stride = (2, 2, 2) + + d2s_strides.append(stride) + blocks.append(("d2s", in_ch, reduction, stride)) + + if not blocks: + return None + + # Determine residual flag: LTX-2 has uniform (2,2,2) strides with reduction=2 → residual=True + # LTX-2.3 has mixed strides or reduction=1 → residual=False + has_mixed_strides = len(set(d2s_strides)) > 1 + has_non_standard_reduction = any(b[2] != 2 for b in blocks if b[0] == "d2s") + use_residual = not has_mixed_strides and not has_non_standard_reduction + + # Apply residual flag to all d2s blocks + final_blocks = [] + for block in blocks: + if block[0] == "d2s": + final_blocks.append(("d2s", block[1], block[2], block[3], use_residual)) + else: + final_blocks.append(block) + + return final_blocks def pixel_norm(self, x: mx.array, eps: float = 1e-8) -> mx.array: """Apply pixel normalization.""" - return x / mx.sqrt(mx.mean(x ** 2, axis=1, keepdims=True) + eps) + return x / mx.sqrt(mx.mean(x**2, axis=1, keepdims=True) + eps) def __call__( self, @@ -366,29 +561,15 @@ class LTX2VideoDecoder(nn.Module): debug: bool = False, chunked_conv: bool = False, ) -> mx.array: - - def debug_stats(name, t): - if debug: - mx.eval(t) - print(f" [VAE] {name}: shape={t.shape}, min={t.min().item():.4f}, max={t.max().item():.4f}, mean={t.mean().item():.4f}") batch_size = sample.shape[0] - if debug: - debug_stats("Input", sample) - # Add noise if timestep conditioning is enabled if self.timestep_conditioning: noise = mx.random.normal(sample.shape) * self.decode_noise_scale sample = noise + (1.0 - self.decode_noise_scale) * sample - if debug: - debug_stats("After noise", sample) - if debug: - print(f" [VAE] Denorm stats - mean: [{self.latents_mean.min().item():.4f}, {self.latents_mean.max().item():.4f}], std: [{self.latents_std.min().item():.4f}, {self.latents_std.max().item():.4f}]") - sample = self.denormalize(sample) - if debug: - debug_stats("After denormalize", sample) + sample = self.per_channel_statistics.un_normalize(sample) if timestep is None and self.timestep_conditioning: timestep = mx.full((batch_size,), self.decode_timestep) @@ -398,8 +579,6 @@ class LTX2VideoDecoder(nn.Module): scaled_timestep = timestep * self.timestep_scale_multiplier x = self.conv_in(sample, causal=causal) - if debug: - debug_stats("After conv_in", x) for i, block in self.up_blocks.items(): if isinstance(block, ResBlockGroup): @@ -408,22 +587,18 @@ class LTX2VideoDecoder(nn.Module): x = block(x, causal=causal, chunked_conv=chunked_conv) else: x = block(x, causal=causal) - if debug: - block_type = type(block).__name__ - debug_stats(f"After up_blocks[{i}] ({block_type})", x) x = self.pixel_norm(x) - if debug: - debug_stats("After pixel_norm", x) if self.timestep_conditioning and scaled_timestep is not None: embedded_timestep = self.last_time_embedder( - scaled_timestep.flatten(), - hidden_dtype=x.dtype + scaled_timestep.flatten(), hidden_dtype=x.dtype ) embedded_timestep = embedded_timestep.reshape(batch_size, -1, 1, 1, 1) - ada_values = self.last_scale_shift_table[None, :, :, None, None, None] # (1, 2, 128, 1, 1, 1) + ada_values = self.last_scale_shift_table[ + None, :, :, None, None, None + ] # (1, 2, 128, 1, 1, 1) ts_reshaped = embedded_timestep.reshape(batch_size, 2, 128, 1, 1, 1) ada_values = ada_values + ts_reshaped @@ -431,21 +606,13 @@ class LTX2VideoDecoder(nn.Module): scale = ada_values[:, 1] x = x * (1 + scale) + shift - if debug: - debug_stats("After timestep modulation", x) x = self.act(x) - if debug: - debug_stats("After activation", x) x = self.conv_out(x, causal=causal) - if debug: - debug_stats("After conv_out", x) # Unpatchify: (B, 48, F', H', W') -> (B, 3, F, H*4, W*4) x = unpatchify(x, patch_size_hw=self.patch_size, patch_size_t=1) - if debug: - debug_stats("After unpatchify", x) return x @@ -502,11 +669,23 @@ class LTX2VideoDecoder(nn.Module): # Auto-enable chunked conv for modes where it helps (larger tiles) # Chunked conv reduces memory by processing conv+depth_to_space in temporal chunks - use_chunked_conv = tiling_mode in ("conservative", "none", "auto", "default", "spatial") + use_chunked_conv = tiling_mode in ( + "conservative", + "none", + "auto", + "default", + "spatial", + ) if not needs_spatial_tiling and not needs_temporal_tiling: # No tiling needed, use regular decode - return self(sample, causal=causal, timestep=timestep, debug=debug, chunked_conv=use_chunked_conv) + return self( + sample, + causal=causal, + timestep=timestep, + debug=debug, + chunked_conv=use_chunked_conv, + ) return decode_with_tiling( decoder_fn=self, @@ -521,101 +700,5 @@ class LTX2VideoDecoder(nn.Module): ) -def load_vae_decoder(model_path: str, timestep_conditioning: Optional[bool] = None) -> LTX2VideoDecoder: - from pathlib import Path - import json - from safetensors import safe_open - - model_path = Path(model_path) - - # Try to find the weights file - if model_path.is_file() and model_path.suffix == ".safetensors": - weights_path = model_path - elif (model_path / "ltx-2-19b-distilled.safetensors").exists(): - weights_path = model_path / "ltx-2-19b-distilled.safetensors" - elif (model_path / "vae" / "diffusion_pytorch_model.safetensors").exists(): - weights_path = model_path / "vae" / "diffusion_pytorch_model.safetensors" - else: - raise FileNotFoundError(f"VAE weights not found at {model_path}") - - print(f"Loading VAE decoder from {weights_path}...") - - # Read config from safetensors metadata to auto-detect timestep_conditioning - if timestep_conditioning is None: - try: - with safe_open(str(weights_path), framework="numpy") as f: - metadata = f.metadata() - if metadata and "config" in metadata: - configs = json.loads(metadata["config"]) - vae_config = configs.get("vae", {}) - timestep_conditioning = vae_config.get("timestep_conditioning", False) - print(f" Auto-detected timestep_conditioning={timestep_conditioning} from weights") - else: - timestep_conditioning = False - except Exception as e: - print(f" Could not read config from metadata: {e}, defaulting to timestep_conditioning=False") - timestep_conditioning = False - - decoder = LTX2VideoDecoder(timestep_conditioning=timestep_conditioning) - - weights = mx.load(str(weights_path)) - - # Determine prefix based on weight keys - has_vae_prefix = any(k.startswith("vae.") for k in weights.keys()) - has_decoder_prefix = any(k.startswith("decoder.") for k in weights.keys()) - - if has_vae_prefix: - prefix = "vae.decoder." - stats_prefix = "vae.per_channel_statistics." - elif has_decoder_prefix: - prefix = "decoder." - stats_prefix = "" - else: - prefix = "" - stats_prefix = "" - - # Load per-channel statistics for denormalization - # Note: use std-of-means (not mean-of-stds) for proper denormalization - mean_key = f"{stats_prefix}mean-of-means" if stats_prefix else "latents_mean" - std_key = f"{stats_prefix}std-of-means" if stats_prefix else "latents_std" - - if mean_key in weights: - decoder.latents_mean = weights[mean_key] - print(f" Loaded latent mean: shape {decoder.latents_mean.shape}") - if std_key in weights: - decoder.latents_std = weights[std_key] - print(f" Loaded latent std: shape {decoder.latents_std.shape}") - - # Build decoder weights dict with key remapping - decoder_weights = {} - for key, value in weights.items(): - if not key.startswith(prefix): - continue - - # Remove prefix - new_key = key[len(prefix):] - - # Handle Conv3d weight transpose: (O, I, D, H, W) -> (O, D, H, W, I) - if ".conv.weight" in key and value.ndim == 5: - value = mx.transpose(value, (0, 2, 3, 4, 1)) - if ".conv.bias" in key: - pass # bias doesn't need transpose - - - if ".conv.weight" in new_key or ".conv.bias" in new_key: - if ".conv.conv.weight" not in new_key and ".conv.conv.bias" not in new_key: - new_key = new_key.replace(".conv.weight", ".conv.conv.weight") - new_key = new_key.replace(".conv.bias", ".conv.conv.bias") - - decoder_weights[new_key] = value - - print(f" Found {len(decoder_weights)} decoder weights") - - ts_keys = [k for k in decoder_weights.keys() if "scale_shift" in k or "time_embedder" in k or "timestep_scale" in k] - print(f" Found {len(ts_keys)} timestep conditioning weights") - - # Load weights - decoder.load_weights(list(decoder_weights.items()), strict=False) - - print("VAE decoder loaded successfully") - return decoder +# Backward-compatible alias +VideoDecoder = LTX2VideoDecoder diff --git a/mlx_video/models/ltx_2/video_vae/encoder.py b/mlx_video/models/ltx_2/video_vae/encoder.py new file mode 100644 index 0000000..2a29458 --- /dev/null +++ b/mlx_video/models/ltx_2/video_vae/encoder.py @@ -0,0 +1,44 @@ +"""Video VAE Encoder for LTX-2 Image-to-Video. + +The encoder compresses input images/videos to latent representations. +Used for I2V (image-to-video) conditioning by encoding the input image +to latent space, which can then be used to condition video generation. +""" + +import mlx.core as mx + +from mlx_video.models.ltx_2.video_vae.video_vae import VideoEncoder + + +def encode_image( + image: mx.array, + encoder: VideoEncoder, +) -> mx.array: + """Encode a single image to latent space. + + Args: + image: Image tensor of shape (H, W, 3) in range [0, 1] or (B, H, W, 3) + encoder: Loaded VAE encoder + + Returns: + Latent tensor of shape (1, 128, 1, H//32, W//32) + """ + # Add batch dimension if needed + if image.ndim == 3: + image = mx.expand_dims(image, axis=0) # (1, H, W, 3) + + # Convert from (B, H, W, C) to (B, C, H, W) + image = mx.transpose(image, (0, 3, 1, 2)) # (B, 3, H, W) + + # Normalize to [-1, 1] + if image.max() > 1.0: + image = image / 255.0 + image = image * 2.0 - 1.0 + + # Add temporal dimension: (B, C, H, W) -> (B, C, 1, H, W) + image = mx.expand_dims(image, axis=2) # (B, 3, 1, H, W) + + # Encode + latent = encoder(image) + + return latent diff --git a/mlx_video/models/ltx/video_vae/ops.py b/mlx_video/models/ltx_2/video_vae/ops.py similarity index 95% rename from mlx_video/models/ltx/video_vae/ops.py rename to mlx_video/models/ltx_2/video_vae/ops.py index d730d2f..a03b643 100644 --- a/mlx_video/models/ltx/video_vae/ops.py +++ b/mlx_video/models/ltx_2/video_vae/ops.py @@ -1,6 +1,5 @@ """Operations for Video VAE.""" -from typing import List, Tuple import mlx.core as mx import mlx.nn as nn @@ -32,7 +31,9 @@ def patchify(x: mx.array, patch_size_hw: int = 4, patch_size_t: int = 1) -> mx.a new_c = c * patch_size_hw * patch_size_hw * patch_size_t # Reshape: (B, C, F, H, W) -> (B, C, F/pt, pt, H/ph, ph, W/pw, pw) - x = mx.reshape(x, (b, c, new_f, patch_size_t, new_h, patch_size_hw, new_w, patch_size_hw)) + x = mx.reshape( + x, (b, c, new_f, patch_size_t, new_h, patch_size_hw, new_w, patch_size_hw) + ) # Permute: (B, C, F', pt, H', ph, W', pw) -> (B, C, pt, pw, ph, F', H', W') # PyTorch einops uses (c, p, r, q) = (c, temporal, width, height), so we need pw before ph @@ -101,7 +102,7 @@ class PerChannelStatistics(nn.Module): Normalized tensor """ # Expand mean and std for broadcasting: (C,) -> (1, C, 1, 1, 1) - dtype = x.dtype + dtype = x.dtype # Cast to float32 for precision mean = self.mean.astype(mx.float32).reshape(1, -1, 1, 1, 1) std = self.std.astype(mx.float32).reshape(1, -1, 1, 1, 1) @@ -117,7 +118,7 @@ class PerChannelStatistics(nn.Module): Returns: Denormalized tensor """ - dtype = x.dtype + dtype = x.dtype # Cast to float32 for precision mean = self.mean.astype(mx.float32).reshape(1, -1, 1, 1, 1) std = self.std.astype(mx.float32).reshape(1, -1, 1, 1, 1) diff --git a/mlx_video/models/ltx/video_vae/resnet.py b/mlx_video/models/ltx_2/video_vae/resnet.py similarity index 97% rename from mlx_video/models/ltx/video_vae/resnet.py rename to mlx_video/models/ltx_2/video_vae/resnet.py index d93754c..0bea4d3 100644 --- a/mlx_video/models/ltx/video_vae/resnet.py +++ b/mlx_video/models/ltx_2/video_vae/resnet.py @@ -6,7 +6,7 @@ from typing import Optional import mlx.core as mx import mlx.nn as nn -from mlx_video.models.ltx.video_vae.convolution import CausalConv3d, PaddingModeType +from mlx_video.models.ltx_2.video_vae.convolution import CausalConv3d, PaddingModeType from mlx_video.utils import PixelNorm @@ -44,7 +44,7 @@ class ResnetBlock3D(nn.Module): timestep_conditioning: bool = False, spatial_padding_mode: PaddingModeType = PaddingModeType.ZEROS, ): - + super().__init__() out_channels = out_channels or in_channels @@ -96,7 +96,7 @@ class ResnetBlock3D(nn.Module): causal: bool = True, generator: Optional[int] = None, ) -> mx.array: - + residual = x # First block @@ -136,7 +136,7 @@ class UNetMidBlock3D(nn.Module): attention_head_dim: Optional[int] = None, spatial_padding_mode: PaddingModeType = PaddingModeType.ZEROS, ): - + super().__init__() self.num_layers = num_layers diff --git a/mlx_video/models/ltx/video_vae/sampling.py b/mlx_video/models/ltx_2/video_vae/sampling.py similarity index 96% rename from mlx_video/models/ltx/video_vae/sampling.py rename to mlx_video/models/ltx_2/video_vae/sampling.py index 76a96bf..7e351ba 100644 --- a/mlx_video/models/ltx/video_vae/sampling.py +++ b/mlx_video/models/ltx_2/video_vae/sampling.py @@ -5,7 +5,7 @@ from typing import Tuple, Union import mlx.core as mx import mlx.nn as nn -from mlx_video.models.ltx.video_vae.convolution import CausalConv3d, PaddingModeType +from mlx_video.models.ltx_2.video_vae.convolution import CausalConv3d, PaddingModeType class SpaceToDepthDownsample(nn.Module): @@ -104,7 +104,7 @@ class SpaceToDepthDownsample(nn.Module): class DepthToSpaceUpsample(nn.Module): - + def __init__( self, dims: int, @@ -114,7 +114,7 @@ class DepthToSpaceUpsample(nn.Module): out_channels_reduction_factor: int = 1, spatial_padding_mode: PaddingModeType = PaddingModeType.ZEROS, ): - + super().__init__() if isinstance(stride, int): @@ -156,7 +156,9 @@ class DepthToSpaceUpsample(nn.Module): return x - def __call__(self, x: mx.array, causal: bool = True, chunked_conv: bool = False) -> mx.array: + def __call__( + self, x: mx.array, causal: bool = True, chunked_conv: bool = False + ) -> mx.array: b, c, d, h, w = x.shape st, sh, sw = self.stride @@ -196,7 +198,9 @@ class DepthToSpaceUpsample(nn.Module): return x - def _chunked_conv_depth_to_space(self, x: mx.array, causal: bool = True) -> mx.array: + def _chunked_conv_depth_to_space( + self, x: mx.array, causal: bool = True + ) -> mx.array: """Chunked conv + depth_to_space that processes in temporal chunks. This reduces peak memory by avoiding the full high-channel intermediate tensor. diff --git a/mlx_video/models/ltx/video_vae/tiling.py b/mlx_video/models/ltx_2/video_vae/tiling.py similarity index 70% rename from mlx_video/models/ltx/video_vae/tiling.py rename to mlx_video/models/ltx_2/video_vae/tiling.py index 72d32e4..75ec47d 100644 --- a/mlx_video/models/ltx/video_vae/tiling.py +++ b/mlx_video/models/ltx_2/video_vae/tiling.py @@ -55,7 +55,9 @@ def compute_trapezoidal_mask_1d( # Apply right ramp (fade out) if ramp_right > 0: # Create fade_out: linspace(1, 0, ramp_right + 2)[1:-1] - fade_out = [(ramp_right + 1 - i) / (ramp_right + 1) for i in range(1, ramp_right + 1)] + fade_out = [ + (ramp_right + 1 - i) / (ramp_right + 1) for i in range(1, ramp_right + 1) + ] for i in range(ramp_right): mask[length - ramp_right + i] *= fade_out[i] @@ -71,11 +73,17 @@ class SpatialTilingConfig: def __post_init__(self) -> None: if self.tile_size_in_pixels < 64: - raise ValueError(f"tile_size_in_pixels must be at least 64, got {self.tile_size_in_pixels}") + raise ValueError( + f"tile_size_in_pixels must be at least 64, got {self.tile_size_in_pixels}" + ) if self.tile_size_in_pixels % 32 != 0: - raise ValueError(f"tile_size_in_pixels must be divisible by 32, got {self.tile_size_in_pixels}") + raise ValueError( + f"tile_size_in_pixels must be divisible by 32, got {self.tile_size_in_pixels}" + ) if self.tile_overlap_in_pixels % 32 != 0: - raise ValueError(f"tile_overlap_in_pixels must be divisible by 32, got {self.tile_overlap_in_pixels}") + raise ValueError( + f"tile_overlap_in_pixels must be divisible by 32, got {self.tile_overlap_in_pixels}" + ) if self.tile_overlap_in_pixels >= self.tile_size_in_pixels: raise ValueError( f"Overlap must be less than tile size, got {self.tile_overlap_in_pixels} and {self.tile_size_in_pixels}" @@ -91,11 +99,17 @@ class TemporalTilingConfig: def __post_init__(self) -> None: if self.tile_size_in_frames < 16: - raise ValueError(f"tile_size_in_frames must be at least 16, got {self.tile_size_in_frames}") + raise ValueError( + f"tile_size_in_frames must be at least 16, got {self.tile_size_in_frames}" + ) if self.tile_size_in_frames % 8 != 0: - raise ValueError(f"tile_size_in_frames must be divisible by 8, got {self.tile_size_in_frames}") + raise ValueError( + f"tile_size_in_frames must be divisible by 8, got {self.tile_size_in_frames}" + ) if self.tile_overlap_in_frames % 8 != 0: - raise ValueError(f"tile_overlap_in_frames must be divisible by 8, got {self.tile_overlap_in_frames}") + raise ValueError( + f"tile_overlap_in_frames must be divisible by 8, got {self.tile_overlap_in_frames}" + ) if self.tile_overlap_in_frames >= self.tile_size_in_frames: raise ValueError( f"Overlap must be less than tile size, got {self.tile_overlap_in_frames} and {self.tile_size_in_frames}" @@ -113,15 +127,21 @@ class TilingConfig: def default(cls) -> "TilingConfig": """Default tiling: 512px spatial, 64 frame temporal.""" return cls( - spatial_config=SpatialTilingConfig(tile_size_in_pixels=512, tile_overlap_in_pixels=64), - temporal_config=TemporalTilingConfig(tile_size_in_frames=64, tile_overlap_in_frames=24), + spatial_config=SpatialTilingConfig( + tile_size_in_pixels=512, tile_overlap_in_pixels=64 + ), + temporal_config=TemporalTilingConfig( + tile_size_in_frames=64, tile_overlap_in_frames=24 + ), ) @classmethod def spatial_only(cls, tile_size: int = 512, overlap: int = 64) -> "TilingConfig": """Spatial tiling only (for short videos with large resolution).""" return cls( - spatial_config=SpatialTilingConfig(tile_size_in_pixels=tile_size, tile_overlap_in_pixels=overlap), + spatial_config=SpatialTilingConfig( + tile_size_in_pixels=tile_size, tile_overlap_in_pixels=overlap + ), temporal_config=None, ) @@ -130,23 +150,33 @@ class TilingConfig: """Temporal tiling only (for long videos with small resolution).""" return cls( spatial_config=None, - temporal_config=TemporalTilingConfig(tile_size_in_frames=tile_size, tile_overlap_in_frames=overlap), + temporal_config=TemporalTilingConfig( + tile_size_in_frames=tile_size, tile_overlap_in_frames=overlap + ), ) @classmethod def aggressive(cls) -> "TilingConfig": """Aggressive tiling for very large videos (smaller tiles, much lower memory).""" return cls( - spatial_config=SpatialTilingConfig(tile_size_in_pixels=256, tile_overlap_in_pixels=64), - temporal_config=TemporalTilingConfig(tile_size_in_frames=32, tile_overlap_in_frames=8), + spatial_config=SpatialTilingConfig( + tile_size_in_pixels=256, tile_overlap_in_pixels=64 + ), + temporal_config=TemporalTilingConfig( + tile_size_in_frames=32, tile_overlap_in_frames=8 + ), ) @classmethod def conservative(cls) -> "TilingConfig": """Conservative tiling (larger tiles, less memory savings but faster).""" return cls( - spatial_config=SpatialTilingConfig(tile_size_in_pixels=768, tile_overlap_in_pixels=64), - temporal_config=TemporalTilingConfig(tile_size_in_frames=96, tile_overlap_in_frames=24), + spatial_config=SpatialTilingConfig( + tile_size_in_pixels=768, tile_overlap_in_pixels=64 + ), + temporal_config=TemporalTilingConfig( + tile_size_in_frames=96, tile_overlap_in_frames=24 + ), ) @classmethod @@ -160,6 +190,9 @@ class TilingConfig: ) -> Optional["TilingConfig"]: """Automatically determine tiling config based on video dimensions. + Uses PyTorch's default tiling (512px spatial, 64f temporal) which provides + enough context for CausalConv3d and sufficient overlap for clean blending. + Args: height: Video height in pixels width: Video width in pixels @@ -176,37 +209,21 @@ class TilingConfig: if not needs_spatial and not needs_temporal: return None - # Estimate memory requirement (rough heuristic) - # Output size in bytes (float32): B * 3 * F * H * W * 4 - estimated_output_gb = (3 * num_frames * height * width * 4) / (1024**3) - - # For very large videos, use aggressive tiling - if estimated_output_gb > 2.0 or (height * width > 768 * 1024 and num_frames > 100): - return cls.aggressive() - + # Use the same defaults as PyTorch (512px spatial, 64f temporal). + # Smaller tiles cause quality degradation because CausalConv3d needs + # sufficient temporal context and overlap for clean blending. spatial_config = None temporal_config = None if needs_spatial: - # Choose tile size based on resolution - max_dim = max(height, width) - if max_dim > 1024: - tile_size = 384 # Smaller tiles for very large resolutions - elif max_dim > 768: - tile_size = 512 - else: - tile_size = 384 - spatial_config = SpatialTilingConfig(tile_size_in_pixels=tile_size, tile_overlap_in_pixels=64) + spatial_config = SpatialTilingConfig( + tile_size_in_pixels=512, tile_overlap_in_pixels=64 + ) if needs_temporal: - # Choose tile size based on frame count - if num_frames > 200: - tile_size, overlap = 32, 8 # Aggressive for very long videos - elif num_frames > 100: - tile_size, overlap = 48, 16 - else: - tile_size, overlap = 64, 24 - temporal_config = TemporalTilingConfig(tile_size_in_frames=tile_size, tile_overlap_in_frames=overlap) + temporal_config = TemporalTilingConfig( + tile_size_in_frames=64, tile_overlap_in_frames=24 + ) return cls(spatial_config=spatial_config, temporal_config=temporal_config) @@ -214,16 +231,21 @@ class TilingConfig: @dataclass class DimensionIntervals: """Intervals for splitting a single dimension.""" + starts: List[int] ends: List[int] left_ramps: List[int] right_ramps: List[int] -def split_in_spatial(size: int, overlap: int, dimension_size: int) -> DimensionIntervals: +def split_in_spatial( + size: int, overlap: int, dimension_size: int +) -> DimensionIntervals: """Split a spatial dimension into intervals.""" if dimension_size <= size: - return DimensionIntervals(starts=[0], ends=[dimension_size], left_ramps=[0], right_ramps=[0]) + return DimensionIntervals( + starts=[0], ends=[dimension_size], left_ramps=[0], right_ramps=[0] + ) amount = (dimension_size + size - 2 * overlap - 1) // (size - overlap) starts = [i * (size - overlap) for i in range(amount)] @@ -232,13 +254,19 @@ def split_in_spatial(size: int, overlap: int, dimension_size: int) -> DimensionI left_ramps = [0] + [overlap] * (amount - 1) right_ramps = [overlap] * (amount - 1) + [0] - return DimensionIntervals(starts=starts, ends=ends, left_ramps=left_ramps, right_ramps=right_ramps) + return DimensionIntervals( + starts=starts, ends=ends, left_ramps=left_ramps, right_ramps=right_ramps + ) -def split_in_temporal(size: int, overlap: int, dimension_size: int) -> DimensionIntervals: +def split_in_temporal( + size: int, overlap: int, dimension_size: int +) -> DimensionIntervals: """Split a temporal dimension into intervals with causal adjustment.""" if dimension_size <= size: - return DimensionIntervals(starts=[0], ends=[dimension_size], left_ramps=[0], right_ramps=[0]) + return DimensionIntervals( + starts=[0], ends=[dimension_size], left_ramps=[0], right_ramps=[0] + ) # Start with spatial split intervals = split_in_spatial(size, overlap, dimension_size) @@ -251,28 +279,41 @@ def split_in_temporal(size: int, overlap: int, dimension_size: int) -> Dimension starts[i] = starts[i] - 1 left_ramps[i] = left_ramps[i] + 1 - return DimensionIntervals(starts=starts, ends=intervals.ends, left_ramps=left_ramps, right_ramps=intervals.right_ramps) + return DimensionIntervals( + starts=starts, + ends=intervals.ends, + left_ramps=left_ramps, + right_ramps=intervals.right_ramps, + ) -def map_temporal_slice(begin: int, end: int, left_ramp: int, right_ramp: int, scale: int) -> Tuple[slice, mx.array]: +def map_temporal_slice( + begin: int, end: int, left_ramp: int, right_ramp: int, scale: int +) -> Tuple[slice, mx.array]: """Map temporal latent interval to output coordinates and mask.""" start = begin * scale stop = 1 + (end - 1) * scale left_ramp_scaled = 1 + (left_ramp - 1) * scale if left_ramp > 0 else 0 right_ramp_scaled = right_ramp * scale - mask = compute_trapezoidal_mask_1d(stop - start, left_ramp_scaled, right_ramp_scaled, True) + mask = compute_trapezoidal_mask_1d( + stop - start, left_ramp_scaled, right_ramp_scaled, True + ) return slice(start, stop), mask -def map_spatial_slice(begin: int, end: int, left_ramp: int, right_ramp: int, scale: int) -> Tuple[slice, mx.array]: +def map_spatial_slice( + begin: int, end: int, left_ramp: int, right_ramp: int, scale: int +) -> Tuple[slice, mx.array]: """Map spatial latent interval to output coordinates and mask.""" start = begin * scale stop = end * scale left_ramp_scaled = left_ramp * scale right_ramp_scaled = right_ramp * scale - mask = compute_trapezoidal_mask_1d(stop - start, left_ramp_scaled, right_ramp_scaled, False) + mask = compute_trapezoidal_mask_1d( + stop - start, left_ramp_scaled, right_ramp_scaled, False + ) return slice(start, stop), mask @@ -332,7 +373,9 @@ def decode_with_tiling( temporal_overlap = 0 # Compute intervals for each dimension - temporal_intervals = split_in_temporal(temporal_tile_size, temporal_overlap, f_latent) + temporal_intervals = split_in_temporal( + temporal_tile_size, temporal_overlap, f_latent + ) height_intervals = split_in_spatial(spatial_tile_size, spatial_overlap, h_latent) width_intervals = split_in_spatial(spatial_tile_size, spatial_overlap, w_latent) @@ -355,7 +398,9 @@ def decode_with_tiling( t_right = temporal_intervals.right_ramps[t_idx] # Map temporal coordinates - out_t_slice, t_mask = map_temporal_slice(t_start, t_end, t_left, t_right, temporal_scale) + out_t_slice, t_mask = map_temporal_slice( + t_start, t_end, t_left, t_right, temporal_scale + ) for h_idx in range(num_h_tiles): h_start = height_intervals.starts[h_idx] @@ -364,7 +409,9 @@ def decode_with_tiling( h_right = height_intervals.right_ramps[h_idx] # Map height coordinates - out_h_slice, h_mask = map_spatial_slice(h_start, h_end, h_left, h_right, spatial_scale) + out_h_slice, h_mask = map_spatial_slice( + h_start, h_end, h_left, h_right, spatial_scale + ) for w_idx in range(num_w_tiles): w_start = width_intervals.starts[w_idx] @@ -373,13 +420,23 @@ def decode_with_tiling( w_right = width_intervals.right_ramps[w_idx] # Map width coordinates - out_w_slice, w_mask = map_spatial_slice(w_start, w_end, w_left, w_right, spatial_scale) + out_w_slice, w_mask = map_spatial_slice( + w_start, w_end, w_left, w_right, spatial_scale + ) # Extract tile latents (small slice) - tile_latents = latents[:, :, t_start:t_end, h_start:h_end, w_start:w_end] + tile_latents = latents[ + :, :, t_start:t_end, h_start:h_end, w_start:w_end + ] # Decode tile - tile_output = decoder_fn(tile_latents, causal=causal, timestep=timestep, debug=False, chunked_conv=chunked_conv) + tile_output = decoder_fn( + tile_latents, + causal=causal, + timestep=timestep, + debug=False, + chunked_conv=chunked_conv, + ) mx.eval(tile_output) # Clear tile_latents reference @@ -402,13 +459,15 @@ def decode_with_tiling( w_mask_slice = w_mask[:actual_w] if len(w_mask) > actual_w else w_mask blend_mask = ( - t_mask_slice.reshape(1, 1, -1, 1, 1) * - h_mask_slice.reshape(1, 1, 1, -1, 1) * - w_mask_slice.reshape(1, 1, 1, 1, -1) + t_mask_slice.reshape(1, 1, -1, 1, 1) + * h_mask_slice.reshape(1, 1, 1, -1, 1) + * w_mask_slice.reshape(1, 1, 1, 1, -1) ) # Slice tile output to match - tile_output_slice = tile_output[:, :, :actual_t, :actual_h, :actual_w].astype(mx.float32) + tile_output_slice = tile_output[ + :, :, :actual_t, :actual_h, :actual_w + ].astype(mx.float32) # Clear full tile_output del tile_output @@ -426,11 +485,37 @@ def decode_with_tiling( weighted_tile = tile_output_slice * blend_mask # Update output using slice assignment - output[:, :, t_out_start:t_out_end, h_out_start:h_out_end, w_out_start:w_out_end] = ( - output[:, :, t_out_start:t_out_end, h_out_start:h_out_end, w_out_start:w_out_end] + weighted_tile + output[ + :, + :, + t_out_start:t_out_end, + h_out_start:h_out_end, + w_out_start:w_out_end, + ] = ( + output[ + :, + :, + t_out_start:t_out_end, + h_out_start:h_out_end, + w_out_start:w_out_end, + ] + + weighted_tile ) - weights[:, :, t_out_start:t_out_end, h_out_start:h_out_end, w_out_start:w_out_end] = ( - weights[:, :, t_out_start:t_out_end, h_out_start:h_out_end, w_out_start:w_out_end] + blend_mask + weights[ + :, + :, + t_out_start:t_out_end, + h_out_start:h_out_end, + w_out_start:w_out_end, + ] = ( + weights[ + :, + :, + t_out_start:t_out_end, + h_out_start:h_out_end, + w_out_start:w_out_end, + ] + + blend_mask ) # Force evaluation to free memory @@ -462,10 +547,12 @@ def decode_with_tiling( if next_tile_start_latent == 0: next_tile_start_out = 0 else: - next_tile_start_out = 1 + (next_tile_start_latent - 1) * temporal_scale + next_tile_start_out = ( + 1 + (next_tile_start_latent - 1) * temporal_scale + ) # We need to track how many frames we've already emitted - if not hasattr(decode_with_tiling, '_emitted_frames'): + if not hasattr(decode_with_tiling, "_emitted_frames"): decode_with_tiling._emitted_frames = 0 emitted = decode_with_tiling._emitted_frames @@ -473,7 +560,10 @@ def decode_with_tiling( # Normalize and emit frames [emitted, next_tile_start_out) finalized_weights = weights[:, :, emitted:next_tile_start_out, :, :] finalized_weights = mx.maximum(finalized_weights, 1e-8) - finalized_output = output[:, :, emitted:next_tile_start_out, :, :] / finalized_weights + finalized_output = ( + output[:, :, emitted:next_tile_start_out, :, :] + / finalized_weights + ) finalized_output = finalized_output.astype(latents.dtype) mx.eval(finalized_output) @@ -490,7 +580,7 @@ def decode_with_tiling( # Emit remaining frames if callback provided if on_frames_ready is not None: - emitted = getattr(decode_with_tiling, '_emitted_frames', 0) + emitted = getattr(decode_with_tiling, "_emitted_frames", 0) if emitted < out_f: remaining_output = output[:, :, emitted:, :, :].astype(latents.dtype) mx.eval(remaining_output) @@ -498,7 +588,7 @@ def decode_with_tiling( del remaining_output # Reset emitted frames counter for next call - if hasattr(decode_with_tiling, '_emitted_frames'): + if hasattr(decode_with_tiling, "_emitted_frames"): del decode_with_tiling._emitted_frames # Clean up weights diff --git a/mlx_video/models/ltx/video_vae/video_vae.py b/mlx_video/models/ltx_2/video_vae/video_vae.py similarity index 73% rename from mlx_video/models/ltx/video_vae/video_vae.py rename to mlx_video/models/ltx_2/video_vae/video_vae.py index cc3ec3a..bd85086 100644 --- a/mlx_video/models/ltx/video_vae/video_vae.py +++ b/mlx_video/models/ltx_2/video_vae/video_vae.py @@ -1,20 +1,24 @@ """Video VAE Encoder and Decoder for LTX-2.""" from enum import Enum +from pathlib import Path from typing import Any, Dict, List, Optional, Tuple import mlx.core as mx import mlx.nn as nn -from mlx_video.models.ltx.video_vae.convolution import CausalConv3d, PaddingModeType -from mlx_video.models.ltx.video_vae.ops import PerChannelStatistics, patchify, unpatchify -from mlx_video.models.ltx.video_vae.resnet import ( +from mlx_video.models.ltx_2.video_vae.convolution import CausalConv3d, PaddingModeType +from mlx_video.models.ltx_2.video_vae.ops import ( + PerChannelStatistics, + patchify, + unpatchify, +) +from mlx_video.models.ltx_2.video_vae.resnet import ( NormLayerType, ResnetBlock3D, UNetMidBlock3D, - get_norm_layer, ) -from mlx_video.models.ltx.video_vae.sampling import ( +from mlx_video.models.ltx_2.video_vae.sampling import ( DepthToSpaceUpsample, SpaceToDepthDownsample, ) @@ -23,6 +27,7 @@ from mlx_video.utils import PixelNorm class LogVarianceType(Enum): """Log variance mode for VAE.""" + PER_CHANNEL = "per_channel" UNIFORM = "uniform" CONSTANT = "constant" @@ -221,46 +226,31 @@ class VideoEncoder(nn.Module): _DEFAULT_NORM_NUM_GROUPS = 32 - def __init__( - self, - convolution_dimensions: int = 3, - in_channels: int = 3, - out_channels: int = 128, - encoder_blocks: List[Tuple[str, Any]] = None, - patch_size: int = 4, - norm_layer: NormLayerType = NormLayerType.PIXEL_NORM, - latent_log_var: LogVarianceType = LogVarianceType.UNIFORM, - encoder_spatial_padding_mode: PaddingModeType = PaddingModeType.ZEROS, - ): - """Initialize VideoEncoder. + def __init__(self, config: "VideoEncoderModelConfig"): + """Initialize VideoEncoder from config. Args: - convolution_dimensions: Number of dimensions (3 for video) - in_channels: Input channels (3 for RGB) - out_channels: Output latent channels - encoder_blocks: List of (block_name, config) tuples - patch_size: Spatial patch size - norm_layer: Normalization layer type - latent_log_var: Log variance mode - encoder_spatial_padding_mode: Padding mode + config: VideoEncoderModelConfig with encoder parameters """ super().__init__() - if encoder_blocks is None: - encoder_blocks = [] - - self.patch_size = patch_size - self.norm_layer = norm_layer - self.latent_channels = out_channels - self.latent_log_var = latent_log_var + self.patch_size = config.patch_size + self.norm_layer = config.norm_layer + self.latent_channels = config.out_channels + self.latent_log_var = config.latent_log_var self._norm_num_groups = self._DEFAULT_NORM_NUM_GROUPS + encoder_blocks = config.encoder_blocks if config.encoder_blocks else [] + encoder_spatial_padding_mode = config.encoder_spatial_padding_mode + # Per-channel statistics for normalizing latents - self.per_channel_statistics = PerChannelStatistics(latent_channels=out_channels) + self.per_channel_statistics = PerChannelStatistics( + latent_channels=config.out_channels + ) # After patchify, channels increase by patch_size^2 - in_channels = in_channels * patch_size ** 2 - feature_channels = out_channels + in_channels = config.in_channels * config.patch_size**2 + feature_channels = config.out_channels # Initial convolution self.conv_in = CausalConv3d( @@ -273,39 +263,47 @@ class VideoEncoder(nn.Module): spatial_padding_mode=encoder_spatial_padding_mode, ) - # Build encoder blocks - use dict with int keys for MLX parameter tracking + # Build encoder blocks + # Use dict with int keys for MLX to track parameters (lists are NOT tracked) self.down_blocks = {} - for i, (block_name, block_params) in enumerate(encoder_blocks): - block_config = {"num_layers": block_params} if isinstance(block_params, int) else block_params + for idx, (block_name, block_params) in enumerate(encoder_blocks): + block_config = ( + {"num_layers": block_params} + if isinstance(block_params, int) + else block_params + ) block, feature_channels = _make_encoder_block( block_name=block_name, block_config=block_config, in_channels=feature_channels, - convolution_dimensions=convolution_dimensions, - norm_layer=norm_layer, + convolution_dimensions=config.convolution_dimensions, + norm_layer=config.norm_layer, norm_num_groups=self._norm_num_groups, spatial_padding_mode=encoder_spatial_padding_mode, ) - self.down_blocks[i] = block + self.down_blocks[idx] = block # Output normalization and convolution - if norm_layer == NormLayerType.GROUP_NORM: + if config.norm_layer == NormLayerType.GROUP_NORM: self.conv_norm_out = nn.GroupNorm( num_groups=self._norm_num_groups, dims=feature_channels, eps=1e-6, ) - elif norm_layer == NormLayerType.PIXEL_NORM: + elif config.norm_layer == NormLayerType.PIXEL_NORM: self.conv_norm_out = PixelNorm() self.conv_act = nn.SiLU() # Calculate output convolution channels - conv_out_channels = out_channels - if latent_log_var == LogVarianceType.PER_CHANNEL: + conv_out_channels = config.out_channels + if config.latent_log_var == LogVarianceType.PER_CHANNEL: conv_out_channels *= 2 - elif latent_log_var in {LogVarianceType.UNIFORM, LogVarianceType.CONSTANT}: + elif config.latent_log_var in { + LogVarianceType.UNIFORM, + LogVarianceType.CONSTANT, + }: conv_out_channels += 1 self.conv_out = CausalConv3d( @@ -341,7 +339,8 @@ class VideoEncoder(nn.Module): sample = self.conv_in(sample, causal=True) # Process through encoder blocks - for down_block in self.down_blocks.values(): + for i in range(len(self.down_blocks)): + down_block = self.down_blocks[i] if isinstance(down_block, (UNetMidBlock3D, ResnetBlock3D)): sample = down_block(sample, causal=True) else: @@ -362,15 +361,99 @@ class VideoEncoder(nn.Module): elif self.latent_log_var == LogVarianceType.CONSTANT: sample = sample[:, :-1, ...] approx_ln_0 = -30 - sample = mx.concatenate([ - sample, - mx.full_like(sample, approx_ln_0), - ], axis=1) + sample = mx.concatenate( + [ + sample, + mx.full_like(sample, approx_ln_0), + ], + axis=1, + ) # Split into means and logvar, normalize means - means = sample[:, :self.latent_channels, ...] + means = sample[:, : self.latent_channels, ...] return self.per_channel_statistics.normalize(means) + def sanitize(self, weights: Dict[str, mx.array]) -> Dict[str, mx.array]: + """Sanitize VAE encoder weights from PyTorch format to MLX format.""" + sanitized = {} + if "per_channel_statistics.mean" in weights: + return weights + + for key, value in weights.items(): + new_key = key + + if "position_ids" in key: + continue + + # Only process VAE encoder weights + if not key.startswith("vae."): + continue + + # Handle per-channel statistics + if "vae.per_channel_statistics" in key: + if key == "vae.per_channel_statistics.mean-of-means": + new_key = "per_channel_statistics.mean" + elif key == "vae.per_channel_statistics.std-of-means": + new_key = "per_channel_statistics.std" + else: + continue + elif key.startswith("vae.encoder."): + new_key = key.replace("vae.encoder.", "") + else: + continue + + # Conv3d: PyTorch (O, I, D, H, W) -> MLX (O, D, H, W, I) + if "conv" in new_key.lower() and "weight" in new_key and value.ndim == 5: + value = mx.transpose(value, (0, 2, 3, 4, 1)) + + # Conv2d: PyTorch (O, I, H, W) -> MLX (O, H, W, I) + if "conv" in new_key.lower() and "weight" in new_key and value.ndim == 4: + value = mx.transpose(value, (0, 2, 3, 1)) + + sanitized[new_key] = value + return sanitized + + @classmethod + def from_pretrained(cls, model_path: Path) -> "VideoEncoder": + """Load a pretrained VideoEncoder from a directory with weights and config. + + Args: + model_path: Path to directory containing safetensors weights and config.json + + Returns: + Loaded VideoEncoder instance + """ + import json + + from mlx_video.models.ltx_2.config import VideoEncoderModelConfig + + # Load config + config_path = model_path / "config.json" + if config_path.exists(): + with open(config_path) as f: + config_dict = json.load(f) + config = VideoEncoderModelConfig(**config_dict) + else: + config = VideoEncoderModelConfig() + + # Load weights + weight_files = sorted(model_path.glob("*.safetensors")) + if not weight_files: + if model_path.is_file(): + weights = mx.load(str(model_path)) + else: + raise FileNotFoundError(f"No safetensors files found in {model_path}") + else: + weights = {} + for wf in weight_files: + weights.update(mx.load(str(wf))) + + # Create model, sanitize and load weights + model = cls(config) + weights = model.sanitize(weights) + model.load_weights(list(weights.items()), strict=False) + return model + class VideoDecoder(nn.Module): @@ -407,7 +490,7 @@ class VideoDecoder(nn.Module): decoder_blocks = [] self.patch_size = patch_size - out_channels = out_channels * patch_size ** 2 + out_channels = out_channels * patch_size**2 self.causal = causal self.timestep_conditioning = timestep_conditioning self._norm_num_groups = self._DEFAULT_NORM_NUM_GROUPS @@ -440,9 +523,14 @@ class VideoDecoder(nn.Module): ) # Build decoder blocks (reversed order) - self.up_blocks = [] - for block_name, block_params in list(reversed(decoder_blocks)): - block_config = {"num_layers": block_params} if isinstance(block_params, int) else block_params + # Use dict with int keys for MLX to track parameters (lists are NOT tracked) + self.up_blocks = {} + for idx, (block_name, block_params) in enumerate(reversed(decoder_blocks)): + block_config = ( + {"num_layers": block_params} + if isinstance(block_params, int) + else block_params + ) block, feature_channels = _make_decoder_block( block_name=block_name, @@ -454,7 +542,7 @@ class VideoDecoder(nn.Module): norm_num_groups=self._norm_num_groups, spatial_padding_mode=decoder_spatial_padding_mode, ) - self.up_blocks.append(block) + self.up_blocks[idx] = block # Output normalization if norm_layer == NormLayerType.GROUP_NORM: @@ -509,7 +597,8 @@ class VideoDecoder(nn.Module): sample = self.conv_in(sample, causal=self.causal) # Process through decoder blocks - for up_block in self.up_blocks: + for i in range(len(self.up_blocks)): + up_block = self.up_blocks[i] if isinstance(up_block, UNetMidBlock3D): sample = up_block(sample, causal=self.causal) elif isinstance(up_block, ResnetBlock3D): diff --git a/mlx_video/models/wan/__init__.py b/mlx_video/models/wan/__init__.py deleted file mode 100644 index c0f37a8..0000000 --- a/mlx_video/models/wan/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -from mlx_video.models.wan.config import WanModelConfig -from mlx_video.models.wan.model import WanModel diff --git a/mlx_video/models/wan/docs/DIAGNOSTICS.md b/mlx_video/models/wan/docs/DIAGNOSTICS.md deleted file mode 100644 index 3b6c456..0000000 --- a/mlx_video/models/wan/docs/DIAGNOSTICS.md +++ /dev/null @@ -1,394 +0,0 @@ -# Wan2.2 I2V-14B Diagnostic Report - -This document records the systematic diagnostic methodology used to debug the Wan2.2 I2V-14B (Image-to-Video, 14 billion parameter) pipeline in mlx-video, along with every bug found, its root cause, and fix. - -## Table of Contents - -- [Overview](#overview) -- [Architecture Summary](#architecture-summary) -- [Diagnostic Methodology](#diagnostic-methodology) -- [Bug 1: Text Embedding Cross-Contamination](#bug-1-text-embedding-cross-contamination) -- [Bug 2: VAE Encoder Weights Excluded from Conversion](#bug-2-vae-encoder-weights-excluded-from-conversion) -- [Bug 3: RoPE Frequency Computation (original)](#bug-3-rope-frequency-computation-original) -- [Bug 6: RoPE Frequency Distribution (Bug 3 Fix Was Wrong)](#bug-6-rope-frequency-distribution-bug-3-fix-was-wrong) -- [Bug 4: VAE Encoder Temporal Downsample Order](#bug-4-vae-encoder-temporal-downsample-order) -- [Bug 5: Non-Chunked VAE Encoding](#bug-5-non-chunked-vae-encoding) -- [Verified Correct Components](#verified-correct-components) -- [Performance Optimizations](#performance-optimizations) -- [Resolved: CFG Effectiveness](#resolved-cfg-effectiveness-was-open-investigation) -- [Reference Implementation](#reference-implementation) -- [Useful Diagnostic Commands](#useful-diagnostic-commands) - ---- - -## Overview - -The I2V-14B pipeline takes an input image and generates a video using a dual-model diffusion transformer. The initial implementation produced severely broken output — first frame showed the image, subsequent frames degraded to noise, checkerboard artifacts, or flat grey. - -Through a systematic component-by-component comparison against the reference PyTorch implementation, **five bugs** were found and fixed. The approach was to verify each component in isolation numerically, then narrow down failures to the subsystem level. - -### Timeline of Symptoms - -| Stage | Symptom | Root Cause | -|-------|---------|------------| -| Initial | Grey/blurry frames after frame 1 | Non-chunked VAE encoding (Bug 5) | -| After chunked encoding fix | First frame OK, rest degrades to noise | Text embedding cross-contamination (Bug 1) + RoPE frequencies (Bug 3) | -| After text + RoPE fix | Severe 8px checkerboard on frames 4+ | VAE encoder temporal downsample order (Bug 4) | -| After VAE fix | Image in frames 0-3, grey frames 4+ | CFG effectiveness issue (open investigation) | - ---- - -## Architecture Summary - -``` -I2V-14B Pipeline: - Input Image → VAE Encoder → [16, T_lat, H_lat, W_lat] - ↓ - Mask Construction → [4, T_lat, H_lat, W_lat] - ↓ - y = concat(mask, encoded_video) → [20, T_lat, H_lat, W_lat] - ↓ - Noise [16, T_lat, H_lat, W_lat] + y → [36, T_lat, H_lat, W_lat] - ↓ - Dual DiT (40 layers, 5120 dim) × 40 denoising steps - ↓ - Denoised Latent [16, T_lat, H_lat, W_lat] - ↓ - VAE Decoder → Video [3, F, H, W] -``` - -**Key parameters:** -- `in_dim=36` (16 noise + 4 mask + 16 image latents), `out_dim=16` -- Dual model: HIGH noise (t ≥ 900) and LOW noise (t < 900) -- 40 steps, shift=5.0, guide_scale=(3.5, 3.5) -- Uses Wan2.1 VAE (z_dim=16, stride 4×8×8) - ---- - -## Diagnostic Methodology - -### 1. Component-Level Numerical Verification - -Each component was tested in isolation against the reference PyTorch implementation: - -1. **Load identical inputs** (same random seed, same image, same prompt) -2. **Run through reference** (on CPU where possible) and save intermediate tensors as `.npy` -3. **Run through MLX** with the same inputs -4. **Compare outputs** with `np.abs(ours - ref).max()` and relative difference metrics - -Components tested this way: -- RoPE frequency parameters and rotation output -- Time embedding (sinusoidal → MLP → projection) -- Patchify (reshape+Linear vs Conv3d) -- Unpatchify (transpose-based vs einsum) -- Scheduler (UniPC) timesteps and step formulas -- VAE encoder output (frame-by-frame comparison) -- Text embeddings (per-model MLP output) -- Cross-attention K/V cache shapes -- Mask construction values - -### 2. Artifact Analysis - -When visual artifacts appeared, quantitative metrics were used to characterize them: - -- **Checkerboard metric**: Difference between even-indexed and odd-indexed pixels at patch boundaries. Values > 20 indicate visible checkerboard. -- **FFT frequency analysis**: Power at the 8px spatial frequency (matches VAE stride). 3× normal power confirmed VAE-stride-aligned artifacts. -- **Per-frame statistics**: Mean, std, min, max for each decoded video frame to track temporal degradation. -- **Frame difference**: `mean(|frame[i] - frame[i-1]|)` to measure motion vs static content. - -### 3. Isolation Testing - -- **VAE round-trip test**: Encode image+zeros → decode. If clean, VAE decoder is not the source. -- **Single-step model output**: Run one diffusion step and compare cond vs uncond predictions to check CFG effectiveness. -- **Patchify/unpatchify synthetic test**: Pass structured gradient through unpatchify to verify spatial ordering. -- **Resolution sweeps**: Test at 480×272, 640×384, 1280×720 to check resolution dependence. -- **Step count sweeps**: Test at 5, 20, 40 steps to distinguish convergence issues from model bugs. - -### 4. Weight Comparison - -Direct comparison of converted MLX weights against original PyTorch weights: -```python -# Load both weight sets -pt_weights = torch.load("model.safetensors") -mlx_weights = mx.load("model.safetensors") -# Compare each key -for key in pt_weights: - diff = np.abs(np.array(pt_weights[key]) - np.array(mlx_weights[key])).max() -``` -Expected: max diff ≈ 0.001 (bfloat16 rounding). Actual: confirmed for all keys. - ---- - -## Bug 1: Text Embedding Cross-Contamination - -**Symptom:** Model ignores text prompt, generated frames lack semantic content. - -**Root Cause:** For the dual-model architecture (high-noise and low-noise experts), text embeddings were computed using only `low_noise_model.embed_text()` and reused for both models' cross-attention K/V caches. The two models have **different** text embedding MLP weights — 42% relative mean difference in output. - -**How Found:** Compared `text_embedding_0.weight` and `text_embedding_1.weight` between `high_noise_model.safetensors` and `low_noise_model.safetensors`. Found 17.9% and 26.3% relative differences in the weight matrices. - -**Fix:** Compute separate text embeddings per model: -```python -# Before (broken): -context_emb = low_noise_model.embed_text([context, context_null]) -cross_kv = low_noise_model.prepare_cross_kv(context_emb) # used for BOTH models - -# After (correct): -context_emb_low = low_noise_model.embed_text([context, context_null]) -context_emb_high = high_noise_model.embed_text([context, context_null]) -cross_kv_low = low_noise_model.prepare_cross_kv(context_emb_low) -cross_kv_high = high_noise_model.prepare_cross_kv(context_emb_high) -``` - -**File:** `mlx_video/generate_wan.py` (lines 333–349) -**Commit:** `a85b1c21` - ---- - -## Bug 2: VAE Encoder Weights Excluded from Conversion - -**Symptom:** VAE encoder produces constant output regardless of input image (all-zero weights after conversion). - -**Root Cause:** The conversion script only included encoder weights for `model_type == "ti2v"` (TI2V-5B), not for `"i2v"` (I2V-14B). Since `load_vae_encoder()` uses `strict=False`, missing encoder weights were silently ignored, resulting in random initialization. - -**How Found:** Traced through `convert_wan.py` and found `include_encoder = config.model_type == "ti2v"`. Cross-referenced with the fact that I2V-14B also requires a VAE encoder (for image conditioning). - -**Fix:** -```python -# Before: -include_encoder = config.model_type == "ti2v" -# After: -include_encoder = config.model_type in ("ti2v", "i2v") -``` - -**Note:** The user's specific model happened to be manually converted with encoder weights already present, so this fix was preventive for future conversions. - -**File:** `mlx_video/convert_wan.py` (line 424) - ---- - -## Bug 3: RoPE Frequency Computation (original) - -**Symptom:** Progressive 2px checkerboard artifacts on generated frames, increasing with temporal distance from the conditioned frame. - -**Root Cause (original):** Our original code called `rope_params` three times but applied them incorrectly (per-axis in the model init, then rope_apply did NOT split). This was initially "fixed" by switching to a single `rope_params(1024, head_dim=128)` call, which reduced checkerboard but introduced Bug 6 (see below). - -**File:** `mlx_video/models/wan/model.py` -**Commit:** `3da4a637` - ---- - -## Bug 6: RoPE Frequency Distribution (Bug 3 Fix Was Wrong) - -**Symptom:** I2V generates input image in frames 0–3, colorful checkerboard on frame 4, then grey frames. CFG cond/uncond predictions nearly identical. Model cannot produce coherent motion. - -**Root Cause:** The Bug 3 "fix" replaced three separate `rope_params` calls with a single `rope_params(1024, 128)`. But the reference (`wan/modules/model.py` lines 400–405) actually uses **three separate calls with different dimension normalizations**, concatenated: - -```python -# Reference (CORRECT): -d = dim // num_heads # 128 -self.freqs = torch.cat([ - rope_params(1024, d - 4 * (d // 6)), # rope_params(1024, 44) - rope_params(1024, 2 * (d // 6)), # rope_params(1024, 42) - rope_params(1024, 2 * (d // 6)) # rope_params(1024, 42) -], dim=1) -``` - -Each axis gets its own full frequency range [θ^0, θ^(-~0.95)]. The single-call approach gave: -- Temporal: low frequencies only [1.0 → 0.049] -- Height: medium frequencies only [0.042 → 0.002] (should start at 1.0!) -- Width: high frequencies only [0.002 → 0.0001] (should start at 1.0!) - -The height/width position encoding was essentially destroyed — nearby spatial positions were indistinguishable (max diff 0.958 for height, 0.998 for width vs reference). - -**How Found:** Direct line-by-line comparison of `WanModel.__init__` freq construction between reference `wan/modules/model.py` and our `models/wan/model.py`. Numerical verification confirmed the three-call approach gives each axis a full [0, ~1) exponent range, while the single-call monotonically assigns low→high across axes. - -**Fix:** -```python -d = dim // config.num_heads -self.freqs = mx.concatenate([ - rope_params(1024, d - 4 * (d // 6)), - rope_params(1024, 2 * (d // 6)), - rope_params(1024, 2 * (d // 6)), -], axis=1) -``` - -**Verification:** Max diff vs reference cos/sin: 0.00000000 (exact float32 match). - -**Impact:** Affects ALL Wan models (T2V, I2V, TI2V). Resolves the "Open Investigation: CFG Effectiveness" issue — the model could not produce meaningful cond/uncond differences because it couldn't encode spatial positions. - -**File:** `mlx_video/models/wan/model.py` (line 155) - ---- - -## Bug 4: VAE Encoder Temporal Downsample Order - -**Symptom:** Massive checkerboard artifacts aligned to VAE spatial stride (8px period). VAE encoder output for frames 1–4 showed decreasing std (0.37→1.19) while reference showed stable std (0.95→1.34). - -**Root Cause:** The VAE encoder has 3 downsampling stages. Two perform spatial+temporal downsampling (`downsample3d`) and one performs spatial-only (`downsample2d`). The order matters: - -``` -Reference: [False, True, True] → stage 0: 2d, stage 1: 3d, stage 2: 3d -Ours: [True, True, False] → stage 0: 3d, stage 1: 3d, stage 2: 2d ← WRONG -``` - -This caused temporal downsampling to happen at the wrong resolution stages (96-dim instead of 384-dim), corrupting temporal feature propagation. - -**How Found:** Installed `einops` in the reference environment and ran the reference PyTorch VAE encoder on CPU. Compared frame-by-frame latent output: -- Frame 0 matched exactly (diff=0.0000) — spatial-only processing was correct -- Frames 1–4 had massive differences — proved temporal processing was broken - -Then traced through the reference `_video_vae()` function and found it sets `temperal_downsample=[False, True, True]`, while our `Encoder3d` class used the wrong default `[True, True, False]`. - -**Fix:** -```python -# In Encoder3d.__init__, change default: -temporal_downsample = [False, True, True] # was [True, True, False] -``` - -**Impact:** Encoder output now matches reference within float32 precision (max_diff=2.2e-5). Checkerboard metric dropped from 60–80 to 0.1–7.7. - -**File:** `mlx_video/models/wan/vae.py` (line 370) -**Commit:** `3da4a637` - ---- - -## Bug 5: Non-Chunked VAE Encoding - -**Symptom:** First 4–5 frames grey, then blurred version of image appears. - -**Root Cause:** The reference VAE encoder uses **chunked encoding** with temporal caching (`feat_cache`): -1. Encode first frame alone (1 frame) -2. Encode remaining frames in chunks of 4, with cached temporal features propagating across chunks -3. Each `CausalConv3d` caches last 2 temporal frames from its output, prepending them to the next chunk's input - -Our original implementation encoded all frames at once with zero-padded causal convolutions. The temporal feature propagation is fundamentally different because: -- Chunked: real features from previous chunks serve as causal context -- Non-chunked: zeros serve as causal context for the start - -**How Found:** Studied the reference `CausalConv3d` caching mechanism (`feat_cache`, `feat_idx`) and traced the temporal dimension through all encoding stages. Confirmed that non-chunked encoding produces different output by comparing tensor shapes and values. - -**Fix:** Implemented full chunked encoding with temporal caching: -- Added `cache_x` parameter to `CausalConv3d.__call__` -- Added `feat_cache`/`feat_idx` propagation to `ResidualBlock`, `Resample`, `Encoder3d` -- Rewrote `WanVAE.encode()` with chunked loop (1-frame first chunk, then 4-frame chunks) -- 24 cache slots across the encoder (1 conv1 + 18 downsamples + 4 middle + 1 head) - -**File:** `mlx_video/models/wan/vae.py` (multiple methods) -**Commit:** `b6a94c4c` - ---- - -## Verified Correct Components - -These components were numerically verified against the reference and are **not** sources of bugs: - -| Component | Method | Max Diff | Notes | -|-----------|--------|----------|-------| -| Weight conversion | Direct tensor comparison | ~0.001 | bfloat16 rounding only | -| RoPE rotation | Standalone comparison (float32 vs float64) | 1.3e-5 | Complex vs real multiplication equivalent | -| Time embedding | Full MLP comparison (sinusoidal→embed→project) | 7e-4 | 0.03% relative | -| Patchify | Conv3d vs reshape+Linear | 3.5e-3 | 0.16% relative | -| Unpatchify | einsum vs transpose(6,0,3,1,4,2,5) | exact | Identical operation | -| Scheduler (UniPC) | Formula-level audit + timestep comparison | exact | Predictor, corrector, lambda, rhos all match | -| Mask construction | Value comparison | exact | [4, T_lat, H_lat, W_lat], first temporal=1 | -| CFG formula | Code audit | — | `uncond + gs * (cond - uncond)` correct order | -| VAE decoder | Round-trip test (encode→decode) | clean | No checkerboard in round-trip output | -| Cross-attention K/V | Shape and value audit | — | Batch dimension preserved correctly | - ---- - -## Performance Optimizations - -Applied alongside bug fixes to improve inference speed: - -### Pre-Computation (Before Diffusion Loop) -- **Cross-attention K/V caching**: Precompute K/V projections for all 40 blocks once -- **RoPE cos/sin precomputation**: Build frequency tensors once instead of per-step broadcast/concat -- **Attention mask precomputation**: Build padding mask once, pass via kwargs -- **Inverse frequency caching**: Store sinusoidal `inv_freq` in `__init__` instead of recomputing -- **Timestep list conversion**: `sched.timesteps.tolist()` before loop to avoid `.item()` sync - -### Per-Step Optimizations -- **Single patchify + broadcast for CFG B=2**: Detect identical batch inputs, patchify once and broadcast instead of duplicating the Linear projection -- **Vectorized RoPE**: When all batch elements share the same grid size, apply rotation to the full batch tensor instead of looping per element -- **Redundant type cast removal**: MLX type promotion handles `bfloat16 * float32 → float32` automatically — removed 240 unnecessary graph nodes per step (6 casts × 40 blocks) -- **Euler scheduler sync fix**: Pre-store sigmas as Python floats to avoid `.item()` evaluation sync - ---- - -## Resolved: CFG Effectiveness (was Open Investigation) - -**Symptom:** Generated video shows the input image in frames 0–3 (latent frame 0), then grey/flat frames for the rest. Cond and uncond predictions were nearly identical. - -**Resolution:** This was caused by Bug 6 (incorrect RoPE frequency distribution). The single `rope_params(1024, 128)` call gave height frequencies starting at 0.042 and width at 0.002 (instead of 1.0 for both), making the model unable to encode spatial positions. This caused the transformer to produce nearly identical outputs regardless of text conditioning, explaining the tiny cond/uncond differences. - ---- - -## Reference Implementation - -The reference PyTorch implementation is at `/Users/daniel/Projects/Wan2.2/`: - -| File | Contents | -|------|----------| -| `wan/image2video.py` | I2V pipeline (y construction, mask, diffusion loop) | -| `wan/modules/model.py` | DiT model (forward pass, RoPE, patchify) | -| `wan/modules/vae2_1.py` | VAE encoder/decoder with chunked encoding | -| `wan/utils/fm_solvers_unipc.py` | UniPC scheduler | -| `wan/configs/wan_i2v_A14B.py` | Model configuration | - -Key structural differences between reference and our implementation: -- Reference runs **separate B=1 forward passes** for cond/uncond; we batch as B=2 -- Reference uses `torch.amp.autocast('cuda', dtype=bfloat16)` with explicit float32 blocks; we cast via weight dtype -- Reference uses `Conv3d` for patchify; we use equivalent `reshape + Linear` -- Reference casts timesteps to `int64`; we keep as float (diff < 1.0) - ---- - -## Useful Diagnostic Commands - -### Run I2V-14B generation -```bash -python -m mlx_video.generate_wan \ - --prompt "A woman smiles at camera" \ - --image start.png \ - --model-dir /Volumes/SSD/Wan-AI/Wan2.2-I2V-A14B-MLX \ - --num-frames 17 --steps 40 \ - --height 384 --width 640 \ - --output output_i2v.mp4 -``` - -### Check VAE encoder output -```python -import mlx.core as mx, numpy as np -from mlx_video.models.wan.vae import WanVAE -# Load VAE and encode an image -latents = vae.encode(video_tensor) # [1, 16, T_lat, H_lat, W_lat] -for t in range(latents.shape[2]): - frame = np.array(latents[0, :, t]) - print(f"Frame {t}: mean={frame.mean():.4f} std={frame.std():.4f}") -``` - -### Analyze video frame quality -```python -import cv2, numpy as np -cap = cv2.VideoCapture("output.mp4") -while True: - ret, frame = cap.read() - if not ret: break - # Checkerboard metric: high values indicate patch-boundary artifacts - checker = np.abs(frame[::2, ::2].astype(float) - frame[1::2, 1::2].astype(float)).mean() - print(f"std={frame.std():.1f} checker={checker:.1f}") -``` - -### Compare weights between PyTorch and MLX -```python -import torch, mlx.core as mx, numpy as np -pt = torch.load("model.pt", map_location="cpu") -mlx_w = mx.load("model.safetensors") -for key in sorted(pt.keys()): - if key in mlx_w: - diff = np.abs(pt[key].float().numpy() - np.array(mlx_w[key])).max() - if diff > 0.01: - print(f"LARGE DIFF {key}: {diff:.6f}") -``` diff --git a/mlx_video/models/wan/docs/IMPLEMENTATION_NOTES.md b/mlx_video/models/wan/docs/IMPLEMENTATION_NOTES.md deleted file mode 100644 index 186aabb..0000000 --- a/mlx_video/models/wan/docs/IMPLEMENTATION_NOTES.md +++ /dev/null @@ -1,285 +0,0 @@ -# Wan2.2 MLX Implementation Notes - -> Learnings and key decisions from porting Wan2.2 (TI2V-5B / T2V-14B / I2V-14B / T2V-1.3B) to Apple MLX. - -## Architecture Overview - -Wan2.2 is a Diffusion Transformer (DiT) for video generation. Despite early reports, the T2V/TI2V models do **not** use Mixture-of-Experts — they are dense DiT models with a dual-model architecture for the 14B variant (separate high-noise and low-noise denoisers with a boundary timestep). - -### Key Parameters - -| Model | dim | heads | layers | FFN mult | VAE z_dim | VAE stride | in_dim | -|-------|-----|-------|--------|----------|-----------|------------|--------| -| T2V-14B | 5120 | 40 | 40 | 4×(5120×4/3) | 16 | (4, 8, 8) | 16 | -| I2V-14B | 5120 | 40 | 40 | 4×(5120×4/3) | 16 | (4, 8, 8) | 36 | -| TI2V-5B | 3072 | 24 | 32 | 4×(3072×4/3) | 48 | (4, 16, 16) | 48 | -| T2V-1.3B | 1536 | 12 | 30 | 4×(1536×4/3) | 16 | (4, 8, 8) | 16 | - -### Codebase Structure (~3900 lines of Wan2.2 code) - -``` -mlx_video/ -├── generate_wan.py # 483L - Generation pipeline (T2V + I2V) -├── convert_wan.py # 564L - Weight conversion from HuggingFace -└── models/wan/ - ├── config.py # 113L - Model configs (dataclass presets) - ├── model.py # 320L - DiT model (time embed, patchify, unpatchify) - ├── transformer.py # 91L - Attention block + FFN - ├── attention.py # 211L - Self-attention + cross-attention - ├── rope.py # 100L - 3D Rotary Position Embeddings - ├── text_encoder.py # 240L - T5 encoder (UMT5-XXL) - ├── scheduler.py # 428L - Euler, DPM++ 2M, UniPC schedulers - ├── vae.py # 315L - Wan2.1 VAE decoder (4×8×8) - ├── vae22.py # 836L - Wan2.2 VAE encoder + decoder (4×16×16) - ├── loading.py # 154L - Model loading utilities - └── i2v_utils.py # 58L - I2V mask/preprocessing -``` - ---- - -## Critical Bugs & Fixes - -### 1. MLX Underscore Attribute Gotcha - -**Problem**: MLX's `nn.Module` silently ignores underscore-prefixed attributes (`_layer_0`, `_layer_1`, etc.) in `parameters()` and `load_weights()`. The Wan2.2 VAE had layers named `_layer_N`, causing **87 out of 110 weights to be silently dropped** during loading. - -**Fix**: Rename all `_layer_N` attributes to `layer_N`. MLX treats underscore-prefixed attributes as "private" and excludes them from the parameter tree. - -**Lesson**: Never use underscore-prefixed names for `nn.Module` sub-modules in MLX. - -### 2. Patchify Channel Ordering - -**Problem**: The patchify/unpatchify operations transposed channels incorrectly — producing `[C fastest]` layout instead of `[C slowest]`, causing completely garbled video output. - -**Fix**: Changed reshape to produce correct `[B, T', H', W', pt*ph*pw*C]` ordering matching PyTorch's contiguous memory layout. - -**Lesson**: When porting PyTorch reshape/view operations to MLX, pay close attention to memory layout — PyTorch is row-major by default, and reshape semantics differ when dimensions are reordered. - -### 3. VAE AttentionBlock Reshape - -**Problem**: Attention block merged batch (B) with channels (C) instead of batch with temporal (T), producing a green checker pattern in output. - -**Fix**: Correct reshape from `[B*C, T, H, W]` to `[B*T, C, H, W]` for spatial attention. - -### 4. RMS Norm vs L2 Norm - -**Problem**: The Wan2.2 VAE uses a class named `RMS_norm` in PyTorch, but it actually computes **L2 normalization** (divide by L2 norm), not RMS normalization (divide by RMS). Using actual RMS norm caused exponential value explosion. - -**Fix**: Implement as `x / ||x||₂` instead of `x / sqrt(mean(x²))`. - -**Lesson**: Don't trust class names in reference code — read the actual computation. - -### 5. Video Codec Green Output - -**Problem**: OpenCV's `mp4v` codec on macOS produces green-tinted video. - -**Fix**: Switch to `imageio` with `libx264` codec. Fallback chain: imageio → cv2 (avc1) → PNG frames. - ---- - -## Precision & Dtype Flow - -### The bfloat16 Autocast Pattern - -The official PyTorch implementation uses `torch.autocast("cuda", dtype=torch.bfloat16)` which automatically casts matmul inputs. In MLX, we replicate this manually: - -| Operation | Official (PyTorch) | MLX Implementation | -|---|---|---| -| Modulation/gates | float32 (explicit `autocast(enabled=False)`) | `x.astype(mx.float32)` before modulation | -| QKV projections | bfloat16 (outer autocast) | Cast input to `self.q.weight.dtype` | -| RoPE computation | float64 → float32 | float32 (MLX lacks float64 on GPU) | -| Q/K after RoPE | bfloat16 (`q.to(v.dtype)`) | Cast back to weight dtype after RoPE | -| FFN matmuls | bfloat16 (outer autocast) | Cast input to `self.fc1.weight.dtype` | -| Residual stream | float32 | float32 (no cast) | - -**Result**: ~16% speedup (47s vs 56s for 20 steps at 480p) with no quality regression. - -**Key insight**: Modulation parameters (scale, shift, gate) must stay in float32 — they are small values (~0.01–0.1) that lose significant precision in bfloat16. The official code explicitly disables autocast for these computations. - -### T5 Encoder Precision - -The T5 text encoder must run in float32. Bfloat16 weights cause the attention softmax to produce degenerate distributions, which corrupts text conditioning and manifests as blurry patches in generated video. Since T5 only runs once per generation, the performance cost is negligible. - -### VAE Decoder Precision - -VAE weights must be float32. Bfloat16 VAE decode introduces visible quality loss in the decoded video frames. - ---- - -## Scheduler Implementation Details - -### Three Schedulers: Euler, DPM++ 2M, UniPC - -All operate in the flow-matching formulation where `sigma` represents the noise level (1.0 = pure noise, 0.0 = clean). - -**Euler**: Simple first-order ODE solver. Most stable, recommended for debugging. - -**DPM++ 2M**: Second-order multistep solver. Uses previous step's model output for higher-order correction. Requires special handling at boundaries (return `±inf` from `_lambda()` when sigma is 0 or 1). - -**UniPC** (default, matches official): Second-order predictor-corrector. The "C" (corrector) part is critical — it refines each step using the already-computed model output at **zero additional model evaluation cost**. - -### UniPC Corrector: Must Be Enabled - -**Discovery**: Our implementation had `use_corrector=False` by default, but the official Wan2.2 code **always** enables it (there's no flag — the corrector runs whenever `step_index > 0`). - -**Impact**: Without the corrector, UniPC degrades to a simple predictor, losing its second-order accuracy advantage. - -### UniPC Corrector Coefficients - -The corrector coefficients (`rhos_c`) must be computed by solving a linear system, not hardcoded. For order ≥ 2, hardcoding `rhos_c[-1] = 0.5` introduces ~6–13% error in the correction term across 47+ steps. The fix uses `np.linalg.solve()` to compute exact coefficients. - -### Sigma Schedule - -```python -# Flow-matching sigma schedule with shift -sigmas = np.linspace(1.0, 1.0 / num_steps, num_steps) -sigmas = shift * sigmas / (1 + (shift - 1) * sigmas) -``` - -Default shifts: T2V-14B uses 5.0, TI2V-5B uses 3.0, T2V-1.3B uses 3.0. - ---- - -## Image-to-Video (I2V) Pipelines - -Wan2.2 supports two distinct I2V approaches: - -### TI2V-5B: Per-Token Timestep Masking - -I2V conditions on a reference first frame by giving first-frame latent patches a timestep of 0 (clean) while other patches get the current diffusion timestep: - -```python -# mask_tokens: [1, L] — 0 for first-frame patches, 1 for rest -t_tokens = mask_tokens * current_timestep # first-frame → t=0 -``` - -The model receives 2D timestep input `[B, L]` instead of scalar, enabling per-token noise levels. - -#### Mask Re-application - -After each scheduler step, the first-frame latent is re-injected to prevent drift: - -```python -latents = (1.0 - mask) * z_img + mask * latents -``` - -#### VAE Encoder Temporal Downsample Order - -The Wan2.2 VAE encoder has `temporal_downsample = (False, True, True)`: -- Stage 0: Spatial-only downsampling -- Stages 1–2: Spatial + temporal downsampling - -This was incorrectly set to `(True, True, False)` initially, causing wrong spatial processing paths. - -### I2V-14B: Channel Concatenation - -The I2V-14B model uses a fundamentally different approach — channel concatenation via a `y` tensor: - -1. **Encode image**: Resize to target (H, W), create video tensor with image as first frame + zeros → VAE encode through Wan2.1 encoder → `[16, T_lat, H_lat, W_lat]` -2. **Build mask**: Binary mask with 1 for first frame, 0 for rest → rearranged to `[4, T_lat, H_lat, W_lat]` -3. **Construct y**: `y = concat([mask_4ch, encoded_16ch])` → `[20, T_lat, H_lat, W_lat]` -4. **Channel concat in model**: Before patchify, `x = concat([noise_16ch, y_20ch])` → 36 channels matching `in_dim=36` - -Key differences from TI2V-5B: -- Uses **Wan2.1 VAE** (z_dim=16, stride 4,8,8), not Wan2.2 VAE -- Requires the **VAE encoder** (for encoding the reference image) -- Uses **scalar timesteps** (same as T2V) — no per-token masking -- **Dual model** pipeline with boundary=0.900 -- Both conditional and unconditional predictions receive the same `y` tensor - ---- - -## Dimension Constraints - -### Patchify Alignment - -Video dimensions must be divisible by `patch_size × vae_stride`: -- **TI2V-5B**: patch=(1,2,2), stride=(4,16,16) → alignment = **32** pixels -- **T2V-14B**: patch=(1,2,2), stride=(4,8,8) → alignment = **16** pixels - -Example: 720p (1280×720) → 720 % 32 ≠ 0, auto-aligns to **704**. - -### Frame Count - -Frames must satisfy `num_frames = 4n + 1` (e.g., 5, 9, 13, ..., 81) due to temporal VAE stride of 4. - ---- - -## Performance Optimizations - -### Batched CFG - -Instead of two separate forward passes for conditional and unconditional predictions, batch them into a single B=2 forward pass: - -```python -preds = model([latents, latents], t=t_batch, context=context_cfg, ...) -noise_pred_cond, noise_pred_uncond = preds[0], preds[1] -``` - -**Result**: ~40% speedup by amortizing attention overhead. - -### Precomputed Text Embeddings & Cross-Attention KV Cache - -Text embeddings and cross-attention K/V projections are constant across all diffusion steps. Computing them once and passing as caches eliminates redundant computation. - -### Memory Management in Diffusion Loop - -```python -# Release temporaries before eval to free memory for graph execution -del noise_pred_cond, noise_pred_uncond, noise_pred, preds -mx.eval(latents) -``` - -MLX's lazy evaluation means `mx.eval()` triggers the full computation graph. Deleting intermediate arrays before eval allows MLX to reuse their memory during execution. - ---- - -## Weight Conversion - -### Key Mapping Patterns - -The PyTorch → MLX conversion (`convert_wan.py`) handles several systematic transforms: - -1. **Conv3d weight transposition**: PyTorch `(out, in, D, H, W)` → MLX `(out, D, H, W, in)` -2. **Linear weight transposition**: PyTorch `(out, in)` → MLX `(out, in)` (same convention for `nn.Linear`) -3. **Nested module paths**: `blocks.0.self_attn.q.weight` → same paths, MLX loads by dotted key - -### Dual-Model Splitting - -The T2V-14B uses dual models (high-noise and low-noise). The conversion script splits a single checkpoint into separate files or handles pre-split checkpoints from HuggingFace. - ---- - -## Testing Strategy - -332 tests across 10 files, all running in ~5 seconds: - -| File | Focus | -|------|-------| -| test_wan_config.py | Config presets, field validation | -| test_wan_attention.py | Self/cross attention, RMSNorm, bf16 autocast | -| test_wan_transformer.py | FFN, attention block, float32 modulation | -| test_wan_model.py | Full DiT forward pass, per-token timesteps | -| test_wan_t5.py | T5 encoder layers and full encoding | -| test_wan_vae.py | VAE 2.1 decoder, VAE 2.2 encoder + decoder | -| test_wan_scheduler.py | All 3 schedulers, cross-scheduler coherence | -| test_wan_convert.py | Weight sanitization and conversion | -| test_wan_generate.py | End-to-end pipeline, I2V masks, dimension alignment | -| test_wan_i2v.py | I2V-14B config, y parameter, VAE encoder, mask construction | - -Tests use a tiny config (`dim=64, heads=2, layers=2`) for fast execution. Cross-scheduler coherence tests verify that all three schedulers produce similar outputs from the same noise. - ---- - -## Known Issues - -### I2V Quality Degradation - -Frames 2–13 gradually degrade, and frame 14 often has a "flash" artifact. All implementation details have been verified against the official PyTorch code with no discrepancies found. Possible causes: -- Subtle numerical differences from float32 vs float64 RoPE (MLX lacks float64 on GPU) -- MLX-specific attention precision behavior -- Better prompts and 720p resolution (the model's native resolution) help reduce artifacts - -### Chinese Negative Prompt - -The official Wan2.2 uses a Chinese negative prompt that prevents oversaturation and comic-style artifacts. Correct tokenization requires `ftfy.fix_text()` to normalize fullwidth characters and double HTML unescaping. Without proper text cleaning, the negative prompt tokens don't match the training distribution, causing blurry patches. diff --git a/mlx_video/models/wan/README.md b/mlx_video/models/wan_2/README.md similarity index 94% rename from mlx_video/models/wan/README.md rename to mlx_video/models/wan_2/README.md index 3d45e2c..6fe7d8a 100644 --- a/mlx_video/models/wan/README.md +++ b/mlx_video/models/wan_2/README.md @@ -70,7 +70,7 @@ The conversion script auto-detects the model version from the directory structur #### Wan2.1 T2V 1.3B ```bash -python -m mlx_video.convert_wan \ +python -m mlx_video.wan2.convert \ --checkpoint-dir ./Wan2.1-T2V-1.3B \ --output-dir ./Wan2.1-T2V-1.3B-MLX ``` @@ -78,7 +78,7 @@ python -m mlx_video.convert_wan \ #### Wan2.1 T2V 14B ```bash -python -m mlx_video.convert_wan \ +python -m mlx_video.wan2.convert \ --checkpoint-dir ./Wan2.1-T2V-14B \ --output-dir ./Wan2.1-T2V-14B-MLX ``` @@ -86,7 +86,7 @@ python -m mlx_video.convert_wan \ #### Wan2.2 T2V 14B ```bash -python -m mlx_video.convert_wan \ +python -m mlx_video.wan2.convert \ --checkpoint-dir ./Wan2.2-T2V-A14B \ --output-dir ./Wan2.2-T2V-A14B-MLX ``` @@ -94,7 +94,7 @@ python -m mlx_video.convert_wan \ #### Wan2.2 I2V 14B ```bash -python -m mlx_video.convert_wan \ +python -m mlx_video.wan2.convert \ --checkpoint-dir ./Wan2.2-I2V-A14B \ --output-dir ./Wan2.2-I2V-A14B-MLX ``` @@ -104,7 +104,7 @@ The I2V model is auto-detected from `config.json`; the output will include a `va #### Wan2.2 TI2V 5B ```bash -python -m mlx_video.convert_wan \ +python -m mlx_video.wan2.convert \ --checkpoint-dir ./Wan2.2-TI2V-5B \ --output-dir ./Wan2.2-TI2V-5B-MLX ``` @@ -144,7 +144,7 @@ wan_mlx/ #### Wan2.1 T2V 1.3B ```bash -python -m mlx_video.generate_wan \ +python -m mlx_video.wan2.gemer \ --model-dir ./Wan2.1-T2V-1.3B-MLX \ --prompt "A cat playing piano in a cozy living room, cinematic lighting" \ --width 832 --height 480 --num-frames 81 \ @@ -156,7 +156,7 @@ python -m mlx_video.generate_wan \ #### Wan2.1 T2V 14B ```bash -python -m mlx_video.generate_wan \ +python -m mlx_video.wan2.gemer \ --model-dir ./Wan2.1-T2V-14B-MLX \ --prompt "A woman walks through a misty forest at dawn, slow motion, cinematic" \ --width 1280 --height 704 --num-frames 81 \ @@ -172,7 +172,7 @@ python -m mlx_video.generate_wan \ Wan2.2 uses a dual-model pipeline (separate high-noise and low-noise transformers) and takes guidance as a `high,low` pair: ```bash -python -m mlx_video.generate_wan \ +python -m mlx_video.wan2.generate \ --model-dir ./Wan2.2-T2V-A14B-MLX \ --prompt "Two astronauts playing chess on the surface of the moon, dramatic lighting, 8K" \ --negative-prompt "low quality, blurry, distorted" \ @@ -189,7 +189,7 @@ python -m mlx_video.generate_wan \ Image-to-video: animates a starting image guided by a text prompt. Pass the image with `--image`: ```bash -python -m mlx_video.generate_wan \ +python -m mlx_video.wan2.generate \ --model-dir ./Wan2.2-I2V-A14B-MLX \ --image ./my_photo.png \ --prompt "The person slowly turns their head and smiles, cinematic, natural lighting" \ @@ -207,7 +207,7 @@ python -m mlx_video.generate_wan \ Text+image-to-video: a single-model variant with a larger VAE (`z_dim=48`). Resolution must be divisible by **32** (not 16 as with other models): ```bash -python -m mlx_video.generate_wan \ +python -m mlx_video.wan2.generate \ --model-dir ./Wan2.2-TI2V-5B-MLX \ --image ./my_photo.png \ --prompt "The subject waves hello, warm sunlight, film grain" \ @@ -251,27 +251,27 @@ Quantize the transformer weights to reduce memory usage by ~3.4×. Quantization ```bash # Convert with 4-bit quantization (works for any variant) -python -m mlx_video.convert_wan \ +python -m mlx_video.wan2.convert \ --checkpoint-dir ./Wan2.1-T2V-1.3B \ --output-dir ./Wan2.1-T2V-1.3B-MLX-Q4 \ --quantize --bits 4 --group-size 64 -python -m mlx_video.convert_wan \ +python -m mlx_video.wan2.convert \ --checkpoint-dir ./Wan2.1-T2V-14B \ --output-dir ./Wan2.1-T2V-14B-MLX-Q4 \ --quantize --bits 4 --group-size 64 -python -m mlx_video.convert_wan \ +python -m mlx_video.wan2.convert \ --checkpoint-dir ./Wan2.2-T2V-A14B \ --output-dir ./Wan2.2-T2V-A14B-MLX-Q4 \ --quantize --bits 4 --group-size 64 -python -m mlx_video.convert_wan \ +python -m mlx_video.wan2.convert \ --checkpoint-dir ./Wan2.2-I2V-A14B \ --output-dir ./Wan2.2-I2V-A14B-MLX-Q4 \ --quantize --bits 4 --group-size 64 -python -m mlx_video.convert_wan \ +python -m mlx_video.wan2.convert \ --checkpoint-dir ./Wan2.2-TI2V-5B \ --output-dir ./Wan2.2-TI2V-5B-MLX-Q4 \ --quantize --bits 4 --group-size 64 @@ -280,7 +280,7 @@ python -m mlx_video.convert_wan \ You can also quantize an already-converted MLX model without re-converting from PyTorch: ```bash -python -m mlx_video.convert_wan \ +python -m mlx_video.wan2.convert \ --checkpoint-dir ./Wan2.2-T2V-A14B-MLX \ --output-dir ./Wan2.2-T2V-A14B-MLX-Q4 \ --quantize-only --bits 4 @@ -289,7 +289,7 @@ python -m mlx_video.convert_wan \ Quantized models are used exactly the same way — the quantization is auto-detected from `config.json`: ```bash -python -m mlx_video.generate_wan \ +python -m mlx_video.wan2.generate \ --model-dir ./Wan2.2-T2V-A14B-MLX-Q4 \ --prompt "A cat playing piano" ``` @@ -330,7 +330,7 @@ LoRA's can be used with the `--lora-high` and `--lora-low` command line switches For example, for using the the distilled [Wan2.2-Lightning](https://huggingface.co/lightx2v/Wan2.2-Lightning) LoRA, use the following command. Lightning speeds up generation by using only 4 steps and a CFG scale of 1. ```bash -python -m mlx_video.generate_wan \ +python -m mlx_video.wan2.generate \ --model-dir /Volumes/SSD/Wan-AI/Wan2.2-T2V-A14B-MLX \ --width 480 \ --height 704 \ diff --git a/mlx_video/models/wan_2/__init__.py b/mlx_video/models/wan_2/__init__.py new file mode 100644 index 0000000..6b96519 --- /dev/null +++ b/mlx_video/models/wan_2/__init__.py @@ -0,0 +1,2 @@ +from mlx_video.models.wan_2.config import WanModelConfig +from mlx_video.models.wan_2.wan_2 import WanModel diff --git a/mlx_video/models/wan/attention.py b/mlx_video/models/wan_2/attention.py similarity index 94% rename from mlx_video/models/wan/attention.py rename to mlx_video/models/wan_2/attention.py index b0a6f2f..36f7608 100644 --- a/mlx_video/models/wan/attention.py +++ b/mlx_video/models/wan_2/attention.py @@ -98,8 +98,12 @@ class WanSelfAttention(nn.Module): v = self.v(x_w).reshape(b, s, n, d) # RoPE in float32 for precision (official uses float64) - q = rope_apply(q.astype(mx.float32), grid_sizes, freqs, precomputed_cos_sin=rope_cos_sin) - k = rope_apply(k.astype(mx.float32), grid_sizes, freqs, precomputed_cos_sin=rope_cos_sin) + q = rope_apply( + q.astype(mx.float32), grid_sizes, freqs, precomputed_cos_sin=rope_cos_sin + ) + k = rope_apply( + k.astype(mx.float32), grid_sizes, freqs, precomputed_cos_sin=rope_cos_sin + ) # Cast back to weight dtype for efficient attention (matching official q.to(v.dtype)) q = q.astype(w_dtype).transpose(0, 2, 1, 3) @@ -120,9 +124,7 @@ class WanSelfAttention(nn.Module): q, k, v, scale=self.scale, mask=mask ) else: - out = mx.fast.scaled_dot_product_attention( - q, k, v, scale=self.scale - ) + out = mx.fast.scaled_dot_product_attention(q, k, v, scale=self.scale) out = out.transpose(0, 2, 1, 3).reshape(b, s, -1) return self.o(out) @@ -213,9 +215,7 @@ class WanCrossAttention(nn.Module): q, k, v, scale=self.scale, mask=mask ) else: - out = mx.fast.scaled_dot_product_attention( - q, k, v, scale=self.scale - ) + out = mx.fast.scaled_dot_product_attention(q, k, v, scale=self.scale) out = out.transpose(0, 2, 1, 3).reshape(b, -1, n * d) return self.o(out) diff --git a/mlx_video/models/wan/config.py b/mlx_video/models/wan_2/config.py similarity index 98% rename from mlx_video/models/wan/config.py rename to mlx_video/models/wan_2/config.py index deb0d78..b3b2019 100644 --- a/mlx_video/models/wan/config.py +++ b/mlx_video/models/wan_2/config.py @@ -1,7 +1,7 @@ from dataclasses import dataclass from typing import Tuple, Union -from mlx_video.models.ltx.config import BaseModelConfig +from mlx_video.models.ltx_2.config import BaseModelConfig @dataclass diff --git a/mlx_video/convert_wan.py b/mlx_video/models/wan_2/convert.py similarity index 92% rename from mlx_video/convert_wan.py rename to mlx_video/models/wan_2/convert.py index 5636565..1bd61cb 100644 --- a/mlx_video/convert_wan.py +++ b/mlx_video/models/wan_2/convert.py @@ -7,7 +7,6 @@ from typing import Dict, List, Optional, Tuple import mlx.core as mx import mlx.utils -import numpy as np logger = logging.getLogger(__name__) @@ -57,7 +56,9 @@ def load_safetensors_weights(path: str) -> Dict[str, mx.array]: return weights -def sanitize_wan_transformer_weights(weights: Dict[str, mx.array]) -> Dict[str, mx.array]: +def sanitize_wan_transformer_weights( + weights: Dict[str, mx.array] +) -> Dict[str, mx.array]: """Convert Wan2.2 transformer weight keys to MLX model structure. Wan2.2 keys follow the pattern: @@ -246,8 +247,8 @@ def _load_lora_configs( Shared between weight-merging and runtime-wrapping paths. """ + from mlx_video.models.wan_2.generate import Colors from mlx_video.lora import LoRAConfig, load_multiple_loras - from mlx_video.generate_wan import Colors print(f"\n{Colors.CYAN}Loading {len(lora_configs)} LoRA(s)...{Colors.RESET}") @@ -264,7 +265,9 @@ def _load_lora_configs( module_to_loras = load_multiple_loras(configs) if not module_to_loras: - print(f"{Colors.YELLOW}Warning: No LoRA weights matched model layers{Colors.RESET}") + print( + f"{Colors.YELLOW}Warning: No LoRA weights matched model layers{Colors.RESET}" + ) return module_to_loras @@ -279,8 +282,8 @@ def load_and_apply_loras( For non-quantized (bf16) models. For quantized models, use apply_loras_to_model(). """ + from mlx_video.models.wan_2.generate import Colors from mlx_video.lora import apply_loras_to_weights - from mlx_video.generate_wan import Colors if not lora_configs: return model_weights @@ -289,12 +292,17 @@ def load_and_apply_loras( if not module_to_loras: return model_weights - print(f"{Colors.GREEN}Applying LoRAs to {len(module_to_loras)} modules...{Colors.RESET}") + print( + f"{Colors.GREEN}Applying LoRAs to {len(module_to_loras)} modules...{Colors.RESET}" + ) if verbose: print(f" Model has {len(model_weights)} weight keys") modified_weights = apply_loras_to_weights( - model_weights, module_to_loras, verbose=verbose, quantization_bits=quantization_bits + model_weights, + module_to_loras, + verbose=verbose, + quantization_bits=quantization_bits, ) print(f"{Colors.GREEN}✓ LoRAs applied successfully{Colors.RESET}") @@ -403,7 +411,7 @@ def convert_wan_checkpoint( print(" Warning: No transformer weights found!") # Save config — detect model size from source config.json or transformer weights - from mlx_video.models.wan.config import WanModelConfig + from mlx_video.models.wan_2.config import WanModelConfig def _detect_config(): """Detect config from source config.json or transformer weight shapes.""" @@ -435,8 +443,10 @@ def convert_wan_checkpoint( src_model_type = src_config.get("model_type", "t2v") src_text_len = src_config.get("text_len", 512) - print(f" Source config: dim={src_dim}, layers={src_num_layers}, " - f"heads={src_num_heads}, type={src_model_type}") + print( + f" Source config: dim={src_dim}, layers={src_num_layers}, " + f"heads={src_num_heads}, type={src_model_type}" + ) # Use preset for known TI2V 5B configuration if src_model_type == "ti2v" and src_dim == 3072: @@ -512,9 +522,12 @@ def convert_wan_checkpoint( print(f"Converting VAE ({'Wan2.2' if is_wan22_vae else 'Wan2.1'})...") weights = load_torch_weights(str(vae_path)) if is_wan22_vae: - from mlx_video.models.wan.vae22 import sanitize_wan22_vae_weights + from mlx_video.models.wan_2.vae22 import sanitize_wan22_vae_weights + include_encoder = config.model_type in ("ti2v", "i2v") - weights = sanitize_wan22_vae_weights(weights, include_encoder=include_encoder) + weights = sanitize_wan22_vae_weights( + weights, include_encoder=include_encoder + ) else: weights = sanitize_wan_vae_weights(weights) # Always save VAE in float32 — official Wan2.2 runs VAE decode in @@ -527,7 +540,9 @@ def convert_wan_checkpoint( # Quantize transformer weights if requested if quantize: - print(f"\nQuantizing transformer weights ({bits}-bit, group_size={group_size})...") + print( + f"\nQuantizing transformer weights ({bits}-bit, group_size={group_size})..." + ) _quantize_saved_model(output_dir, config, is_dual, bits, group_size) print(f"\nConversion complete! Output: {output_dir}") @@ -543,9 +558,16 @@ def _quantize_predicate(path: str, module) -> bool: return False # Quantize attention Q/K/V/O and FFN fc1/fc2 quantize_patterns = ( - ".self_attn.q", ".self_attn.k", ".self_attn.v", ".self_attn.o", - ".cross_attn.q", ".cross_attn.k", ".cross_attn.v", ".cross_attn.o", - ".ffn.fc1", ".ffn.fc2", + ".self_attn.q", + ".self_attn.k", + ".self_attn.v", + ".self_attn.o", + ".cross_attn.q", + ".cross_attn.k", + ".cross_attn.v", + ".cross_attn.o", + ".ffn.fc1", + ".ffn.fc2", ) return any(path.endswith(p) for p in quantize_patterns) @@ -572,7 +594,7 @@ def _quantize_saved_model( import mlx.nn as nn - from mlx_video.models.wan.model import WanModel + from mlx_video.models.wan_2.wan_2 import WanModel if source_dir is None: source_dir = output_dir @@ -682,16 +704,22 @@ def quantize_mlx_model( ).exists() # Build model config - from mlx_video.models.wan.config import WanModelConfig + from mlx_video.models.wan_2.config import WanModelConfig - config_dict = {k: v for k, v in cfg.items() if k in WanModelConfig.__dataclass_fields__} + config_dict = { + k: v for k, v in cfg.items() if k in WanModelConfig.__dataclass_fields__ + } for key in ("patch_size", "vae_stride", "window_size", "sample_guide_scale"): if key in config_dict and isinstance(config_dict[key], list): config_dict[key] = tuple(config_dict[key]) config = WanModelConfig(**config_dict) # Copy non-transformer files to output dir (skip large model weights) - transformer_files = {"low_noise_model.safetensors", "high_noise_model.safetensors", "model.safetensors"} + transformer_files = { + "low_noise_model.safetensors", + "high_noise_model.safetensors", + "model.safetensors", + } if dst.resolve() != src.resolve(): dst.mkdir(parents=True, exist_ok=True) for f in src.iterdir(): @@ -763,11 +791,18 @@ if __name__ == "__main__": if args.quantize_only: quantize_mlx_model( - args.checkpoint_dir, args.output_dir, - bits=args.bits, group_size=args.group_size, + args.checkpoint_dir, + args.output_dir, + bits=args.bits, + group_size=args.group_size, ) else: convert_wan_checkpoint( - args.checkpoint_dir, args.output_dir, args.dtype, args.model_version, - quantize=args.quantize, bits=args.bits, group_size=args.group_size, + args.checkpoint_dir, + args.output_dir, + args.dtype, + args.model_version, + quantize=args.quantize, + bits=args.bits, + group_size=args.group_size, ) diff --git a/mlx_video/generate_wan.py b/mlx_video/models/wan_2/generate.py similarity index 77% rename from mlx_video/generate_wan.py rename to mlx_video/models/wan_2/generate.py index cc5d895..f455911 100644 --- a/mlx_video/generate_wan.py +++ b/mlx_video/models/wan_2/generate.py @@ -4,25 +4,23 @@ import argparse import gc import math import random -import sys import time from pathlib import Path import mlx.core as mx -import mlx.nn as nn import numpy as np from tqdm import tqdm -from mlx_video.models.wan.i2v_utils import build_i2v_mask, preprocess_image -from mlx_video.models.wan.loading import ( - _clean_text, +from mlx_video.models.wan_2.i2v_utils import build_i2v_mask, preprocess_image +from mlx_video.models.wan_2.utils import ( encode_text, load_t5_encoder, load_vae_decoder, load_vae_encoder, load_wan_model, ) -from mlx_video.models.wan.postprocess import save_video +from mlx_video.models.wan_2.postprocess import save_video + class Colors: """ANSI color codes for terminal output.""" @@ -37,6 +35,7 @@ class Colors: DIM = "\033[2m" RESET = "\033[0m" + # Backward-compat alias (tests and external code may use the old name) _build_i2v_mask = build_i2v_mask @@ -122,8 +121,8 @@ def generate_video( """ import json - from mlx_video.models.wan.config import WanModelConfig - from mlx_video.models.wan.scheduler import ( + from mlx_video.models.wan_2.config import WanModelConfig + from mlx_video.models.wan_2.scheduler import ( FlowDPMPP2MScheduler, FlowMatchEulerScheduler, FlowUniPCScheduler, @@ -143,10 +142,13 @@ def generate_video( for key in ("patch_size", "vae_stride", "window_size", "sample_guide_scale"): if key in config_dict and isinstance(config_dict[key], list): config_dict[key] = tuple(config_dict[key]) - config = WanModelConfig(**{ - k: v for k, v in config_dict.items() - if k in WanModelConfig.__dataclass_fields__ - }) + config = WanModelConfig( + **{ + k: v + for k, v in config_dict.items() + if k in WanModelConfig.__dataclass_fields__ + } + ) else: # Auto-detect: dual model files → 2.2, single model → 2.1 if (model_dir / "low_noise_model.safetensors").exists(): @@ -182,7 +184,9 @@ def generate_video( if "patch_embedding_proj.weight" in k: actual_dim = v.shape[0] if actual_dim != config.dim: - print(f"{Colors.YELLOW} Config dim={config.dim} doesn't match weights dim={actual_dim}, auto-correcting...{Colors.RESET}") + print( + f"{Colors.YELLOW} Config dim={config.dim} doesn't match weights dim={actual_dim}, auto-correcting...{Colors.RESET}" + ) if actual_dim <= 2048: config = WanModelConfig.wan21_t2v_1_3b() else: @@ -192,13 +196,20 @@ def generate_video( # Auto-correct Wan2.2 VAE params from stale configs if config.in_dim == 48 and config.vae_z_dim != 48: - print(f"{Colors.YELLOW} Auto-correcting Wan2.2 VAE params (in_dim=48 but vae_z_dim={config.vae_z_dim}){Colors.RESET}") - config = WanModelConfig(**{ - **{f.name: getattr(config, f.name) for f in config.__dataclass_fields__.values()}, - "vae_z_dim": 48, - "vae_stride": (4, 16, 16), - "sample_fps": 24, - }) + print( + f"{Colors.YELLOW} Auto-correcting Wan2.2 VAE params (in_dim=48 but vae_z_dim={config.vae_z_dim}){Colors.RESET}" + ) + config = WanModelConfig( + **{ + **{ + f.name: getattr(config, f.name) + for f in config.__dataclass_fields__.values() + }, + "vae_z_dim": 48, + "vae_stride": (4, 16, 16), + "sample_fps": 24, + } + ) # Apply defaults from config if not overridden if steps is None: @@ -227,7 +238,9 @@ def generate_video( gen_frames = num_frames if trim_first_frames > 0: gen_frames = num_frames + trim_first_frames * 4 - print(f"{Colors.DIM} Trim: generating {gen_frames} frames, will discard first {trim_first_frames * 4}{Colors.RESET}") + print( + f"{Colors.DIM} Trim: generating {gen_frames} frames, will discard first {trim_first_frames * 4}{Colors.RESET}" + ) version_str = f"Wan{config.model_version}" mode_str = "dual-model" if is_dual else "single-model" @@ -247,10 +260,16 @@ def generate_video( if is_i2v: print(f" Image: {image}") if neg_prompt_resolved and neg_prompt_resolved.strip(): - neg_display = neg_prompt_resolved[:60] + "..." if len(neg_prompt_resolved) > 60 else neg_prompt_resolved + neg_display = ( + neg_prompt_resolved[:60] + "..." + if len(neg_prompt_resolved) > 60 + else neg_prompt_resolved + ) print(f" Neg prompt: {neg_display}") print(f" Size: {width}x{height}, Frames: {num_frames}") - print(f" Steps: {steps}, Guide: {guide_scale}, Shift: {shift}, Solver: {scheduler}") + print( + f" Steps: {steps}, Guide: {guide_scale}, Shift: {shift}, Solver: {scheduler}" + ) if cfg_disabled: print(f" CFG: disabled (guide_scale≤1 → B=1 fast path, 2x denoising speedup)") print(f"{Colors.RESET}") @@ -275,12 +294,16 @@ def generate_video( height = align_h if width == 0: width = align_w - print(f"{Colors.DIM} Aligned {old_w}x{old_h} → {width}x{height} (must be divisible by {align_w}x{align_h}){Colors.RESET}") + print( + f"{Colors.DIM} Aligned {old_w}x{old_h} → {width}x{height} (must be divisible by {align_w}x{align_h}){Colors.RESET}" + ) # Enforce max_area constraint (model-specific resolution limit) if config.max_area > 0 and height * width > config.max_area: old_h, old_w = height, width - width, height = _best_output_size(width, height, align_w, align_h, config.max_area) + width, height = _best_output_size( + width, height, align_w, align_h, config.max_area + ) print( f"{Colors.YELLOW} ⚠ Resolution {old_w}x{old_h} exceeds model's max area " f"({config.max_area:,}px). Adjusted → {width}x{height}{Colors.RESET}" @@ -309,6 +332,7 @@ def generate_video( # Load tokenizer from transformers import AutoTokenizer + tokenizer = AutoTokenizer.from_pretrained("google/umt5-xxl") # Encode prompts @@ -318,12 +342,15 @@ def generate_video( context_null = None mx.eval(context) else: - context_null = encode_text(t5_encoder, tokenizer, neg_prompt_resolved, config.text_len) + context_null = encode_text( + t5_encoder, tokenizer, neg_prompt_resolved, config.text_len + ) mx.eval(context, context_null) # Free T5 from memory del t5_encoder - gc.collect(); mx.clear_cache() + gc.collect() + mx.clear_cache() print(f"{Colors.DIM} T5 encoding: {time.time() - t1:.1f}s{Colors.RESET}") # I2V: encode image to latent space @@ -346,18 +373,25 @@ def generate_video( img = Image.open(image).convert("RGB") scale = max(width / img.width, height / img.height) - img = img.resize((round(img.width * scale), round(img.height * scale)), Image.LANCZOS) + img = img.resize( + (round(img.width * scale), round(img.height * scale)), Image.LANCZOS + ) x1, y1 = (img.width - width) // 2, (img.height - height) // 2 img = img.crop((x1, y1, x1 + width, y1 + height)) - img_arr = mx.array(np.array(img, dtype=np.float32) / 255.0 * 2.0 - 1.0) # [H, W, 3] + img_arr = mx.array( + np.array(img, dtype=np.float32) / 255.0 * 2.0 - 1.0 + ) # [H, W, 3] img_chw = img_arr.transpose(2, 0, 1) # [3, H, W] # Build video: first frame = image, rest = zeros -> [3, F, H, W] # Chunked encoding processes 1-frame + 4-frame chunks with temporal caching - video = mx.concatenate([ - img_chw[:, None, :, :], - mx.zeros((3, num_frames - 1, height, width)), - ], axis=1) + video = mx.concatenate( + [ + img_chw[:, None, :, :], + mx.zeros((3, num_frames - 1, height, width)), + ], + axis=1, + ) # Encode through Wan2.1 VAE -> [1, z_dim, T_lat, H_lat, W_lat] vae_enc = load_vae_encoder(vae_path, config) @@ -367,12 +401,17 @@ def generate_video( # Build mask: 1 for first frame, 0 for rest -> rearrange to [4, T_lat, H, W] msk = mx.ones((1, num_frames, h_latent, w_latent)) - msk = mx.concatenate([msk[:, :1], mx.zeros((1, num_frames - 1, h_latent, w_latent))], axis=1) + msk = mx.concatenate( + [msk[:, :1], mx.zeros((1, num_frames - 1, h_latent, w_latent))], axis=1 + ) # Repeat first frame 4x, concat rest: [1, 4 + (F-1), H_lat, W_lat] - msk = mx.concatenate([ - mx.repeat(msk[:, :1], 4, axis=1), - msk[:, 1:], - ], axis=1) + msk = mx.concatenate( + [ + mx.repeat(msk[:, :1], 4, axis=1), + msk[:, 1:], + ], + axis=1, + ) # Reshape to [1, T_lat, 4, H_lat, W_lat] then transpose -> [4, T_lat, H_lat, W_lat] msk = msk.reshape(1, msk.shape[1] // 4, 4, h_latent, w_latent) msk = msk.transpose(0, 2, 1, 3, 4)[0] # [4, T_lat, H_lat, W_lat] @@ -395,13 +434,16 @@ def generate_video( del vae_enc, img_tensor - gc.collect(); mx.clear_cache() + gc.collect() + mx.clear_cache() print(f"{Colors.DIM} Image encoding: {time.time() - t_img:.1f}s{Colors.RESET}") # Load transformer models print(f"\n{Colors.BLUE}Loading transformer model(s)...{Colors.RESET}") if quantization: - print(f"{Colors.DIM} Using {quantization['bits']}-bit quantized weights (group_size={quantization['group_size']}){Colors.RESET}") + print( + f"{Colors.DIM} Using {quantization['bits']}-bit quantized weights (group_size={quantization['group_size']}){Colors.RESET}" + ) t2 = time.time() # Merge per-model LoRAs with shared LoRAs @@ -412,10 +454,16 @@ def generate_video( if is_dual: low_noise_path = model_dir / "low_noise_model.safetensors" high_noise_path = model_dir / "high_noise_model.safetensors" - low_noise_model = load_wan_model(low_noise_path, config, quantization, loras=_loras_low) - high_noise_model = load_wan_model(high_noise_path, config, quantization, loras=_loras_high) + low_noise_model = load_wan_model( + low_noise_path, config, quantization, loras=_loras_low + ) + high_noise_model = load_wan_model( + high_noise_path, config, quantization, loras=_loras_high + ) else: - single_model = load_wan_model(model_dir / "model.safetensors", config, quantization, loras=_loras_single) + single_model = load_wan_model( + model_dir / "model.safetensors", config, quantization, loras=_loras_single + ) print(f"{Colors.DIM} Models loaded: {time.time() - t2:.1f}s{Colors.RESET}") # Precompute text embeddings once (avoids redundant MLP in every step) @@ -437,8 +485,12 @@ def generate_video( context_emb_low = low_noise_model.embed_text([context, context_null]) context_emb_high = high_noise_model.embed_text([context, context_null]) mx.eval(context_emb_low, context_emb_high) - context_cfg_low = mx.concatenate([context_emb_low[0:1], context_emb_low[1:2]], axis=0) - context_cfg_high = mx.concatenate([context_emb_high[0:1], context_emb_high[1:2]], axis=0) + context_cfg_low = mx.concatenate( + [context_emb_low[0:1], context_emb_low[1:2]], axis=0 + ) + context_cfg_high = mx.concatenate( + [context_emb_high[0:1], context_emb_high[1:2]], axis=0 + ) else: context_emb = single_model.embed_text([context, context_null]) mx.eval(context_emb) @@ -534,7 +586,7 @@ def generate_video( rcs = rope_cos_sin # Use compiled forward when available (faster after first trace) - _call = getattr(model, '_compiled', model) + _call = getattr(model, "_compiled", model) if cfg_disabled: # No CFG: B=1 forward pass (2x faster than B=2 CFG batch) @@ -552,7 +604,9 @@ def generate_video( y_arg = [y_i2v] if is_i2v_channel_concat else None if is_dual: - ctx = context_cond_high if timestep_val >= boundary else context_cond_low + ctx = ( + context_cond_high if timestep_val >= boundary else context_cond_low + ) else: ctx = context_cond preds = _call( @@ -571,7 +625,11 @@ def generate_video( if is_dual: gs = guide_scale[1] if timestep_val >= boundary else guide_scale[0] else: - gs = guide_scale if isinstance(guide_scale, (int, float)) else guide_scale[0] + gs = ( + guide_scale + if isinstance(guide_scale, (int, float)) + else guide_scale[0] + ) if is_i2v_mask_blend: t_tokens = i2v_mask_tokens * timestep_val @@ -586,8 +644,10 @@ def generate_video( y_arg = [y_i2v, y_i2v] if is_i2v_channel_concat else None - ctx = context_cfg if not is_dual else ( - context_cfg_high if timestep_val >= boundary else context_cfg_low + ctx = ( + context_cfg + if not is_dual + else (context_cfg_high if timestep_val >= boundary else context_cfg_low) ) preds = _call( [latents, latents], @@ -618,16 +678,24 @@ def generate_video( if debug_latents: lat_np = np.array(latents) # [C, T, H, W] n_t = lat_np.shape[1] - print(f"\n{Colors.CYAN} Latent diagnostics (shape {lat_np.shape}):{Colors.RESET}") - print(f" {'Pos':>4s} {'Mean':>8s} {'Std':>8s} {'Min':>8s} {'Max':>8s} {'AbsMean':>8s}") + print( + f"\n{Colors.CYAN} Latent diagnostics (shape {lat_np.shape}):{Colors.RESET}" + ) + print( + f" {'Pos':>4s} {'Mean':>8s} {'Std':>8s} {'Min':>8s} {'Max':>8s} {'AbsMean':>8s}" + ) for t_pos in range(min(n_t, 8)): frame = lat_np[:, t_pos, :, :] - print(f" {t_pos:4d} {frame.mean():8.4f} {frame.std():8.4f} " - f"{frame.min():8.4f} {frame.max():8.4f} {np.abs(frame).mean():8.4f}") + print( + f" {t_pos:4d} {frame.mean():8.4f} {frame.std():8.4f} " + f"{frame.min():8.4f} {frame.max():8.4f} {np.abs(frame).mean():8.4f}" + ) if n_t > 8: interior = lat_np[:, 4:, :, :] - print(f" {'4+':>4s} {interior.mean():8.4f} {interior.std():8.4f} " - f"{interior.min():8.4f} {interior.max():8.4f} {np.abs(interior).mean():8.4f}") + print( + f" {'4+':>4s} {interior.mean():8.4f} {interior.std():8.4f} " + f"{interior.min():8.4f} {interior.max():8.4f} {np.abs(interior).mean():8.4f}" + ) print() # Free transformer models and text embeddings @@ -646,7 +714,8 @@ def generate_video( del model, kv, context if context_null is not None: del context_null - gc.collect(); mx.clear_cache() + gc.collect() + mx.clear_cache() # Load VAE and decode print(f"\n{Colors.BLUE}Decoding with VAE...{Colors.RESET}") @@ -660,7 +729,7 @@ def generate_video( # the CausalConv3d zero-padding artifacts fall on the prefix (which we crop). # This gives the first real frame a full temporal receptive field of real data. # Select tiling configuration - from mlx_video.models.ltx.video_vae.tiling import TilingConfig + from mlx_video.models.ltx_2.video_vae.tiling import TilingConfig if tiling == "none": tiling_config = None @@ -677,16 +746,28 @@ def generate_video( elif tiling == "temporal": tiling_config = TilingConfig.temporal_only() else: - print(f"{Colors.YELLOW} Unknown tiling mode '{tiling}', using auto{Colors.RESET}") + print( + f"{Colors.YELLOW} Unknown tiling mode '{tiling}', using auto{Colors.RESET}" + ) tiling_config = TilingConfig.auto(height, width, num_frames) if tiling_config is not None: - spatial_info = f"{tiling_config.spatial_config.tile_size_in_pixels}px" if tiling_config.spatial_config else "none" - temporal_info = f"{tiling_config.temporal_config.tile_size_in_frames}f" if tiling_config.temporal_config else "none" - print(f"{Colors.DIM} Tiling ({tiling}): spatial={spatial_info}, temporal={temporal_info}{Colors.RESET}") + spatial_info = ( + f"{tiling_config.spatial_config.tile_size_in_pixels}px" + if tiling_config.spatial_config + else "none" + ) + temporal_info = ( + f"{tiling_config.temporal_config.tile_size_in_frames}f" + if tiling_config.temporal_config + else "none" + ) + print( + f"{Colors.DIM} Tiling ({tiling}): spatial={spatial_info}, temporal={temporal_info}{Colors.RESET}" + ) if is_wan22_vae: - from mlx_video.models.wan.vae22 import denormalize_latents + from mlx_video.models.wan_2.vae22 import denormalize_latents # latents: [C, T, H, W] → [1, T, H, W, C] (channels-last for Wan2.2 VAE) z = latents.transpose(1, 2, 3, 0)[None] @@ -718,7 +799,9 @@ def generate_video( if trim_first_frames > 0: trim_pixels = trim_first_frames * 4 video = video[trim_pixels:] - print(f"{Colors.DIM} Trimmed first {trim_pixels} frames ({video.shape[0]} remaining){Colors.RESET}") + print( + f"{Colors.DIM} Trimmed first {trim_pixels} frames ({video.shape[0]} remaining){Colors.RESET}" + ) save_video(video, output_path, fps=config.sample_fps) print(f"\n{Colors.GREEN}✓ Video saved to {output_path}{Colors.RESET}") @@ -727,58 +810,124 @@ def generate_video( def main(): parser = argparse.ArgumentParser(description="Wan Text-to-Video Generation (MLX)") - parser.add_argument("--model-dir", type=str, required=True, help="Path to converted MLX model directory") - parser.add_argument("--prompt", type=str, required=True, help="Text prompt") - parser.add_argument("--image", type=str, default=None, - help="Path to input image for I2V (omit for T2V mode)") - parser.add_argument("--negative-prompt", type=str, default=None, - help="Negative prompt for CFG (default: official Chinese prompt from config)") - parser.add_argument("--no-negative-prompt", action="store_true", - help="Disable negative prompt (use empty string instead of config default)") - parser.add_argument("--width", type=int, default=1280, help="Video width (default: 1280)") - parser.add_argument("--height", type=int, default=704, help="Video height (default: 704; 720p models use 704)") - parser.add_argument("--num-frames", type=int, default=81, help="Number of frames (must be 4n+1)") - parser.add_argument("--steps", type=int, default=None, help="Number of diffusion steps (default: from config)") - parser.add_argument("--guide-scale", type=str, default=None, help="Guidance scale: single float or low,high pair") - parser.add_argument("--shift", type=float, default=None, help="Noise schedule shift (default: from config)") - parser.add_argument("--seed", type=int, default=-1, help="Random seed") - parser.add_argument("--output-path", type=str, default="output.mp4", help="Output video path") parser.add_argument( - "--scheduler", type=str, default="unipc", + "--model-dir", + type=str, + required=True, + help="Path to converted MLX model directory", + ) + parser.add_argument("--prompt", type=str, required=True, help="Text prompt") + parser.add_argument( + "--image", + type=str, + default=None, + help="Path to input image for I2V (omit for T2V mode)", + ) + parser.add_argument( + "--negative-prompt", + type=str, + default=None, + help="Negative prompt for CFG (default: official Chinese prompt from config)", + ) + parser.add_argument( + "--no-negative-prompt", + action="store_true", + help="Disable negative prompt (use empty string instead of config default)", + ) + parser.add_argument( + "--width", type=int, default=1280, help="Video width (default: 1280)" + ) + parser.add_argument( + "--height", + type=int, + default=704, + help="Video height (default: 704; 720p models use 704)", + ) + parser.add_argument( + "--num-frames", type=int, default=81, help="Number of frames (must be 4n+1)" + ) + parser.add_argument( + "--steps", + type=int, + default=None, + help="Number of diffusion steps (default: from config)", + ) + parser.add_argument( + "--guide-scale", + type=str, + default=None, + help="Guidance scale: single float or low,high pair", + ) + parser.add_argument( + "--shift", + type=float, + default=None, + help="Noise schedule shift (default: from config)", + ) + parser.add_argument("--seed", type=int, default=-1, help="Random seed") + parser.add_argument( + "--output-path", type=str, default="output.mp4", help="Output video path" + ) + parser.add_argument( + "--scheduler", + type=str, + default="unipc", choices=["euler", "dpm++", "unipc"], help="Diffusion solver: euler (1st order), dpm++ (2nd order), unipc (2nd order PC, default/official)", ) parser.add_argument( - "--lora", nargs=2, action="append", metavar=("PATH", "STRENGTH"), + "--lora", + nargs=2, + action="append", + metavar=("PATH", "STRENGTH"), help="Apply a LoRA to all models (repeatable). Format: --lora path.safetensors 0.8", ) parser.add_argument( - "--lora-high", nargs=2, action="append", metavar=("PATH", "STRENGTH"), + "--lora-high", + nargs=2, + action="append", + metavar=("PATH", "STRENGTH"), help="Apply a LoRA to high-noise model only (dual-model, repeatable)", ) parser.add_argument( - "--lora-low", nargs=2, action="append", metavar=("PATH", "STRENGTH"), + "--lora-low", + nargs=2, + action="append", + metavar=("PATH", "STRENGTH"), help="Apply a LoRA to low-noise model only (dual-model, repeatable)", ) parser.add_argument( "--tiling", type=str, default="auto", - choices=["auto", "none", "default", "aggressive", "conservative", "spatial", "temporal"], + choices=[ + "auto", + "none", + "default", + "aggressive", + "conservative", + "spatial", + "temporal", + ], help="VAE tiling mode to reduce memory during decoding (default: auto)", ) parser.add_argument( - "--no-compile", action="store_true", + "--no-compile", + action="store_true", help="Disable mx.compile on models (for debugging)", ) parser.add_argument( - "--trim-first-frames", type=int, default=0, metavar="N", + "--trim-first-frames", + type=int, + default=0, + metavar="N", help="Generate N extra temporal chunks (N×4 frames) and discard them from the start. " - "Fixes first-frame color/lighting artifacts on 14B models. Try 1 first (4 frames). " - "Default: 0 (disabled)", + "Fixes first-frame color/lighting artifacts on 14B models. Try 1 first (4 frames). " + "Default: 0 (disabled)", ) parser.add_argument( - "--debug-latents", action="store_true", + "--debug-latents", + action="store_true", help="Print per-temporal-position latent statistics after denoising (diagnostic)", ) args = parser.parse_args() diff --git a/mlx_video/models/wan/i2v_utils.py b/mlx_video/models/wan_2/i2v_utils.py similarity index 94% rename from mlx_video/models/wan/i2v_utils.py rename to mlx_video/models/wan_2/i2v_utils.py index 98a4752..0558130 100644 --- a/mlx_video/models/wan/i2v_utils.py +++ b/mlx_video/models/wan_2/i2v_utils.py @@ -21,7 +21,9 @@ def preprocess_image(image_path: str, width: int, height: int) -> mx.array: # Resize so that the image covers the target size (LANCZOS) scale = max(width / img.width, height / img.height) - img = img.resize((round(img.width * scale), round(img.height * scale)), Image.LANCZOS) + img = img.resize( + (round(img.width * scale), round(img.height * scale)), Image.LANCZOS + ) # Center crop x1 = (img.width - width) // 2 diff --git a/mlx_video/models/wan/postprocess.py b/mlx_video/models/wan_2/postprocess.py similarity index 90% rename from mlx_video/models/wan/postprocess.py rename to mlx_video/models/wan_2/postprocess.py index 4c24fc6..f916a0f 100644 --- a/mlx_video/models/wan/postprocess.py +++ b/mlx_video/models/wan_2/postprocess.py @@ -1,6 +1,8 @@ -import numpy as np from pathlib import Path +import numpy as np + + def save_video(frames: np.ndarray, output_path: str, fps: int = 16): """Save video frames to MP4. @@ -11,6 +13,7 @@ def save_video(frames: np.ndarray, output_path: str, fps: int = 16): """ try: import imageio + writer = imageio.get_writer(output_path, fps=fps, codec="libx264", quality=8) for frame in frames: writer.append_data(frame) @@ -18,6 +21,7 @@ def save_video(frames: np.ndarray, output_path: str, fps: int = 16): except ImportError: try: import cv2 + h, w = frames.shape[1], frames.shape[2] fourcc = cv2.VideoWriter_fourcc(*"avc1") writer = cv2.VideoWriter(output_path, fourcc, fps, (w, h)) @@ -27,9 +31,11 @@ def save_video(frames: np.ndarray, output_path: str, fps: int = 16): except (ImportError, Exception): # Last resort: save as individual PNGs from PIL import Image + out_dir = Path(output_path).parent / Path(output_path).stem out_dir.mkdir(parents=True, exist_ok=True) for i, frame in enumerate(frames): Image.fromarray(frame).save(out_dir / f"frame_{i:04d}.png") - print(f" (no video encoder available, saved {len(frames)} frames to {out_dir}/)") - + print( + f" (no video encoder available, saved {len(frames)} frames to {out_dir}/)" + ) diff --git a/mlx_video/models/wan/rope.py b/mlx_video/models/wan_2/rope.py similarity index 89% rename from mlx_video/models/wan/rope.py rename to mlx_video/models/wan_2/rope.py index d992607..1ad93ae 100644 --- a/mlx_video/models/wan/rope.py +++ b/mlx_video/models/wan_2/rope.py @@ -1,4 +1,3 @@ -import math import mlx.core as mx import numpy as np @@ -11,13 +10,16 @@ def rope_params(max_seq_len: int, dim: int, theta: float = 10000.0) -> mx.array: Complex frequency tensor of shape [max_seq_len, dim // 2]. """ assert dim % 2 == 0 - freqs = np.arange(max_seq_len, dtype=np.float64)[:, None] * ( - 1.0 - / np.power( - theta, - np.arange(0, dim, 2, dtype=np.float64) / dim, - ) - )[None, :] + freqs = ( + np.arange(max_seq_len, dtype=np.float64)[:, None] + * ( + 1.0 + / np.power( + theta, + np.arange(0, dim, 2, dtype=np.float64) / dim, + ) + )[None, :] + ) # Store as (cos, sin) pairs: shape [max_seq_len, dim // 2, 2] cos_freqs = np.cos(freqs).astype(np.float32) sin_freqs = np.sin(freqs).astype(np.float32) @@ -46,9 +48,9 @@ def rope_apply( # Check if all batch elements have the same grid (common for CFG B=2) f0, h0, w0 = grid_sizes[0] seq_len = f0 * h0 * w0 - all_same_grid = all( - grid_sizes[i] == grid_sizes[0] for i in range(1, b) - ) if b > 1 else True + all_same_grid = ( + all(grid_sizes[i] == grid_sizes[0] for i in range(1, b)) if b > 1 else True + ) if all_same_grid: # Vectorized path: apply RoPE to all batch elements at once @@ -57,7 +59,9 @@ def rope_apply( x_imag = x_seq[..., 1] out_real = x_real * cos_f - x_imag * sin_f out_imag = x_real * sin_f + x_imag * cos_f - x_rotated = mx.stack([out_real, out_imag], axis=-1).reshape(b, seq_len, n, d) + x_rotated = mx.stack([out_real, out_imag], axis=-1).reshape( + b, seq_len, n, d + ) if seq_len < s: x_rotated = mx.concatenate([x_rotated, x[:, seq_len:]], axis=1) return x_rotated @@ -102,17 +106,11 @@ def rope_apply( # Build per-position frequencies by expanding along grid dims # temporal: [f,1,1,d_t,2] -> [f,h,w,d_t,2] - ft = mx.broadcast_to( - freqs_t[:f].reshape(f, 1, 1, d_t, 2), (f, h, w, d_t, 2) - ) + ft = mx.broadcast_to(freqs_t[:f].reshape(f, 1, 1, d_t, 2), (f, h, w, d_t, 2)) # height: [1,h,1,d_h,2] -> [f,h,w,d_h,2] - fh = mx.broadcast_to( - freqs_h[:h].reshape(1, h, 1, d_h, 2), (f, h, w, d_h, 2) - ) + fh = mx.broadcast_to(freqs_h[:h].reshape(1, h, 1, d_h, 2), (f, h, w, d_h, 2)) # width: [1,1,w,d_w,2] -> [f,h,w,d_w,2] - fw = mx.broadcast_to( - freqs_w[:w].reshape(1, 1, w, d_w, 2), (f, h, w, d_w, 2) - ) + fw = mx.broadcast_to(freqs_w[:w].reshape(1, 1, w, d_w, 2), (f, h, w, d_w, 2)) # Concatenate: [f*h*w, half_d, 2] freqs_i = mx.concatenate([ft, fh, fw], axis=3).reshape(seq_len, 1, half_d, 2) diff --git a/mlx_video/models/wan/scheduler.py b/mlx_video/models/wan_2/scheduler.py similarity index 97% rename from mlx_video/models/wan/scheduler.py rename to mlx_video/models/wan_2/scheduler.py index 15de21b..067b14e 100644 --- a/mlx_video/models/wan/scheduler.py +++ b/mlx_video/models/wan_2/scheduler.py @@ -7,9 +7,8 @@ for the same quality as Euler. import math -import numpy as np - import mlx.core as mx +import numpy as np def _compute_sigmas( @@ -25,9 +24,7 @@ def _compute_sigmas( Returns num_steps+1 values (the last being 0.0 for the terminal state). """ # sigma bounds from unshifted training schedule (constructor uses shift=1) - alphas = np.linspace(1.0, 1.0 / num_train_timesteps, num_train_timesteps)[ - ::-1 - ] + alphas = np.linspace(1.0, 1.0 / num_train_timesteps, num_train_timesteps)[::-1] sigmas_unshifted = 1.0 - alphas sigma_max = float(sigmas_unshifted[0]) # (N-1)/N sigma_min = float(sigmas_unshifted[-1]) # 0.0 @@ -65,7 +62,10 @@ class FlowMatchEulerScheduler: sample: mx.array, ) -> mx.array: """Euler step: x_next = x + (sigma_next - sigma_cur) * v.""" - dt = self._sigmas_float[self._step_index + 1] - self._sigmas_float[self._step_index] + dt = ( + self._sigmas_float[self._step_index + 1] + - self._sigmas_float[self._step_index] + ) x_next = sample + dt * model_output self._step_index += 1 return x_next @@ -139,13 +139,8 @@ class FlowDPMPP2MScheduler: # Decide order: 1st for first step, last step (if lower_order_final # and few steps), otherwise 2nd - use_first_order = ( - self._prev_x0 is None - or ( - self.lower_order_final - and i == self._num_steps - 1 - and self._num_steps < 15 - ) + use_first_order = self._prev_x0 is None or ( + self.lower_order_final and i == self._num_steps - 1 and self._num_steps < 15 ) if use_first_order or sigma_next == 0.0: diff --git a/mlx_video/models/wan/text_encoder.py b/mlx_video/models/wan_2/text_encoder.py similarity index 94% rename from mlx_video/models/wan/text_encoder.py rename to mlx_video/models/wan_2/text_encoder.py index b81a072..604e63e 100644 --- a/mlx_video/models/wan/text_encoder.py +++ b/mlx_video/models/wan_2/text_encoder.py @@ -49,20 +49,19 @@ class T5RelativeEmbedding(nn.Module): is_small = rel_pos < max_exact rel_pos_f = rel_pos.astype(mx.float32) - rel_pos_large = ( - max_exact - + ( - mx.log(rel_pos_f / max_exact) - / math.log(self.max_dist / max_exact) - * (num_buckets - max_exact) - ).astype(mx.int32) - ) + rel_pos_large = max_exact + ( + mx.log(rel_pos_f / max_exact) + / math.log(self.max_dist / max_exact) + * (num_buckets - max_exact) + ).astype(mx.int32) rel_pos_large = mx.minimum( rel_pos_large, mx.full(rel_pos_large.shape, num_buckets - 1, dtype=mx.int32), ) - rel_buckets = rel_buckets + mx.where(is_small, rel_pos.astype(mx.int32), rel_pos_large) + rel_buckets = rel_buckets + mx.where( + is_small, rel_pos.astype(mx.int32), rel_pos_large + ) return rel_buckets def __call__(self, lq: int, lk: int) -> mx.array: @@ -115,7 +114,7 @@ class T5Attention(nn.Module): v = v.transpose(0, 2, 1, 3) # QK^T (no scaling) — compute in float32 for precision - attn = (q.astype(mx.float32) @ k.astype(mx.float32).transpose(0, 1, 3, 2)) + attn = q.astype(mx.float32) @ k.astype(mx.float32).transpose(0, 1, 3, 2) # Add position bias if pos_bias is not None: diff --git a/mlx_video/models/wan/tiling.py b/mlx_video/models/wan_2/tiling.py similarity index 75% rename from mlx_video/models/wan/tiling.py rename to mlx_video/models/wan_2/tiling.py index 73f2624..1d144b7 100644 --- a/mlx_video/models/wan/tiling.py +++ b/mlx_video/models/wan_2/tiling.py @@ -6,7 +6,7 @@ for non-causal temporal decoders (e.g. Wan2.1 where T latent frames → T*scale output frames rather than LTX's 1+(T-1)*scale mapping). # TODO: This function can be refactored to consolidate with -# mlx_video.models.ltx.video_vae.tiling.decode_with_tiling once the +# mlx_video.models.ltx_2.video_vae.tiling.decode_with_tiling once the # causal_temporal generalisation is accepted upstream. """ @@ -14,7 +14,7 @@ from typing import Callable, Optional import mlx.core as mx -from mlx_video.models.ltx.video_vae.tiling import ( +from mlx_video.models.ltx_2.video_vae.tiling import ( SpatialTilingConfig, TemporalTilingConfig, TilingConfig, @@ -75,7 +75,11 @@ def decode_with_tiling( b, c, f_latent, h_latent, w_latent = latents.shape # Compute output shape - out_f = (1 + (f_latent - 1) * temporal_scale) if causal_temporal else (f_latent * temporal_scale) + out_f = ( + (1 + (f_latent - 1) * temporal_scale) + if causal_temporal + else (f_latent * temporal_scale) + ) out_h = h_latent * spatial_scale out_w = w_latent * spatial_scale @@ -98,9 +102,13 @@ def decode_with_tiling( # Compute intervals for each dimension if causal_temporal: - temporal_intervals = split_in_temporal(temporal_tile_size, temporal_overlap, f_latent) + temporal_intervals = split_in_temporal( + temporal_tile_size, temporal_overlap, f_latent + ) else: - temporal_intervals = split_in_spatial(temporal_tile_size, temporal_overlap, f_latent) + temporal_intervals = split_in_spatial( + temporal_tile_size, temporal_overlap, f_latent + ) height_intervals = split_in_spatial(spatial_tile_size, spatial_overlap, h_latent) width_intervals = split_in_spatial(spatial_tile_size, spatial_overlap, w_latent) @@ -124,9 +132,13 @@ def decode_with_tiling( # Map temporal coordinates if causal_temporal: - out_t_slice, t_mask = map_temporal_slice(t_start, t_end, t_left, t_right, temporal_scale) + out_t_slice, t_mask = map_temporal_slice( + t_start, t_end, t_left, t_right, temporal_scale + ) else: - out_t_slice, t_mask = map_spatial_slice(t_start, t_end, t_left, t_right, temporal_scale) + out_t_slice, t_mask = map_spatial_slice( + t_start, t_end, t_left, t_right, temporal_scale + ) for h_idx in range(num_h_tiles): h_start = height_intervals.starts[h_idx] @@ -135,7 +147,9 @@ def decode_with_tiling( h_right = height_intervals.right_ramps[h_idx] # Map height coordinates - out_h_slice, h_mask = map_spatial_slice(h_start, h_end, h_left, h_right, spatial_scale) + out_h_slice, h_mask = map_spatial_slice( + h_start, h_end, h_left, h_right, spatial_scale + ) for w_idx in range(num_w_tiles): w_start = width_intervals.starts[w_idx] @@ -144,13 +158,23 @@ def decode_with_tiling( w_right = width_intervals.right_ramps[w_idx] # Map width coordinates - out_w_slice, w_mask = map_spatial_slice(w_start, w_end, w_left, w_right, spatial_scale) + out_w_slice, w_mask = map_spatial_slice( + w_start, w_end, w_left, w_right, spatial_scale + ) # Extract tile latents (small slice) - tile_latents = latents[:, :, t_start:t_end, h_start:h_end, w_start:w_end] + tile_latents = latents[ + :, :, t_start:t_end, h_start:h_end, w_start:w_end + ] # Decode tile - tile_output = decoder_fn(tile_latents, causal=causal, timestep=timestep, debug=False, chunked_conv=chunked_conv) + tile_output = decoder_fn( + tile_latents, + causal=causal, + timestep=timestep, + debug=False, + chunked_conv=chunked_conv, + ) mx.eval(tile_output) # Clear tile_latents reference @@ -173,13 +197,15 @@ def decode_with_tiling( w_mask_slice = w_mask[:actual_w] if len(w_mask) > actual_w else w_mask blend_mask = ( - t_mask_slice.reshape(1, 1, -1, 1, 1) * - h_mask_slice.reshape(1, 1, 1, -1, 1) * - w_mask_slice.reshape(1, 1, 1, 1, -1) + t_mask_slice.reshape(1, 1, -1, 1, 1) + * h_mask_slice.reshape(1, 1, 1, -1, 1) + * w_mask_slice.reshape(1, 1, 1, 1, -1) ) # Slice tile output to match - tile_output_slice = tile_output[:, :, :actual_t, :actual_h, :actual_w].astype(mx.float32) + tile_output_slice = tile_output[ + :, :, :actual_t, :actual_h, :actual_w + ].astype(mx.float32) # Clear full tile_output del tile_output @@ -196,11 +222,37 @@ def decode_with_tiling( weighted_tile = tile_output_slice * blend_mask # Update output using slice assignment - output[:, :, t_out_start:t_out_end, h_out_start:h_out_end, w_out_start:w_out_end] = ( - output[:, :, t_out_start:t_out_end, h_out_start:h_out_end, w_out_start:w_out_end] + weighted_tile + output[ + :, + :, + t_out_start:t_out_end, + h_out_start:h_out_end, + w_out_start:w_out_end, + ] = ( + output[ + :, + :, + t_out_start:t_out_end, + h_out_start:h_out_end, + w_out_start:w_out_end, + ] + + weighted_tile ) - weights[:, :, t_out_start:t_out_end, h_out_start:h_out_end, w_out_start:w_out_end] = ( - weights[:, :, t_out_start:t_out_end, h_out_start:h_out_end, w_out_start:w_out_end] + blend_mask + weights[ + :, + :, + t_out_start:t_out_end, + h_out_start:h_out_end, + w_out_start:w_out_end, + ] = ( + weights[ + :, + :, + t_out_start:t_out_end, + h_out_start:h_out_end, + w_out_start:w_out_end, + ] + + blend_mask ) # Force evaluation to free memory @@ -232,12 +284,14 @@ def decode_with_tiling( if next_tile_start_latent == 0: next_tile_start_out = 0 elif causal_temporal: - next_tile_start_out = 1 + (next_tile_start_latent - 1) * temporal_scale + next_tile_start_out = ( + 1 + (next_tile_start_latent - 1) * temporal_scale + ) else: next_tile_start_out = next_tile_start_latent * temporal_scale # We need to track how many frames we've already emitted - if not hasattr(decode_with_tiling, '_emitted_frames'): + if not hasattr(decode_with_tiling, "_emitted_frames"): decode_with_tiling._emitted_frames = 0 emitted = decode_with_tiling._emitted_frames @@ -245,7 +299,10 @@ def decode_with_tiling( # Normalize and emit frames [emitted, next_tile_start_out) finalized_weights = weights[:, :, emitted:next_tile_start_out, :, :] finalized_weights = mx.maximum(finalized_weights, 1e-8) - finalized_output = output[:, :, emitted:next_tile_start_out, :, :] / finalized_weights + finalized_output = ( + output[:, :, emitted:next_tile_start_out, :, :] + / finalized_weights + ) finalized_output = finalized_output.astype(latents.dtype) mx.eval(finalized_output) @@ -262,7 +319,7 @@ def decode_with_tiling( # Emit remaining frames if callback provided if on_frames_ready is not None: - emitted = getattr(decode_with_tiling, '_emitted_frames', 0) + emitted = getattr(decode_with_tiling, "_emitted_frames", 0) if emitted < out_f: remaining_output = output[:, :, emitted:, :, :].astype(latents.dtype) mx.eval(remaining_output) @@ -270,7 +327,7 @@ def decode_with_tiling( del remaining_output # Reset emitted frames counter for next call - if hasattr(decode_with_tiling, '_emitted_frames'): + if hasattr(decode_with_tiling, "_emitted_frames"): del decode_with_tiling._emitted_frames # Clean up weights diff --git a/mlx_video/models/wan/transformer.py b/mlx_video/models/wan_2/transformer.py similarity index 90% rename from mlx_video/models/wan/transformer.py rename to mlx_video/models/wan_2/transformer.py index 7186b82..ea1c058 100644 --- a/mlx_video/models/wan/transformer.py +++ b/mlx_video/models/wan_2/transformer.py @@ -25,9 +25,7 @@ class WanAttentionBlock(nn.Module): # Cross-attention (with optional norm on context) self.norm3 = ( - WanLayerNorm(dim, eps, elementwise_affine=True) - if cross_attn_norm - else None + WanLayerNorm(dim, eps, elementwise_affine=True) if cross_attn_norm else None ) self.cross_attn = WanCrossAttention(dim, num_heads, qk_norm, eps) @@ -36,7 +34,9 @@ class WanAttentionBlock(nn.Module): self.ffn = WanFFN(dim, ffn_dim) # Learned modulation: 6 vectors for scale/shift/gate (kept in float32 for precision) - self.modulation = (mx.random.normal((1, 6, dim)) * (dim**-0.5)).astype(mx.float32) + self.modulation = (mx.random.normal((1, 6, dim)) * (dim**-0.5)).astype( + mx.float32 + ) def __call__( self, @@ -67,7 +67,14 @@ class WanAttentionBlock(nn.Module): # Self-attention with modulation (hidden state stays in w_dtype) x_mod = self.norm1(x) * (1 + e1) + e0 - y = self.self_attn(x_mod, seq_lens, grid_sizes, freqs, rope_cos_sin=rope_cos_sin, attn_mask=attn_mask) + y = self.self_attn( + x_mod, + seq_lens, + grid_sizes, + freqs, + rope_cos_sin=rope_cos_sin, + attn_mask=attn_mask, + ) x = x + y * e2 # Cross-attention (no modulation, just norm) diff --git a/mlx_video/models/wan/loading.py b/mlx_video/models/wan_2/utils.py similarity index 88% rename from mlx_video/models/wan/loading.py rename to mlx_video/models/wan_2/utils.py index 35e3d12..262e41d 100644 --- a/mlx_video/models/wan/loading.py +++ b/mlx_video/models/wan_2/utils.py @@ -6,7 +6,12 @@ import mlx.core as mx import mlx.nn as nn -def load_wan_model(model_path: Path, config, quantization: dict | None = None, loras: list | None = None): +def load_wan_model( + model_path: Path, + config, + quantization: dict | None = None, + loras: list | None = None, +): """Load and initialize WanModel, with optional quantization and LoRA support. Args: @@ -16,12 +21,12 @@ def load_wan_model(model_path: Path, config, quantization: dict | None = None, l If provided, creates QuantizedLinear stubs before loading. loras: Optional list of (lora_path, strength) tuples to apply. """ - from mlx_video.models.wan.model import WanModel + from mlx_video.models.wan_2.wan_2 import WanModel model = WanModel(config) if quantization: - from mlx_video.convert_wan import _quantize_predicate + from mlx_video.models.wan_2.convert import _quantize_predicate nn.quantize( model, @@ -37,7 +42,7 @@ def load_wan_model(model_path: Path, config, quantization: dict | None = None, l if quantization: # Dequantize LoRA-targeted layers, merge delta, replace with bf16 Linear. # Non-LoRA layers stay 4-bit. Zero per-step overhead. - from mlx_video.convert_wan import _load_lora_configs + from mlx_video.models.wan_2.convert import _load_lora_configs from mlx_video.lora import apply_loras_to_model model.load_weights(list(weights.items()), strict=False) @@ -48,7 +53,7 @@ def load_wan_model(model_path: Path, config, quantization: dict | None = None, l return model else: # Weight merging: fold LoRA into bf16 weights before loading - from mlx_video.convert_wan import load_and_apply_loras + from mlx_video.models.wan_2.convert import load_and_apply_loras weights = load_and_apply_loras(dict(weights), loras) @@ -64,7 +69,7 @@ def load_t5_encoder(model_path: Path, config): only runs once per generation, so performance impact is negligible. This matches the official which computes softmax in float32 explicitly. """ - from mlx_video.models.wan.text_encoder import T5Encoder + from mlx_video.models.wan_2.text_encoder import T5Encoder encoder = T5Encoder( vocab_size=config.t5_vocab_size, @@ -92,10 +97,12 @@ def load_vae_decoder(model_path: Path, config=None): is_wan22 = config is not None and config.vae_z_dim == 48 if is_wan22: - from mlx_video.models.wan.vae22 import Wan22VAEDecoder + from mlx_video.models.wan_2.vae22 import Wan22VAEDecoder + vae = Wan22VAEDecoder(z_dim=48) else: - from mlx_video.models.wan.vae import WanVAE + from mlx_video.models.wan_2.vae import WanVAE + vae = WanVAE(z_dim=16) weights = mx.load(str(model_path)) @@ -113,11 +120,11 @@ def load_vae_encoder(model_path: Path, config=None): For Wan2.1/I2V-14B (vae_z_dim=16), uses WanVAE with encoder=True. """ if config is not None and config.vae_z_dim == 16: - from mlx_video.models.wan.vae import WanVAE + from mlx_video.models.wan_2.vae import WanVAE vae = WanVAE(z_dim=16, encoder=True) else: - from mlx_video.models.wan.vae22 import Wan22VAEEncoder + from mlx_video.models.wan_2.vae22 import Wan22VAEEncoder vae = Wan22VAEEncoder(z_dim=config.vae_z_dim if config else 48) @@ -140,6 +147,7 @@ def _clean_text(text: str) -> str: try: import ftfy + text = ftfy.fix_text(text) except ImportError: pass diff --git a/mlx_video/models/wan/vae.py b/mlx_video/models/wan_2/vae.py similarity index 91% rename from mlx_video/models/wan/vae.py rename to mlx_video/models/wan_2/vae.py index faa2372..379ec24 100644 --- a/mlx_video/models/wan/vae.py +++ b/mlx_video/models/wan_2/vae.py @@ -6,19 +6,45 @@ so weights load directly without key sanitization. import mlx.core as mx import mlx.nn as nn -import numpy as np - CACHE_T = 2 # Per-channel normalization statistics for z_dim=16 VAE_MEAN = [ - -0.7571, -0.7089, -0.9113, 0.1075, -0.1745, 0.9653, -0.1517, 1.5508, - 0.4134, -0.0715, 0.5517, -0.3632, -0.1922, -0.9497, 0.2503, -0.2921, + -0.7571, + -0.7089, + -0.9113, + 0.1075, + -0.1745, + 0.9653, + -0.1517, + 1.5508, + 0.4134, + -0.0715, + 0.5517, + -0.3632, + -0.1922, + -0.9497, + 0.2503, + -0.2921, ] VAE_STD = [ - 2.8184, 1.4541, 2.3275, 2.6558, 1.2196, 1.7708, 2.6052, 2.0743, - 3.2687, 2.1526, 2.8652, 1.5579, 1.6382, 1.1253, 2.8251, 1.9160, + 2.8184, + 1.4541, + 2.3275, + 2.6558, + 1.2196, + 1.7708, + 2.6052, + 2.0743, + 3.2687, + 2.1526, + 2.8652, + 1.5579, + 1.6382, + 1.1253, + 2.8251, + 1.9160, ] @@ -50,7 +76,9 @@ class CausalConv3d(nn.Module): self._pad_w = padding[2] # MLX Conv3d: weight shape [O, D, H, W, I] - self.weight = mx.zeros((out_channels, kernel_size[0], kernel_size[1], kernel_size[2], in_channels)) + self.weight = mx.zeros( + (out_channels, kernel_size[0], kernel_size[1], kernel_size[2], in_channels) + ) self.bias = mx.zeros((out_channels,)) def __call__(self, x: mx.array, cache_x: mx.array = None) -> mx.array: @@ -67,8 +95,16 @@ class CausalConv3d(nn.Module): x = mx.concatenate([pad_t, x], axis=2) if self._pad_h > 0 or self._pad_w > 0: - x = mx.pad(x, [(0, 0), (0, 0), (0, 0), - (self._pad_h, self._pad_h), (self._pad_w, self._pad_w)]) + x = mx.pad( + x, + [ + (0, 0), + (0, 0), + (0, 0), + (self._pad_h, self._pad_h), + (self._pad_w, self._pad_w), + ], + ) x = x.transpose(0, 2, 3, 4, 1) # [B, T, H, W, C] out = self._conv3d(x) @@ -118,7 +154,11 @@ class RMS_norm(nn.Module): def __call__(self, x: mx.array) -> mx.array: norm_dim = 1 if self.channel_first else -1 # L2 normalize along channel dim (matches F.normalize) - norm = mx.sqrt(mx.clip(mx.sum(x * x, axis=norm_dim, keepdims=True), a_min=1e-12, a_max=None)) + norm = mx.sqrt( + mx.clip( + mx.sum(x * x, axis=norm_dim, keepdims=True), a_min=1e-12, a_max=None + ) + ) return (x / norm) * self.scale * self.gamma @@ -133,12 +173,12 @@ class ResidualBlock(nn.Module): def __init__(self, in_dim: int, out_dim: int): super().__init__() self.residual = [ - RMS_norm(in_dim, images=False), # [0] - None, # [1] SiLU + RMS_norm(in_dim, images=False), # [0] + None, # [1] SiLU CausalConv3d(in_dim, out_dim, 3, padding=1), # [2] - RMS_norm(out_dim, images=False), # [3] - None, # [4] SiLU - None, # [5] Dropout + RMS_norm(out_dim, images=False), # [3] + None, # [4] SiLU + None, # [5] Dropout CausalConv3d(out_dim, out_dim, 3, padding=1), # [6] ] self.shortcut = CausalConv3d(in_dim, out_dim, 1) if in_dim != out_dim else None @@ -226,13 +266,16 @@ class Resample(nn.Module): # resample.0 = Upsample (no params), resample.1 = Conv2d self.resample = [None, nn.Conv2d(dim, dim // 2, 3, padding=1)] if mode == "upsample3d": - self.time_conv = CausalConv3d(dim, dim * 2, (3, 1, 1), padding=(1, 0, 0)) + self.time_conv = CausalConv3d( + dim, dim * 2, (3, 1, 1), padding=(1, 0, 0) + ) else: # resample.0 = ZeroPad2d (no params), resample.1 = Conv2d(stride=2) self.resample = [None, nn.Conv2d(dim, dim, 3, stride=2)] if mode == "downsample3d": self.time_conv = CausalConv3d( - dim, dim, (3, 1, 1), stride=(2, 1, 1), padding=(0, 0, 0)) + dim, dim, (3, 1, 1), stride=(2, 1, 1), padding=(0, 0, 0) + ) def __call__(self, x: mx.array, feat_cache=None, feat_idx=None) -> mx.array: """x: [B, C, T, H, W]""" @@ -272,8 +315,7 @@ class Resample(nn.Module): else: # Subsequent chunks: use cached frame as temporal context cache_x = x[:, :, -1:] - x = self.time_conv( - x, cache_x=feat_cache[idx][:, :, -1:]) + x = self.time_conv(x, cache_x=feat_cache[idx][:, :, -1:]) feat_cache[idx] = cache_x feat_idx[0] += 1 else: @@ -328,8 +370,8 @@ class Decoder3d(nn.Module): # Output head: [RMS_norm, SiLU (no params), CausalConv3d] self.head = [ - RMS_norm(dims[-1], images=False), # [0] - None, # [1] SiLU + RMS_norm(dims[-1], images=False), # [0] + None, # [1] SiLU CausalConv3d(dims[-1], 3, 3, padding=1), # [2] ] @@ -405,8 +447,7 @@ class Encoder3d(nn.Module): idx = feat_idx[0] cache_x = x[:, :, -CACHE_T:] if cache_x.shape[2] < CACHE_T and feat_cache[idx] is not None: - cache_x = mx.concatenate( - [feat_cache[idx][:, :, -1:], cache_x], axis=2) + cache_x = mx.concatenate([feat_cache[idx][:, :, -1:], cache_x], axis=2) x = self.conv1(x, cache_x=feat_cache[idx]) feat_cache[idx] = cache_x feat_idx[0] += 1 @@ -431,8 +472,7 @@ class Encoder3d(nn.Module): idx = feat_idx[0] cache_x = x[:, :, -CACHE_T:] if cache_x.shape[2] < CACHE_T and feat_cache[idx] is not None: - cache_x = mx.concatenate( - [feat_cache[idx][:, :, -1:], cache_x], axis=2) + cache_x = mx.concatenate([feat_cache[idx][:, :, -1:], cache_x], axis=2) x = self.head[2](x, cache_x=feat_cache[idx]) feat_cache[idx] = cache_x feat_idx[0] += 1 @@ -549,7 +589,7 @@ class WanVAE(nn.Module): Returns: Video [B, 3, T_out, H_out, W_out] clamped to [-1, 1] """ - from mlx_video.models.wan.tiling import TilingConfig, decode_with_tiling + from mlx_video.models.wan_2.tiling import TilingConfig, decode_with_tiling if tiling_config is None: tiling_config = TilingConfig.default() @@ -583,7 +623,7 @@ class WanVAE(nn.Module): decoder_fn=tile_decode, latents=z_denorm, tiling_config=tiling_config, - spatial_scale=8, # 3× spatial 2× upsamples = 8× - temporal_scale=4, # 2× temporal upsamples × 2 = 4× + spatial_scale=8, # 3× spatial 2× upsamples = 8× + temporal_scale=4, # 2× temporal upsamples × 2 = 4× causal_temporal=False, # Wan2.1 uses non-causal temporal (T → 4T) ) diff --git a/mlx_video/models/wan/vae22.py b/mlx_video/models/wan_2/vae22.py similarity index 88% rename from mlx_video/models/wan/vae22.py rename to mlx_video/models/wan_2/vae22.py index a1b233f..7063746 100644 --- a/mlx_video/models/wan/vae22.py +++ b/mlx_video/models/wan_2/vae22.py @@ -8,7 +8,6 @@ conversion (channels-first → channels-last) is needed. """ import logging -import math import mlx.core as mx import mlx.nn as nn @@ -19,23 +18,111 @@ logger = logging.getLogger(__name__) CACHE_T = 2 # Per-channel normalization for z_dim=48 latent space -VAE22_MEAN = mx.array([ - -0.2289, -0.0052, -0.1323, -0.2339, -0.2799, 0.0174, 0.1838, 0.1557, - -0.1382, 0.0542, 0.2813, 0.0891, 0.1570, -0.0098, 0.0375, -0.1825, - -0.2246, -0.1207, -0.0698, 0.5109, 0.2665, -0.2108, -0.2158, 0.2502, - -0.2055, -0.0322, 0.1109, 0.1567, -0.0729, 0.0899, -0.2799, -0.1230, - -0.0313, -0.1649, 0.0117, 0.0723, -0.2839, -0.2083, -0.0520, 0.3748, - 0.0152, 0.1957, 0.1433, -0.2944, 0.3573, -0.0548, -0.1681, -0.0667, -]) +VAE22_MEAN = mx.array( + [ + -0.2289, + -0.0052, + -0.1323, + -0.2339, + -0.2799, + 0.0174, + 0.1838, + 0.1557, + -0.1382, + 0.0542, + 0.2813, + 0.0891, + 0.1570, + -0.0098, + 0.0375, + -0.1825, + -0.2246, + -0.1207, + -0.0698, + 0.5109, + 0.2665, + -0.2108, + -0.2158, + 0.2502, + -0.2055, + -0.0322, + 0.1109, + 0.1567, + -0.0729, + 0.0899, + -0.2799, + -0.1230, + -0.0313, + -0.1649, + 0.0117, + 0.0723, + -0.2839, + -0.2083, + -0.0520, + 0.3748, + 0.0152, + 0.1957, + 0.1433, + -0.2944, + 0.3573, + -0.0548, + -0.1681, + -0.0667, + ] +) -VAE22_STD = mx.array([ - 0.4765, 1.0364, 0.4514, 1.1677, 0.5313, 0.4990, 0.4818, 0.5013, - 0.8158, 1.0344, 0.5894, 1.0901, 0.6885, 0.6165, 0.8454, 0.4978, - 0.5759, 0.3523, 0.7135, 0.6804, 0.5833, 1.4146, 0.8986, 0.5659, - 0.7069, 0.5338, 0.4889, 0.4917, 0.4069, 0.4999, 0.6866, 0.4093, - 0.5709, 0.6065, 0.6415, 0.4944, 0.5726, 1.2042, 0.5458, 1.6887, - 0.3971, 1.0600, 0.3943, 0.5537, 0.5444, 0.4089, 0.7468, 0.7744, -]) +VAE22_STD = mx.array( + [ + 0.4765, + 1.0364, + 0.4514, + 1.1677, + 0.5313, + 0.4990, + 0.4818, + 0.5013, + 0.8158, + 1.0344, + 0.5894, + 1.0901, + 0.6885, + 0.6165, + 0.8454, + 0.4978, + 0.5759, + 0.3523, + 0.7135, + 0.6804, + 0.5833, + 1.4146, + 0.8986, + 0.5659, + 0.7069, + 0.5338, + 0.4889, + 0.4917, + 0.4069, + 0.4999, + 0.6866, + 0.4093, + 0.5709, + 0.6065, + 0.6415, + 0.4944, + 0.5726, + 1.2042, + 0.5458, + 1.6887, + 0.3971, + 1.0600, + 0.3943, + 0.5537, + 0.5444, + 0.4089, + 0.7468, + 0.7744, + ] +) class CausalConv3d(nn.Module): @@ -65,9 +152,9 @@ class CausalConv3d(nn.Module): self._pad_w = padding[2] # Weight: [O, D, H, W, I] for MLX - self.weight = mx.zeros(( - out_channels, kernel_size[0], kernel_size[1], kernel_size[2], in_channels - )) + self.weight = mx.zeros( + (out_channels, kernel_size[0], kernel_size[1], kernel_size[2], in_channels) + ) self.bias = mx.zeros((out_channels,)) def __call__(self, x, cache_x=None): @@ -96,8 +183,16 @@ class CausalConv3d(nn.Module): # Spatial padding if self._pad_h > 0 or self._pad_w > 0: - x = mx.pad(x, [(0, 0), (0, 0), (self._pad_h, self._pad_h), - (self._pad_w, self._pad_w), (0, 0)]) + x = mx.pad( + x, + [ + (0, 0), + (0, 0), + (self._pad_h, self._pad_h), + (self._pad_w, self._pad_w), + (0, 0), + ], + ) T_padded = x.shape[1] H_padded, W_padded = x.shape[2], x.shape[3] @@ -113,8 +208,9 @@ class CausalConv3d(nn.Module): for d in range(kd): frame = x[:, t_start + d] # [B, H_padded, W_padded, C] w2d = self.weight[:, d, :, :, :] # [O, kh, kw, I] - conv_out = mx.conv_general(frame, w2d, - stride=(self.stride[1], self.stride[2])) + conv_out = mx.conv_general( + frame, w2d, stride=(self.stride[1], self.stride[2]) + ) accum = conv_out if accum is None else accum + conv_out outputs.append(accum + self.bias) @@ -126,7 +222,7 @@ class RMS_norm(nn.Module): def __init__(self, dim): super().__init__() - self.scale = dim ** 0.5 + self.scale = dim**0.5 # Weight stored as (dim,) — PyTorch stores (dim, 1, 1, 1) but we squeeze self.gamma = mx.ones((dim,)) @@ -134,7 +230,9 @@ class RMS_norm(nn.Module): # x: [..., C] (channels-last) # PyTorch uses F.normalize (L2 norm), not RMS: x / max(||x||_2, eps) l2_sq = mx.sum(x * x, axis=-1, keepdims=True) - return x * mx.rsqrt(mx.maximum(l2_sq, mx.array(1e-24))) * self.scale * self.gamma + return ( + x * mx.rsqrt(mx.maximum(l2_sq, mx.array(1e-24))) * self.scale * self.gamma + ) class ResidualBlock(nn.Module): @@ -145,11 +243,7 @@ class ResidualBlock(nn.Module): # Sequential residual path: [norm, silu, conv3d, norm, silu, dropout, conv3d] # We store as named layers matching PyTorch's indices self.residual = ResidualBlockLayers(in_dim, out_dim) - self.shortcut = ( - CausalConv3d(in_dim, out_dim, 1) - if in_dim != out_dim - else None - ) + self.shortcut = CausalConv3d(in_dim, out_dim, 1) if in_dim != out_dim else None def __call__(self, x, feat_cache=None, feat_idx=None): h = self.shortcut(x) if self.shortcut is not None else x @@ -182,9 +276,7 @@ class ResidualBlockLayers(nn.Module): # Save last CACHE_T frames before conv (for next chunk's context) cache_x = x[:, -CACHE_T:] if cache_x.shape[1] < 2 and feat_cache[idx] is not None: - cache_x = mx.concatenate( - [feat_cache[idx][:, -1:], cache_x], axis=1 - ) + cache_x = mx.concatenate([feat_cache[idx][:, -1:], cache_x], axis=1) out = conv(x, cache_x=feat_cache[idx]) feat_cache[idx] = cache_x feat_idx[0] += 1 @@ -231,7 +323,9 @@ class AttentionBlock(nn.Module): x = self.norm(x) # QKV via 1x1 conv2d (equivalent to linear on last dim) - qkv = mx.conv_general(x, self.to_qkv_weight) + self.to_qkv_bias # [BT, H, W, 3C] + qkv = ( + mx.conv_general(x, self.to_qkv_weight) + self.to_qkv_bias + ) # [BT, H, W, 3C] qkv = qkv.reshape(B * T, H * W, 3 * C) q, k, v = mx.split(qkv, 3, axis=-1) # each [BT, HW, C] @@ -240,8 +334,10 @@ class AttentionBlock(nn.Module): k = k[:, None, :, :] v = v[:, None, :, :] - scale = C ** -0.5 - out = mx.fast.scaled_dot_product_attention(q, k, v, scale=scale) # [BT, 1, HW, C] + scale = C**-0.5 + out = mx.fast.scaled_dot_product_attention( + q, k, v, scale=scale + ) # [BT, 1, HW, C] out = out.squeeze(1).reshape(B * T, H, W, C) # Project output @@ -270,16 +366,24 @@ class DupUp3D(nn.Module): x = mx.repeat(x, self.repeats, axis=-1) # [B, T, H, W, C*repeats] # Reshape to [B, T, H, W, out_C, factor_t, factor_s, factor_s] - x = x.reshape(B, T, H, W, self.out_channels, self.factor_t, self.factor_s, self.factor_s) + x = x.reshape( + B, T, H, W, self.out_channels, self.factor_t, self.factor_s, self.factor_s + ) # Permute to interleave: [B, T, factor_t, H, factor_s, W, factor_s, out_C] x = x.transpose(0, 1, 5, 2, 6, 3, 7, 4) # Reshape to final: [B, T*factor_t, H*factor_s, W*factor_s, out_C] - x = x.reshape(B, T * self.factor_t, H * self.factor_s, W * self.factor_s, self.out_channels) + x = x.reshape( + B, + T * self.factor_t, + H * self.factor_s, + W * self.factor_s, + self.out_channels, + ) if first_chunk: - x = x[:, self.factor_t - 1:, :, :, :] + x = x[:, self.factor_t - 1 :, :, :, :] return x @@ -348,7 +452,9 @@ class Resample(nn.Module): self.resample_weight = mx.zeros((dim, 3, 3, dim)) self.resample_bias = mx.zeros((dim,)) # time_conv: CausalConv3d(dim, dim, (3,1,1), stride=(2,1,1)) - self.time_conv = CausalConv3d(dim, dim, (3, 1, 1), stride=(2, 1, 1), padding=(0, 0, 0)) + self.time_conv = CausalConv3d( + dim, dim, (3, 1, 1), stride=(2, 1, 1), padding=(0, 0, 0) + ) else: raise ValueError(f"Unsupported mode: {mode}") @@ -369,7 +475,9 @@ class Resample(nn.Module): """Apply strided Conv2d for downsampling. x: [N, H, W, C].""" # ZeroPad2d((0,1,0,1)): pad right=1, bottom=1 x = mx.pad(x, [(0, 0), (0, 1), (0, 1), (0, 0)]) - return mx.conv_general(x, self.resample_weight, stride=(2, 2)) + self.resample_bias + return ( + mx.conv_general(x, self.resample_weight, stride=(2, 2)) + self.resample_bias + ) def __call__(self, x, first_chunk=False, feat_cache=None, feat_idx=None): # x: [B, T, H, W, C] @@ -444,14 +552,17 @@ class Resample(nn.Module): class Up_ResidualBlock(nn.Module): """Upsampling residual block with optional DupUp3D shortcut.""" - def __init__(self, in_dim, out_dim, num_res_blocks, temperal_upsample=False, up_flag=False): + def __init__( + self, in_dim, out_dim, num_res_blocks, temperal_upsample=False, up_flag=False + ): super().__init__() self.up_flag = up_flag # DupUp3D shortcut (no learnable params) if up_flag: self.avg_shortcut = DupUp3D( - in_dim, out_dim, + in_dim, + out_dim, factor_t=2 if temperal_upsample else 1, factor_s=2 if up_flag else 1, ) @@ -490,13 +601,21 @@ class Up_ResidualBlock(nn.Module): class Down_ResidualBlock(nn.Module): """Downsampling residual block with AvgDown3D shortcut.""" - def __init__(self, in_dim, out_dim, num_res_blocks, temperal_downsample=False, down_flag=False): + def __init__( + self, + in_dim, + out_dim, + num_res_blocks, + temperal_downsample=False, + down_flag=False, + ): super().__init__() self.down_flag = down_flag # AvgDown3D shortcut (no learnable params, always present) self.avg_shortcut = AvgDown3D( - in_dim, out_dim, + in_dim, + out_dim, factor_t=2 if temperal_downsample else 1, factor_s=2 if down_flag else 1, ) @@ -562,13 +681,15 @@ class Decoder3d(nn.Module): self.upsamples = [] for i, (in_dim, out_dim) in enumerate(zip(dims[:-1], dims[1:])): t_up = temperal_upsample[i] if i < len(temperal_upsample) else False - self.upsamples.append(Up_ResidualBlock( - in_dim=in_dim, - out_dim=out_dim, - num_res_blocks=num_res_blocks + 1, - temperal_upsample=t_up, - up_flag=(i != len(dim_mult) - 1), - )) + self.upsamples.append( + Up_ResidualBlock( + in_dim=in_dim, + out_dim=out_dim, + num_res_blocks=num_res_blocks + 1, + temperal_upsample=t_up, + up_flag=(i != len(dim_mult) - 1), + ) + ) # Output head: [RMS_norm, SiLU, CausalConv3d] self.head = Head22(dims[-1]) @@ -612,13 +733,15 @@ class Encoder3d(nn.Module): for i in range(len(dim_mult)): in_d, out_d = dims[i], dims[i + 1] t_down = temperal_downsample[i] if i < len(temperal_downsample) else False - self.downsamples.append(Down_ResidualBlock( - in_dim=in_d, - out_dim=out_d, - num_res_blocks=num_res_blocks, - temperal_downsample=t_down, - down_flag=(i < len(dim_mult) - 1), - )) + self.downsamples.append( + Down_ResidualBlock( + in_dim=in_d, + out_dim=out_d, + num_res_blocks=num_res_blocks, + temperal_downsample=t_down, + down_flag=(i < len(dim_mult) - 1), + ) + ) # Middle blocks (same as decoder) out_dim = dims[-1] @@ -658,9 +781,7 @@ class Encoder3d(nn.Module): idx = feat_idx[0] cache_x = x[:, -CACHE_T:] if cache_x.shape[1] < 2 and feat_cache[idx] is not None: - cache_x = mx.concatenate( - [feat_cache[idx][:, -1:], cache_x], axis=1 - ) + cache_x = mx.concatenate([feat_cache[idx][:, -1:], cache_x], axis=1) x = self.conv1(x, cache_x=feat_cache[idx]) feat_cache[idx] = cache_x feat_idx[0] += 1 @@ -700,9 +821,7 @@ class Head22(nn.Module): idx = feat_idx[0] cache_x = x[:, -CACHE_T:] if cache_x.shape[1] < 2 and feat_cache[idx] is not None: - cache_x = mx.concatenate( - [feat_cache[idx][:, -1:], cache_x], axis=1 - ) + cache_x = mx.concatenate([feat_cache[idx][:, -1:], cache_x], axis=1) x = self.layer_2(x, cache_x=feat_cache[idx]) feat_cache[idx] = cache_x feat_idx[0] += 1 @@ -768,7 +887,7 @@ class Wan22VAEEncoder(nn.Module): if i == 0: chunk = x[:, :1] else: - chunk = x[:, 1 + 4 * (i - 1):1 + 4 * i] + chunk = x[:, 1 + 4 * (i - 1) : 1 + 4 * i] chunk_out = self.encoder(chunk, feat_cache=feat_cache, feat_idx=feat_idx) if out is None: out = chunk_out @@ -778,7 +897,7 @@ class Wan22VAEEncoder(nn.Module): # conv1 (pointwise) + split into mu, log_var out = self.conv1(out) - mu = out[:, :, :, :, :self.z_dim] + mu = out[:, :, :, :, : self.z_dim] # Normalize mu = normalize_latents(mu) @@ -847,7 +966,7 @@ class Wan22VAEDecoder(nn.Module): Returns: video: [B, T', H', W', 3] decoded RGB in [-1, 1] """ - from mlx_video.models.wan.tiling import TilingConfig, decode_with_tiling + from mlx_video.models.wan_2.tiling import TilingConfig, decode_with_tiling if tiling_config is None: tiling_config = TilingConfig.default() @@ -885,8 +1004,8 @@ class Wan22VAEDecoder(nn.Module): decoder_fn=tile_decode, latents=z_cf, tiling_config=tiling_config, - spatial_scale=16, # 8× conv upsample + 2× unpatchify - temporal_scale=4, # two 2× temporal upsamples (first_chunk=True → causal) + spatial_scale=16, # 8× conv upsample + 2× unpatchify + temporal_scale=4, # two 2× temporal upsamples (first_chunk=True → causal) causal_temporal=True, ) diff --git a/mlx_video/models/wan/model.py b/mlx_video/models/wan_2/wan_2.py similarity index 93% rename from mlx_video/models/wan/model.py rename to mlx_video/models/wan_2/wan_2.py index 989e712..6684537 100644 --- a/mlx_video/models/wan/model.py +++ b/mlx_video/models/wan_2/wan_2.py @@ -1,4 +1,5 @@ import math + import mlx.core as mx import mlx.nn as nn import numpy as np @@ -37,7 +38,9 @@ class Head(nn.Module): proj_dim = math.prod(patch_size) * out_dim self.norm = WanLayerNorm(dim, eps) self.head = nn.Linear(dim, proj_dim) - self.modulation = (mx.random.normal((1, 2, dim)) * (dim**-0.5)).astype(mx.float32) + self.modulation = (mx.random.normal((1, 2, dim)) * (dim**-0.5)).astype( + mx.float32 + ) def __call__(self, x: mx.array, e: mx.array) -> mx.array: """ @@ -111,20 +114,23 @@ class WanModel(nn.Module): # Reference computes three rope_params with different dim normalizations # so each axis (temporal/height/width) gets its own full frequency range. d = dim // config.num_heads - self.freqs = mx.concatenate([ - rope_params(1024, d - 4 * (d // 6)), - rope_params(1024, 2 * (d // 6)), - rope_params(1024, 2 * (d // 6)), - ], axis=1) + self.freqs = mx.concatenate( + [ + rope_params(1024, d - 4 * (d // 6)), + rope_params(1024, 2 * (d // 6)), + rope_params(1024, 2 * (d // 6)), + ], + axis=1, + ) # Precompute sinusoidal inv_freq for time embedding. half = config.freq_dim // 2 self._inv_freq = mx.array( - np.power(10000.0, -np.arange(half, dtype=np.float64) / half - ).astype(np.float32) + np.power(10000.0, -np.arange(half, dtype=np.float64) / half).astype( + np.float32 + ) ) - def _patchify(self, x: mx.array) -> tuple: """Convert video tensor to patch embeddings. @@ -297,12 +303,19 @@ class WanModel(nn.Module): seq_lens_list.append(p.shape[1]) x = mx.concatenate( [ - mx.concatenate( - [p, mx.zeros((1, seq_len - p.shape[1], self.dim), dtype=p.dtype)], - axis=1, + ( + mx.concatenate( + [ + p, + mx.zeros( + (1, seq_len - p.shape[1], self.dim), dtype=p.dtype + ), + ], + axis=1, + ) + if p.shape[1] < seq_len + else p ) - if p.shape[1] < seq_len - else p for p in patches ], axis=0, @@ -315,9 +328,7 @@ class WanModel(nn.Module): t = t[None] sinusoid = t[..., None].astype(mx.float32) * self._inv_freq - sin_emb = mx.concatenate( - [mx.cos(sinusoid), mx.sin(sinusoid)], axis=-1 - ) + sin_emb = mx.concatenate([mx.cos(sinusoid), mx.sin(sinusoid)], axis=-1) if t.ndim == 1: # Standard T2V: scalar timestep per batch element [B] diff --git a/mlx_video/text_projection.py b/mlx_video/text_projection.py deleted file mode 100644 index 311d6cc..0000000 --- a/mlx_video/text_projection.py +++ /dev/null @@ -1,32 +0,0 @@ -import mlx.core as mx -import mlx.nn as nn - - -class PixArtAlphaTextProjection(nn.Module): - - def __init__( - self, - in_features: int, - hidden_size: int, - out_features: int | None = None, - bias: bool = True, - act_fn: str = "gelu_tanh", - ): - - super().__init__() - - out_features = out_features or hidden_size - self.linear1 = nn.Linear(in_features, hidden_size, bias=bias) - if act_fn == "gelu_tanh": - self.act = nn.GELU(approx="tanh") - elif act_fn == "silu": - self.act = nn.SiLU() - else: - raise ValueError(f"Unknown activation function: {act_fn}") - self.linear2 = nn.Linear(hidden_size, out_features, bias=bias) - - def __call__(self, x: mx.array) -> mx.array: - x = self.linear1(x) - x = self.act(x) - x = self.linear2(x) - return x diff --git a/mlx_video/utils.py b/mlx_video/utils.py index cebbed7..eb8903b 100644 --- a/mlx_video/utils.py +++ b/mlx_video/utils.py @@ -1,29 +1,36 @@ import math -from typing import Optional, Tuple, Union +from functools import partial +from pathlib import Path +from typing import Optional, Union import mlx.core as mx import mlx.nn as nn import numpy as np -from functools import partial -from pathlib import Path from huggingface_hub import snapshot_download from PIL import Image + def get_model_path(model_repo: str): """Get or download LTX-2 model path.""" try: + if Path(model_repo).exists(): + return Path(model_repo) return Path(snapshot_download(repo_id=model_repo, local_files_only=True)) except Exception: print("Downloading LTX-2 model weights...") - return Path(snapshot_download( - repo_id=model_repo, - local_files_only=False, - resume_download=True, - allow_patterns=["*.safetensors", "*.json"], - )) + return Path( + snapshot_download( + repo_id=model_repo, + local_files_only=False, + resume_download=True, + allow_patterns=["*.safetensors", "*.json"], + ) + ) + def apply_quantization(model: nn.Module, weights: mx.array, quantization: dict): if quantization is not None: + def get_class_predicate(p, m): # Handle custom per layer quantizations if p in quantization: @@ -44,23 +51,24 @@ def apply_quantization(model: nn.Module, weights: mx.array, quantization: dict): class_predicate=get_class_predicate, ) -@partial(mx.compile, shapeless=True) + +@partial(mx.compile, shapeless=True) def rms_norm(x: mx.array, eps: float = 1e-6) -> mx.array: return mx.fast.rms_norm(x, mx.ones((x.shape[-1],), dtype=x.dtype), eps) - @partial(mx.compile, shapeless=True) def to_denoised( - noisy: mx.array, - velocity: mx.array, - sigma: mx.array | float + noisy: mx.array, velocity: mx.array, sigma: mx.array | float ) -> mx.array: """Convert velocity prediction to denoised output. Given noisy input x_t and velocity prediction v, compute denoised x_0: x_0 = x_t - sigma * v + Uses float32 for computation precision (matching PyTorch behavior), + then converts back to input dtype. + Args: noisy: Noisy input tensor x_t velocity: Velocity prediction v @@ -69,16 +77,21 @@ def to_denoised( Returns: Denoised tensor x_0 """ + original_dtype = noisy.dtype + + # Cast to float32 for precision (PyTorch uses calc_dtype=torch.float32) + noisy_f32 = noisy.astype(mx.float32) + velocity_f32 = velocity.astype(mx.float32) + if isinstance(sigma, (int, float)): - # Convert to array with matching dtype to avoid float32 promotion - sigma_arr = mx.array(sigma, dtype=velocity.dtype) - return noisy - sigma_arr * velocity + sigma_f32 = mx.array(sigma, dtype=mx.float32) else: - # sigma is per-sample - ensure dtype matches - sigma = sigma.astype(velocity.dtype) - while sigma.ndim < velocity.ndim: - sigma = mx.expand_dims(sigma, axis=-1) - return noisy - sigma * velocity + sigma_f32 = sigma.astype(mx.float32) + while sigma_f32.ndim < velocity_f32.ndim: + sigma_f32 = mx.expand_dims(sigma_f32, axis=-1) + + result = noisy_f32 - sigma_f32 * velocity_f32 + return result.astype(original_dtype) def repeat_interleave(x: mx.array, repeats: int, axis: int = -1) -> mx.array: @@ -274,7 +287,9 @@ def prepare_image_for_encoding( if image_np.max() <= 1.0: image_np = (image_np * 255).astype(np.uint8) pil_image = Image.fromarray(image_np) - pil_image = pil_image.resize((target_width, target_height), Image.Resampling.LANCZOS) + pil_image = pil_image.resize( + (target_width, target_height), Image.Resampling.LANCZOS + ) image = mx.array(np.array(pil_image).astype(np.float32) / 255.0) # Normalize to [-1, 1] diff --git a/mlx_video/version.py b/mlx_video/version.py index b3c06d4..f102a9c 100644 --- a/mlx_video/version.py +++ b/mlx_video/version.py @@ -1 +1 @@ -__version__ = "0.0.1" \ No newline at end of file +__version__ = "0.0.1" diff --git a/pyproject.toml b/pyproject.toml index 198956d..6a4d3ad 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -20,6 +20,8 @@ dependencies = [ "opencv-python>=4.12.0.88", "Pillow>=10.3.0", "mlx-vlm", + "rich>=14.2.0", + "librosa>=0.10.0", "imageio>=2.37.2", "imageio-ffmpeg>=0.6.0", "ftfy", @@ -44,8 +46,8 @@ Repository = "https://github.com/Blaizzy/mlx-video" Issues = "https://github.com/Blaizzy/mlx-video/issues" [project.scripts] -"mlx_video.generate" = "mlx_video.generate:main" -"mlx_video.generate_wan" = "mlx_video.generate_wan:main" +"mlx_video.ltx_2.generate" = "mlx_video.models.ltx_2.generate:main" +"mlx_video.wan_2.generate" = "mlx_video.models.wan_2.generate:main" [tool.setuptools.packages.find] include = ["mlx_video*"] @@ -57,3 +59,4 @@ version = {attr = "mlx_video.version.__version__"} dev = [ "pytest", ] + diff --git a/scripts/video/compare_videos.py b/scripts/video/compare_videos.py index 1d18804..462e282 100644 --- a/scripts/video/compare_videos.py +++ b/scripts/video/compare_videos.py @@ -170,19 +170,33 @@ def print_report(results, ref_path, test_path): print("AGGREGATE METRICS") print("-" * 40) - print(f" PSNR (dB): mean={np.mean(psnr):6.2f} min={np.min(psnr):6.2f} max={np.max(psnr):6.2f}") - print(f" SSIM: mean={np.mean(ssim):.4f} min={np.min(ssim):.4f} max={np.max(ssim):.4f}") - print(f" Mean diff: mean={np.mean(md):6.2f} min={np.min(md):6.2f} max={np.max(md):6.2f}") - print(f" Max diff: mean={np.mean(mx):6.1f} min={np.min(mx):6.1f} max={np.max(mx):6.1f}") - print(f" Color dist: mean={np.mean(cd):.4f} min={np.min(cd):.4f} max={np.max(cd):.4f}") + print( + f" PSNR (dB): mean={np.mean(psnr):6.2f} min={np.min(psnr):6.2f} max={np.max(psnr):6.2f}" + ) + print( + f" SSIM: mean={np.mean(ssim):.4f} min={np.min(ssim):.4f} max={np.max(ssim):.4f}" + ) + print( + f" Mean diff: mean={np.mean(md):6.2f} min={np.min(md):6.2f} max={np.max(md):6.2f}" + ) + print( + f" Max diff: mean={np.mean(mx):6.1f} min={np.min(mx):6.1f} max={np.max(mx):6.1f}" + ) + print( + f" Color dist: mean={np.mean(cd):.4f} min={np.min(cd):.4f} max={np.max(cd):.4f}" + ) print() print("TEMPORAL COHERENCE (mean frame-to-frame diff, lower = smoother)") print("-" * 40) print(f" Reference: {results['ref_temporal_coherence']:.2f}") print(f" Test: {results['test_temporal_coherence']:.2f}") - ratio = results["test_temporal_coherence"] / (results["ref_temporal_coherence"] + 1e-10) - print(f" Ratio: {ratio:.2f}x {'(test is smoother)' if ratio < 1 else '(test is jerkier)' if ratio > 1.05 else '(similar)'}") + ratio = results["test_temporal_coherence"] / ( + results["ref_temporal_coherence"] + 1e-10 + ) + print( + f" Ratio: {ratio:.2f}x {'(test is smoother)' if ratio < 1 else '(test is jerkier)' if ratio > 1.05 else '(similar)'}" + ) print() # Identify worst frames @@ -190,7 +204,9 @@ def print_report(results, ref_path, test_path): print("-" * 40) worst_idx = np.argsort(psnr)[:5] for i in worst_idx: - print(f" Frame {i:4d}: PSNR={psnr[i]:6.2f} dB SSIM={ssim[i]:.4f} mean_diff={md[i]:.2f}") + print( + f" Frame {i:4d}: PSNR={psnr[i]:6.2f} dB SSIM={ssim[i]:.4f} mean_diff={md[i]:.2f}" + ) print() # Quality assessment @@ -210,7 +226,9 @@ def print_report(results, ref_path, test_path): grade = "Very different" print(f" Overall: {grade} (PSNR={mean_psnr:.1f} dB, SSIM={mean_ssim:.4f})") if mean_psnr < 30: - print(" ⚠ Videos differ significantly — likely a bug or different generation seed") + print( + " ⚠ Videos differ significantly — likely a bug or different generation seed" + ) print("=" * 72) @@ -242,9 +260,7 @@ def main(): parser.add_argument( "--diff-video", help="Save side-by-side diff visualization to this path" ) - parser.add_argument( - "--max-frames", type=int, help="Compare only first N frames" - ) + parser.add_argument("--max-frames", type=int, help="Compare only first N frames") parser.add_argument( "--ssim-win", type=int, default=7, help="SSIM window size (default: 7)" ) @@ -254,26 +270,29 @@ def main(): default=5.0, help="Diff heatmap amplification (default: 5.0)", ) - parser.add_argument( - "--csv", help="Export per-frame metrics to CSV file" - ) + parser.add_argument("--csv", help="Export per-frame metrics to CSV file") args = parser.parse_args() print(f"Loading reference: {args.reference}") ref_frames, ref_fps = load_video(args.reference, args.max_frames) - print(f" → {len(ref_frames)} frames, {ref_fps:.1f} fps, {ref_frames[0].shape[1]}x{ref_frames[0].shape[0]}") + print( + f" → {len(ref_frames)} frames, {ref_fps:.1f} fps, {ref_frames[0].shape[1]}x{ref_frames[0].shape[0]}" + ) print(f"Loading test: {args.test}") test_frames, test_fps = load_video(args.test, args.max_frames) - print(f" → {len(test_frames)} frames, {test_fps:.1f} fps, {test_frames[0].shape[1]}x{test_frames[0].shape[0]}") + print( + f" → {len(test_frames)} frames, {test_fps:.1f} fps, {test_frames[0].shape[1]}x{test_frames[0].shape[0]}" + ) if ref_frames[0].shape != test_frames[0].shape: - print(f"Warning: resolution mismatch {ref_frames[0].shape} vs {test_frames[0].shape}") + print( + f"Warning: resolution mismatch {ref_frames[0].shape} vs {test_frames[0].shape}" + ) print("Resizing test frames to match reference...") h, w = ref_frames[0].shape[:2] test_frames = [ - cv2.resize(f, (w, h), interpolation=cv2.INTER_LANCZOS4) - for f in test_frames + cv2.resize(f, (w, h), interpolation=cv2.INTER_LANCZOS4) for f in test_frames ] print("Computing metrics...") @@ -282,23 +301,29 @@ def main(): print_report(results, args.reference, args.test) if args.diff_video: - save_diff_video(ref_frames, test_frames, args.diff_video, ref_fps, args.diff_scale) + save_diff_video( + ref_frames, test_frames, args.diff_video, ref_fps, args.diff_scale + ) if args.csv: import csv with open(args.csv, "w", newline="") as f: writer = csv.writer(f) - writer.writerow(["frame", "psnr", "ssim", "mean_diff", "max_diff", "color_dist"]) + writer.writerow( + ["frame", "psnr", "ssim", "mean_diff", "max_diff", "color_dist"] + ) for i in range(results["num_frames"]): - writer.writerow([ - i, - f"{results['psnr'][i]:.4f}", - f"{results['ssim'][i]:.6f}", - f"{results['mean_diff'][i]:.4f}", - f"{results['max_diff'][i]:.1f}", - f"{results['color_dist'][i]:.6f}", - ]) + writer.writerow( + [ + i, + f"{results['psnr'][i]:.4f}", + f"{results['ssim'][i]:.6f}", + f"{results['mean_diff'][i]:.4f}", + f"{results['max_diff'][i]:.1f}", + f"{results['color_dist'][i]:.6f}", + ] + ) print(f"Per-frame metrics saved to {args.csv}") diff --git a/scripts/video/video_quality.py b/scripts/video/video_quality.py index f756b5a..9ed287a 100644 --- a/scripts/video/video_quality.py +++ b/scripts/video/video_quality.py @@ -158,10 +158,14 @@ def analyze_video(frames, chunk_size=None, compute_flow=False): boundary_metrics = [] for b in boundaries: if b < n and b > 0: - pre = metrics["frame_diff"][b - 1] if b > 1 else metrics["frame_diff"][1] + pre = ( + metrics["frame_diff"][b - 1] if b > 1 else metrics["frame_diff"][1] + ) at = metrics["frame_diff"][b] ratio = at / (pre + 1e-10) - brightness_jump = metrics["brightness"][b] - metrics["brightness"][b - 1] + brightness_jump = ( + metrics["brightness"][b] - metrics["brightness"][b - 1] + ) contrast_jump = ( (metrics["contrast"][b] - metrics["contrast"][b - 1]) / (metrics["contrast"][b - 1] + 1e-10) @@ -198,7 +202,9 @@ def print_report(metrics, path, fps, total_frames, frames_analyzed): print("VIDEO QUALITY REPORT") print("=" * 72) print(f" File: {path}") - print(f" Total frames: {total_frames} Analyzed: {frames_analyzed} FPS: {fps:.1f}") + print( + f" Total frames: {total_frames} Analyzed: {frames_analyzed} FPS: {fps:.1f}" + ) duration = total_frames / fps if fps > 0 else 0 print(f" Duration: {duration:.1f}s") print() @@ -211,52 +217,76 @@ def print_report(metrics, path, fps, total_frames, frames_analyzed): print("-" * 40) if n_uniform: frames_list = np.where(metrics["is_uniform"])[0][:10] - print(f" Uniform/blank frames: {n_uniform} — frames {list(frames_list)}{'...' if n_uniform > 10 else ''}") + print( + f" Uniform/blank frames: {n_uniform} — frames {list(frames_list)}{'...' if n_uniform > 10 else ''}" + ) if n_noisy: frames_list = np.where(metrics["is_noisy"])[0][:10] - print(f" Noisy frames: {n_noisy} — frames {list(frames_list)}{'...' if n_noisy > 10 else ''}") + print( + f" Noisy frames: {n_noisy} — frames {list(frames_list)}{'...' if n_noisy > 10 else ''}" + ) print() print("SHARPNESS") print("-" * 40) - print(f" Laplacian var: mean={np.mean(sl):8.1f} min={np.min(sl):8.1f} max={np.max(sl):8.1f} std={np.std(sl):.1f}") - print(f" Gradient mag: mean={np.mean(sg):8.2f} min={np.min(sg):8.2f} max={np.max(sg):8.2f} std={np.std(sg):.2f}") + print( + f" Laplacian var: mean={np.mean(sl):8.1f} min={np.min(sl):8.1f} max={np.max(sl):8.1f} std={np.std(sl):.1f}" + ) + print( + f" Gradient mag: mean={np.mean(sg):8.2f} min={np.min(sg):8.2f} max={np.max(sg):8.2f} std={np.std(sg):.2f}" + ) if np.std(sl) / (np.mean(sl) + 1e-10) > 0.3: print(" ⚠ High sharpness variation — possible blur artifacts") print() print("BRIGHTNESS & CONTRAST") print("-" * 40) - print(f" Brightness: mean={np.mean(br):6.1f} min={np.min(br):6.1f} max={np.max(br):6.1f} std={np.std(br):.2f}") - print(f" Contrast (std): mean={np.mean(ct):6.1f} min={np.min(ct):6.1f} max={np.max(ct):6.1f} std={np.std(ct):.2f}") + print( + f" Brightness: mean={np.mean(br):6.1f} min={np.min(br):6.1f} max={np.max(br):6.1f} std={np.std(br):.2f}" + ) + print( + f" Contrast (std): mean={np.mean(ct):6.1f} min={np.min(ct):6.1f} max={np.max(ct):6.1f} std={np.std(ct):.2f}" + ) if np.std(br) > 3.0: print(" ⚠ Brightness instability — may indicate chunk boundary artifacts") print() print("COLOR DISTRIBUTION (BGR)") print("-" * 40) - print(f" Blue: mean={np.mean(metrics['color_mean_b']):6.1f} std={np.std(metrics['color_mean_b']):.2f}") - print(f" Green: mean={np.mean(metrics['color_mean_g']):6.1f} std={np.std(metrics['color_mean_g']):.2f}") - print(f" Red: mean={np.mean(metrics['color_mean_r']):6.1f} std={np.std(metrics['color_mean_r']):.2f}") + print( + f" Blue: mean={np.mean(metrics['color_mean_b']):6.1f} std={np.std(metrics['color_mean_b']):.2f}" + ) + print( + f" Green: mean={np.mean(metrics['color_mean_g']):6.1f} std={np.std(metrics['color_mean_g']):.2f}" + ) + print( + f" Red: mean={np.mean(metrics['color_mean_r']):6.1f} std={np.std(metrics['color_mean_r']):.2f}" + ) print() print("TEMPORAL STABILITY") print("-" * 40) fd_nz = fd[1:] # skip first frame (always 0) if len(fd_nz) > 0: - print(f" Frame diff: mean={np.mean(fd_nz):6.2f} min={np.min(fd_nz):6.2f} max={np.max(fd_nz):6.2f} std={np.std(fd_nz):.2f}") + print( + f" Frame diff: mean={np.mean(fd_nz):6.2f} min={np.min(fd_nz):6.2f} max={np.max(fd_nz):6.2f} std={np.std(fd_nz):.2f}" + ) if np.std(fd_nz) / (np.mean(fd_nz) + 1e-10) > 0.5: print(" ⚠ High diff variance — jitter or discontinuities") if "flow_mean" in metrics: fm = metrics["flow_mean"][1:] - print(f" Optical flow: mean={np.mean(fm):6.2f} max_frame={np.max(metrics['flow_max'][1:]):.1f}") + print( + f" Optical flow: mean={np.mean(fm):6.2f} max_frame={np.max(metrics['flow_max'][1:]):.1f}" + ) print() # Chunk boundaries if "boundaries" in metrics and metrics["boundaries"]: print("CHUNK BOUNDARIES") print("-" * 40) - print(f" {'Frame':>6} {'Diff ratio':>10} {'Brightness':>10} {'Contrast %':>10} {'Sharpness %':>11}") + print( + f" {'Frame':>6} {'Diff ratio':>10} {'Brightness':>10} {'Contrast %':>10} {'Sharpness %':>11}" + ) for bm in metrics["boundaries"]: print( f" {bm['frame']:6d}" @@ -267,7 +297,9 @@ def print_report(metrics, path, fps, total_frames, frames_analyzed): ) avg_ratio = np.mean([b["diff_ratio"] for b in metrics["boundaries"]]) if avg_ratio > 2.0: - print(f" ⚠ Boundary diff ratio {avg_ratio:.1f}x — visible chunk transitions") + print( + f" ⚠ Boundary diff ratio {avg_ratio:.1f}x — visible chunk transitions" + ) print() # Overall grade @@ -303,9 +335,7 @@ def main(): type=int, help="Frames per chunk for boundary analysis (e.g., 32)", ) - parser.add_argument( - "--start", type=int, default=0, help="Start frame (default: 0)" - ) + parser.add_argument("--start", type=int, default=0, help="Start frame (default: 0)") parser.add_argument("--end", type=int, help="End frame (default: all)") parser.add_argument( "--flow", @@ -329,8 +359,14 @@ def main(): import csv keys = [ - "sharpness_lap", "sharpness_grad", "brightness", "contrast", - "color_mean_b", "color_mean_g", "color_mean_r", "frame_diff", + "sharpness_lap", + "sharpness_grad", + "brightness", + "contrast", + "color_mean_b", + "color_mean_g", + "color_mean_r", + "frame_diff", ] if args.flow: keys += ["flow_mean", "flow_max"] diff --git a/tests/test_generate_dev.py b/tests/test_generate_dev.py new file mode 100644 index 0000000..3e85f61 --- /dev/null +++ b/tests/test_generate_dev.py @@ -0,0 +1,443 @@ +"""Tests for LTX-2 dev model generation pipeline.""" + +import mlx.core as mx +import pytest + +from mlx_video.generate_dev import ( + AUDIO_LATENTS_PER_SECOND, + AUDIO_SAMPLE_RATE, + DEFAULT_NEGATIVE_PROMPT, + cfg_delta, + compute_audio_frames, + create_audio_position_grid, + create_position_grid, + ltx2_scheduler, +) + + +class TestLTX2Scheduler: + """Tests for the LTX-2 sigma scheduler.""" + + def test_scheduler_output_shape(self): + """Scheduler should return steps+1 sigma values.""" + steps = 20 + sigmas = ltx2_scheduler(steps=steps) + assert sigmas.shape == ( + steps + 1, + ), f"Expected ({steps + 1},), got {sigmas.shape}" + + def test_scheduler_starts_at_one(self): + """Sigma schedule should start at 1.0.""" + sigmas = ltx2_scheduler(steps=20) + assert ( + abs(sigmas[0].item() - 1.0) < 1e-5 + ), f"Expected 1.0, got {sigmas[0].item()}" + + def test_scheduler_ends_at_zero(self): + """Sigma schedule should end at 0.0.""" + sigmas = ltx2_scheduler(steps=20) + assert abs(sigmas[-1].item()) < 1e-5, f"Expected 0.0, got {sigmas[-1].item()}" + + def test_scheduler_monotonically_decreasing(self): + """Sigma values should monotonically decrease.""" + sigmas = ltx2_scheduler(steps=20) + sigmas_list = sigmas.tolist() + for i in range(len(sigmas_list) - 1): + assert ( + sigmas_list[i] >= sigmas_list[i + 1] + ), f"Sigma not decreasing at index {i}: {sigmas_list[i]} < {sigmas_list[i + 1]}" + + def test_scheduler_dtype(self): + """Scheduler should return float32 array.""" + sigmas = ltx2_scheduler(steps=20) + assert sigmas.dtype == mx.float32, f"Expected float32, got {sigmas.dtype}" + + def test_scheduler_with_num_tokens(self): + """Scheduler should accept num_tokens parameter.""" + sigmas_default = ltx2_scheduler(steps=20, num_tokens=None) + sigmas_custom = ltx2_scheduler(steps=20, num_tokens=1920) + + # Both should be valid arrays + assert sigmas_default.shape == (21,) + assert sigmas_custom.shape == (21,) + + def test_scheduler_no_stretch(self): + """Scheduler without stretching should still work.""" + sigmas = ltx2_scheduler(steps=20, stretch=False) + assert sigmas.shape == (21,) + assert sigmas[0].item() > 0 + assert sigmas[-1].item() == 0.0 + + def test_scheduler_different_steps(self): + """Scheduler should work with different step counts.""" + for steps in [5, 10, 20, 40, 50]: + sigmas = ltx2_scheduler(steps=steps) + assert sigmas.shape == (steps + 1,), f"Failed for steps={steps}" + + +class TestCreatePositionGrid: + """Tests for position grid creation.""" + + def test_position_grid_shape(self): + """Position grid should have correct shape (B, 3, num_patches, 2).""" + batch_size = 1 + num_frames = 5 + height = 16 + width = 24 + + positions = create_position_grid(batch_size, num_frames, height, width) + num_patches = num_frames * height * width + + expected_shape = (batch_size, 3, num_patches, 2) + assert ( + positions.shape == expected_shape + ), f"Expected {expected_shape}, got {positions.shape}" + + def test_position_grid_dtype(self): + """Position grid should be float32 for RoPE precision.""" + positions = create_position_grid(1, 5, 16, 24) + assert ( + positions.dtype == mx.float32 + ), f"Expected float32 for RoPE precision, got {positions.dtype}" + + def test_position_grid_batch_size(self): + """Position grid should respect batch size.""" + for batch_size in [1, 2, 4]: + positions = create_position_grid(batch_size, 5, 16, 24) + assert positions.shape[0] == batch_size + + def test_position_grid_temporal_dimension(self): + """Temporal dimension should have values scaled by fps.""" + positions = create_position_grid(1, 5, 16, 24, fps=24.0) + temporal = positions[0, 0, :, :] # (num_patches, 2) + + # Values should be in seconds (divided by fps) + max_temporal = mx.max(temporal).item() + # For 5 latent frames at scale 8, max pixel frame ~ 40, divided by 24 fps ~ 1.67s + assert max_temporal < 10, f"Temporal values too large: {max_temporal}" + + def test_position_grid_spatial_dimensions(self): + """Spatial dimensions should have pixel-space values.""" + positions = create_position_grid(1, 5, 16, 24, spatial_scale=32) + + # Height dimension + height_vals = positions[0, 1, :, :] + max_height = mx.max(height_vals).item() + # 16 latent * 32 scale = 512 pixels + assert max_height <= 512, f"Height values too large: {max_height}" + + # Width dimension + width_vals = positions[0, 2, :, :] + max_width = mx.max(width_vals).item() + # 24 latent * 32 scale = 768 pixels + assert max_width <= 768, f"Width values too large: {max_width}" + + def test_position_grid_causal_fix(self): + """Causal fix should adjust first frame temporal values.""" + positions_causal = create_position_grid(1, 5, 16, 24, causal_fix=True) + positions_no_causal = create_position_grid(1, 5, 16, 24, causal_fix=False) + + # They should be different due to causal fix + diff = mx.abs(positions_causal - positions_no_causal) + assert mx.max(diff).item() > 0, "Causal fix should change position values" + + def test_position_grid_no_nan_or_inf(self): + """Position grid should not contain NaN or Inf values.""" + positions = create_position_grid(1, 5, 16, 24) + + assert not mx.any(mx.isnan(positions)).item(), "Position grid contains NaN" + assert not mx.any(mx.isinf(positions)).item(), "Position grid contains Inf" + + +class TestCFGDelta: + """Tests for CFG (Classifier-Free Guidance) delta calculation.""" + + def test_cfg_delta_shape(self): + """CFG delta should have same shape as inputs.""" + shape = (1, 1920, 128) + cond = mx.random.normal(shape) + uncond = mx.random.normal(shape) + + delta = cfg_delta(cond, uncond, scale=4.0) + assert delta.shape == shape + + def test_cfg_delta_scale_one(self): + """CFG with scale=1.0 should return zero delta.""" + shape = (1, 1920, 128) + cond = mx.random.normal(shape) + uncond = mx.random.normal(shape) + mx.eval(cond, uncond) + + delta = cfg_delta(cond, uncond, scale=1.0) + mx.eval(delta) + + # Scale=1.0 means (1.0 - 1.0) * (cond - uncond) = 0 + assert ( + mx.max(mx.abs(delta)).item() < 1e-6 + ), "CFG delta with scale=1.0 should be zero" + + def test_cfg_delta_formula(self): + """CFG delta should follow the formula: (scale-1) * (cond - uncond).""" + cond = mx.array([[[1.0, 2.0, 3.0]]]) + uncond = mx.array([[[0.5, 1.0, 1.5]]]) + scale = 4.0 + + delta = cfg_delta(cond, uncond, scale) + expected = (scale - 1.0) * (cond - uncond) + + mx.eval(delta, expected) + diff = mx.max(mx.abs(delta - expected)).item() + assert diff < 1e-6, f"CFG delta formula mismatch: diff={diff}" + + def test_cfg_delta_dtype_preservation(self): + """CFG delta should preserve input dtype.""" + for dtype in [mx.float32, mx.bfloat16]: + cond = mx.random.normal((1, 100, 64)).astype(dtype) + uncond = mx.random.normal((1, 100, 64)).astype(dtype) + + delta = cfg_delta(cond, uncond, scale=4.0) + assert delta.dtype == dtype, f"Expected {dtype}, got {delta.dtype}" + + +class TestDefaultNegativePrompt: + """Tests for the default negative prompt.""" + + def test_default_negative_prompt_exists(self): + """Default negative prompt should be defined.""" + assert DEFAULT_NEGATIVE_PROMPT is not None + assert len(DEFAULT_NEGATIVE_PROMPT) > 0 + + def test_default_negative_prompt_contains_quality_terms(self): + """Default negative prompt should contain quality-related terms.""" + prompt_lower = DEFAULT_NEGATIVE_PROMPT.lower() + + # Check for common negative quality terms + assert "blurry" in prompt_lower, "Should contain 'blurry'" + assert ( + "low quality" in prompt_lower or "low contrast" in prompt_lower + ), "Should contain quality-related terms" + + +class TestInputValidation: + """Tests for input validation in generate_video_dev.""" + + def test_height_divisible_by_32(self): + """Height must be divisible by 32.""" + # This would be tested via the actual function, but we can test the validation logic + valid_heights = [256, 384, 512, 640, 768] + invalid_heights = [100, 300, 500, 700] + + for h in valid_heights: + assert h % 32 == 0, f"Height {h} should be valid" + + for h in invalid_heights: + assert h % 32 != 0, f"Height {h} should be invalid" + + def test_width_divisible_by_32(self): + """Width must be divisible by 32.""" + valid_widths = [256, 384, 512, 640, 768, 1024] + invalid_widths = [100, 300, 500, 700] + + for w in valid_widths: + assert w % 32 == 0, f"Width {w} should be valid" + + for w in invalid_widths: + assert w % 32 != 0, f"Width {w} should be invalid" + + def test_num_frames_formula(self): + """Number of frames should be 1 + 8*k.""" + valid_frames = [1, 9, 17, 25, 33, 41, 49, 57, 65] + + for f in valid_frames: + assert (f - 1) % 8 == 0, f"Frames {f} should be valid (1 + 8*k)" + + def test_num_frames_adjustment(self): + """Invalid frame counts should be adjusted to nearest valid value.""" + # Test the adjustment logic + test_cases = [ + (30, 33), # 30 -> nearest valid is 33 + (35, 33), # 35 -> nearest valid is 33 + (40, 41), # 40 -> nearest valid is 41 + (1, 1), # 1 is already valid + (33, 33), # 33 is already valid + ] + + for input_frames, expected in test_cases: + if input_frames % 8 != 1: + adjusted = round((input_frames - 1) / 8) * 8 + 1 + assert ( + adjusted == expected + ), f"Expected {expected} for input {input_frames}, got {adjusted}" + + +class TestDenoiseWithCFGMocked: + """Tests for denoise_with_cfg with mocked transformer.""" + + def test_sigmas_list_conversion(self): + """Sigmas should be convertible to list.""" + sigmas = ltx2_scheduler(steps=5) + sigmas_list = sigmas.tolist() + + assert isinstance(sigmas_list, list) + assert len(sigmas_list) == 6 # steps + 1 + + +class TestTilingDefault: + """Tests for tiling default behavior.""" + + def test_tiling_default_is_none(self): + """Default tiling should be 'none' for performance.""" + import inspect + + from mlx_video.generate_dev import generate_video_dev + + sig = inspect.signature(generate_video_dev) + + tiling_param = sig.parameters.get("tiling") + assert tiling_param is not None + assert ( + tiling_param.default == "none" + ), f"Expected default tiling='none', got '{tiling_param.default}'" + + +class TestLatentDimensions: + """Tests for latent dimension calculations.""" + + def test_latent_height_calculation(self): + """Latent height should be height // 32.""" + test_cases = [(512, 16), (768, 24), (1024, 32)] + + for height, expected_latent_h in test_cases: + latent_h = height // 32 + assert ( + latent_h == expected_latent_h + ), f"Expected latent_h={expected_latent_h} for height={height}, got {latent_h}" + + def test_latent_width_calculation(self): + """Latent width should be width // 32.""" + test_cases = [(512, 16), (768, 24), (1024, 32)] + + for width, expected_latent_w in test_cases: + latent_w = width // 32 + assert ( + latent_w == expected_latent_w + ), f"Expected latent_w={expected_latent_w} for width={width}, got {latent_w}" + + def test_latent_frames_calculation(self): + """Latent frames should be 1 + (num_frames - 1) // 8.""" + test_cases = [(1, 1), (9, 2), (17, 3), (33, 5), (65, 9)] + + for num_frames, expected_latent_f in test_cases: + latent_f = 1 + (num_frames - 1) // 8 + assert ( + latent_f == expected_latent_f + ), f"Expected latent_f={expected_latent_f} for num_frames={num_frames}, got {latent_f}" + + def test_num_tokens_calculation(self): + """Number of tokens should be latent_f * latent_h * latent_w.""" + # For 33 frames at 512x768 + num_frames, height, width = 33, 512, 768 + + latent_f = 1 + (num_frames - 1) // 8 # 5 + latent_h = height // 32 # 16 + latent_w = width // 32 # 24 + + num_tokens = latent_f * latent_h * latent_w + expected = 5 * 16 * 24 # 1920 + + assert num_tokens == expected, f"Expected {expected} tokens, got {num_tokens}" + + +class TestAudioPositionGrid: + """Tests for audio position grid creation.""" + + def test_audio_position_grid_shape(self): + """Audio position grid should have correct shape (B, 1, T, 2).""" + batch_size = 1 + audio_frames = 34 # ~1.36 seconds at 25 latent frames/sec + + positions = create_audio_position_grid(batch_size, audio_frames) + expected_shape = (batch_size, 1, audio_frames, 2) + + assert ( + positions.shape == expected_shape + ), f"Expected {expected_shape}, got {positions.shape}" + + def test_audio_position_grid_dtype(self): + """Audio position grid should be float32.""" + positions = create_audio_position_grid(1, 34) + assert positions.dtype == mx.float32, f"Expected float32, got {positions.dtype}" + + def test_audio_position_grid_batch_size(self): + """Audio position grid should respect batch size.""" + for batch_size in [1, 2, 4]: + positions = create_audio_position_grid(batch_size, 34) + assert positions.shape[0] == batch_size + + def test_audio_position_grid_temporal_values(self): + """Audio positions should be in seconds.""" + positions = create_audio_position_grid(1, 34) + + # Values should be in seconds (small values for ~1 second of audio) + max_val = mx.max(positions).item() + assert max_val < 10, f"Audio positions seem too large: {max_val}" + assert max_val > 0, "Audio positions should be positive" + + def test_audio_position_grid_no_nan_or_inf(self): + """Audio position grid should not contain NaN or Inf.""" + positions = create_audio_position_grid(1, 34) + + assert not mx.any( + mx.isnan(positions) + ).item(), "Audio position grid contains NaN" + assert not mx.any( + mx.isinf(positions) + ).item(), "Audio position grid contains Inf" + + +class TestComputeAudioFrames: + """Tests for audio frame count calculation.""" + + def test_audio_frames_basic(self): + """Audio frames should be proportional to video duration.""" + # 33 frames at 24 fps = ~1.375 seconds + # At 25 latent frames/sec = ~34 audio frames + audio_frames = compute_audio_frames(33, 24.0) + assert audio_frames > 0 + assert isinstance(audio_frames, int) + + def test_audio_frames_scales_with_video(self): + """More video frames should produce more audio frames.""" + audio_33 = compute_audio_frames(33, 24.0) + audio_65 = compute_audio_frames(65, 24.0) + + assert ( + audio_65 > audio_33 + ), f"Expected more audio frames for longer video: {audio_65} <= {audio_33}" + + def test_audio_frames_formula(self): + """Audio frames should match expected formula.""" + num_video_frames = 33 + fps = 24.0 + + duration = num_video_frames / fps # ~1.375 seconds + expected = round(duration * AUDIO_LATENTS_PER_SECOND) + + actual = compute_audio_frames(num_video_frames, fps) + assert actual == expected, f"Expected {expected}, got {actual}" + + +class TestAudioConstants: + """Tests for audio constants.""" + + def test_audio_sample_rate(self): + """Audio sample rate should be 24000 Hz.""" + assert AUDIO_SAMPLE_RATE == 24000 + + def test_audio_latents_per_second(self): + """Audio latents per second should be 25.""" + assert AUDIO_LATENTS_PER_SECOND == 25.0 + + +if __name__ == "__main__": + pytest.main([__file__, "-v"]) diff --git a/tests/test_rope.py b/tests/test_rope.py index cef8d6f..f05574c 100644 --- a/tests/test_rope.py +++ b/tests/test_rope.py @@ -1,11 +1,9 @@ -import pytest import mlx.core as mx import numpy as np +import pytest -from mlx_video.models.ltx.rope import ( - precompute_freqs_cis, -) -from mlx_video.models.ltx.config import LTXRopeType +from mlx_video.models.ltx_2.config import LTXModelConfig, LTXRopeType +from mlx_video.models.ltx_2.rope import precompute_freqs_cis def create_video_position_grid( @@ -20,7 +18,7 @@ def create_video_position_grid( h_coords = np.arange(0, height) w_coords = np.arange(0, width) - t_grid, h_grid, w_grid = np.meshgrid(t_coords, h_coords, w_coords, indexing='ij') + t_grid, h_grid, w_grid = np.meshgrid(t_coords, h_coords, w_coords, indexing="ij") patch_starts = np.stack([t_grid, h_grid, w_grid], axis=0) patch_ends = patch_starts + 1 @@ -36,6 +34,73 @@ def create_video_position_grid( return mx.array(pixel_coords, dtype=dtype) + +def _numpy_reference_rope(positions_np, dim, theta, max_pos, num_heads): + """Compute RoPE cos/sin using NumPy float64 as ground truth reference. + + This mirrors the regular (non-double-precision) path in rope.py exactly, + but uses float64 throughout, so we can verify that the float32 MLX path + stays close to the true values. + """ + # positions_np: (B, 3, T, 2) in float64 + n_pos_dims = positions_np.shape[1] + n_elem = 2 * n_pos_dims + + # Middle-of-interval positions + mid = (positions_np[..., 0] + positions_np[..., 1]) / 2.0 # (B, 3, T) + + # Frequency grid — matches generate_freq_grid() in rope.py: + # log_start = log(1)/log(theta) = 0 + # log_end = log(theta)/log(theta) = 1 + # pow_indices = theta^linspace(0, 1, num_indices) * pi/2 + num_indices = dim // n_elem + if num_indices == 0: + num_indices = 1 + lin_space = np.linspace(0.0, 1.0, num_indices, dtype=np.float64) + freq_indices = np.power(theta, lin_space) * (np.pi / 2) # (num_indices,) + + # Fractional positions and scaling — matches generate_freqs() + # frac = pos / max_pos for each dim, then scale to [-1, 1] + frac_list = [] + for d in range(n_pos_dims): + frac = mid[:, d, :] / max_pos[d] # (B, T) + frac_list.append(frac) + fractional = np.stack(frac_list, axis=-1) # (B, T, n_dims) + scaled = fractional * 2 - 1 # [-1, 1] + + # Outer product: (B, T, n_dims, 1) * (1, 1, 1, num_indices) + freqs = ( + scaled[..., np.newaxis] * freq_indices[np.newaxis, np.newaxis, np.newaxis, :] + ) + # (B, T, n_dims, num_indices) -> swap last two -> (B, T, num_indices, n_dims) -> flatten + freqs = np.swapaxes(freqs, -1, -2) + freqs = freqs.reshape( + freqs.shape[0], freqs.shape[1], -1 + ) # (B, T, num_indices * n_dims) + + cos_ref = np.cos(freqs) + sin_ref = np.sin(freqs) + + # Split RoPE: pad to dim//2, reshape to (B, H, T, dim_per_head//2) + expected = dim // 2 + pad_size = expected - cos_ref.shape[-1] + if pad_size > 0: + # Padding is prepended (ones for cos, zeros for sin) — matches split_freqs_cis() + cos_ref = np.concatenate( + [np.ones((*cos_ref.shape[:-1], pad_size)), cos_ref], axis=-1 + ) + sin_ref = np.concatenate( + [np.zeros((*sin_ref.shape[:-1], pad_size)), sin_ref], axis=-1 + ) + + B, T, _ = cos_ref.shape + dim_per_head = dim // num_heads + cos_ref = cos_ref.reshape(B, T, num_heads, dim_per_head // 2).transpose(0, 2, 1, 3) + sin_ref = sin_ref.reshape(B, T, num_heads, dim_per_head // 2).transpose(0, 2, 1, 3) + + return cos_ref, sin_ref + + class TestRoPEPositionPrecision: """Test suite for RoPE position precision requirements.""" @@ -65,10 +130,12 @@ class TestRoPEPositionPrecision: assert not mx.any(mx.isinf(sin_freq)).item(), "sin_freq contains Inf" # Verify cos/sin are in valid range [-1, 1] - assert mx.all(cos_freq >= -1.0).item() and mx.all(cos_freq <= 1.0).item(), \ - "cos_freq values out of [-1, 1] range" - assert mx.all(sin_freq >= -1.0).item() and mx.all(sin_freq <= 1.0).item(), \ - "sin_freq values out of [-1, 1] range" + assert ( + mx.all(cos_freq >= -1.0).item() and mx.all(cos_freq <= 1.0).item() + ), "cos_freq values out of [-1, 1] range" + assert ( + mx.all(sin_freq >= -1.0).item() and mx.all(sin_freq <= 1.0).item() + ), "sin_freq values out of [-1, 1] range" def test_bfloat16_positions_cause_precision_loss(self): """bfloat16 positions should produce different (less precise) results than float32. @@ -116,7 +183,9 @@ class TestRoPEPositionPrecision: # The threshold here is intentionally low to catch the issue precision_threshold = 1e-6 - has_precision_loss = max_cos_diff > precision_threshold or max_sin_diff > precision_threshold + has_precision_loss = ( + max_cos_diff > precision_threshold or max_sin_diff > precision_threshold + ) # Document the precision loss (this is expected behavior) if has_precision_loss: @@ -125,18 +194,14 @@ class TestRoPEPositionPrecision: print(f" Max sin difference: {max_sin_diff:.6e}") # This assertion documents the issue - bfloat16 positions cause precision loss - assert has_precision_loss, \ - "Expected precision loss with bfloat16 positions - if this fails, the issue may be fixed" + assert ( + has_precision_loss + ), "Expected precision loss with bfloat16 positions - if this fails, the issue may be fixed" def test_double_precision_converts_to_float32_internally(self): """Verify that double_precision mode converts bfloat16 to float32 first.""" positions_bf16 = create_video_position_grid(1, 4, 4, 4, dtype=mx.bfloat16) - # The double precision path in rope.py line 434: - # indices_grid_np = np.array(indices_grid.astype(mx.float32)).astype(np.float64) - # This means bfloat16 -> float32 -> float64 - # The precision is already lost at the bfloat16 -> float32 step - cos_freq, sin_freq = precompute_freqs_cis( indices_grid=positions_bf16, dim=128, @@ -161,20 +226,127 @@ class TestRoPEPositionPrecision: # Recommended: create positions in float32 positions = create_video_position_grid(1, 4, 4, 4, dtype=mx.float32) - assert positions.dtype == mx.float32, \ - "Position grids should be created in float32 for RoPE precision" + assert ( + positions.dtype == mx.float32 + ), "Position grids should be created in float32 for RoPE precision" # Verify the position values are reasonable # Temporal positions should be small (seconds) temporal_positions = positions[:, 0, :, :] - assert mx.max(temporal_positions).item() < 100, \ - "Temporal positions should be in seconds (small values)" + assert ( + mx.max(temporal_positions).item() < 100 + ), "Temporal positions should be in seconds (small values)" # Spatial positions should be larger (pixels) spatial_h = positions[:, 1, :, :] spatial_w = positions[:, 2, :, :] - assert mx.max(spatial_h).item() > 0, "Spatial height positions should be positive" - assert mx.max(spatial_w).item() > 0, "Spatial width positions should be positive" + assert ( + mx.max(spatial_h).item() > 0 + ), "Spatial height positions should be positive" + assert ( + mx.max(spatial_w).item() > 0 + ), "Spatial width positions should be positive" + + def test_float32_positions_match_numpy_float64_reference(self): + """Regression test: float32 RoPE must closely match a NumPy float64 reference. + + This is the key correctness test. We compute RoPE with NumPy in float64 + (ground truth) and verify that the MLX float32 path produces nearly + identical results. The max allowed diff (1e-5) is well below the error + we saw with bfloat16 positions (~2.0 max diff, cosine sim 0.88). + """ + positions = create_video_position_grid(1, 4, 4, 4, dtype=mx.float32) + positions_np = np.array(positions).astype(np.float64) + + dim = 128 + theta = 10000.0 + max_pos = [20, 2048, 2048] + num_heads = 32 + + # MLX result (float32 path, non-double-precision) + cos_mlx, sin_mlx = precompute_freqs_cis( + indices_grid=positions, + dim=dim, + theta=theta, + max_pos=max_pos, + use_middle_indices_grid=True, + num_attention_heads=num_heads, + rope_type=LTXRopeType.SPLIT, + double_precision=False, + ) + + # NumPy float64 reference + cos_ref, sin_ref = _numpy_reference_rope( + positions_np, dim, theta, max_pos, num_heads + ) + + cos_mlx_np = np.array(cos_mlx) + sin_mlx_np = np.array(sin_mlx) + + max_cos_diff = np.max(np.abs(cos_mlx_np - cos_ref)) + max_sin_diff = np.max(np.abs(sin_mlx_np - sin_ref)) + + # Cosine similarity (flatten for single scalar) + cos_flat = cos_mlx_np.flatten() + ref_flat = cos_ref.flatten() + cosine_sim = np.dot(cos_flat, ref_flat) / ( + np.linalg.norm(cos_flat) * np.linalg.norm(ref_flat) + ) + + # float32 vs float64: expect small diffs from 23-bit vs 52-bit mantissa. + # Threshold 0.01 is well below the bfloat16 failure mode (~2.0 max diff). + assert ( + max_cos_diff < 0.01 + ), f"cos max diff {max_cos_diff:.2e} exceeds 0.01 — float32 positions may not be preserved" + assert ( + max_sin_diff < 0.01 + ), f"sin max diff {max_sin_diff:.2e} exceeds 0.01 — float32 positions may not be preserved" + assert ( + cosine_sim > 0.9999 + ), f"cos cosine similarity {cosine_sim:.6f} too low — expected >0.9999" + + def test_high_frequency_amplification_regression(self): + """Regression test for the specific failure mode: high-frequency index amplification. + + With production-sized grids (5x16x16 = 1280 tokens), fractional positions + like 0.000391 get multiplied by frequency indices up to ~15708. In bfloat16 + the fractional part is quantized, producing raw freq errors of ~6.14 and + cos/sin sign flips (max_diff ~2.0). Float32 must keep max_diff < 0.01. + """ + # Use a production-like grid size + positions = create_video_position_grid(1, 5, 16, 16, dtype=mx.float32) + positions_np = np.array(positions).astype(np.float64) + + dim = 128 + theta = 10000.0 + max_pos = [20, 2048, 2048] + num_heads = 32 + + cos_mlx, sin_mlx = precompute_freqs_cis( + indices_grid=positions, + dim=dim, + theta=theta, + max_pos=max_pos, + use_middle_indices_grid=True, + num_attention_heads=num_heads, + rope_type=LTXRopeType.SPLIT, + double_precision=False, + ) + + cos_ref, sin_ref = _numpy_reference_rope( + positions_np, dim, theta, max_pos, num_heads + ) + + max_cos_diff = np.max(np.abs(np.array(cos_mlx) - cos_ref)) + max_sin_diff = np.max(np.abs(np.array(sin_mlx) - sin_ref)) + + # Float32 should keep errors well below the bfloat16 failure threshold of ~2.0 + assert ( + max_cos_diff < 0.01 + ), f"Production grid cos max diff {max_cos_diff:.4f} — high-freq amplification detected" + assert ( + max_sin_diff < 0.01 + ), f"Production grid sin max diff {max_sin_diff:.4f} — high-freq amplification detected" class TestRoPEInterleaved: @@ -201,43 +373,144 @@ class TestRoPEInterleaved: assert not mx.any(mx.isnan(sin_freq)).item() -class TestRoPEWarnings: - """Tests for RoPE warnings.""" +class TestRoPEInputCasting: + """Tests that precompute_freqs_cis casts positions to float32 internally. - def test_bfloat16_positions_trigger_warning(self): - """Verify that bfloat16 positions trigger a UserWarning.""" - positions_bf16 = create_video_position_grid(1, 4, 4, 4, dtype=mx.bfloat16) + The fix in rope.py ensures that regardless of the input dtype, positions are + cast to float32 before any computation. This class verifies that behavior + for both the regular and double-precision paths. + """ - with pytest.warns(UserWarning, match="Position grid has dtype bfloat16"): - precompute_freqs_cis( - indices_grid=positions_bf16, - dim=128, - theta=10000.0, - max_pos=[20, 2048, 2048], - use_middle_indices_grid=True, - num_attention_heads=32, - rope_type=LTXRopeType.SPLIT, - double_precision=True, - ) - - def test_float32_positions_no_warning(self): - """Verify that float32 positions do NOT trigger a warning.""" + def test_regular_path_outputs_float32(self): + """Regular path: both float32 and bfloat16 inputs produce float32 output.""" positions_f32 = create_video_position_grid(1, 4, 4, 4, dtype=mx.float32) + positions_bf16 = positions_f32.astype(mx.bfloat16) - # This should not raise any warnings - import warnings - with warnings.catch_warnings(): - warnings.simplefilter("error") # Turn warnings into errors - precompute_freqs_cis( - indices_grid=positions_f32, - dim=128, - theta=10000.0, - max_pos=[20, 2048, 2048], - use_middle_indices_grid=True, - num_attention_heads=32, - rope_type=LTXRopeType.SPLIT, - double_precision=True, - ) + kwargs = dict( + dim=128, + theta=10000.0, + max_pos=[20, 2048, 2048], + use_middle_indices_grid=True, + num_attention_heads=32, + rope_type=LTXRopeType.SPLIT, + double_precision=False, + ) + + cos_f32, sin_f32 = precompute_freqs_cis(indices_grid=positions_f32, **kwargs) + cos_bf16, sin_bf16 = precompute_freqs_cis(indices_grid=positions_bf16, **kwargs) + + # Both produce float32 output regardless of input dtype + assert cos_f32.dtype == mx.float32 + assert cos_bf16.dtype == mx.float32 + assert sin_f32.dtype == mx.float32 + assert sin_bf16.dtype == mx.float32 + + # No NaN/Inf in either + assert not mx.any(mx.isnan(cos_bf16)).item() + assert not mx.any(mx.isinf(cos_bf16)).item() + + def test_double_precision_path_outputs_float32(self): + """Double-precision path: both float32 and bfloat16 inputs produce float32 output.""" + positions_f32 = create_video_position_grid(1, 4, 4, 4, dtype=mx.float32) + positions_bf16 = positions_f32.astype(mx.bfloat16) + + kwargs = dict( + dim=128, + theta=10000.0, + max_pos=[20, 2048, 2048], + use_middle_indices_grid=True, + num_attention_heads=32, + rope_type=LTXRopeType.SPLIT, + double_precision=True, + ) + + cos_f32, sin_f32 = precompute_freqs_cis(indices_grid=positions_f32, **kwargs) + cos_bf16, sin_bf16 = precompute_freqs_cis(indices_grid=positions_bf16, **kwargs) + + assert cos_f32.dtype == mx.float32 + assert cos_bf16.dtype == mx.float32 + assert sin_f32.dtype == mx.float32 + assert sin_bf16.dtype == mx.float32 + + assert not mx.any(mx.isnan(cos_bf16)).item() + assert not mx.any(mx.isinf(cos_bf16)).item() + + def test_float16_input_also_cast_to_float32(self): + """Float16 input should also be handled correctly.""" + positions_f16 = create_video_position_grid(1, 4, 4, 4, dtype=mx.float16) + + cos_freq, sin_freq = precompute_freqs_cis( + indices_grid=positions_f16, + dim=128, + theta=10000.0, + max_pos=[20, 2048, 2048], + use_middle_indices_grid=True, + num_attention_heads=32, + rope_type=LTXRopeType.SPLIT, + double_precision=False, + ) + + assert cos_freq.dtype == mx.float32 + assert sin_freq.dtype == mx.float32 + assert not mx.any(mx.isnan(cos_freq)).item() + + +class TestDoublePrecisionRopeConfig: + """Tests for the conditional double_precision_rope logic in LTXModelConfig.""" + + def test_ltx2_forces_double_precision_rope_false(self): + """LTX-2 (no prompt adaln) must have double_precision_rope=False.""" + config = LTXModelConfig(has_prompt_adaln=False, double_precision_rope=True) + assert ( + config.double_precision_rope is False + ), "LTX-2 should force double_precision_rope=False regardless of input" + + def test_ltx23_preserves_double_precision_rope_true(self): + """LTX-2.3 (has_prompt_adaln=True) should keep double_precision_rope=True.""" + config = LTXModelConfig(has_prompt_adaln=True, double_precision_rope=True) + assert ( + config.double_precision_rope is True + ), "LTX-2.3 should preserve double_precision_rope=True" + + def test_ltx23_preserves_double_precision_rope_false(self): + """LTX-2.3 with double_precision_rope=False should stay False.""" + config = LTXModelConfig(has_prompt_adaln=True, double_precision_rope=False) + assert ( + config.double_precision_rope is False + ), "LTX-2.3 should respect double_precision_rope=False when explicitly set" + + def test_ltx2_default_double_precision_rope(self): + """LTX-2 default (double_precision_rope not set) should be False.""" + config = LTXModelConfig(has_prompt_adaln=False) + assert config.double_precision_rope is False + + def test_ltx23_default_double_precision_rope(self): + """LTX-2.3 default (double_precision_rope not set) should be False (field default).""" + config = LTXModelConfig(has_prompt_adaln=True) + # The field default is False and __post_init__ doesn't override for LTX-2.3 + assert config.double_precision_rope is False + + def test_config_from_dict_ltx2(self): + """Config created from dict for LTX-2 should force double_precision_rope=False.""" + config = LTXModelConfig.from_dict( + { + "has_prompt_adaln": False, + "double_precision_rope": True, + "rope_type": "split", + } + ) + assert config.double_precision_rope is False + + def test_config_from_dict_ltx23(self): + """Config created from dict for LTX-2.3 should preserve double_precision_rope.""" + config = LTXModelConfig.from_dict( + { + "has_prompt_adaln": True, + "double_precision_rope": True, + "rope_type": "split", + } + ) + assert config.double_precision_rope is True class TestRoPESplit: @@ -270,10 +543,12 @@ class TestRoPESplit: # dim=128, num_heads=32, so dim_per_head=4, and split uses half=2 dim_per_head = dim // num_heads expected_shape = (batch_size, num_heads, num_tokens, dim_per_head // 2) - assert cos_freq.shape == expected_shape, \ - f"Expected shape {expected_shape}, got {cos_freq.shape}" - assert sin_freq.shape == expected_shape, \ - f"Expected shape {expected_shape}, got {sin_freq.shape}" + assert ( + cos_freq.shape == expected_shape + ), f"Expected shape {expected_shape}, got {cos_freq.shape}" + assert ( + sin_freq.shape == expected_shape + ), f"Expected shape {expected_shape}, got {sin_freq.shape}" if __name__ == "__main__": diff --git a/tests/test_vae_streaming.py b/tests/test_vae_streaming.py index be29d00..13d1a82 100644 --- a/tests/test_vae_streaming.py +++ b/tests/test_vae_streaming.py @@ -1,11 +1,11 @@ """Tests for VAE streaming and chunked conv features.""" -import pytest import mlx.core as mx import numpy as np +import pytest -from mlx_video.models.ltx.video_vae.sampling import DepthToSpaceUpsample -from mlx_video.models.ltx.video_vae.tiling import ( +from mlx_video.models.ltx_2.video_vae.sampling import DepthToSpaceUpsample +from mlx_video.models.ltx_2.video_vae.tiling import ( TilingConfig, compute_trapezoidal_mask_1d, decode_with_tiling, @@ -50,7 +50,7 @@ class TestChunkedConv: np.array(out_chunked), rtol=1e-5, atol=1e-5, - err_msg="Chunked conv output differs from regular output" + err_msg="Chunked conv output differs from regular output", ) def test_chunked_conv_small_input_passthrough(self): @@ -117,13 +117,17 @@ class TestProgressiveFrameSaving: frames_received = [] def on_frames_ready(frames: mx.array, start_idx: int): - frames_received.append({ - 'shape': frames.shape, - 'start_idx': start_idx, - }) + frames_received.append( + { + "shape": frames.shape, + "start_idx": start_idx, + } + ) # Create a mock decoder that just returns scaled input - def mock_decoder(x, causal=False, timestep=None, debug=False, chunked_conv=False): + def mock_decoder( + x, causal=False, timestep=None, debug=False, chunked_conv=False + ): # Simulate VAE output: upsample 8x temporal, 32x spatial b, c, f, h, w = x.shape out_f = 1 + (f - 1) * 8 @@ -154,7 +158,9 @@ class TestProgressiveFrameSaving: # All received frames should have correct channel count for received in frames_received: - assert received['shape'][1] == 3, f"Expected 3 channels, got {received['shape'][1]}" + assert ( + received["shape"][1] == 3 + ), f"Expected 3 channels, got {received['shape'][1]}" def test_on_frames_ready_covers_all_frames(self): """Verify all frames are emitted via callbacks.""" @@ -165,7 +171,9 @@ class TestProgressiveFrameSaving: for i in range(num_frames): all_frame_indices.add(start_idx + i) - def mock_decoder(x, causal=False, timestep=None, debug=False, chunked_conv=False): + def mock_decoder( + x, causal=False, timestep=None, debug=False, chunked_conv=False + ): b, c, f, h, w = x.shape out_f = 1 + (f - 1) * 8 out_h = h * 32 @@ -191,24 +199,29 @@ class TestProgressiveFrameSaving: expected_frames = 1 + (12 - 1) * 8 # 89 frames # All frames should have been emitted - assert len(all_frame_indices) == expected_frames, \ - f"Expected {expected_frames} frames, got {len(all_frame_indices)}" - assert all_frame_indices == set(range(expected_frames)), \ - "Not all frame indices were covered" + assert ( + len(all_frame_indices) == expected_frames + ), f"Expected {expected_frames} frames, got {len(all_frame_indices)}" + assert all_frame_indices == set( + range(expected_frames) + ), "Not all frame indices were covered" class TestAutoChunkedConv: """Tests for auto-enabling chunked_conv based on tiling mode.""" - @pytest.mark.parametrize("tiling_mode,should_enable", [ - ("conservative", True), - ("none", True), - ("auto", True), - ("default", True), - ("spatial", True), - ("aggressive", False), - ("temporal", False), - ]) + @pytest.mark.parametrize( + "tiling_mode,should_enable", + [ + ("conservative", True), + ("none", True), + ("auto", True), + ("default", True), + ("spatial", True), + ("aggressive", False), + ("temporal", False), + ], + ) def test_chunked_conv_auto_enable(self, tiling_mode: str, should_enable: bool): """Verify chunked_conv is auto-enabled for correct tiling modes.""" # The logic is: tiling_mode in ("conservative", "none", "auto", "default", "spatial") @@ -216,8 +229,9 @@ class TestAutoChunkedConv: use_chunked_conv = tiling_mode in expected_modes - assert use_chunked_conv == should_enable, \ - f"For tiling_mode='{tiling_mode}', expected chunked_conv={should_enable}" + assert ( + use_chunked_conv == should_enable + ), f"For tiling_mode='{tiling_mode}', expected chunked_conv={should_enable}" class TestTrapezoidalMask: @@ -250,7 +264,9 @@ class TestTrapezoidalMask: # Right ramp should be decreasing right_ramp = mask_np[-8:] - assert np.all(np.diff(right_ramp) <= 0), "Right ramp not monotonically decreasing" + assert np.all( + np.diff(right_ramp) <= 0 + ), "Right ramp not monotonically decreasing" def test_temporal_mask_starts_from_zero(self): """Verify temporal mask (left_starts_from_0=True) starts from 0.""" diff --git a/tests/test_wan_attention.py b/tests/test_wan_attention.py index 02e471b..0b48bf9 100644 --- a/tests/test_wan_attention.py +++ b/tests/test_wan_attention.py @@ -2,31 +2,33 @@ import mlx.core as mx import numpy as np -import pytest - # --------------------------------------------------------------------------- # RoPE Tests # --------------------------------------------------------------------------- + class TestRoPE: """Tests for 3-way factorized RoPE.""" def test_rope_params_shape(self): - from mlx_video.models.wan.rope import rope_params + from mlx_video.models.wan_2.rope import rope_params + freqs = rope_params(1024, 64) mx.eval(freqs) assert freqs.shape == (1024, 32, 2) # [max_seq_len, dim//2, 2] def test_rope_params_different_dims(self): - from mlx_video.models.wan.rope import rope_params + from mlx_video.models.wan_2.rope import rope_params + for dim in [32, 64, 128]: freqs = rope_params(512, dim) mx.eval(freqs) assert freqs.shape == (512, dim // 2, 2) def test_rope_params_cos_sin_range(self): - from mlx_video.models.wan.rope import rope_params + from mlx_video.models.wan_2.rope import rope_params + freqs = rope_params(256, 64) mx.eval(freqs) cos_vals = np.array(freqs[:, :, 0]) @@ -36,14 +38,16 @@ class TestRoPE: def test_rope_params_position_zero(self): """At position 0, cos should be 1 and sin should be 0.""" - from mlx_video.models.wan.rope import rope_params + from mlx_video.models.wan_2.rope import rope_params + freqs = rope_params(10, 64) mx.eval(freqs) np.testing.assert_allclose(np.array(freqs[0, :, 0]), 1.0, atol=1e-6) np.testing.assert_allclose(np.array(freqs[0, :, 1]), 0.0, atol=1e-6) def test_rope_apply_output_shape(self): - from mlx_video.models.wan.rope import rope_params, rope_apply + from mlx_video.models.wan_2.rope import rope_apply, rope_params + B, L, N, D = 1, 24, 4, 32 # batch, seq, heads, head_dim x = mx.random.normal((B, L, N, D)) freqs = rope_params(1024, D) @@ -54,7 +58,8 @@ class TestRoPE: def test_rope_apply_preserves_norm(self): """RoPE rotation should preserve vector norms.""" - from mlx_video.models.wan.rope import rope_params, rope_apply + from mlx_video.models.wan_2.rope import rope_apply, rope_params + B, N, D = 1, 2, 16 F, H, W = 2, 3, 4 L = F * H * W @@ -74,7 +79,8 @@ class TestRoPE: def test_rope_apply_with_padding(self): """When seq_len < L, extra tokens should be preserved unchanged.""" - from mlx_video.models.wan.rope import rope_params, rope_apply + from mlx_video.models.wan_2.rope import rope_apply, rope_params + B, N, D = 1, 2, 16 F, H, W = 2, 2, 2 seq_len = F * H * W # 8 @@ -94,7 +100,8 @@ class TestRoPE: def test_rope_apply_batch(self): """Test with batch_size > 1 and different grid sizes.""" - from mlx_video.models.wan.rope import rope_params, rope_apply + from mlx_video.models.wan_2.rope import rope_apply, rope_params + B, N, D = 2, 2, 16 grids = [(2, 3, 4), (2, 3, 4)] L = 2 * 3 * 4 @@ -122,9 +129,11 @@ class TestRoPE: # Attention Tests # --------------------------------------------------------------------------- + class TestWanRMSNorm: def test_output_shape(self): - from mlx_video.models.wan.attention import WanRMSNorm + from mlx_video.models.wan_2.attention import WanRMSNorm + norm = WanRMSNorm(64) x = mx.random.normal((2, 10, 64)) out = norm(x) @@ -133,7 +142,8 @@ class TestWanRMSNorm: def test_zero_mean_variance(self): """RMS norm should make RMS ≈ 1 before scaling.""" - from mlx_video.models.wan.attention import WanRMSNorm + from mlx_video.models.wan_2.attention import WanRMSNorm + norm = WanRMSNorm(64) x = mx.random.normal((1, 5, 64)) * 10.0 out = norm(x) @@ -146,7 +156,8 @@ class TestWanRMSNorm: def test_dtype_preservation(self): """RMSNorm weight is float32, so output is promoted to float32.""" - from mlx_video.models.wan.attention import WanRMSNorm + from mlx_video.models.wan_2.attention import WanRMSNorm + norm = WanRMSNorm(32) x = mx.random.normal((1, 4, 32)).astype(mx.bfloat16) out = norm(x) @@ -157,7 +168,8 @@ class TestWanRMSNorm: class TestWanLayerNorm: def test_output_shape(self): - from mlx_video.models.wan.attention import WanLayerNorm + from mlx_video.models.wan_2.attention import WanLayerNorm + norm = WanLayerNorm(64) x = mx.random.normal((2, 10, 64)) out = norm(x) @@ -165,7 +177,8 @@ class TestWanLayerNorm: assert out.shape == (2, 10, 64) def test_without_affine(self): - from mlx_video.models.wan.attention import WanLayerNorm + from mlx_video.models.wan_2.attention import WanLayerNorm + norm = WanLayerNorm(64, elementwise_affine=False) x = mx.random.normal((1, 4, 64)) out = norm(x) @@ -177,7 +190,8 @@ class TestWanLayerNorm: np.testing.assert_allclose(np.std(out_np[i]), 1.0, rtol=0.1) def test_with_affine(self): - from mlx_video.models.wan.attention import WanLayerNorm + from mlx_video.models.wan_2.attention import WanLayerNorm + norm = WanLayerNorm(32, elementwise_affine=True) assert hasattr(norm, "weight") assert hasattr(norm, "bias") @@ -194,8 +208,9 @@ class TestWanSelfAttention: self.num_heads = 4 def test_output_shape(self): - from mlx_video.models.wan.attention import WanSelfAttention - from mlx_video.models.wan.rope import rope_params + from mlx_video.models.wan_2.attention import WanSelfAttention + from mlx_video.models.wan_2.rope import rope_params + attn = WanSelfAttention(self.dim, self.num_heads) B, L = 1, 24 F, H, W = 2, 3, 4 @@ -206,21 +221,24 @@ class TestWanSelfAttention: assert out.shape == (B, L, self.dim) def test_with_qk_norm(self): - from mlx_video.models.wan.attention import WanSelfAttention + from mlx_video.models.wan_2.attention import WanSelfAttention + attn = WanSelfAttention(self.dim, self.num_heads, qk_norm=True) assert attn.norm_q is not None assert attn.norm_k is not None def test_without_qk_norm(self): - from mlx_video.models.wan.attention import WanSelfAttention + from mlx_video.models.wan_2.attention import WanSelfAttention + attn = WanSelfAttention(self.dim, self.num_heads, qk_norm=False) assert attn.norm_q is None assert attn.norm_k is None def test_masking(self): """Test that masking works: shorter seq_lens should mask later tokens.""" - from mlx_video.models.wan.attention import WanSelfAttention - from mlx_video.models.wan.rope import rope_params + from mlx_video.models.wan_2.attention import WanSelfAttention + from mlx_video.models.wan_2.rope import rope_params + attn = WanSelfAttention(self.dim, self.num_heads, qk_norm=False) B, L = 1, 24 F, H, W = 2, 3, 4 @@ -244,7 +262,8 @@ class TestWanCrossAttention: self.num_heads = 4 def test_output_shape(self): - from mlx_video.models.wan.attention import WanCrossAttention + from mlx_video.models.wan_2.attention import WanCrossAttention + attn = WanCrossAttention(self.dim, self.num_heads) B, L_q, L_kv = 1, 24, 16 x = mx.random.normal((B, L_q, self.dim)) @@ -254,7 +273,8 @@ class TestWanCrossAttention: assert out.shape == (B, L_q, self.dim) def test_with_context_mask(self): - from mlx_video.models.wan.attention import WanCrossAttention + from mlx_video.models.wan_2.attention import WanCrossAttention + attn = WanCrossAttention(self.dim, self.num_heads) B, L_q, L_kv = 1, 12, 16 x = mx.random.normal((B, L_q, self.dim)) @@ -268,6 +288,7 @@ class TestWanCrossAttention: # bfloat16 Autocast Tests # --------------------------------------------------------------------------- + class TestBFloat16Autocast: """Tests that attention and FFN cast inputs to weight dtype (bfloat16) for efficient matmul, matching official PyTorch autocast behavior.""" @@ -290,8 +311,9 @@ class TestBFloat16Autocast: def test_self_attn_casts_to_weight_dtype(self): """Self-attention should cast input to weight dtype for QKV projections.""" - from mlx_video.models.wan.attention import WanSelfAttention - from mlx_video.models.wan.rope import rope_params + from mlx_video.models.wan_2.attention import WanSelfAttention + from mlx_video.models.wan_2.rope import rope_params + attn = WanSelfAttention(self.dim, self.num_heads) attn.update(self._to_bf16(attn.parameters())) @@ -304,7 +326,8 @@ class TestBFloat16Autocast: def test_cross_attn_casts_to_weight_dtype(self): """Cross-attention should cast input to weight dtype.""" - from mlx_video.models.wan.attention import WanCrossAttention + from mlx_video.models.wan_2.attention import WanCrossAttention + attn = WanCrossAttention(self.dim, self.num_heads) attn.update(self._to_bf16(attn.parameters())) @@ -317,7 +340,8 @@ class TestBFloat16Autocast: def test_cross_attn_kv_cache_uses_weight_dtype(self): """prepare_kv should cast context to weight dtype.""" - from mlx_video.models.wan.attention import WanCrossAttention + from mlx_video.models.wan_2.attention import WanCrossAttention + attn = WanCrossAttention(self.dim, self.num_heads) attn.update(self._to_bf16(attn.parameters())) @@ -329,7 +353,8 @@ class TestBFloat16Autocast: def test_ffn_casts_to_weight_dtype(self): """FFN should cast input to weight dtype for linear layers.""" - from mlx_video.models.wan.transformer import WanFFN + from mlx_video.models.wan_2.transformer import WanFFN + ffn = WanFFN(self.dim, 128) ffn.update(self._to_bf16(ffn.parameters())) @@ -341,8 +366,9 @@ class TestBFloat16Autocast: def test_self_attn_rope_in_float32(self): """RoPE should be applied in float32 for precision, even with bf16 weights.""" - from mlx_video.models.wan.attention import WanSelfAttention - from mlx_video.models.wan.rope import rope_params + from mlx_video.models.wan_2.attention import WanSelfAttention + from mlx_video.models.wan_2.rope import rope_params + attn = WanSelfAttention(self.dim, self.num_heads) attn.update(self._to_bf16(attn.parameters())) @@ -355,8 +381,9 @@ class TestBFloat16Autocast: def test_block_float32_residual_with_bf16_weights(self): """Full block: residual stream stays float32, matmuls use bf16 weights.""" - from mlx_video.models.wan.transformer import WanAttentionBlock - from mlx_video.models.wan.rope import rope_params + from mlx_video.models.wan_2.rope import rope_params + from mlx_video.models.wan_2.transformer import WanAttentionBlock + block = WanAttentionBlock(self.dim, 128, self.num_heads, cross_attn_norm=True) block.update(self._to_bf16(block.parameters())) diff --git a/tests/test_wan_config.py b/tests/test_wan_config.py index 5b943df..a5f19ed 100644 --- a/tests/test_wan_config.py +++ b/tests/test_wan_config.py @@ -1,17 +1,17 @@ """Tests for Wan model configuration.""" -import pytest - # --------------------------------------------------------------------------- # Config Tests # --------------------------------------------------------------------------- + class TestWanModelConfig: """Tests for WanModelConfig dataclass.""" def test_default_values(self): - from mlx_video.models.wan.config import WanModelConfig + from mlx_video.models.wan_2.config import WanModelConfig + config = WanModelConfig() assert config.dim == 5120 assert config.ffn_dim == 13824 @@ -32,12 +32,14 @@ class TestWanModelConfig: assert config.text_len == 512 def test_head_dim_property(self): - from mlx_video.models.wan.config import WanModelConfig + from mlx_video.models.wan_2.config import WanModelConfig + config = WanModelConfig() assert config.head_dim == 128 # 5120 // 40 def test_to_dict_roundtrip(self): - from mlx_video.models.wan.config import WanModelConfig + from mlx_video.models.wan_2.config import WanModelConfig + config = WanModelConfig() d = config.to_dict() assert isinstance(d, dict) @@ -46,7 +48,8 @@ class TestWanModelConfig: assert d["boundary"] == 0.875 def test_t5_config_values(self): - from mlx_video.models.wan.config import WanModelConfig + from mlx_video.models.wan_2.config import WanModelConfig + config = WanModelConfig() assert config.t5_vocab_size == 256384 assert config.t5_dim == 4096 @@ -61,11 +64,13 @@ class TestWanModelConfig: # Wan2.1 Config Tests # --------------------------------------------------------------------------- + class TestWan21Config: """Tests for Wan2.1 config presets.""" def test_wan21_14b_factory(self): - from mlx_video.models.wan.config import WanModelConfig + from mlx_video.models.wan_2.config import WanModelConfig + config = WanModelConfig.wan21_t2v_14b() assert config.model_version == "2.1" assert config.dual_model is False @@ -80,7 +85,8 @@ class TestWan21Config: assert config.boundary == 0.0 def test_wan21_1_3b_factory(self): - from mlx_video.models.wan.config import WanModelConfig + from mlx_video.models.wan_2.config import WanModelConfig + config = WanModelConfig.wan21_t2v_1_3b() assert config.model_version == "2.1" assert config.dual_model is False @@ -92,7 +98,8 @@ class TestWan21Config: assert config.sample_guide_scale == 5.0 def test_wan22_14b_factory(self): - from mlx_video.models.wan.config import WanModelConfig + from mlx_video.models.wan_2.config import WanModelConfig + config = WanModelConfig.wan22_t2v_14b() assert config.model_version == "2.2" assert config.dual_model is True @@ -103,7 +110,8 @@ class TestWan21Config: assert config.boundary == 0.875 def test_wan21_config_to_dict(self): - from mlx_video.models.wan.config import WanModelConfig + from mlx_video.models.wan_2.config import WanModelConfig + config = WanModelConfig.wan21_t2v_14b() d = config.to_dict() assert d["model_version"] == "2.1" @@ -111,7 +119,8 @@ class TestWan21Config: assert d["sample_guide_scale"] == 5.0 def test_wan21_1_3b_config_to_dict(self): - from mlx_video.models.wan.config import WanModelConfig + from mlx_video.models.wan_2.config import WanModelConfig + config = WanModelConfig.wan21_t2v_1_3b() d = config.to_dict() assert d["dim"] == 1536 @@ -119,7 +128,8 @@ class TestWan21Config: def test_default_config_is_wan22(self): """Default WanModelConfig() should be Wan2.2 14B.""" - from mlx_video.models.wan.config import WanModelConfig + from mlx_video.models.wan_2.config import WanModelConfig + config = WanModelConfig() assert config.model_version == "2.2" assert config.dual_model is True diff --git a/tests/test_wan_convert.py b/tests/test_wan_convert.py index 81630ce..1483f9c 100644 --- a/tests/test_wan_convert.py +++ b/tests/test_wan_convert.py @@ -3,17 +3,16 @@ import logging import mlx.core as mx -import numpy as np -import pytest - # --------------------------------------------------------------------------- # Transformer Weight Conversion Tests # --------------------------------------------------------------------------- + class TestSanitizeTransformerWeights: def test_patch_embedding_reshape(self): - from mlx_video.convert_wan import sanitize_wan_transformer_weights + from mlx_video.models.wan_2.convert import sanitize_wan_transformer_weights + weights = { "patch_embedding.weight": mx.random.normal((5120, 16, 1, 2, 2)), "patch_embedding.bias": mx.random.normal((5120,)), @@ -24,7 +23,8 @@ class TestSanitizeTransformerWeights: assert out["patch_embedding_proj.weight"].shape == (5120, 16 * 1 * 2 * 2) def test_text_embedding_rename(self): - from mlx_video.convert_wan import sanitize_wan_transformer_weights + from mlx_video.models.wan_2.convert import sanitize_wan_transformer_weights + weights = { "text_embedding.0.weight": mx.zeros((64, 32)), "text_embedding.0.bias": mx.zeros((64,)), @@ -38,7 +38,8 @@ class TestSanitizeTransformerWeights: assert "text_embedding_1.bias" in out def test_time_embedding_rename(self): - from mlx_video.convert_wan import sanitize_wan_transformer_weights + from mlx_video.models.wan_2.convert import sanitize_wan_transformer_weights + weights = { "time_embedding.0.weight": mx.zeros((64, 32)), "time_embedding.2.weight": mx.zeros((64, 64)), @@ -48,7 +49,8 @@ class TestSanitizeTransformerWeights: assert "time_embedding_1.weight" in out def test_time_projection_rename(self): - from mlx_video.convert_wan import sanitize_wan_transformer_weights + from mlx_video.models.wan_2.convert import sanitize_wan_transformer_weights + weights = { "time_projection.1.weight": mx.zeros((384, 64)), "time_projection.1.bias": mx.zeros((384,)), @@ -58,7 +60,8 @@ class TestSanitizeTransformerWeights: assert "time_projection.bias" in out def test_ffn_rename(self): - from mlx_video.convert_wan import sanitize_wan_transformer_weights + from mlx_video.models.wan_2.convert import sanitize_wan_transformer_weights + weights = { "blocks.0.ffn.0.weight": mx.zeros((128, 64)), "blocks.0.ffn.0.bias": mx.zeros((128,)), @@ -72,7 +75,8 @@ class TestSanitizeTransformerWeights: assert "blocks.0.ffn.fc2.bias" in out def test_freqs_skipped(self): - from mlx_video.convert_wan import sanitize_wan_transformer_weights + from mlx_video.models.wan_2.convert import sanitize_wan_transformer_weights + weights = { "freqs": mx.zeros((1024, 64, 2)), "blocks.0.norm1.weight": mx.zeros((64,)), @@ -82,7 +86,8 @@ class TestSanitizeTransformerWeights: assert "blocks.0.norm1.weight" in out def test_passthrough_keys(self): - from mlx_video.convert_wan import sanitize_wan_transformer_weights + from mlx_video.models.wan_2.convert import sanitize_wan_transformer_weights + weights = { "blocks.0.self_attn.q.weight": mx.zeros((64, 64)), "blocks.0.self_attn.k.weight": mx.zeros((64, 64)), @@ -97,7 +102,8 @@ class TestSanitizeTransformerWeights: assert key in out def test_no_unconsumed_keys(self, caplog): - from mlx_video.convert_wan import sanitize_wan_transformer_weights + from mlx_video.models.wan_2.convert import sanitize_wan_transformer_weights + weights = { "patch_embedding.weight": mx.random.normal((5120, 16, 1, 2, 2)), "patch_embedding.bias": mx.random.normal((5120,)), @@ -113,14 +119,15 @@ class TestSanitizeTransformerWeights: "head.head.weight": mx.zeros((64, 64)), "freqs": mx.zeros((1024, 64, 2)), } - with caplog.at_level(logging.WARNING, logger="mlx_video.convert_wan"): + with caplog.at_level(logging.WARNING, logger="mlx_video.models.wan_2.convert"): sanitize_wan_transformer_weights(weights) assert "Unconsumed" not in caplog.text class TestSanitizeT5Weights: def test_gate_rename(self): - from mlx_video.convert_wan import sanitize_wan_t5_weights + from mlx_video.models.wan_2.convert import sanitize_wan_t5_weights + weights = { "blocks.0.ffn.gate.0.weight": mx.zeros((128, 64)), "blocks.0.ffn.fc1.weight": mx.zeros((128, 64)), @@ -132,7 +139,8 @@ class TestSanitizeT5Weights: assert "blocks.0.ffn.fc2.weight" in out def test_passthrough(self): - from mlx_video.convert_wan import sanitize_wan_t5_weights + from mlx_video.models.wan_2.convert import sanitize_wan_t5_weights + weights = { "token_embedding.weight": mx.zeros((100, 64)), "blocks.0.attn.q.weight": mx.zeros((64, 64)), @@ -143,7 +151,8 @@ class TestSanitizeT5Weights: assert key in out def test_no_unconsumed_keys(self, caplog): - from mlx_video.convert_wan import sanitize_wan_t5_weights + from mlx_video.models.wan_2.convert import sanitize_wan_t5_weights + weights = { "token_embedding.weight": mx.zeros((100, 64)), "blocks.0.ffn.gate.0.weight": mx.zeros((128, 64)), @@ -151,14 +160,15 @@ class TestSanitizeT5Weights: "blocks.0.ffn.fc2.weight": mx.zeros((64, 128)), "norm.weight": mx.zeros((64,)), } - with caplog.at_level(logging.WARNING, logger="mlx_video.convert_wan"): + with caplog.at_level(logging.WARNING, logger="mlx_video.models.wan_2.convert"): sanitize_wan_t5_weights(weights) assert "Unconsumed" not in caplog.text class TestSanitizeVAEWeights: def test_conv3d_transpose(self): - from mlx_video.convert_wan import sanitize_wan_vae_weights + from mlx_video.models.wan_2.convert import sanitize_wan_vae_weights + weights = { "decoder.conv1.weight": mx.zeros((8, 4, 3, 3, 3)), # [O, I, D, H, W] } @@ -166,7 +176,8 @@ class TestSanitizeVAEWeights: assert out["decoder.conv1.weight"].shape == (8, 3, 3, 3, 4) # [O, D, H, W, I] def test_conv2d_transpose(self): - from mlx_video.convert_wan import sanitize_wan_vae_weights + from mlx_video.models.wan_2.convert import sanitize_wan_vae_weights + weights = { "decoder.proj.weight": mx.zeros((16, 8, 3, 3)), # [O, I, H, W] } @@ -174,7 +185,8 @@ class TestSanitizeVAEWeights: assert out["decoder.proj.weight"].shape == (16, 3, 3, 8) # [O, H, W, I] def test_non_conv_passthrough(self): - from mlx_video.convert_wan import sanitize_wan_vae_weights + from mlx_video.models.wan_2.convert import sanitize_wan_vae_weights + weights = { "decoder.norm.weight": mx.zeros((64,)), # 1D, no transpose "decoder.bias": mx.zeros((16,)), @@ -184,7 +196,8 @@ class TestSanitizeVAEWeights: assert out["decoder.bias"].shape == (16,) def test_mixed_weights(self): - from mlx_video.convert_wan import sanitize_wan_vae_weights + from mlx_video.models.wan_2.convert import sanitize_wan_vae_weights + weights = { "conv3d.weight": mx.zeros((8, 4, 3, 3, 3)), # 5D "conv2d.weight": mx.zeros((8, 4, 3, 3)), # 4D @@ -198,14 +211,15 @@ class TestSanitizeVAEWeights: assert out["norm.weight"].shape == (8,) def test_no_unconsumed_keys(self, caplog): - from mlx_video.convert_wan import sanitize_wan_vae_weights + from mlx_video.models.wan_2.convert import sanitize_wan_vae_weights + weights = { "decoder.conv1.weight": mx.zeros((8, 4, 3, 3, 3)), "decoder.proj.weight": mx.zeros((16, 8, 3, 3)), "decoder.norm.weight": mx.zeros((64,)), "decoder.bias": mx.zeros((16,)), } - with caplog.at_level(logging.WARNING, logger="mlx_video.convert_wan"): + with caplog.at_level(logging.WARNING, logger="mlx_video.models.wan_2.convert"): sanitize_wan_vae_weights(weights) assert "Unconsumed" not in caplog.text @@ -214,6 +228,7 @@ class TestSanitizeVAEWeights: # Wan2.1 Conversion Tests # --------------------------------------------------------------------------- + class TestWan21Convert: """Tests for Wan2.1 conversion support.""" @@ -222,7 +237,7 @@ class TestWan21Convert: # Create a Wan2.1-style directory (no low_noise_model subdir) (tmp_path / "dummy.safetensors").touch() # The auto-detect logic: no low_noise_model dir → 2.1 - from pathlib import Path + low = tmp_path / "low_noise_model" assert not low.exists() # Simulates auto detection @@ -233,7 +248,7 @@ class TestWan21Convert: """Auto-detect dual-model directory as Wan2.2.""" (tmp_path / "low_noise_model").mkdir() (tmp_path / "high_noise_model").mkdir() - from pathlib import Path + low = tmp_path / "low_noise_model" assert low.exists() version = "2.2" if low.exists() else "2.1" @@ -241,7 +256,8 @@ class TestWan21Convert: def test_wan21_config_saved_correctly(self): """Verify config dict has correct fields for Wan2.1.""" - from mlx_video.models.wan.config import WanModelConfig + from mlx_video.models.wan_2.config import WanModelConfig + config = WanModelConfig.wan21_t2v_14b() d = config.to_dict() assert d["model_version"] == "2.1" @@ -254,11 +270,12 @@ class TestWan21Convert: # Encoder Weight Sanitization Tests # --------------------------------------------------------------------------- + class TestSanitizeEncoderWeights: """Tests for sanitize_wan22_vae_weights with include_encoder.""" def test_exclude_encoder_by_default(self): - from mlx_video.models.wan.vae22 import sanitize_wan22_vae_weights + from mlx_video.models.wan_2.vae22 import sanitize_wan22_vae_weights weights = { "encoder.conv1.weight": mx.zeros((8, 1, 3, 3, 3)), @@ -270,7 +287,7 @@ class TestSanitizeEncoderWeights: assert not any("encoder" in k or k.startswith("conv1") for k in out) def test_include_encoder(self): - from mlx_video.models.wan.vae22 import sanitize_wan22_vae_weights + from mlx_video.models.wan_2.vae22 import sanitize_wan22_vae_weights weights = { "encoder.conv1.weight": mx.zeros((8, 1, 3, 3, 3)), @@ -283,25 +300,25 @@ class TestSanitizeEncoderWeights: assert "conv2.weight" in out def test_no_unconsumed_keys(self, caplog): - from mlx_video.models.wan.vae22 import sanitize_wan22_vae_weights + from mlx_video.models.wan_2.vae22 import sanitize_wan22_vae_weights weights = { "encoder.conv1.weight": mx.zeros((8, 1, 3, 3, 3)), "conv1.weight": mx.zeros((8, 1, 1, 1, 8)), "conv2.weight": mx.zeros((8, 1, 1, 1, 8)), } - with caplog.at_level(logging.WARNING, logger="mlx_video.models.wan.vae22"): + with caplog.at_level(logging.WARNING, logger="mlx_video.models.wan_2.vae22"): sanitize_wan22_vae_weights(weights, include_encoder=True) assert "Unconsumed" not in caplog.text def test_no_unconsumed_keys_exclude_encoder(self, caplog): - from mlx_video.models.wan.vae22 import sanitize_wan22_vae_weights + from mlx_video.models.wan_2.vae22 import sanitize_wan22_vae_weights weights = { "encoder.conv1.weight": mx.zeros((8, 1, 3, 3, 3)), "conv1.weight": mx.zeros((8, 1, 1, 1, 8)), "conv2.weight": mx.zeros((8, 1, 1, 1, 8)), } - with caplog.at_level(logging.WARNING, logger="mlx_video.models.wan.vae22"): + with caplog.at_level(logging.WARNING, logger="mlx_video.models.wan_2.vae22"): sanitize_wan22_vae_weights(weights, include_encoder=False) assert "Unconsumed" not in caplog.text diff --git a/tests/test_wan_generate.py b/tests/test_wan_generate.py index a643d9e..2972d1f 100644 --- a/tests/test_wan_generate.py +++ b/tests/test_wan_generate.py @@ -2,22 +2,20 @@ import mlx.core as mx import numpy as np -import pytest - from wan_test_helpers import _make_tiny_config - # --------------------------------------------------------------------------- # Integration: end-to-end tiny model forward pass # --------------------------------------------------------------------------- + class TestEndToEnd: """End-to-end test with tiny model (no real weights needed).""" def test_tiny_model_denoise_step(self): """Simulate one denoising step with tiny model.""" - from mlx_video.models.wan.model import WanModel - from mlx_video.models.wan.scheduler import FlowMatchEulerScheduler + from mlx_video.models.wan_2.wan_2 import WanModel + from mlx_video.models.wan_2.scheduler import FlowMatchEulerScheduler mx.random.seed(42) config = _make_tiny_config() @@ -45,8 +43,8 @@ class TestEndToEnd: def test_tiny_model_full_loop(self): """Run a complete (tiny) diffusion loop.""" - from mlx_video.models.wan.model import WanModel - from mlx_video.models.wan.scheduler import FlowMatchEulerScheduler + from mlx_video.models.wan_2.wan_2 import WanModel + from mlx_video.models.wan_2.scheduler import FlowMatchEulerScheduler mx.random.seed(123) config = _make_tiny_config() @@ -78,11 +76,12 @@ class TestEndToEnd: # I2V Mask Tests # --------------------------------------------------------------------------- + class TestI2VMask: """Tests for _build_i2v_mask.""" def test_mask_shapes(self): - from mlx_video.generate_wan import _build_i2v_mask + from mlx_video.models.wan_2.generate import _build_i2v_mask z_shape = (48, 5, 4, 4) # C, T, H, W patch_size = (1, 2, 2) @@ -92,7 +91,7 @@ class TestI2VMask: assert mask_tokens.shape == (1, 20) def test_first_frame_zero(self): - from mlx_video.generate_wan import _build_i2v_mask + from mlx_video.models.wan_2.generate import _build_i2v_mask z_shape = (48, 5, 4, 4) mask, mask_tokens = _build_i2v_mask(z_shape, (1, 2, 2)) @@ -112,7 +111,8 @@ class TestI2VMaskAlignment: def test_mask_with_ti2v_dimensions(self): """Mask should work with TI2V-5B typical dimensions.""" - from mlx_video.generate_wan import _build_i2v_mask + from mlx_video.models.wan_2.generate import _build_i2v_mask + # TI2V: z_dim=48, vae_stride=(4,16,16), patch=(1,2,2) # 704x1280 → latent 44x80, t_latent=21 for 81 frames z_shape = (48, 21, 44, 80) @@ -132,7 +132,8 @@ class TestI2VMaskAlignment: def test_mask_per_token_timestep(self): """Per-token timesteps: first-frame tokens get t=0, rest get t=sigma.""" - from mlx_video.generate_wan import _build_i2v_mask + from mlx_video.models.wan_2.generate import _build_i2v_mask + z_shape = (4, 3, 4, 4) patch_size = (1, 2, 2) _, mask_tokens = _build_i2v_mask(z_shape, patch_size) @@ -144,13 +145,16 @@ class TestI2VMaskAlignment: first_tokens = 1 * 2 * 2 # pt * (H/ph) * (W/pw) np.testing.assert_allclose(np.array(t_tokens[0, :first_tokens]), 0.0, atol=1e-7) - np.testing.assert_allclose(np.array(t_tokens[0, first_tokens:]), timestep_val, atol=1e-7) + np.testing.assert_allclose( + np.array(t_tokens[0, first_tokens:]), timestep_val, atol=1e-7 + ) # --------------------------------------------------------------------------- # Dimension Alignment Tests # --------------------------------------------------------------------------- + class TestDimensionAlignment: """Tests for automatic dimension alignment in generate_wan.""" @@ -197,7 +201,8 @@ class TestDimensionAlignment: def test_patchify_valid_after_alignment(self): """After alignment, patchify should succeed without reshape errors.""" - from mlx_video.models.wan.model import WanModel + from mlx_video.models.wan_2.wan_2 import WanModel + config = _make_tiny_config() model = WanModel(config) @@ -222,11 +227,16 @@ class TestDimensionAlignment: patches, grid_size = model._patchify(vid) mx.eval(patches) assert patches.ndim == 3 # [1, L, dim] - assert grid_size == (t_latent, h_latent // patch_size[1], w_latent // patch_size[2]) + assert grid_size == ( + t_latent, + h_latent // patch_size[1], + w_latent // patch_size[2], + ) def test_alignment_with_ti2v_config(self): """TI2V-5B uses vae_stride=(4,16,16), patch_size=(1,2,2) → align=32.""" - from mlx_video.models.wan.config import WanModelConfig + from mlx_video.models.wan_2.config import WanModelConfig + config = WanModelConfig.wan22_ti2v_5b() align_h = config.patch_size[1] * config.vae_stride[1] align_w = config.patch_size[2] * config.vae_stride[2] diff --git a/tests/test_wan_i2v.py b/tests/test_wan_i2v.py index 067d6c3..2b4789d 100644 --- a/tests/test_wan_i2v.py +++ b/tests/test_wan_i2v.py @@ -1,9 +1,6 @@ """Tests for Wan2.2 I2V-14B support.""" import mlx.core as mx -import numpy as np -import pytest - from wan_test_helpers import _make_tiny_config @@ -26,7 +23,7 @@ class TestI2VConfig: """Test I2V-14B config preset.""" def test_wan22_i2v_14b_preset(self): - from mlx_video.models.wan.config import WanModelConfig + from mlx_video.models.wan_2.config import WanModelConfig config = WanModelConfig.wan22_i2v_14b() assert config.model_type == "i2v" @@ -42,7 +39,7 @@ class TestI2VConfig: assert config.vae_z_dim == 16 def test_i2v_vs_t2v_differences(self): - from mlx_video.models.wan.config import WanModelConfig + from mlx_video.models.wan_2.config import WanModelConfig i2v = WanModelConfig.wan22_i2v_14b() t2v = WanModelConfig.wan22_t2v_14b() @@ -54,7 +51,7 @@ class TestI2VConfig: assert i2v.sample_shift == 5.0 and t2v.sample_shift == 12.0 def test_i2v_serialization_roundtrip(self): - from mlx_video.models.wan.config import WanModelConfig + from mlx_video.models.wan_2.config import WanModelConfig config = WanModelConfig.wan22_i2v_14b() d = config.to_dict() @@ -69,7 +66,7 @@ class TestModelYParameter: def test_forward_without_y(self): """Standard T2V forward pass (no y) still works.""" - from mlx_video.models.wan.model import WanModel + from mlx_video.models.wan_2.wan_2 import WanModel config = _make_tiny_config() model = WanModel(config) @@ -88,7 +85,7 @@ class TestModelYParameter: def test_forward_with_y(self): """I2V forward pass with y channel concatenation.""" - from mlx_video.models.wan.model import WanModel + from mlx_video.models.wan_2.wan_2 import WanModel config = _make_tiny_i2v_config() model = WanModel(config) @@ -111,7 +108,7 @@ class TestModelYParameter: def test_y_none_is_noop(self): """Passing y=None should be identical to not passing y.""" - from mlx_video.models.wan.model import WanModel + from mlx_video.models.wan_2.wan_2 import WanModel config = _make_tiny_config() model = WanModel(config) @@ -132,7 +129,7 @@ class TestModelYParameter: def test_batched_cfg_with_y(self): """Batched CFG (B=2) with y should work.""" - from mlx_video.models.wan.model import WanModel + from mlx_video.models.wan_2.wan_2 import WanModel config = _make_tiny_i2v_config() model = WanModel(config) @@ -145,7 +142,10 @@ class TestModelYParameter: latents = mx.random.normal((C_noise, F, H, W)) y = mx.random.normal((C_y, F, H, W)) t = mx.array([500.0, 500.0]) - ctx = [mx.random.normal((6, config.text_dim)), mx.random.normal((6, config.text_dim))] + ctx = [ + mx.random.normal((6, config.text_dim)), + mx.random.normal((6, config.text_dim)), + ] out = model([latents, latents], t, ctx, seq_len, y=[y, y]) mx.eval(out[0], out[1]) @@ -158,16 +158,18 @@ class TestVAEEncoder: """Test Wan2.1 VAE encoder.""" def test_encoder3d_instantiation(self): - from mlx_video.models.wan.vae import Encoder3d + from mlx_video.models.wan_2.vae import Encoder3d - enc = Encoder3d(dim=32, z_dim=8) # z_dim=8 (will output 8ch, but WanVAE wraps with z*2) + enc = Encoder3d( + dim=32, z_dim=8 + ) # z_dim=8 (will output 8ch, but WanVAE wraps with z*2) assert enc.conv1 is not None assert len(enc.downsamples) > 0 assert len(enc.middle) == 3 def test_encoder3d_output_shape(self): """Encoder should downsample spatially by 8x and temporally by 4x.""" - from mlx_video.models.wan.vae import Encoder3d + from mlx_video.models.wan_2.vae import Encoder3d enc = Encoder3d(dim=32, z_dim=8) # Random input: [B=1, 3, T=5, H=32, W=32] @@ -184,7 +186,7 @@ class TestVAEEncoder: def test_wan_vae_encode(self): """WanVAE with encoder=True should produce normalized latents.""" - from mlx_video.models.wan.vae import WanVAE + from mlx_video.models.wan_2.vae import WanVAE vae = WanVAE(z_dim=16, encoder=True) # Input: [B=1, 3, T=5, H=32, W=32] @@ -196,20 +198,20 @@ class TestVAEEncoder: def test_wan_vae_encoder_flag(self): """WanVAE without encoder flag should not have encoder attribute.""" - from mlx_video.models.wan.vae import WanVAE + from mlx_video.models.wan_2.vae import WanVAE vae_no_enc = WanVAE(z_dim=4, encoder=False) - assert not hasattr(vae_no_enc, 'encoder') + assert not hasattr(vae_no_enc, "encoder") vae_enc = WanVAE(z_dim=4, encoder=True) - assert hasattr(vae_enc, 'encoder') + assert hasattr(vae_enc, "encoder") class TestResampleDownsample: """Test downsample modes in Resample.""" def test_downsample2d(self): - from mlx_video.models.wan.vae import Resample + from mlx_video.models.wan_2.vae import Resample r = Resample(dim=16, mode="downsample2d") x = mx.random.normal((1, 16, 2, 8, 8)) @@ -219,7 +221,7 @@ class TestResampleDownsample: assert out.shape == (1, 16, 2, 4, 4) def test_downsample3d(self): - from mlx_video.models.wan.vae import Resample + from mlx_video.models.wan_2.vae import Resample r = Resample(dim=16, mode="downsample3d") x = mx.random.normal((1, 16, 4, 8, 8)) @@ -229,7 +231,7 @@ class TestResampleDownsample: assert out.shape == (1, 16, 2, 4, 4) def test_upsample2d_still_works(self): - from mlx_video.models.wan.vae import Resample + from mlx_video.models.wan_2.vae import Resample r = Resample(dim=16, mode="upsample2d") x = mx.random.normal((1, 16, 2, 4, 4)) @@ -238,7 +240,7 @@ class TestResampleDownsample: assert out.shape == (1, 8, 2, 8, 8) def test_upsample3d_still_works(self): - from mlx_video.models.wan.vae import Resample + from mlx_video.models.wan_2.vae import Resample r = Resample(dim=16, mode="upsample3d") x = mx.random.normal((1, 16, 2, 4, 4)) @@ -258,7 +260,9 @@ class TestI2VMaskConstruction: # Build mask following reference logic msk = mx.ones((1, num_frames, h_latent, w_latent)) - msk = mx.concatenate([msk[:, :1], mx.zeros((1, num_frames - 1, h_latent, w_latent))], axis=1) + msk = mx.concatenate( + [msk[:, :1], mx.zeros((1, num_frames - 1, h_latent, w_latent))], axis=1 + ) msk = mx.concatenate([mx.repeat(msk[:, :1], 4, axis=1), msk[:, 1:]], axis=1) msk = msk.reshape(1, msk.shape[1] // 4, 4, h_latent, w_latent) msk = msk.transpose(0, 2, 1, 3, 4)[0] # [4, T_lat, H_lat, W_lat] @@ -272,7 +276,9 @@ class TestI2VMaskConstruction: t_latent = (num_frames - 1) // 4 + 1 # = 3 msk = mx.ones((1, num_frames, h_latent, w_latent)) - msk = mx.concatenate([msk[:, :1], mx.zeros((1, num_frames - 1, h_latent, w_latent))], axis=1) + msk = mx.concatenate( + [msk[:, :1], mx.zeros((1, num_frames - 1, h_latent, w_latent))], axis=1 + ) msk = mx.concatenate([mx.repeat(msk[:, :1], 4, axis=1), msk[:, 1:]], axis=1) msk = msk.reshape(1, msk.shape[1] // 4, 4, h_latent, w_latent) msk = msk.transpose(0, 2, 1, 3, 4)[0] @@ -301,9 +307,9 @@ class TestI2VEndToEndPipeline: def test_full_i2v_pipeline(self): """End-to-end I2V: synthetic image → VAE encode → build y → denoise → VAE decode.""" - from mlx_video.models.wan.model import WanModel - from mlx_video.models.wan.scheduler import FlowMatchEulerScheduler - from mlx_video.models.wan.vae import WanVAE + from mlx_video.models.wan_2.wan_2 import WanModel + from mlx_video.models.wan_2.scheduler import FlowMatchEulerScheduler + from mlx_video.models.wan_2.vae import WanVAE mx.random.seed(0) @@ -311,7 +317,9 @@ class TestI2VEndToEndPipeline: config = _make_tiny_i2v_config() config.vae_z_dim = 16 config.out_dim = 16 # must match VAE z_dim for decode - config.in_dim = 16 + 4 + 16 # noise(out_dim=16) + mask(4) + image(z_dim=16) = 36 + config.in_dim = ( + 16 + 4 + 16 + ) # noise(out_dim=16) + mask(4) + image(z_dim=16) = 36 model = WanModel(config) # --- Tiny VAE (with encoder) --- @@ -323,10 +331,13 @@ class TestI2VEndToEndPipeline: img = mx.random.uniform(-1, 1, (1, 3, 1, height, width)) # Build video: first frame = image, rest = zeros -> [1, 3, F, H, W] - video = mx.concatenate([ - img, - mx.zeros((1, 3, num_frames - 1, height, width)), - ], axis=2) + video = mx.concatenate( + [ + img, + mx.zeros((1, 3, num_frames - 1, height, width)), + ], + axis=2, + ) # --- VAE encode --- z_video = vae.encode(video) # [1, z_dim, T_lat, H_lat, W_lat] @@ -341,7 +352,9 @@ class TestI2VEndToEndPipeline: # --- Build I2V mask (4 channels) --- msk = mx.ones((1, num_frames, h_latent, w_latent)) - msk = mx.concatenate([msk[:, :1], mx.zeros((1, num_frames - 1, h_latent, w_latent))], axis=1) + msk = mx.concatenate( + [msk[:, :1], mx.zeros((1, num_frames - 1, h_latent, w_latent))], axis=1 + ) msk = mx.concatenate([mx.repeat(msk[:, :1], 4, axis=1), msk[:, 1:]], axis=1) msk = msk.reshape(1, msk.shape[1] // 4, 4, h_latent, w_latent) msk = msk.transpose(0, 2, 1, 3, 4)[0] # [4, T_lat, H_lat, W_lat] @@ -397,8 +410,8 @@ class TestDualModelSwitching: def test_model_selection_by_timestep(self): """Verify high_noise model used for timesteps >= boundary, low_noise otherwise.""" - from mlx_video.models.wan.model import WanModel - from mlx_video.models.wan.scheduler import FlowMatchEulerScheduler + from mlx_video.models.wan_2.wan_2 import WanModel + from mlx_video.models.wan_2.scheduler import FlowMatchEulerScheduler mx.random.seed(1) config = _make_tiny_i2v_config() @@ -453,7 +466,9 @@ class TestDualModelSwitching: noise_pred_cond, noise_pred_uncond = preds[0], preds[1] noise_pred = noise_pred_uncond + gs * (noise_pred_cond - noise_pred_uncond) - latents = sched.step(noise_pred[None], timestep_val, latents[None]).squeeze(0) + latents = sched.step(noise_pred[None], timestep_val, latents[None]).squeeze( + 0 + ) mx.eval(latents) # With shift=5.0, early timesteps should be high (>=900), later ones low @@ -461,17 +476,17 @@ class TestDualModelSwitching: assert len(low_used_steps) > 0, "Low-noise model was never selected" # High-noise steps should come before low-noise steps (timesteps decrease) if high_used_steps and low_used_steps: - assert max(high_used_steps) < min(low_used_steps) or \ - min(high_used_steps) < max(low_used_steps), \ - "Model switching should happen during the loop" + assert max(high_used_steps) < min(low_used_steps) or min( + high_used_steps + ) < max(low_used_steps), "Model switching should happen during the loop" assert latents.shape == (C_noise, F, H, W) assert not mx.any(mx.isnan(latents)).item() def test_guide_scale_tuple_applied_per_model(self): """Verify (low_gs, high_gs) tuple applies different scales per model.""" - from mlx_video.models.wan.model import WanModel - from mlx_video.models.wan.scheduler import FlowMatchEulerScheduler + from mlx_video.models.wan_2.wan_2 import WanModel + from mlx_video.models.wan_2.scheduler import FlowMatchEulerScheduler mx.random.seed(2) config = _make_tiny_i2v_config() @@ -515,7 +530,9 @@ class TestDualModelSwitching: y=[y_i2v, y_i2v], ) noise_pred = pred[1] + gs * (pred[0] - pred[1]) - latents = sched.step(noise_pred[None], timestep_val, latents[None]).squeeze(0) + latents = sched.step(noise_pred[None], timestep_val, latents[None]).squeeze( + 0 + ) mx.eval(latents) # Verify both guide scales were used @@ -528,8 +545,8 @@ class TestDualModelSwitching: def test_single_model_fallback_with_tuple_guide_scale(self): """When dual_model=False, guide_scale tuple should use first element.""" - from mlx_video.models.wan.model import WanModel - from mlx_video.models.wan.scheduler import FlowMatchEulerScheduler + from mlx_video.models.wan_2.wan_2 import WanModel + from mlx_video.models.wan_2.scheduler import FlowMatchEulerScheduler mx.random.seed(3) config = _make_tiny_config() diff --git a/tests/test_wan_lora.py b/tests/test_wan_lora.py index 1670d84..9d5e57d 100644 --- a/tests/test_wan_lora.py +++ b/tests/test_wan_lora.py @@ -4,7 +4,6 @@ import tempfile from pathlib import Path import mlx.core as mx -import numpy as np import pytest @@ -40,7 +39,9 @@ class TestLoRATypes: lora_a = mx.ones((2, 4)) lora_b = mx.ones((8, 2)) - w = LoRAWeights(lora_A=lora_a, lora_B=lora_b, rank=2, alpha=2.0, module_name="test") + w = LoRAWeights( + lora_A=lora_a, lora_B=lora_b, rank=2, alpha=2.0, module_name="test" + ) applied = AppliedLoRA(weights=w, strength=0.5) delta = applied.compute_delta() # scale=1.0, strength=0.5, B@A = [[2,2,2,2]]*8 (each row sum of 2 ones) @@ -51,7 +52,9 @@ class TestLoRATypes: class TestLoRALoader: """Test LoRA weight loading from safetensors.""" - def _make_lora_file(self, tmp_dir, module_names, rank=4, in_dim=64, out_dim=128, key_format="AB"): + def _make_lora_file( + self, tmp_dir, module_names, rank=4, in_dim=64, out_dim=128, key_format="AB" + ): """Helper to create a mock LoRA safetensors file.""" weights = {} for name in module_names: @@ -133,8 +136,16 @@ class TestWanKeyNormalization: """Simulate typical Wan2.2 MLX model weight keys.""" keys = set() for i in range(2): - for layer in ["self_attn.q", "self_attn.k", "self_attn.v", "self_attn.o", - "cross_attn.q", "cross_attn.k", "cross_attn.v", "cross_attn.o"]: + for layer in [ + "self_attn.q", + "self_attn.k", + "self_attn.v", + "self_attn.o", + "cross_attn.q", + "cross_attn.k", + "cross_attn.v", + "cross_attn.o", + ]: keys.add(f"blocks.{i}.{layer}.weight") keys.add(f"blocks.{i}.ffn.fc1.weight") keys.add(f"blocks.{i}.ffn.fc2.weight") @@ -150,7 +161,10 @@ class TestWanKeyNormalization: from mlx_video.lora.apply import _normalize_wan_lora_key keys = self._wan_model_keys() - assert _normalize_wan_lora_key("blocks.0.self_attn.q", keys) == "blocks.0.self_attn.q" + assert ( + _normalize_wan_lora_key("blocks.0.self_attn.q", keys) + == "blocks.0.self_attn.q" + ) def test_strip_diffusion_model_prefix(self): from mlx_video.lora.apply import _normalize_wan_lora_key @@ -163,7 +177,9 @@ class TestWanKeyNormalization: from mlx_video.lora.apply import _normalize_wan_lora_key keys = self._wan_model_keys() - result = _normalize_wan_lora_key("model.diffusion_model.blocks.0.self_attn.k", keys) + result = _normalize_wan_lora_key( + "model.diffusion_model.blocks.0.self_attn.k", keys + ) assert result == "blocks.0.self_attn.k" def test_ffn_key_mapping(self): @@ -197,7 +213,9 @@ class TestWanKeyNormalization: from mlx_video.lora.apply import _normalize_wan_lora_key keys = self._wan_model_keys() - assert _normalize_wan_lora_key("patch_embedding", keys) == "patch_embedding_proj" + assert ( + _normalize_wan_lora_key("patch_embedding", keys) == "patch_embedding_proj" + ) def test_combined_prefix_and_ffn(self): from mlx_video.lora.apply import _normalize_wan_lora_key @@ -219,7 +237,9 @@ class TestApplyLoRA: # LoRA weights in float32 (typical when loaded from safetensors) lora_a = mx.ones((2, 4), dtype=mx.float32) * 0.1 lora_b = mx.ones((8, 2), dtype=mx.float32) * 0.1 - w = LoRAWeights(lora_A=lora_a, lora_B=lora_b, rank=2, alpha=2.0, module_name="test") + w = LoRAWeights( + lora_A=lora_a, lora_B=lora_b, rank=2, alpha=2.0, module_name="test" + ) result = apply_lora_to_linear(original, [(w, 1.0)]) assert result.dtype == mx.bfloat16, f"Expected bfloat16, got {result.dtype}" @@ -230,7 +250,9 @@ class TestApplyLoRA: original = mx.ones((8, 4), dtype=mx.float16) lora_a = mx.ones((2, 4), dtype=mx.float32) * 0.1 lora_b = mx.ones((8, 2), dtype=mx.float32) * 0.1 - w = LoRAWeights(lora_A=lora_a, lora_B=lora_b, rank=2, alpha=2.0, module_name="test") + w = LoRAWeights( + lora_A=lora_a, lora_B=lora_b, rank=2, alpha=2.0, module_name="test" + ) result = apply_lora_to_linear(original, [(w, 1.0)]) assert result.dtype == mx.float16, f"Expected float16, got {result.dtype}" @@ -241,7 +263,9 @@ class TestApplyLoRA: original = mx.ones((8, 4)) lora_a = mx.ones((2, 4)) * 0.1 lora_b = mx.ones((8, 2)) * 0.1 - w = LoRAWeights(lora_A=lora_a, lora_B=lora_b, rank=2, alpha=2.0, module_name="test") + w = LoRAWeights( + lora_A=lora_a, lora_B=lora_b, rank=2, alpha=2.0, module_name="test" + ) result = apply_lora_to_linear(original, [(w, 1.0)]) # delta = 1.0 * (B @ A) = ones(8,2)*0.1 @ ones(2,4)*0.1 = 0.02 * ones(8,4) expected = original + 0.02 * mx.ones((8, 4)) @@ -255,12 +279,16 @@ class TestApplyLoRA: w1 = LoRAWeights( lora_A=mx.ones((2, 4)), lora_B=mx.ones((8, 2)), - rank=2, alpha=2.0, module_name="a", + rank=2, + alpha=2.0, + module_name="a", ) w2 = LoRAWeights( lora_A=mx.ones((2, 4)) * 2, lora_B=mx.ones((8, 2)) * 2, - rank=2, alpha=4.0, module_name="b", + rank=2, + alpha=4.0, + module_name="b", ) result = apply_lora_to_linear(original, [(w1, 1.0), (w2, 0.5)]) # w1 delta: 1.0 * 1.0 * (ones(8,2) @ ones(2,4)) = 2 * ones(8,4) @@ -282,7 +310,9 @@ class TestApplyLoRA: w = LoRAWeights( lora_A=mx.ones((4, 64)) * 0.01, lora_B=mx.ones((128, 4)) * 0.01, - rank=4, alpha=4.0, module_name="blocks.0.self_attn.q", + rank=4, + alpha=4.0, + module_name="blocks.0.self_attn.q", ) module_to_loras = {"blocks.0.self_attn.q": [(w, 1.0)]} result = apply_loras_to_weights(model_weights, module_to_loras) @@ -301,7 +331,7 @@ class TestEndToEnd: """End-to-end LoRA loading and application.""" def test_load_and_apply_loras(self): - from mlx_video.convert_wan import load_and_apply_loras + from mlx_video.models.wan_2.convert import load_and_apply_loras with tempfile.TemporaryDirectory() as tmp: # Create mock LoRA safetensors @@ -319,9 +349,7 @@ class TestEndToEnd: "blocks.0.self_attn.k.weight": mx.ones((128, 64)), } - result = load_and_apply_loras( - model_weights, [(str(lora_path), 1.0)] - ) + result = load_and_apply_loras(model_weights, [(str(lora_path), 1.0)]) # q weight should be modified, k unchanged assert not mx.array_equal( diff --git a/tests/test_wan_model.py b/tests/test_wan_model.py index caaae89..b386fb3 100644 --- a/tests/test_wan_model.py +++ b/tests/test_wan_model.py @@ -3,18 +3,17 @@ import mlx.core as mx import mlx.nn as nn import numpy as np -import pytest - from wan_test_helpers import _make_tiny_config - # --------------------------------------------------------------------------- # Sinusoidal Embedding Tests # --------------------------------------------------------------------------- + class TestSinusoidalEmbedding: def test_output_shape(self): - from mlx_video.models.wan.model import sinusoidal_embedding_1d + from mlx_video.models.wan_2.wan_2 import sinusoidal_embedding_1d + pos = mx.arange(10).astype(mx.float32) emb = sinusoidal_embedding_1d(256, pos) mx.eval(emb) @@ -22,7 +21,8 @@ class TestSinusoidalEmbedding: def test_position_zero(self): """Position 0 should have cos=1 for all dims and sin=0.""" - from mlx_video.models.wan.model import sinusoidal_embedding_1d + from mlx_video.models.wan_2.wan_2 import sinusoidal_embedding_1d + pos = mx.array([0.0]) emb = sinusoidal_embedding_1d(64, pos) mx.eval(emb) @@ -33,7 +33,8 @@ class TestSinusoidalEmbedding: np.testing.assert_allclose(emb_np[32:], 0.0, atol=1e-5) def test_different_positions_differ(self): - from mlx_video.models.wan.model import sinusoidal_embedding_1d + from mlx_video.models.wan_2.wan_2 import sinusoidal_embedding_1d + pos = mx.array([0.0, 100.0, 999.0]) emb = sinusoidal_embedding_1d(128, pos) mx.eval(emb) @@ -46,9 +47,11 @@ class TestSinusoidalEmbedding: # Head Tests # --------------------------------------------------------------------------- + class TestHead: def test_output_shape(self): - from mlx_video.models.wan.model import Head + from mlx_video.models.wan_2.wan_2 import Head + head = Head(dim=64, out_dim=16, patch_size=(1, 2, 2)) B, L = 1, 24 x = mx.random.normal((B, L, 64)) @@ -59,7 +62,8 @@ class TestHead: assert out.shape == (B, L, expected_proj_dim) def test_modulation_shape(self): - from mlx_video.models.wan.model import Head + from mlx_video.models.wan_2.wan_2 import Head + head = Head(dim=64, out_dim=16, patch_size=(1, 2, 2)) assert head.modulation.shape == (1, 2, 64) @@ -68,19 +72,22 @@ class TestHead: # WanModel (Tiny) Tests # --------------------------------------------------------------------------- + class TestWanModel: def setup_method(self): mx.random.seed(42) def test_instantiation(self): - from mlx_video.models.wan.model import WanModel + from mlx_video.models.wan_2.wan_2 import WanModel + config = _make_tiny_config() model = WanModel(config) num_params = sum(p.size for _, p in nn.utils.tree_flatten(model.parameters())) assert num_params > 0 def test_patchify_shape(self): - from mlx_video.models.wan.model import WanModel + from mlx_video.models.wan_2.wan_2 import WanModel + config = _make_tiny_config() model = WanModel(config) # Input: [C=4, F=1, H=4, W=4] @@ -92,7 +99,8 @@ class TestWanModel: assert patches.shape == (1, 1 * 2 * 2, config.dim) def test_patchify_various_sizes(self): - from mlx_video.models.wan.model import WanModel + from mlx_video.models.wan_2.wan_2 import WanModel + config = _make_tiny_config() model = WanModel(config) for f, h, w in [(1, 4, 4), (2, 6, 8), (3, 4, 6)]: @@ -107,7 +115,8 @@ class TestWanModel: def test_unpatchify_inverse(self): """Patchify then unpatchify should reconstruct original spatial dims.""" - from mlx_video.models.wan.model import WanModel + from mlx_video.models.wan_2.wan_2 import WanModel + config = _make_tiny_config() model = WanModel(config) C, F, H, W = config.in_dim, 2, 4, 6 @@ -122,7 +131,8 @@ class TestWanModel: assert out[0].shape == (config.out_dim, F, H, W) def test_forward_pass(self): - from mlx_video.models.wan.model import WanModel + from mlx_video.models.wan_2.wan_2 import WanModel + config = _make_tiny_config() model = WanModel(config) C, F, H, W = config.in_dim, 1, 4, 4 @@ -139,7 +149,8 @@ class TestWanModel: assert out[0].shape == (C, F, H, W) def test_forward_batch(self): - from mlx_video.models.wan.model import WanModel + from mlx_video.models.wan_2.wan_2 import WanModel + config = _make_tiny_config() model = WanModel(config) C, F, H, W = config.in_dim, 1, 4, 4 @@ -148,7 +159,10 @@ class TestWanModel: x_list = [mx.random.normal((C, F, H, W)), mx.random.normal((C, F, H, W))] t = mx.array([500.0, 200.0]) - context = [mx.random.normal((6, config.text_dim)), mx.random.normal((4, config.text_dim))] + context = [ + mx.random.normal((6, config.text_dim)), + mx.random.normal((4, config.text_dim)), + ] out = model(x_list, t, context, seq_len) mx.eval(out[0], out[1]) @@ -157,13 +171,18 @@ class TestWanModel: assert o.shape == (C, F, H, W) def test_output_is_float32(self): - from mlx_video.models.wan.model import WanModel + from mlx_video.models.wan_2.wan_2 import WanModel + config = _make_tiny_config() model = WanModel(config) C, F, H, W = config.in_dim, 1, 4, 4 seq_len = (F // 1) * (H // 2) * (W // 2) - out = model([mx.random.normal((C, F, H, W))], mx.array([100.0]), - [mx.random.normal((4, config.text_dim))], seq_len) + out = model( + [mx.random.normal((C, F, H, W))], + mx.array([100.0]), + [mx.random.normal((4, config.text_dim))], + seq_len, + ) mx.eval(out[0]) assert out[0].dtype == mx.float32 @@ -172,6 +191,7 @@ class TestWanModel: # Wan2.1 Model Tests # --------------------------------------------------------------------------- + class TestWan21Model: """Test tiny Wan2.1-style model (single model mode).""" @@ -180,7 +200,8 @@ class TestWan21Model: def _make_tiny_wan21_config(self): """Create a tiny config mimicking Wan2.1 (single model).""" - from mlx_video.models.wan.config import WanModelConfig + from mlx_video.models.wan_2.config import WanModelConfig + config = WanModelConfig.wan21_t2v_14b() # Override to tiny values config.dim = 64 @@ -196,7 +217,8 @@ class TestWan21Model: def _make_tiny_wan21_1_3b_config(self): """Create a tiny config mimicking Wan2.1 1.3B.""" - from mlx_video.models.wan.config import WanModelConfig + from mlx_video.models.wan_2.config import WanModelConfig + config = WanModelConfig.wan21_t2v_1_3b() # Override to tiny values (preserve 1.3B head structure: 12 heads) config.dim = 48 @@ -212,7 +234,7 @@ class TestWan21Model: def test_wan21_tiny_model_forward(self): """Forward pass with Wan2.1 tiny config.""" - from mlx_video.models.wan.model import WanModel + from mlx_video.models.wan_2.wan_2 import WanModel config = self._make_tiny_wan21_config() model = WanModel(config) @@ -230,7 +252,7 @@ class TestWan21Model: def test_wan21_1_3b_tiny_model_forward(self): """Forward pass with Wan2.1 1.3B tiny config.""" - from mlx_video.models.wan.model import WanModel + from mlx_video.models.wan_2.wan_2 import WanModel config = self._make_tiny_wan21_1_3b_config() model = WanModel(config) @@ -248,8 +270,8 @@ class TestWan21Model: def test_wan21_single_model_loop(self): """Full diffusion loop with single model (Wan2.1 style).""" - from mlx_video.models.wan.model import WanModel - from mlx_video.models.wan.scheduler import FlowMatchEulerScheduler + from mlx_video.models.wan_2.wan_2 import WanModel + from mlx_video.models.wan_2.scheduler import FlowMatchEulerScheduler config = self._make_tiny_wan21_config() model = WanModel(config) @@ -271,7 +293,9 @@ class TestWan21Model: for i in range(3): t = sched.timesteps[i] pred_cond = model([latents], mx.array([t.item()]), [context], seq_len)[0] - pred_uncond = model([latents], mx.array([t.item()]), [context_null], seq_len)[0] + pred_uncond = model( + [latents], mx.array([t.item()]), [context_null], seq_len + )[0] pred = pred_uncond + gs * (pred_cond - pred_uncond) latents = sched.step(pred[None], t, latents[None]).squeeze(0) mx.eval(latents) @@ -281,7 +305,7 @@ class TestWan21Model: def test_wan21_vs_wan22_config_differences(self): """Verify key differences between Wan2.1 and Wan2.2 configs.""" - from mlx_video.models.wan.config import WanModelConfig + from mlx_video.models.wan_2.config import WanModelConfig c21 = WanModelConfig.wan21_t2v_14b() c22 = WanModelConfig.wan22_t2v_14b() @@ -304,25 +328,26 @@ class TestWan21Model: # Per-Token Timestep Tests # --------------------------------------------------------------------------- + class TestPerTokenTimestep: """Tests for per-token sinusoidal embedding.""" def test_1d_unchanged(self): - from mlx_video.models.wan.model import sinusoidal_embedding_1d + from mlx_video.models.wan_2.wan_2 import sinusoidal_embedding_1d pos = mx.array([0.0, 100.0, 500.0]) emb = sinusoidal_embedding_1d(256, pos) assert emb.shape == (3, 256) def test_2d_per_token(self): - from mlx_video.models.wan.model import sinusoidal_embedding_1d + from mlx_video.models.wan_2.wan_2 import sinusoidal_embedding_1d pos = mx.array([[0.0, 100.0, 100.0], [50.0, 50.0, 50.0]]) emb = sinusoidal_embedding_1d(256, pos) assert emb.shape == (2, 3, 256) def test_consistency(self): - from mlx_video.models.wan.model import sinusoidal_embedding_1d + from mlx_video.models.wan_2.wan_2 import sinusoidal_embedding_1d pos_1d = mx.array([0.0, 100.0]) emb_1d = sinusoidal_embedding_1d(256, pos_1d) diff --git a/tests/test_wan_quantization.py b/tests/test_wan_quantization.py index a219eb7..fda29e3 100644 --- a/tests/test_wan_quantization.py +++ b/tests/test_wan_quantization.py @@ -1,67 +1,82 @@ """Tests for Wan model quantization pipeline.""" import json + import mlx.core as mx import mlx.nn as nn import mlx.utils import numpy as np -import pytest - from wan_test_helpers import _make_tiny_config - # --------------------------------------------------------------------------- # Quantize Predicate Tests # --------------------------------------------------------------------------- + class TestQuantizePredicate: def test_matches_self_attention_layers(self): - from mlx_video.convert_wan import _quantize_predicate + from mlx_video.models.wan_2.convert import _quantize_predicate + mock_linear = nn.Linear(64, 64) for suffix in ["q", "k", "v", "o"]: path = f"blocks.0.self_attn.{suffix}" assert _quantize_predicate(path, mock_linear), f"Should match {path}" def test_matches_cross_attention_layers(self): - from mlx_video.convert_wan import _quantize_predicate + from mlx_video.models.wan_2.convert import _quantize_predicate + mock_linear = nn.Linear(64, 64) for suffix in ["q", "k", "v", "o"]: path = f"blocks.0.cross_attn.{suffix}" assert _quantize_predicate(path, mock_linear), f"Should match {path}" def test_matches_ffn_layers(self): - from mlx_video.convert_wan import _quantize_predicate + from mlx_video.models.wan_2.convert import _quantize_predicate + mock_linear = nn.Linear(64, 64) assert _quantize_predicate("blocks.0.ffn.fc1", mock_linear) assert _quantize_predicate("blocks.0.ffn.fc2", mock_linear) def test_rejects_embeddings(self): - from mlx_video.convert_wan import _quantize_predicate + from mlx_video.models.wan_2.convert import _quantize_predicate + mock_linear = nn.Linear(64, 64) - for path in ["patch_embedding_proj", "text_embedding_fc1", "time_embedding.fc1"]: + for path in [ + "patch_embedding_proj", + "text_embedding_fc1", + "time_embedding.fc1", + ]: assert not _quantize_predicate(path, mock_linear), f"Should reject {path}" def test_rejects_norms(self): - from mlx_video.convert_wan import _quantize_predicate + from mlx_video.models.wan_2.convert import _quantize_predicate + mock_norm = nn.RMSNorm(64) assert not _quantize_predicate("blocks.0.self_attn.norm_q", mock_norm) def test_rejects_non_quantizable_modules(self): - from mlx_video.convert_wan import _quantize_predicate + from mlx_video.models.wan_2.convert import _quantize_predicate + mock_norm = nn.RMSNorm(64) # Even if path matches, module must have to_quantized assert not _quantize_predicate("blocks.0.self_attn.q", mock_norm) def test_all_10_patterns_covered(self): """Verify exactly 10 layer patterns are targeted.""" - from mlx_video.convert_wan import _quantize_predicate + from mlx_video.models.wan_2.convert import _quantize_predicate + mock_linear = nn.Linear(64, 64) patterns = [ - "blocks.0.self_attn.q", "blocks.0.self_attn.k", - "blocks.0.self_attn.v", "blocks.0.self_attn.o", - "blocks.0.cross_attn.q", "blocks.0.cross_attn.k", - "blocks.0.cross_attn.v", "blocks.0.cross_attn.o", - "blocks.0.ffn.fc1", "blocks.0.ffn.fc2", + "blocks.0.self_attn.q", + "blocks.0.self_attn.k", + "blocks.0.self_attn.v", + "blocks.0.self_attn.o", + "blocks.0.cross_attn.q", + "blocks.0.cross_attn.k", + "blocks.0.cross_attn.v", + "blocks.0.cross_attn.o", + "blocks.0.ffn.fc1", + "blocks.0.ffn.fc2", ] matched = [p for p in patterns if _quantize_predicate(p, mock_linear)] assert len(matched) == 10 @@ -71,11 +86,12 @@ class TestQuantizePredicate: # Quantize Round-Trip Tests # --------------------------------------------------------------------------- + class TestQuantizeRoundTrip: def _quantize_and_save(self, config, tmp_path, bits=4, group_size=64): """Helper: create model, quantize, save to tmp_path.""" - from mlx_video.models.wan.model import WanModel - from mlx_video.convert_wan import _quantize_predicate + from mlx_video.models.wan_2.convert import _quantize_predicate + from mlx_video.models.wan_2.wan_2 import WanModel model = WanModel(config) nn.quantize( @@ -100,9 +116,11 @@ class TestQuantizeRoundTrip: config = _make_tiny_config() model_path, saved_weights = self._quantize_and_save(config, tmp_path, bits=4) - from mlx_video.models.wan.loading import load_wan_model + from mlx_video.models.wan_2.utils import load_wan_model + loaded = load_wan_model( - model_path, config, + model_path, + config, quantization={"bits": 4, "group_size": 64}, ) @@ -118,9 +136,11 @@ class TestQuantizeRoundTrip: config = _make_tiny_config() model_path, saved_weights = self._quantize_and_save(config, tmp_path, bits=8) - from mlx_video.models.wan.loading import load_wan_model + from mlx_video.models.wan_2.utils import load_wan_model + loaded = load_wan_model( - model_path, config, + model_path, + config, quantization={"bits": 8, "group_size": 64}, ) @@ -131,9 +151,11 @@ class TestQuantizeRoundTrip: config = _make_tiny_config() model_path, _ = self._quantize_and_save(config, tmp_path, bits=4) - from mlx_video.models.wan.loading import load_wan_model + from mlx_video.models.wan_2.utils import load_wan_model + loaded = load_wan_model( - model_path, config, + model_path, + config, quantization={"bits": 4, "group_size": 64}, ) @@ -142,7 +164,7 @@ class TestQuantizeRoundTrip: def test_loading_without_quantization_flag(self, tmp_path): """Loading a non-quantized model should have standard Linear layers.""" - from mlx_video.models.wan.model import WanModel + from mlx_video.models.wan_2.wan_2 import WanModel config = _make_tiny_config() model = WanModel(config) @@ -150,7 +172,8 @@ class TestQuantizeRoundTrip: model_path = tmp_path / "model.safetensors" mx.save_safetensors(str(model_path), weights_dict) - from mlx_video.models.wan.loading import load_wan_model + from mlx_video.models.wan_2.utils import load_wan_model + loaded = load_wan_model(model_path, config, quantization=None) assert isinstance(loaded.blocks[0].self_attn.q, nn.Linear) @@ -161,10 +184,11 @@ class TestQuantizeRoundTrip: # Quantized Inference Tests # --------------------------------------------------------------------------- + class TestQuantizedInference: def _make_quantized_model(self, config, bits=4): - from mlx_video.models.wan.model import WanModel - from mlx_video.convert_wan import _quantize_predicate + from mlx_video.models.wan_2.convert import _quantize_predicate + from mlx_video.models.wan_2.wan_2 import WanModel model = WanModel(config) nn.quantize( @@ -214,8 +238,8 @@ class TestQuantizedInference: def test_quantized_output_differs_from_unquantized(self): """Sanity check: quantization should change the weights.""" - from mlx_video.models.wan.model import WanModel - from mlx_video.convert_wan import _quantize_predicate + from mlx_video.models.wan_2.convert import _quantize_predicate + from mlx_video.models.wan_2.wan_2 import WanModel config = _make_tiny_config() mx.random.seed(42) @@ -243,11 +267,12 @@ class TestQuantizedInference: # Config Metadata Tests # --------------------------------------------------------------------------- + class TestQuantizationConfig: def test_config_metadata_written(self, tmp_path): """Verify _quantize_saved_model writes quantization metadata to config.json.""" - from mlx_video.models.wan.model import WanModel - from mlx_video.convert_wan import _quantize_saved_model + from mlx_video.models.wan_2.convert import _quantize_saved_model + from mlx_video.models.wan_2.wan_2 import WanModel config = _make_tiny_config() model = WanModel(config) @@ -270,8 +295,8 @@ class TestQuantizationConfig: assert cfg["quantization"]["group_size"] == 64 def test_config_metadata_8bit(self, tmp_path): - from mlx_video.models.wan.model import WanModel - from mlx_video.convert_wan import _quantize_saved_model + from mlx_video.models.wan_2.convert import _quantize_saved_model + from mlx_video.models.wan_2.wan_2 import WanModel config = _make_tiny_config() model = WanModel(config) @@ -291,8 +316,8 @@ class TestQuantizationConfig: def test_dual_model_quantization(self, tmp_path): """Verify dual-model quantization writes both model files.""" - from mlx_video.models.wan.model import WanModel - from mlx_video.convert_wan import _quantize_saved_model + from mlx_video.models.wan_2.convert import _quantize_saved_model + from mlx_video.models.wan_2.wan_2 import WanModel config = _make_tiny_config() diff --git a/tests/test_wan_rope_freqs.py b/tests/test_wan_rope_freqs.py index 9e41c5a..0b64bdb 100644 --- a/tests/test_wan_rope_freqs.py +++ b/tests/test_wan_rope_freqs.py @@ -27,8 +27,8 @@ class TestRoPEFrequencyConstruction: def _get_model_freqs(self, dim=64, num_heads=4): """Instantiate a tiny WanModel and return its .freqs tensor.""" - from mlx_video.models.wan.config import WanModelConfig - from mlx_video.models.wan.model import WanModel + from mlx_video.models.wan_2.config import WanModelConfig + from mlx_video.models.wan_2.wan_2 import WanModel config = WanModelConfig() config.dim = dim @@ -51,22 +51,27 @@ class TestRoPEFrequencyConstruction: def test_three_call_vs_single_call_differ(self): """Three separate rope_params calls must differ from single call.""" - from mlx_video.models.wan.rope import rope_params + from mlx_video.models.wan_2.rope import rope_params d = 128 # head_dim for all Wan models # Reference: three separate calls - correct = mx.concatenate([ - rope_params(1024, d - 4 * (d // 6)), - rope_params(1024, 2 * (d // 6)), - rope_params(1024, 2 * (d // 6)), - ], axis=1) + correct = mx.concatenate( + [ + rope_params(1024, d - 4 * (d // 6)), + rope_params(1024, 2 * (d // 6)), + rope_params(1024, 2 * (d // 6)), + ], + axis=1, + ) # Wrong: single call wrong = rope_params(1024, d) mx.eval(correct, wrong) assert correct.shape == wrong.shape diff = np.abs(np.array(correct) - np.array(wrong)).max() - assert diff > 0.1, f"Three-call and single-call should differ significantly, got max diff {diff}" + assert ( + diff > 0.1 + ), f"Three-call and single-call should differ significantly, got max diff {diff}" def test_each_axis_starts_at_frequency_one(self): """Each axis (temporal/height/width) should have cos=1, sin=0 at position 0. @@ -74,14 +79,17 @@ class TestRoPEFrequencyConstruction: This verifies each axis gets its own independent frequency range starting from theta^0 = 1.0 (i.e., exponent 0/dim). """ - from mlx_video.models.wan.rope import rope_params + from mlx_video.models.wan_2.rope import rope_params d = 128 - freqs = mx.concatenate([ - rope_params(1024, d - 4 * (d // 6)), - rope_params(1024, 2 * (d // 6)), - rope_params(1024, 2 * (d // 6)), - ], axis=1) + freqs = mx.concatenate( + [ + rope_params(1024, d - 4 * (d // 6)), + rope_params(1024, 2 * (d // 6)), + rope_params(1024, 2 * (d // 6)), + ], + axis=1, + ) mx.eval(freqs) f = np.array(freqs) @@ -95,29 +103,35 @@ class TestRoPEFrequencyConstruction: # At position 1, each axis should have its FIRST frequency near cos(1/theta^0)=cos(1) # Temporal axis first freq - np.testing.assert_allclose(f[1, 0, 0], np.cos(1.0), atol=1e-5, - err_msg="temporal[0] cos at pos 1") + np.testing.assert_allclose( + f[1, 0, 0], np.cos(1.0), atol=1e-5, err_msg="temporal[0] cos at pos 1" + ) # Height axis first freq (starts at index d_t) - np.testing.assert_allclose(f[1, d_t, 0], np.cos(1.0), atol=1e-5, - err_msg="height[0] cos at pos 1") + np.testing.assert_allclose( + f[1, d_t, 0], np.cos(1.0), atol=1e-5, err_msg="height[0] cos at pos 1" + ) # Width axis first freq (starts at index d_t + d_h) - np.testing.assert_allclose(f[1, d_t + d_h, 0], np.cos(1.0), atol=1e-5, - err_msg="width[0] cos at pos 1") + np.testing.assert_allclose( + f[1, d_t + d_h, 0], np.cos(1.0), atol=1e-5, err_msg="width[0] cos at pos 1" + ) def test_height_width_frequencies_identical(self): """Height and width axes should have identical frequency tables. Both use rope_params(1024, 2*(d//6)) = rope_params(1024, 42). """ - from mlx_video.models.wan.rope import rope_params + from mlx_video.models.wan_2.rope import rope_params d = 128 d_h_dim = 2 * (d // 6) # 42 - freqs = mx.concatenate([ - rope_params(1024, d - 4 * (d // 6)), - rope_params(1024, d_h_dim), - rope_params(1024, d_h_dim), - ], axis=1) + freqs = mx.concatenate( + [ + rope_params(1024, d - 4 * (d // 6)), + rope_params(1024, d_h_dim), + rope_params(1024, d_h_dim), + ], + axis=1, + ) mx.eval(freqs) f = np.array(freqs) @@ -125,8 +139,8 @@ class TestRoPEFrequencyConstruction: d_t = half_d - 2 * (half_d // 3) d_h = half_d // 3 - height_freqs = f[:, d_t:d_t + d_h] - width_freqs = f[:, d_t + d_h:] + height_freqs = f[:, d_t : d_t + d_h] + width_freqs = f[:, d_t + d_h :] np.testing.assert_array_equal(height_freqs, width_freqs) def test_frequency_range_per_axis(self): @@ -136,14 +150,17 @@ class TestRoPEFrequencyConstruction: axis should be 1.0 (theta^0). A single-call approach would give height starting at ~0.04 and width at ~0.002 instead of 1.0. """ - from mlx_video.models.wan.rope import rope_params + from mlx_video.models.wan_2.rope import rope_params d = 128 - freqs = mx.concatenate([ - rope_params(1024, d - 4 * (d // 6)), - rope_params(1024, 2 * (d // 6)), - rope_params(1024, 2 * (d // 6)), - ], axis=1) + freqs = mx.concatenate( + [ + rope_params(1024, d - 4 * (d // 6)), + rope_params(1024, 2 * (d // 6)), + rope_params(1024, 2 * (d // 6)), + ], + axis=1, + ) mx.eval(freqs) f = np.array(freqs) @@ -157,37 +174,46 @@ class TestRoPEFrequencyConstruction: pos1_h = f[1, d_t, 0] # height first freq pos1_w = f[1, d_t + d_h, 0] # width first freq - assert pos1_t > 0.5, f"Temporal first freq at pos 1 should be >0.5, got {pos1_t}" + assert ( + pos1_t > 0.5 + ), f"Temporal first freq at pos 1 should be >0.5, got {pos1_t}" assert pos1_h > 0.5, f"Height first freq at pos 1 should be >0.5, got {pos1_h}" assert pos1_w > 0.5, f"Width first freq at pos 1 should be >0.5, got {pos1_w}" def test_model_freqs_match_manual_construction(self): """WanModel.freqs should match manually constructed three-call freqs.""" - from mlx_video.models.wan.rope import rope_params + from mlx_video.models.wan_2.rope import rope_params freqs_model, head_dim = self._get_model_freqs(dim=64, num_heads=4) d = head_dim # 16 - freqs_manual = mx.concatenate([ - rope_params(1024, d - 4 * (d // 6)), - rope_params(1024, 2 * (d // 6)), - rope_params(1024, 2 * (d // 6)), - ], axis=1) + freqs_manual = mx.concatenate( + [ + rope_params(1024, d - 4 * (d // 6)), + rope_params(1024, 2 * (d // 6)), + rope_params(1024, 2 * (d // 6)), + ], + axis=1, + ) mx.eval(freqs_model, freqs_manual) np.testing.assert_array_equal( - np.array(freqs_model), np.array(freqs_manual), - err_msg="WanModel.freqs should use three-call construction" + np.array(freqs_model), + np.array(freqs_manual), + err_msg="WanModel.freqs should use three-call construction", ) def test_model_freqs_14b_dimensions(self): """Verify freq dimensions for 14B-scale head_dim=128.""" - from mlx_video.models.wan.rope import rope_params + from mlx_video.models.wan_2.rope import rope_params d = 128 - freqs = mx.concatenate([ - rope_params(1024, d - 4 * (d // 6)), # dim=44 → 22 freq pairs - rope_params(1024, 2 * (d // 6)), # dim=42 → 21 freq pairs - rope_params(1024, 2 * (d // 6)), # dim=42 → 21 freq pairs - ], axis=1) + freqs = mx.concatenate( + [ + rope_params(1024, d - 4 * (d // 6)), # dim=44 → 22 freq pairs + rope_params(1024, 2 * (d // 6)), # dim=42 → 21 freq pairs + rope_params(1024, 2 * (d // 6)), # dim=42 → 21 freq pairs + ], + axis=1, + ) mx.eval(freqs) assert freqs.shape == (1024, 64, 2) @@ -206,7 +232,8 @@ class TestRoPEFrequencyMatchesReference: @pytest.fixture def has_torch(self): try: - import torch + pass + return True except ImportError: pytest.skip("PyTorch not installed") @@ -214,7 +241,8 @@ class TestRoPEFrequencyMatchesReference: def test_freqs_match_pytorch_reference(self, has_torch): """Numerically compare MLX and PyTorch frequency tables.""" import torch - from mlx_video.models.wan.rope import rope_params + + from mlx_video.models.wan_2.rope import rope_params d = 128 @@ -222,22 +250,30 @@ class TestRoPEFrequencyMatchesReference: def pt_rope_params(max_seq_len, dim, theta=10000): freqs = torch.outer( torch.arange(max_seq_len), - 1.0 / torch.pow(theta, torch.arange(0, dim, 2).to(torch.float64).div(dim))) + 1.0 + / torch.pow(theta, torch.arange(0, dim, 2).to(torch.float64).div(dim)), + ) freqs = torch.polar(torch.ones_like(freqs), freqs) return freqs - ref = torch.cat([ - pt_rope_params(1024, d - 4 * (d // 6)), - pt_rope_params(1024, 2 * (d // 6)), - pt_rope_params(1024, 2 * (d // 6)), - ], dim=1) + ref = torch.cat( + [ + pt_rope_params(1024, d - 4 * (d // 6)), + pt_rope_params(1024, 2 * (d // 6)), + pt_rope_params(1024, 2 * (d // 6)), + ], + dim=1, + ) # MLX - ours = mx.concatenate([ - rope_params(1024, d - 4 * (d // 6)), - rope_params(1024, 2 * (d // 6)), - rope_params(1024, 2 * (d // 6)), - ], axis=1) + ours = mx.concatenate( + [ + rope_params(1024, d - 4 * (d // 6)), + rope_params(1024, 2 * (d // 6)), + rope_params(1024, 2 * (d // 6)), + ], + axis=1, + ) mx.eval(ours) our_cos = np.array(ours[:, :, 0]) @@ -245,10 +281,12 @@ class TestRoPEFrequencyMatchesReference: ref_cos = ref.real.float().numpy() ref_sin = ref.imag.float().numpy() - np.testing.assert_allclose(our_cos, ref_cos, atol=1e-6, - err_msg="cos mismatch vs PyTorch reference") - np.testing.assert_allclose(our_sin, ref_sin, atol=1e-6, - err_msg="sin mismatch vs PyTorch reference") + np.testing.assert_allclose( + our_cos, ref_cos, atol=1e-6, err_msg="cos mismatch vs PyTorch reference" + ) + np.testing.assert_allclose( + our_sin, ref_sin, atol=1e-6, err_msg="sin mismatch vs PyTorch reference" + ) class TestRoPEApplyWithCorrectFreqs: @@ -260,14 +298,17 @@ class TestRoPEApplyWithCorrectFreqs: This is the key property that was broken by the single-call bug: height/width frequencies were too low to distinguish nearby positions. """ - from mlx_video.models.wan.rope import rope_params, rope_apply + from mlx_video.models.wan_2.rope import rope_apply, rope_params d = 128 - freqs = mx.concatenate([ - rope_params(1024, d - 4 * (d // 6)), - rope_params(1024, 2 * (d // 6)), - rope_params(1024, 2 * (d // 6)), - ], axis=1) + freqs = mx.concatenate( + [ + rope_params(1024, d - 4 * (d // 6)), + rope_params(1024, 2 * (d // 6)), + rope_params(1024, 2 * (d // 6)), + ], + axis=1, + ) B, N = 1, 4 F, H, W = 1, 4, 4 @@ -289,30 +330,37 @@ class TestRoPEApplyWithCorrectFreqs: # Max diff should be >0.5 for both axes. With the bug, height was ~0.04 # and width was ~0.002. With correct freqs, both are ~1.3. - assert height_diff > 0.5, ( - f"Adjacent height positions should differ significantly, got {height_diff:.4f}" - ) - assert width_diff > 0.5, ( - f"Adjacent width positions should differ significantly, got {width_diff:.4f}" - ) + assert ( + height_diff > 0.5 + ), f"Adjacent height positions should differ significantly, got {height_diff:.4f}" + assert ( + width_diff > 0.5 + ), f"Adjacent width positions should differ significantly, got {width_diff:.4f}" # Height and width should have identical frequency tables → same diffs - np.testing.assert_allclose(height_diff, width_diff, rtol=1e-5, - err_msg="Height and width should use identical frequency tables") + np.testing.assert_allclose( + height_diff, + width_diff, + rtol=1e-5, + err_msg="Height and width should use identical frequency tables", + ) def test_precomputed_matches_online(self): """rope_precompute_cos_sin + rope_apply should match non-precomputed path.""" - from mlx_video.models.wan.rope import ( + from mlx_video.models.wan_2.rope import ( rope_apply, rope_params, rope_precompute_cos_sin, ) d = 128 - freqs = mx.concatenate([ - rope_params(1024, d - 4 * (d // 6)), - rope_params(1024, 2 * (d // 6)), - rope_params(1024, 2 * (d // 6)), - ], axis=1) + freqs = mx.concatenate( + [ + rope_params(1024, d - 4 * (d // 6)), + rope_params(1024, 2 * (d // 6)), + rope_params(1024, 2 * (d // 6)), + ], + axis=1, + ) B, N = 2, 4 F, H, W = 2, 3, 4 @@ -329,6 +377,8 @@ class TestRoPEApplyWithCorrectFreqs: mx.eval(out_online, out_precomp) np.testing.assert_allclose( - np.array(out_online), np.array(out_precomp), atol=1e-5, - err_msg="Precomputed and online RoPE should match" + np.array(out_online), + np.array(out_precomp), + atol=1e-5, + err_msg="Precomputed and online RoPE should match", ) diff --git a/tests/test_wan_scheduler.py b/tests/test_wan_scheduler.py index d16ff49..3789a8d 100644 --- a/tests/test_wan_scheduler.py +++ b/tests/test_wan_scheduler.py @@ -6,21 +6,23 @@ import mlx.core as mx import numpy as np import pytest - # --------------------------------------------------------------------------- # Euler Scheduler Tests # --------------------------------------------------------------------------- + class TestFlowMatchEulerScheduler: def test_initialization(self): - from mlx_video.models.wan.scheduler import FlowMatchEulerScheduler + from mlx_video.models.wan_2.scheduler import FlowMatchEulerScheduler + sched = FlowMatchEulerScheduler() assert sched.num_train_timesteps == 1000 assert sched.timesteps is None assert sched.sigmas is None def test_set_timesteps(self): - from mlx_video.models.wan.scheduler import FlowMatchEulerScheduler + from mlx_video.models.wan_2.scheduler import FlowMatchEulerScheduler + sched = FlowMatchEulerScheduler() sched.set_timesteps(40, shift=12.0) mx.eval(sched.timesteps, sched.sigmas) @@ -28,7 +30,8 @@ class TestFlowMatchEulerScheduler: assert sched.sigmas.shape == (41,) # 40 steps + terminal def test_timesteps_decreasing(self): - from mlx_video.models.wan.scheduler import FlowMatchEulerScheduler + from mlx_video.models.wan_2.scheduler import FlowMatchEulerScheduler + sched = FlowMatchEulerScheduler() sched.set_timesteps(40, shift=12.0) mx.eval(sched.timesteps) @@ -37,7 +40,8 @@ class TestFlowMatchEulerScheduler: assert np.all(np.diff(ts) < 0), f"Timesteps not decreasing: {ts[:5]}..." def test_sigmas_decreasing(self): - from mlx_video.models.wan.scheduler import FlowMatchEulerScheduler + from mlx_video.models.wan_2.scheduler import FlowMatchEulerScheduler + sched = FlowMatchEulerScheduler() sched.set_timesteps(20, shift=1.0) mx.eval(sched.sigmas) @@ -45,7 +49,8 @@ class TestFlowMatchEulerScheduler: assert np.all(np.diff(sigmas) <= 0), "Sigmas not decreasing" def test_terminal_sigma_is_zero(self): - from mlx_video.models.wan.scheduler import FlowMatchEulerScheduler + from mlx_video.models.wan_2.scheduler import FlowMatchEulerScheduler + sched = FlowMatchEulerScheduler() sched.set_timesteps(20, shift=5.0) mx.eval(sched.sigmas) @@ -53,7 +58,8 @@ class TestFlowMatchEulerScheduler: def test_shift_effect(self): """Larger shift should push sigmas toward higher values.""" - from mlx_video.models.wan.scheduler import FlowMatchEulerScheduler + from mlx_video.models.wan_2.scheduler import FlowMatchEulerScheduler + sched1 = FlowMatchEulerScheduler() sched2 = FlowMatchEulerScheduler() sched1.set_timesteps(20, shift=1.0) @@ -64,7 +70,8 @@ class TestFlowMatchEulerScheduler: assert mean2 > mean1, "Higher shift should push sigmas higher" def test_step_euler(self): - from mlx_video.models.wan.scheduler import FlowMatchEulerScheduler + from mlx_video.models.wan_2.scheduler import FlowMatchEulerScheduler + sched = FlowMatchEulerScheduler() sched.set_timesteps(10, shift=1.0) mx.eval(sched.sigmas) @@ -82,11 +89,14 @@ class TestFlowMatchEulerScheduler: # Euler: x_next = x + (sigma_next - sigma) * v expected = 1.0 + (sigma_next - sigma) * 0.5 np.testing.assert_allclose( - np.array(result).flatten()[0], expected, rtol=1e-4, + np.array(result).flatten()[0], + expected, + rtol=1e-4, ) def test_step_index_increments(self): - from mlx_video.models.wan.scheduler import FlowMatchEulerScheduler + from mlx_video.models.wan_2.scheduler import FlowMatchEulerScheduler + sched = FlowMatchEulerScheduler() sched.set_timesteps(5, shift=1.0) assert sched._step_index == 0 @@ -98,7 +108,8 @@ class TestFlowMatchEulerScheduler: assert sched._step_index == 2 def test_reset(self): - from mlx_video.models.wan.scheduler import FlowMatchEulerScheduler + from mlx_video.models.wan_2.scheduler import FlowMatchEulerScheduler + sched = FlowMatchEulerScheduler() sched.set_timesteps(5, shift=1.0) sample = mx.ones((1, 1, 1, 1, 1)) @@ -110,7 +121,8 @@ class TestFlowMatchEulerScheduler: @pytest.mark.parametrize("steps", [10, 20, 40, 50]) def test_various_step_counts(self, steps): - from mlx_video.models.wan.scheduler import FlowMatchEulerScheduler + from mlx_video.models.wan_2.scheduler import FlowMatchEulerScheduler + sched = FlowMatchEulerScheduler() sched.set_timesteps(steps, shift=12.0) mx.eval(sched.timesteps, sched.sigmas) @@ -119,7 +131,8 @@ class TestFlowMatchEulerScheduler: def test_full_denoise_loop(self): """Run a complete denoise loop with zero velocity -> sample unchanged.""" - from mlx_video.models.wan.scheduler import FlowMatchEulerScheduler + from mlx_video.models.wan_2.scheduler import FlowMatchEulerScheduler + sched = FlowMatchEulerScheduler() sched.set_timesteps(5, shift=1.0) sample = mx.ones((1, 2, 1, 2, 2)) @@ -140,23 +153,27 @@ class TestComputeSigmas: """Tests for the shared _compute_sigmas helper.""" def test_length(self): - from mlx_video.models.wan.scheduler import _compute_sigmas + from mlx_video.models.wan_2.scheduler import _compute_sigmas + sigmas = _compute_sigmas(20, shift=5.0) assert len(sigmas) == 21 # num_steps + terminal def test_terminal_zero(self): - from mlx_video.models.wan.scheduler import _compute_sigmas + from mlx_video.models.wan_2.scheduler import _compute_sigmas + sigmas = _compute_sigmas(10, shift=1.0) assert sigmas[-1] == 0.0 def test_starts_near_one(self): - from mlx_video.models.wan.scheduler import _compute_sigmas + from mlx_video.models.wan_2.scheduler import _compute_sigmas + sigmas = _compute_sigmas(20, shift=5.0) # Reference applies shift twice, so sigma[0] ≈ 0.99996 (not exactly 1.0) np.testing.assert_allclose(sigmas[0], 1.0, atol=1e-3) def test_decreasing(self): - from mlx_video.models.wan.scheduler import _compute_sigmas + from mlx_video.models.wan_2.scheduler import _compute_sigmas + sigmas = _compute_sigmas(20, shift=5.0) assert np.all(np.diff(sigmas) <= 0) @@ -168,7 +185,8 @@ class TestComputeSigmas: sigma_max/sigma_min come from the *unshifted* training schedule, and the shift is applied only once (single-shift). """ - from mlx_video.models.wan.scheduler import _compute_sigmas + from mlx_video.models.wan_2.scheduler import _compute_sigmas + steps, shift, N = 50, 5.0, 1000 sigmas = _compute_sigmas(steps, shift, N) # Official single-shift: unshifted bounds, then shift once @@ -182,7 +200,8 @@ class TestComputeSigmas: np.testing.assert_allclose(sigmas, official, atol=1e-6) def test_shift_one_is_near_linear(self): - from mlx_video.models.wan.scheduler import _compute_sigmas + from mlx_video.models.wan_2.scheduler import _compute_sigmas + sigmas = _compute_sigmas(10, shift=1.0) # With shift=1, f(sigma)=sigma, but sigma_max = 0.999 (from alpha schedule) # so schedule is nearly linear from ~0.999 to 0 @@ -191,11 +210,12 @@ class TestComputeSigmas: def test_all_schedulers_same_sigmas(self): """All three schedulers should produce identical sigma schedules.""" - from mlx_video.models.wan.scheduler import ( + from mlx_video.models.wan_2.scheduler import ( FlowDPMPP2MScheduler, FlowMatchEulerScheduler, FlowUniPCScheduler, ) + scheds = [ FlowMatchEulerScheduler(1000), FlowDPMPP2MScheduler(1000), @@ -209,11 +229,12 @@ class TestComputeSigmas: np.testing.assert_allclose(np.array(s.sigmas), ref, atol=1e-6) def test_all_schedulers_same_timesteps(self): - from mlx_video.models.wan.scheduler import ( + from mlx_video.models.wan_2.scheduler import ( FlowDPMPP2MScheduler, FlowMatchEulerScheduler, FlowUniPCScheduler, ) + scheds = [ FlowMatchEulerScheduler(1000), FlowDPMPP2MScheduler(1000), @@ -234,13 +255,15 @@ class TestComputeSigmas: class TestFlowDPMPP2MScheduler: def test_initialization(self): - from mlx_video.models.wan.scheduler import FlowDPMPP2MScheduler + from mlx_video.models.wan_2.scheduler import FlowDPMPP2MScheduler + sched = FlowDPMPP2MScheduler() assert sched.num_train_timesteps == 1000 assert sched.lower_order_final is True def test_set_timesteps(self): - from mlx_video.models.wan.scheduler import FlowDPMPP2MScheduler + from mlx_video.models.wan_2.scheduler import FlowDPMPP2MScheduler + sched = FlowDPMPP2MScheduler() sched.set_timesteps(20, shift=5.0) mx.eval(sched.timesteps, sched.sigmas) @@ -248,7 +271,8 @@ class TestFlowDPMPP2MScheduler: assert sched.sigmas.shape == (21,) def test_step_index_increments(self): - from mlx_video.models.wan.scheduler import FlowDPMPP2MScheduler + from mlx_video.models.wan_2.scheduler import FlowDPMPP2MScheduler + sched = FlowDPMPP2MScheduler() sched.set_timesteps(5, shift=1.0) sample = mx.ones((1, 4, 1, 2, 2)) @@ -260,7 +284,8 @@ class TestFlowDPMPP2MScheduler: assert sched._step_index == 2 def test_reset(self): - from mlx_video.models.wan.scheduler import FlowDPMPP2MScheduler + from mlx_video.models.wan_2.scheduler import FlowDPMPP2MScheduler + sched = FlowDPMPP2MScheduler() sched.set_timesteps(5, shift=1.0) sample = mx.ones((1, 1, 1, 1, 1)) @@ -271,7 +296,8 @@ class TestFlowDPMPP2MScheduler: def test_full_loop_finite(self): """Full loop with constant velocity should produce finite output.""" - from mlx_video.models.wan.scheduler import FlowDPMPP2MScheduler + from mlx_video.models.wan_2.scheduler import FlowDPMPP2MScheduler + sched = FlowDPMPP2MScheduler() sched.set_timesteps(10, shift=1.0) sample = mx.ones((1, 2, 1, 2, 2)) @@ -283,7 +309,8 @@ class TestFlowDPMPP2MScheduler: def test_first_step_is_first_order(self): """First step should use 1st-order (no prev_x0 available).""" - from mlx_video.models.wan.scheduler import FlowDPMPP2MScheduler + from mlx_video.models.wan_2.scheduler import FlowDPMPP2MScheduler + sched = FlowDPMPP2MScheduler() sched.set_timesteps(10, shift=5.0) sample = mx.random.normal((1, 4, 2, 4, 4)) @@ -297,7 +324,8 @@ class TestFlowDPMPP2MScheduler: def test_second_step_uses_correction(self): """After first step, DPM++ should have stored prev_x0 for correction.""" - from mlx_video.models.wan.scheduler import FlowDPMPP2MScheduler + from mlx_video.models.wan_2.scheduler import FlowDPMPP2MScheduler + sched = FlowDPMPP2MScheduler() sched.set_timesteps(10, shift=5.0) sample = mx.random.normal((1, 4, 1, 2, 2)) @@ -314,11 +342,14 @@ class TestFlowDPMPP2MScheduler: x0_after_second = sched._prev_x0 assert x0_after_second is not None # The stored x0 should differ from the first step's - assert not np.allclose(np.array(x0_after_first), np.array(x0_after_second), atol=1e-6) + assert not np.allclose( + np.array(x0_after_first), np.array(x0_after_second), atol=1e-6 + ) def test_denoise_to_target(self): """Perfect oracle should denoise to target with any solver.""" - from mlx_video.models.wan.scheduler import FlowDPMPP2MScheduler + from mlx_video.models.wan_2.scheduler import FlowDPMPP2MScheduler + sched = FlowDPMPP2MScheduler() sched.set_timesteps(20, shift=5.0) target = mx.zeros((1, 2, 1, 4, 4)) @@ -332,7 +363,8 @@ class TestFlowDPMPP2MScheduler: @pytest.mark.parametrize("steps", [5, 10, 20, 50]) def test_various_step_counts(self, steps): - from mlx_video.models.wan.scheduler import FlowDPMPP2MScheduler + from mlx_video.models.wan_2.scheduler import FlowDPMPP2MScheduler + sched = FlowDPMPP2MScheduler() sched.set_timesteps(steps, shift=5.0) mx.eval(sched.timesteps, sched.sigmas) @@ -341,7 +373,8 @@ class TestFlowDPMPP2MScheduler: def test_terminal_sigma_produces_x0(self): """When sigma_next=0 the scheduler should return x0 directly.""" - from mlx_video.models.wan.scheduler import FlowDPMPP2MScheduler + from mlx_video.models.wan_2.scheduler import FlowDPMPP2MScheduler + sched = FlowDPMPP2MScheduler() sched.set_timesteps(5, shift=1.0) sample = mx.ones((1, 1, 1, 1, 1)) * 3.0 @@ -361,14 +394,16 @@ class TestFlowDPMPP2MScheduler: class TestFlowUniPCScheduler: def test_initialization(self): - from mlx_video.models.wan.scheduler import FlowUniPCScheduler + from mlx_video.models.wan_2.scheduler import FlowUniPCScheduler + sched = FlowUniPCScheduler() assert sched.num_train_timesteps == 1000 assert sched.solver_order == 2 assert sched.lower_order_final is True def test_set_timesteps(self): - from mlx_video.models.wan.scheduler import FlowUniPCScheduler + from mlx_video.models.wan_2.scheduler import FlowUniPCScheduler + sched = FlowUniPCScheduler() sched.set_timesteps(30, shift=12.0) mx.eval(sched.timesteps, sched.sigmas) @@ -376,7 +411,8 @@ class TestFlowUniPCScheduler: assert sched.sigmas.shape == (31,) def test_step_index_increments(self): - from mlx_video.models.wan.scheduler import FlowUniPCScheduler + from mlx_video.models.wan_2.scheduler import FlowUniPCScheduler + sched = FlowUniPCScheduler() sched.set_timesteps(5, shift=1.0) sample = mx.ones((1, 1, 1, 1, 1)) @@ -386,7 +422,8 @@ class TestFlowUniPCScheduler: assert sched._step_index == 1 def test_reset(self): - from mlx_video.models.wan.scheduler import FlowUniPCScheduler + from mlx_video.models.wan_2.scheduler import FlowUniPCScheduler + sched = FlowUniPCScheduler() sched.set_timesteps(5, shift=1.0) sample = mx.ones((1, 1, 1, 1, 1)) @@ -398,7 +435,8 @@ class TestFlowUniPCScheduler: assert all(m is None for m in sched._model_outputs) def test_full_loop_finite(self): - from mlx_video.models.wan.scheduler import FlowUniPCScheduler + from mlx_video.models.wan_2.scheduler import FlowUniPCScheduler + sched = FlowUniPCScheduler() sched.set_timesteps(10, shift=1.0) sample = mx.ones((1, 2, 1, 2, 2)) @@ -410,7 +448,8 @@ class TestFlowUniPCScheduler: def test_corrector_not_applied_first_step(self): """First step should skip the corrector (no history).""" - from mlx_video.models.wan.scheduler import FlowUniPCScheduler + from mlx_video.models.wan_2.scheduler import FlowUniPCScheduler + sched = FlowUniPCScheduler(use_corrector=True) sched.set_timesteps(10, shift=5.0) sample = mx.random.normal((1, 4, 1, 2, 2)) @@ -423,7 +462,8 @@ class TestFlowUniPCScheduler: def test_corrector_applied_after_first_step(self): """Steps after the first should use the corrector when enabled.""" - from mlx_video.models.wan.scheduler import FlowUniPCScheduler + from mlx_video.models.wan_2.scheduler import FlowUniPCScheduler + sched = FlowUniPCScheduler(use_corrector=True) sched.set_timesteps(10, shift=5.0) sample = mx.random.normal((1, 2, 1, 4, 4)) @@ -435,7 +475,8 @@ class TestFlowUniPCScheduler: assert sched._lower_order_nums >= 2 def test_denoise_to_target(self): - from mlx_video.models.wan.scheduler import FlowUniPCScheduler + from mlx_video.models.wan_2.scheduler import FlowUniPCScheduler + sched = FlowUniPCScheduler() sched.set_timesteps(20, shift=5.0) target = mx.zeros((1, 2, 1, 4, 4)) @@ -449,7 +490,8 @@ class TestFlowUniPCScheduler: @pytest.mark.parametrize("steps", [5, 10, 20, 50]) def test_various_step_counts(self, steps): - from mlx_video.models.wan.scheduler import FlowUniPCScheduler + from mlx_video.models.wan_2.scheduler import FlowUniPCScheduler + sched = FlowUniPCScheduler() sched.set_timesteps(steps, shift=5.0) mx.eval(sched.timesteps, sched.sigmas) @@ -458,7 +500,8 @@ class TestFlowUniPCScheduler: def test_disable_corrector(self): """Disabling corrector on step 0 should still work without error.""" - from mlx_video.models.wan.scheduler import FlowUniPCScheduler + from mlx_video.models.wan_2.scheduler import FlowUniPCScheduler + sched = FlowUniPCScheduler(use_corrector=True, disable_corrector=[0]) sched.set_timesteps(5, shift=1.0) sample = mx.ones((1, 1, 1, 2, 2)) @@ -470,7 +513,8 @@ class TestFlowUniPCScheduler: def test_solver_order_3(self): """Order 3 should work without error.""" - from mlx_video.models.wan.scheduler import FlowUniPCScheduler + from mlx_video.models.wan_2.scheduler import FlowUniPCScheduler + sched = FlowUniPCScheduler(solver_order=3, use_corrector=True) sched.set_timesteps(10, shift=5.0) sample = mx.random.normal((1, 2, 1, 2, 2)) @@ -483,10 +527,11 @@ class TestFlowUniPCScheduler: def test_corrector_rhos_c_not_hardcoded(self): """Corrector rhos_c should be computed via linalg.solve, not hardcoded 0.5.""" import math + # For 50-step schedule with shift=5.0, order 2 corrector at step 5: # rhos_c[0] (history) should be ~0.07, NOT 0.5 # rhos_c[1] (D1_t) should be ~0.45, NOT 0.5 - from mlx_video.models.wan.scheduler import _compute_sigmas + from mlx_video.models.wan_2.scheduler import _compute_sigmas sigmas = _compute_sigmas(50, shift=5.0) @@ -525,16 +570,23 @@ class TestFlowUniPCScheduler: rhos_c = np.linalg.solve(R, b) # History weight should be small (~0.07-0.09), not 0.5 - assert rhos_c[0] < 0.15, f"Step {step_idx}: rhos_c[0]={rhos_c[0]:.4f} too large" - assert rhos_c[0] > 0.0, f"Step {step_idx}: rhos_c[0]={rhos_c[0]:.4f} should be positive" + assert ( + rhos_c[0] < 0.15 + ), f"Step {step_idx}: rhos_c[0]={rhos_c[0]:.4f} too large" + assert ( + rhos_c[0] > 0.0 + ), f"Step {step_idx}: rhos_c[0]={rhos_c[0]:.4f} should be positive" # D1_t weight should be ~0.42-0.45, not 0.5 - assert 0.3 < rhos_c[1] < 0.5, f"Step {step_idx}: rhos_c[1]={rhos_c[1]:.4f} out of range" + assert ( + 0.3 < rhos_c[1] < 0.5 + ), f"Step {step_idx}: rhos_c[1]={rhos_c[1]:.4f} out of range" # --------------------------------------------------------------------------- # Scheduler Coherence Tests # --------------------------------------------------------------------------- + class TestSchedulerCoherence: """Tests that Euler, DPM++, and UniPC schedulers produce coherent results. @@ -545,7 +597,7 @@ class TestSchedulerCoherence: @staticmethod def _make_schedulers(steps=10, shift=5.0): - from mlx_video.models.wan.scheduler import ( + from mlx_video.models.wan_2.scheduler import ( FlowDPMPP2MScheduler, FlowMatchEulerScheduler, FlowUniPCScheduler, @@ -599,11 +651,15 @@ class TestSchedulerCoherence: results[name] = np.array(r) np.testing.assert_allclose( - results["dpm++"], results["euler"], atol=1e-5, + results["dpm++"], + results["euler"], + atol=1e-5, err_msg="DPM++ step 0 should match Euler", ) np.testing.assert_allclose( - results["unipc"], results["euler"], atol=1e-5, + results["unipc"], + results["euler"], + atol=1e-5, err_msg="UniPC step 0 should match Euler", ) @@ -621,11 +677,15 @@ class TestSchedulerCoherence: unipc_r = scheds["unipc"].step(vel, scheds["unipc"].timesteps[0], noise) mx.eval(euler_r, dpm_r, unipc_r) np.testing.assert_allclose( - np.array(dpm_r), np.array(euler_r), atol=1e-5, + np.array(dpm_r), + np.array(euler_r), + atol=1e-5, err_msg=f"DPM++ step 0 differs from Euler at shift={shift}", ) np.testing.assert_allclose( - np.array(unipc_r), np.array(euler_r), atol=1e-5, + np.array(unipc_r), + np.array(euler_r), + atol=1e-5, err_msg=f"UniPC step 0 differs from Euler at shift={shift}", ) @@ -644,7 +704,9 @@ class TestSchedulerCoherence: latents = sched.step(v, sched.timesteps[i], latents) mx.eval(latents) np.testing.assert_allclose( - np.array(latents), 0.0, atol=1e-3, + np.array(latents), + 0.0, + atol=1e-3, err_msg=f"{name} did not converge to target with oracle", ) @@ -669,12 +731,12 @@ class TestSchedulerCoherence: # Higher-order solvers should not be significantly worse than Euler # (add small epsilon to handle near-zero errors from floating point noise) eps = 1e-6 - assert errors["dpm++"] <= errors["euler"] * 1.5 + eps, ( - f"DPM++ error {errors['dpm++']:.6f} much worse than Euler {errors['euler']:.6f}" - ) - assert errors["unipc"] <= errors["euler"] * 1.5 + eps, ( - f"UniPC error {errors['unipc']:.6f} much worse than Euler {errors['euler']:.6f}" - ) + assert ( + errors["dpm++"] <= errors["euler"] * 1.5 + eps + ), f"DPM++ error {errors['dpm++']:.6f} much worse than Euler {errors['euler']:.6f}" + assert ( + errors["unipc"] <= errors["euler"] * 1.5 + eps + ), f"UniPC error {errors['unipc']:.6f} much worse than Euler {errors['euler']:.6f}" def test_multistep_trajectory_similar_magnitude(self): """Over a full denoising loop with constant velocity, all solvers @@ -696,9 +758,9 @@ class TestSchedulerCoherence: # All solvers should produce results within the same order of magnitude vals = list(final_means.values()) ratio = max(vals) / max(min(vals), 1e-10) - assert ratio < 10.0, ( - f"Scheduler outputs diverge too much: {final_means}, ratio={ratio:.1f}" - ) + assert ( + ratio < 10.0 + ), f"Scheduler outputs diverge too much: {final_means}, ratio={ratio:.1f}" def test_intermediate_values_finite(self): """Every intermediate latent value must be finite for all solvers.""" @@ -712,33 +774,33 @@ class TestSchedulerCoherence: vel = mx.random.normal(shape) latents = sched.step(vel, sched.timesteps[i], latents) mx.eval(latents) - assert np.isfinite(np.array(latents)).all(), ( - f"{name} produced non-finite values at step {i}" - ) + assert np.isfinite( + np.array(latents) + ).all(), f"{name} produced non-finite values at step {i}" def test_lambda_boundary_values(self): """_lambda must return -inf at sigma=1.0 and +inf at sigma=0.0.""" - from mlx_video.models.wan.scheduler import ( + from mlx_video.models.wan_2.scheduler import ( FlowDPMPP2MScheduler, FlowUniPCScheduler, ) for cls in (FlowDPMPP2MScheduler, FlowUniPCScheduler): - assert cls._lambda(1.0) == -math.inf, ( - f"{cls.__name__}._lambda(1.0) should be -inf" - ) - assert cls._lambda(0.0) == math.inf, ( - f"{cls.__name__}._lambda(0.0) should be +inf" - ) + assert ( + cls._lambda(1.0) == -math.inf + ), f"{cls.__name__}._lambda(1.0) should be -inf" + assert ( + cls._lambda(0.0) == math.inf + ), f"{cls.__name__}._lambda(0.0) should be +inf" # Interior values should be finite lam = cls._lambda(0.5) - assert math.isfinite(lam) and lam == 0.0, ( - f"{cls.__name__}._lambda(0.5) should be 0.0" - ) + assert ( + math.isfinite(lam) and lam == 0.0 + ), f"{cls.__name__}._lambda(0.5) should be 0.0" def test_lambda_monotonically_decreasing(self): """_lambda(sigma) should decrease as sigma increases (more noise → lower SNR).""" - from mlx_video.models.wan.scheduler import FlowDPMPP2MScheduler + from mlx_video.models.wan_2.scheduler import FlowDPMPP2MScheduler sigmas = [0.01, 0.1, 0.3, 0.5, 0.7, 0.9, 0.99] lambdas = [FlowDPMPP2MScheduler._lambda(s) for s in sigmas] @@ -770,7 +832,9 @@ class TestSchedulerCoherence: result = scheds[name].step(vel, scheds[name].timesteps[0], sample) mx.eval(result) np.testing.assert_allclose( - np.array(result), np.array(expected), atol=5e-4, + np.array(result), + np.array(expected), + atol=5e-4, err_msg=f"{name} step 0 doesn't match DDIM formula (shift={shift})", ) @@ -790,10 +854,14 @@ class TestSchedulerCoherence: results[name] = np.array(r) np.testing.assert_allclose( - results["dpm++"], results["euler"], atol=1e-5, + results["dpm++"], + results["euler"], + atol=1e-5, ) np.testing.assert_allclose( - results["unipc"], results["euler"], atol=1e-5, + results["unipc"], + results["euler"], + atol=1e-5, ) def test_dpmpp_unipc_agree_on_step1(self): @@ -834,7 +902,10 @@ class TestSchedulerCoherence: shape = (1, 2, 1, 2, 2) noise = mx.random.normal(shape) - from mlx_video.models.wan.scheduler import FlowDPMPP2MScheduler, FlowUniPCScheduler + from mlx_video.models.wan_2.scheduler import ( + FlowDPMPP2MScheduler, + FlowUniPCScheduler, + ) for cls in (FlowDPMPP2MScheduler, FlowUniPCScheduler): sched = cls() @@ -857,27 +928,34 @@ class TestSchedulerCoherence: mx.eval(latents) result2 = np.array(latents) - np.testing.assert_allclose(result1, result2, atol=1e-5, - err_msg=f"{cls.__name__} not reproducible after reset()") + np.testing.assert_allclose( + result1, + result2, + atol=1e-5, + err_msg=f"{cls.__name__} not reproducible after reset()", + ) # --------------------------------------------------------------------------- # UniPC Corrector Default Tests # --------------------------------------------------------------------------- + class TestUniPCCorrectorDefault: """Tests that the UniPC corrector is enabled by default, matching official FlowUniPCMultistepScheduler behavior.""" def test_corrector_enabled_by_default(self): """Default construction should have corrector enabled.""" - from mlx_video.models.wan.scheduler import FlowUniPCScheduler + from mlx_video.models.wan_2.scheduler import FlowUniPCScheduler + sched = FlowUniPCScheduler() assert sched._use_corrector is True def test_corrector_affects_output(self): """Corrector should produce different results than no corrector after step 1.""" - from mlx_video.models.wan.scheduler import FlowUniPCScheduler + from mlx_video.models.wan_2.scheduler import FlowUniPCScheduler + mx.random.seed(42) shape = (1, 4, 1, 4, 4) noise = mx.random.normal(shape) @@ -900,7 +978,8 @@ class TestUniPCCorrectorDefault: def test_corrector_does_not_affect_first_step(self): """Step 0 should be identical regardless of corrector setting.""" - from mlx_video.models.wan.scheduler import FlowUniPCScheduler + from mlx_video.models.wan_2.scheduler import FlowUniPCScheduler + mx.random.seed(42) shape = (1, 4, 1, 4, 4) noise = mx.random.normal(shape) diff --git a/tests/test_wan_t5.py b/tests/test_wan_t5.py index 7cb064f..0c606d8 100644 --- a/tests/test_wan_t5.py +++ b/tests/test_wan_t5.py @@ -3,16 +3,16 @@ import mlx.core as mx import mlx.nn as nn import numpy as np -import pytest - # --------------------------------------------------------------------------- # T5 Encoder Tests # --------------------------------------------------------------------------- + class TestT5LayerNorm: def test_output_shape(self): - from mlx_video.models.wan.text_encoder import T5LayerNorm + from mlx_video.models.wan_2.text_encoder import T5LayerNorm + norm = T5LayerNorm(64) x = mx.random.normal((2, 10, 64)) out = norm(x) @@ -21,7 +21,8 @@ class TestT5LayerNorm: def test_rms_normalization(self): """After T5LayerNorm with weight=1, RMS should be ~1.""" - from mlx_video.models.wan.text_encoder import T5LayerNorm + from mlx_video.models.wan_2.text_encoder import T5LayerNorm + norm = T5LayerNorm(128) x = mx.random.normal((1, 5, 128)) * 5.0 out = norm(x) @@ -34,14 +35,16 @@ class TestT5LayerNorm: class TestT5RelativeEmbedding: def test_output_shape(self): - from mlx_video.models.wan.text_encoder import T5RelativeEmbedding + from mlx_video.models.wan_2.text_encoder import T5RelativeEmbedding + rel_emb = T5RelativeEmbedding(num_buckets=32, num_heads=4) out = rel_emb(10, 10) mx.eval(out) assert out.shape == (1, 4, 10, 10) # [1, N, lq, lk] def test_asymmetric_lengths(self): - from mlx_video.models.wan.text_encoder import T5RelativeEmbedding + from mlx_video.models.wan_2.text_encoder import T5RelativeEmbedding + rel_emb = T5RelativeEmbedding(num_buckets=32, num_heads=4) out = rel_emb(8, 12) mx.eval(out) @@ -49,7 +52,8 @@ class TestT5RelativeEmbedding: def test_symmetry(self): """Position bias should have structure (not all zeros/random).""" - from mlx_video.models.wan.text_encoder import T5RelativeEmbedding + from mlx_video.models.wan_2.text_encoder import T5RelativeEmbedding + rel_emb = T5RelativeEmbedding(num_buckets=32, num_heads=2) out = rel_emb(6, 6) mx.eval(out) @@ -63,7 +67,8 @@ class TestT5RelativeEmbedding: class TestT5Attention: def test_output_shape(self): - from mlx_video.models.wan.text_encoder import T5Attention + from mlx_video.models.wan_2.text_encoder import T5Attention + attn = T5Attention(dim=64, dim_attn=64, num_heads=4) x = mx.random.normal((1, 10, 64)) out = attn(x) @@ -72,13 +77,15 @@ class TestT5Attention: def test_no_scaling(self): """T5 attention famously has no sqrt(d) scaling. Verify structure.""" - from mlx_video.models.wan.text_encoder import T5Attention + from mlx_video.models.wan_2.text_encoder import T5Attention + attn = T5Attention(dim=64, dim_attn=64, num_heads=4) # No scale attribute (unlike standard attention) assert not hasattr(attn, "scale") def test_with_position_bias(self): - from mlx_video.models.wan.text_encoder import T5Attention, T5RelativeEmbedding + from mlx_video.models.wan_2.text_encoder import T5Attention, T5RelativeEmbedding + attn = T5Attention(dim=64, dim_attn=64, num_heads=4) rel_emb = T5RelativeEmbedding(32, 4) x = mx.random.normal((1, 10, 64)) @@ -88,7 +95,8 @@ class TestT5Attention: assert out.shape == (1, 10, 64) def test_with_mask(self): - from mlx_video.models.wan.text_encoder import T5Attention + from mlx_video.models.wan_2.text_encoder import T5Attention + attn = T5Attention(dim=64, dim_attn=64, num_heads=4) x = mx.random.normal((1, 10, 64)) mask = mx.ones((1, 10)) @@ -100,7 +108,8 @@ class TestT5Attention: class TestT5FeedForward: def test_output_shape(self): - from mlx_video.models.wan.text_encoder import T5FeedForward + from mlx_video.models.wan_2.text_encoder import T5FeedForward + ffn = T5FeedForward(64, 256) x = mx.random.normal((1, 10, 64)) out = ffn(x) @@ -109,7 +118,8 @@ class TestT5FeedForward: def test_gated_structure(self): """T5 FFN is gated: gate(x) * fc1(x).""" - from mlx_video.models.wan.text_encoder import T5FeedForward + from mlx_video.models.wan_2.text_encoder import T5FeedForward + ffn = T5FeedForward(32, 64) assert hasattr(ffn, "gate_proj") assert hasattr(ffn, "fc1") @@ -121,10 +131,17 @@ class TestT5Encoder: mx.random.seed(42) def test_output_shape(self): - from mlx_video.models.wan.text_encoder import T5Encoder + from mlx_video.models.wan_2.text_encoder import T5Encoder + encoder = T5Encoder( - vocab_size=100, dim=64, dim_attn=64, dim_ffn=128, - num_heads=4, num_layers=2, num_buckets=32, shared_pos=False, + vocab_size=100, + dim=64, + dim_attn=64, + dim_ffn=128, + num_heads=4, + num_layers=2, + num_buckets=32, + shared_pos=False, ) ids = mx.array([[1, 5, 10, 0, 0]]) mask = mx.array([[1, 1, 1, 0, 0]]) @@ -133,39 +150,67 @@ class TestT5Encoder: assert out.shape == (1, 5, 64) def test_shared_pos(self): - from mlx_video.models.wan.text_encoder import T5Encoder + from mlx_video.models.wan_2.text_encoder import T5Encoder + encoder = T5Encoder( - vocab_size=100, dim=64, dim_attn=64, dim_ffn=128, - num_heads=4, num_layers=2, num_buckets=32, shared_pos=True, + vocab_size=100, + dim=64, + dim_attn=64, + dim_ffn=128, + num_heads=4, + num_layers=2, + num_buckets=32, + shared_pos=True, ) assert encoder.pos_embedding is not None for block in encoder.blocks: assert block.pos_embedding is None def test_per_layer_pos(self): - from mlx_video.models.wan.text_encoder import T5Encoder + from mlx_video.models.wan_2.text_encoder import T5Encoder + encoder = T5Encoder( - vocab_size=100, dim=64, dim_attn=64, dim_ffn=128, - num_heads=4, num_layers=2, num_buckets=32, shared_pos=False, + vocab_size=100, + dim=64, + dim_attn=64, + dim_ffn=128, + num_heads=4, + num_layers=2, + num_buckets=32, + shared_pos=False, ) assert encoder.pos_embedding is None for block in encoder.blocks: assert block.pos_embedding is not None def test_param_count(self): - from mlx_video.models.wan.text_encoder import T5Encoder + from mlx_video.models.wan_2.text_encoder import T5Encoder + encoder = T5Encoder( - vocab_size=100, dim=64, dim_attn=64, dim_ffn=128, - num_heads=4, num_layers=2, num_buckets=32, shared_pos=False, + vocab_size=100, + dim=64, + dim_attn=64, + dim_ffn=128, + num_heads=4, + num_layers=2, + num_buckets=32, + shared_pos=False, ) num_params = sum(p.size for _, p in nn.utils.tree_flatten(encoder.parameters())) assert num_params > 0 def test_without_mask(self): - from mlx_video.models.wan.text_encoder import T5Encoder + from mlx_video.models.wan_2.text_encoder import T5Encoder + encoder = T5Encoder( - vocab_size=100, dim=64, dim_attn=64, dim_ffn=128, - num_heads=4, num_layers=2, num_buckets=32, shared_pos=False, + vocab_size=100, + dim=64, + dim_attn=64, + dim_ffn=128, + num_heads=4, + num_layers=2, + num_buckets=32, + shared_pos=False, ) ids = mx.array([[1, 5, 10]]) out = encoder(ids) diff --git a/tests/test_wan_tiling.py b/tests/test_wan_tiling.py index 3353dd4..b90eab9 100644 --- a/tests/test_wan_tiling.py +++ b/tests/test_wan_tiling.py @@ -2,13 +2,11 @@ import mlx.core as mx import numpy as np -import pytest -from mlx_video.models.ltx.video_vae.tiling import ( +from mlx_video.models.ltx_2.video_vae.tiling import ( TilingConfig, decode_with_tiling, split_in_spatial, - split_in_temporal, ) @@ -49,16 +47,24 @@ class TestNonCausalTemporal: # Causal: 1 + (4-1)*4 = 13 out_causal = decode_with_tiling( - dummy_decoder_causal, latents, config, - spatial_scale=scale, temporal_scale=scale, causal_temporal=True, + dummy_decoder_causal, + latents, + config, + spatial_scale=scale, + temporal_scale=scale, + causal_temporal=True, ) mx.eval(out_causal) assert out_causal.shape[2] == 1 + (t - 1) * scale # 13 # Non-causal: 4*4 = 16 out_noncausal = decode_with_tiling( - dummy_decoder_noncausal, latents, config, - spatial_scale=scale, temporal_scale=scale, causal_temporal=False, + dummy_decoder_noncausal, + latents, + config, + spatial_scale=scale, + temporal_scale=scale, + causal_temporal=False, ) mx.eval(out_noncausal) assert out_noncausal.shape[2] == t * scale # 16 @@ -69,7 +75,7 @@ class TestWan22TiledDecoding: def _make_small_wan22_decoder(self): """Create a small Wan2.2 decoder for testing.""" - from mlx_video.models.wan.vae22 import Wan22VAEDecoder + from mlx_video.models.wan_2.vae22 import Wan22VAEDecoder # Use very small dimensions for fast testing vae = Wan22VAEDecoder(z_dim=48, dim=16, dec_dim=16) @@ -100,9 +106,9 @@ class TestWan22TiledDecoding: mx.eval(out_tiled) # Both should produce the same shape - assert out_regular.shape == out_tiled.shape, ( - f"Shape mismatch: regular={out_regular.shape} vs tiled={out_tiled.shape}" - ) + assert ( + out_regular.shape == out_tiled.shape + ), f"Shape mismatch: regular={out_regular.shape} vs tiled={out_tiled.shape}" def test_decode_tiled_falls_through_when_small(self): """When input is smaller than tile size, decode_tiled should produce same output as __call__.""" @@ -120,8 +126,10 @@ class TestWan22TiledDecoding: mx.eval(out_tiled) np.testing.assert_allclose( - np.array(out_regular), np.array(out_tiled), - rtol=1e-4, atol=1e-4, + np.array(out_regular), + np.array(out_tiled), + rtol=1e-4, + atol=1e-4, err_msg="Tiled decode should match regular decode for small inputs", ) @@ -131,7 +139,7 @@ class TestWan21TiledDecoding: def _make_small_wan21_vae(self): """Create a small Wan2.1 VAE for testing.""" - from mlx_video.models.wan.vae import WanVAE + from mlx_video.models.wan_2.vae import WanVAE vae = WanVAE(z_dim=16) mx.eval(vae.parameters()) @@ -152,9 +160,9 @@ class TestWan21TiledDecoding: out_tiled = vae.decode_tiled(z, tiling_config=TilingConfig.default()) mx.eval(out_tiled) - assert out_regular.shape == out_tiled.shape, ( - f"Shape mismatch: regular={out_regular.shape} vs tiled={out_tiled.shape}" - ) + assert ( + out_regular.shape == out_tiled.shape + ), f"Shape mismatch: regular={out_regular.shape} vs tiled={out_tiled.shape}" def test_decode_tiled_falls_through_when_small(self): """When input is smaller than tile size, decode_tiled should produce same output as decode.""" @@ -171,8 +179,10 @@ class TestWan21TiledDecoding: mx.eval(out_tiled) np.testing.assert_allclose( - np.array(out_regular), np.array(out_tiled), - rtol=1e-4, atol=1e-4, + np.array(out_regular), + np.array(out_tiled), + rtol=1e-4, + atol=1e-4, err_msg="Tiled decode should match regular decode for small inputs", ) @@ -182,11 +192,16 @@ class TestWan21TemporalScale: def test_wan21_decoder_temporal_output(self): """Wan2.1 Decoder3d should produce T*4 temporal output (non-causal doubling).""" - from mlx_video.models.wan.vae import Decoder3d + from mlx_video.models.wan_2.vae import Decoder3d # Small decoder for fast test - dec = Decoder3d(dim=16, z_dim=4, dim_mult=[1, 1, 1, 1], num_res_blocks=1, - temporal_upsample=[True, True, False]) + dec = Decoder3d( + dim=16, + z_dim=4, + dim_mult=[1, 1, 1, 1], + num_res_blocks=1, + temporal_upsample=[True, True, False], + ) mx.eval(dec.parameters()) x = mx.random.normal((1, 4, 3, 4, 4)) # T=3 diff --git a/tests/test_wan_transformer.py b/tests/test_wan_transformer.py index dd9acec..0722958 100644 --- a/tests/test_wan_transformer.py +++ b/tests/test_wan_transformer.py @@ -2,16 +2,16 @@ import mlx.core as mx import numpy as np -import pytest - # --------------------------------------------------------------------------- # Transformer Block Tests # --------------------------------------------------------------------------- + class TestWanFFN: def test_output_shape(self): - from mlx_video.models.wan.transformer import WanFFN + from mlx_video.models.wan_2.transformer import WanFFN + ffn = WanFFN(64, 256) x = mx.random.normal((2, 10, 64)) out = ffn(x) @@ -20,7 +20,8 @@ class TestWanFFN: def test_gelu_activation(self): """FFN should use GELU activation (non-linearity).""" - from mlx_video.models.wan.transformer import WanFFN + from mlx_video.models.wan_2.transformer import WanFFN + ffn = WanFFN(32, 128) x = mx.ones((1, 1, 32)) * 2.0 out1 = ffn(x) @@ -39,10 +40,13 @@ class TestWanAttentionBlock: self.num_heads = 4 def test_output_shape(self): - from mlx_video.models.wan.transformer import WanAttentionBlock - from mlx_video.models.wan.rope import rope_params + from mlx_video.models.wan_2.rope import rope_params + from mlx_video.models.wan_2.transformer import WanAttentionBlock + block = WanAttentionBlock( - self.dim, self.ffn_dim, self.num_heads, + self.dim, + self.ffn_dim, + self.num_heads, cross_attn_norm=True, ) B, L = 1, 24 @@ -53,37 +57,49 @@ class TestWanAttentionBlock: freqs = rope_params(1024, self.dim // self.num_heads) out = block( - x, e, seq_lens=[L], grid_sizes=[(F, H, W)], - freqs=freqs, context=context, + x, + e, + seq_lens=[L], + grid_sizes=[(F, H, W)], + freqs=freqs, + context=context, ) mx.eval(out) assert out.shape == (B, L, self.dim) def test_modulation_shape(self): - from mlx_video.models.wan.transformer import WanAttentionBlock + from mlx_video.models.wan_2.transformer import WanAttentionBlock + block = WanAttentionBlock(self.dim, self.ffn_dim, self.num_heads) assert block.modulation.shape == (1, 6, self.dim) def test_with_cross_attn_norm(self): - from mlx_video.models.wan.transformer import WanAttentionBlock + from mlx_video.models.wan_2.transformer import WanAttentionBlock + block = WanAttentionBlock( - self.dim, self.ffn_dim, self.num_heads, + self.dim, + self.ffn_dim, + self.num_heads, cross_attn_norm=True, ) assert block.norm3 is not None def test_without_cross_attn_norm(self): - from mlx_video.models.wan.transformer import WanAttentionBlock + from mlx_video.models.wan_2.transformer import WanAttentionBlock + block = WanAttentionBlock( - self.dim, self.ffn_dim, self.num_heads, + self.dim, + self.ffn_dim, + self.num_heads, cross_attn_norm=False, ) assert block.norm3 is None def test_residual_connection(self): """Output should differ from zero even with small random init.""" - from mlx_video.models.wan.transformer import WanAttentionBlock - from mlx_video.models.wan.rope import rope_params + from mlx_video.models.wan_2.rope import rope_params + from mlx_video.models.wan_2.transformer import WanAttentionBlock + block = WanAttentionBlock(self.dim, self.ffn_dim, self.num_heads) B, L = 1, 8 F, H, W = 2, 2, 2 @@ -102,6 +118,7 @@ class TestWanAttentionBlock: # Float32 Modulation Precision Tests # --------------------------------------------------------------------------- + class TestFloat32Modulation: """Tests that modulation/gate operations are computed in float32, matching official torch.amp.autocast('cuda', dtype=torch.float32).""" @@ -112,14 +129,16 @@ class TestFloat32Modulation: def test_block_modulation_in_float32(self): """Modulation param starts random but should be usable as float32.""" - from mlx_video.models.wan.transformer import WanAttentionBlock + from mlx_video.models.wan_2.transformer import WanAttentionBlock + block = WanAttentionBlock(self.dim, 128, 4, cross_attn_norm=True) assert block.modulation.dtype == mx.float32 def test_block_output_float32_with_bf16_modulation_input(self): """Even if e (time embedding) arrives as bf16, modulation should cast to f32.""" - from mlx_video.models.wan.transformer import WanAttentionBlock - from mlx_video.models.wan.rope import rope_params + from mlx_video.models.wan_2.rope import rope_params + from mlx_video.models.wan_2.transformer import WanAttentionBlock + block = WanAttentionBlock(self.dim, 128, 4) B, L = 1, 8 x = mx.random.normal((B, L, self.dim)) @@ -134,7 +153,8 @@ class TestFloat32Modulation: def test_head_modulation_float32(self): """Head modulation should be float32 even with bf16 e input.""" - from mlx_video.models.wan.model import Head + from mlx_video.models.wan_2.wan_2 import Head + head = Head(self.dim, 4, (1, 2, 2)) x = mx.random.normal((1, 8, self.dim)) e = mx.random.normal((1, 8, self.dim)).astype(mx.bfloat16) @@ -144,7 +164,8 @@ class TestFloat32Modulation: def test_model_time_embedding_float32(self): """sinusoidal_embedding_1d output must be float32.""" - from mlx_video.models.wan.model import sinusoidal_embedding_1d + from mlx_video.models.wan_2.wan_2 import sinusoidal_embedding_1d + t = mx.array([500.0]) emb = sinusoidal_embedding_1d(256, t) mx.eval(emb) @@ -152,7 +173,8 @@ class TestFloat32Modulation: def test_model_per_token_time_embedding_float32(self): """Per-token time embeddings (I2V) should also be float32.""" - from mlx_video.models.wan.model import sinusoidal_embedding_1d + from mlx_video.models.wan_2.wan_2 import sinusoidal_embedding_1d + t = mx.array([[0.0, 100.0, 200.0, 300.0]]) # [B=1, L=4] emb = sinusoidal_embedding_1d(256, t) mx.eval(emb) diff --git a/tests/test_wan_vae.py b/tests/test_wan_vae.py index cd2cf94..255ef71 100644 --- a/tests/test_wan_vae.py +++ b/tests/test_wan_vae.py @@ -4,16 +4,16 @@ import math import mlx.core as mx import numpy as np -import pytest - # --------------------------------------------------------------------------- # VAE 2.1 Tests # --------------------------------------------------------------------------- + class TestCausalConv3d: def test_output_shape_stride1(self): - from mlx_video.models.wan.vae import CausalConv3d + from mlx_video.models.wan_2.vae import CausalConv3d + conv = CausalConv3d(4, 8, kernel_size=3, stride=1, padding=1) # Initialize weights conv.weight = mx.random.normal(conv.weight.shape) * 0.02 @@ -28,7 +28,8 @@ class TestCausalConv3d: assert out.shape[4] == 8 # W preserved def test_output_shape_kernel1(self): - from mlx_video.models.wan.vae import CausalConv3d + from mlx_video.models.wan_2.vae import CausalConv3d + conv = CausalConv3d(4, 8, kernel_size=1, stride=1, padding=0) conv.weight = mx.random.normal(conv.weight.shape) * 0.02 x = mx.random.normal((1, 4, 2, 4, 4)) @@ -38,7 +39,8 @@ class TestCausalConv3d: def test_causal_padding(self): """Causal conv should only use past/current frames, not future.""" - from mlx_video.models.wan.vae import CausalConv3d + from mlx_video.models.wan_2.vae import CausalConv3d + conv = CausalConv3d(2, 2, kernel_size=3, stride=1, padding=1) conv.weight = mx.random.normal(conv.weight.shape) * 0.1 conv.bias = mx.zeros((2,)) @@ -54,7 +56,8 @@ class TestCausalConv3d: class TestResidualBlock: def test_same_dim(self): - from mlx_video.models.wan.vae import ResidualBlock + from mlx_video.models.wan_2.vae import ResidualBlock + block = ResidualBlock(8, 8) x = mx.random.normal((1, 8, 2, 4, 4)) out = block(x) @@ -62,7 +65,8 @@ class TestResidualBlock: assert out.shape == (1, 8, 2, 4, 4) def test_different_dim(self): - from mlx_video.models.wan.vae import ResidualBlock + from mlx_video.models.wan_2.vae import ResidualBlock + block = ResidualBlock(8, 16) x = mx.random.normal((1, 8, 2, 4, 4)) out = block(x) @@ -70,19 +74,22 @@ class TestResidualBlock: assert out.shape == (1, 16, 2, 4, 4) def test_shortcut_exists_when_dims_differ(self): - from mlx_video.models.wan.vae import ResidualBlock + from mlx_video.models.wan_2.vae import ResidualBlock + block = ResidualBlock(8, 16) assert block.shortcut is not None def test_no_shortcut_when_dims_same(self): - from mlx_video.models.wan.vae import ResidualBlock + from mlx_video.models.wan_2.vae import ResidualBlock + block = ResidualBlock(8, 8) assert block.shortcut is None class TestAttentionBlock: def test_output_shape(self): - from mlx_video.models.wan.vae import AttentionBlock + from mlx_video.models.wan_2.vae import AttentionBlock + block = AttentionBlock(8) x = mx.random.normal((1, 8, 2, 4, 4)) out = block(x) @@ -90,7 +97,8 @@ class TestAttentionBlock: assert out.shape == (1, 8, 2, 4, 4) def test_residual_connection(self): - from mlx_video.models.wan.vae import AttentionBlock + from mlx_video.models.wan_2.vae import AttentionBlock + block = AttentionBlock(8) x = mx.random.normal((1, 8, 1, 3, 3)) out = block(x) @@ -101,14 +109,16 @@ class TestAttentionBlock: class TestWanVAE: def test_instantiation(self): - from mlx_video.models.wan.vae import WanVAE + from mlx_video.models.wan_2.vae import WanVAE + vae = WanVAE(z_dim=16) assert vae.z_dim == 16 assert vae.mean.shape == (16,) assert vae.std.shape == (16,) def test_normalization_stats(self): - from mlx_video.models.wan.vae import WanVAE, VAE_MEAN, VAE_STD + from mlx_video.models.wan_2.vae import VAE_MEAN, VAE_STD + assert len(VAE_MEAN) == 16 assert len(VAE_STD) == 16 assert all(s > 0 for s in VAE_STD) @@ -123,7 +133,8 @@ class TestVAE22CausalConv3d: """Tests for vae22.CausalConv3d (channels-last).""" def test_output_shape_k3(self): - from mlx_video.models.wan.vae22 import CausalConv3d + from mlx_video.models.wan_2.vae22 import CausalConv3d + conv = CausalConv3d(8, 16, kernel_size=3, padding=1) x = mx.random.normal((1, 4, 8, 8, 8)) # [B, T, H, W, C] out = conv(x) @@ -131,7 +142,8 @@ class TestVAE22CausalConv3d: assert out.shape == (1, 4, 8, 8, 16) def test_output_shape_k1(self): - from mlx_video.models.wan.vae22 import CausalConv3d + from mlx_video.models.wan_2.vae22 import CausalConv3d + conv = CausalConv3d(8, 16, kernel_size=1) x = mx.random.normal((1, 2, 4, 4, 8)) out = conv(x) @@ -140,7 +152,8 @@ class TestVAE22CausalConv3d: def test_temporal_causal(self): """Output at t=0 should not depend on t>0.""" - from mlx_video.models.wan.vae22 import CausalConv3d + from mlx_video.models.wan_2.vae22 import CausalConv3d + conv = CausalConv3d(2, 2, kernel_size=3, padding=1) conv.weight = mx.random.normal(conv.weight.shape) * 0.1 conv.bias = mx.zeros(conv.bias.shape) @@ -151,10 +164,13 @@ class TestVAE22CausalConv3d: t0_ref = np.array(out_zero[0, 0]) # Modify t=2..3; output at t=0 should be unchanged - x_mod = mx.concatenate([ - x[:, :2], - mx.ones((1, 2, 4, 4, 2)), - ], axis=1) + x_mod = mx.concatenate( + [ + x[:, :2], + mx.ones((1, 2, 4, 4, 2)), + ], + axis=1, + ) out_mod = conv(x_mod) mx.eval(out_mod) t0_mod = np.array(out_mod[0, 0]) @@ -162,7 +178,8 @@ class TestVAE22CausalConv3d: def test_channels_last_format(self): """Verify input/output are channels-last [B, T, H, W, C].""" - from mlx_video.models.wan.vae22 import CausalConv3d + from mlx_video.models.wan_2.vae22 import CausalConv3d + conv = CausalConv3d(4, 8, kernel_size=3, padding=1) x = mx.random.normal((2, 3, 6, 6, 4)) out = conv(x) @@ -174,7 +191,8 @@ class TestRMSNorm: """Tests for vae22.RMS_norm (actually L2 normalization).""" def test_output_shape(self): - from mlx_video.models.wan.vae22 import RMS_norm + from mlx_video.models.wan_2.vae22 import RMS_norm + norm = RMS_norm(16) x = mx.random.normal((2, 4, 4, 4, 16)) out = norm(x) @@ -183,7 +201,8 @@ class TestRMSNorm: def test_l2_normalization(self): """RMS_norm should normalize to unit L2 norm * sqrt(dim).""" - from mlx_video.models.wan.vae22 import RMS_norm + from mlx_video.models.wan_2.vae22 import RMS_norm + dim = 32 norm = RMS_norm(dim) x = mx.random.normal((1, 1, 1, 1, dim)) * 5.0 # large values @@ -196,7 +215,8 @@ class TestRMSNorm: def test_scale_invariant(self): """Scaling input by constant should not change output (L2 norm property).""" - from mlx_video.models.wan.vae22 import RMS_norm + from mlx_video.models.wan_2.vae22 import RMS_norm + norm = RMS_norm(8) x = mx.random.normal((1, 1, 1, 1, 8)) out1 = norm(x) @@ -206,7 +226,8 @@ class TestRMSNorm: def test_gamma_effect(self): """Non-unit gamma should scale output.""" - from mlx_video.models.wan.vae22 import RMS_norm + from mlx_video.models.wan_2.vae22 import RMS_norm + norm = RMS_norm(4) norm.gamma = mx.array([2.0, 2.0, 2.0, 2.0]) x = mx.ones((1, 1, 1, 1, 4)) @@ -220,7 +241,8 @@ class TestDupUp3D: """Tests for vae22.DupUp3D spatial/temporal upsampling.""" def test_spatial_only(self): - from mlx_video.models.wan.vae22 import DupUp3D + from mlx_video.models.wan_2.vae22 import DupUp3D + up = DupUp3D(8, 4, factor_t=1, factor_s=2) x = mx.random.normal((1, 3, 4, 4, 8)) out = up(x) @@ -228,7 +250,8 @@ class TestDupUp3D: assert out.shape == (1, 3, 8, 8, 4) def test_temporal_and_spatial(self): - from mlx_video.models.wan.vae22 import DupUp3D + from mlx_video.models.wan_2.vae22 import DupUp3D + up = DupUp3D(16, 8, factor_t=2, factor_s=2) x = mx.random.normal((1, 3, 4, 4, 16)) out = up(x) @@ -236,7 +259,8 @@ class TestDupUp3D: assert out.shape == (1, 6, 8, 8, 8) def test_first_chunk_trims(self): - from mlx_video.models.wan.vae22 import DupUp3D + from mlx_video.models.wan_2.vae22 import DupUp3D + up = DupUp3D(8, 4, factor_t=2, factor_s=2) x = mx.random.normal((1, 3, 4, 4, 8)) out_normal = up(x, first_chunk=False) @@ -247,7 +271,8 @@ class TestDupUp3D: assert out_trimmed.shape[1] == 5 def test_no_temporal_first_chunk_noop(self): - from mlx_video.models.wan.vae22 import DupUp3D + from mlx_video.models.wan_2.vae22 import DupUp3D + up = DupUp3D(8, 4, factor_t=1, factor_s=2) x = mx.random.normal((1, 3, 4, 4, 8)) out_normal = up(x, first_chunk=False) @@ -261,7 +286,8 @@ class TestVAE22Resample: """Tests for vae22.Resample (spatial/temporal upsampling).""" def test_upsample2d_shape(self): - from mlx_video.models.wan.vae22 import Resample + from mlx_video.models.wan_2.vae22 import Resample + r = Resample(8, "upsample2d") r.resample_weight = mx.random.normal(r.resample_weight.shape) * 0.01 x = mx.random.normal((1, 2, 4, 4, 8)) @@ -270,7 +296,8 @@ class TestVAE22Resample: assert out.shape == (1, 2, 8, 8, 8) # 2x spatial, same temporal def test_upsample3d_shape(self): - from mlx_video.models.wan.vae22 import Resample + from mlx_video.models.wan_2.vae22 import Resample + r = Resample(8, "upsample3d") r.resample_weight = mx.random.normal(r.resample_weight.shape) * 0.01 x = mx.random.normal((1, 2, 4, 4, 8)) @@ -279,7 +306,8 @@ class TestVAE22Resample: assert out.shape == (1, 4, 8, 8, 8) # 2x spatial + 2x temporal def test_upsample3d_first_chunk(self): - from mlx_video.models.wan.vae22 import Resample + from mlx_video.models.wan_2.vae22 import Resample + r = Resample(8, "upsample3d") r.resample_weight = mx.random.normal(r.resample_weight.shape) * 0.01 x = mx.random.normal((1, 2, 4, 4, 8)) @@ -290,7 +318,8 @@ class TestVAE22Resample: def test_upsample3d_first_chunk_single_frame(self): """Single-frame input with first_chunk: no temporal upsample.""" - from mlx_video.models.wan.vae22 import Resample + from mlx_video.models.wan_2.vae22 import Resample + r = Resample(8, "upsample3d") r.resample_weight = mx.random.normal(r.resample_weight.shape) * 0.01 x = mx.random.normal((1, 1, 4, 4, 8)) @@ -307,7 +336,8 @@ class TestVAE22Resample: We verify this by checking that the first output frame depends only on the first input frame (not on time_conv parameters). """ - from mlx_video.models.wan.vae22 import Resample + from mlx_video.models.wan_2.vae22 import Resample + C = 8 r = Resample(C, "upsample3d") # Set time_conv weights to large values so its effect is detectable @@ -334,15 +364,17 @@ class TestVAE22Resample: # Compare first output frame to reference first_out = out[:, 0:1].reshape(1, out.shape[2], out.shape[3], C) mx.eval(first_out) - assert mx.allclose(first_out, ref, atol=1e-5).item(), \ - "First frame should bypass time_conv and match spatial-only upsample" + assert mx.allclose( + first_out, ref, atol=1e-5 + ).item(), "First frame should bypass time_conv and match spatial-only upsample" class TestVAE22ResidualBlock: """Tests for vae22.ResidualBlock.""" def test_same_dim(self): - from mlx_video.models.wan.vae22 import ResidualBlock + from mlx_video.models.wan_2.vae22 import ResidualBlock + block = ResidualBlock(8, 8) x = mx.random.normal((1, 2, 4, 4, 8)) out = block(x) @@ -350,7 +382,8 @@ class TestVAE22ResidualBlock: assert out.shape == (1, 2, 4, 4, 8) def test_different_dim(self): - from mlx_video.models.wan.vae22 import ResidualBlock + from mlx_video.models.wan_2.vae22 import ResidualBlock + block = ResidualBlock(8, 16) x = mx.random.normal((1, 2, 4, 4, 8)) out = block(x) @@ -358,12 +391,14 @@ class TestVAE22ResidualBlock: assert out.shape == (1, 2, 4, 4, 16) def test_shortcut_when_dims_differ(self): - from mlx_video.models.wan.vae22 import ResidualBlock + from mlx_video.models.wan_2.vae22 import ResidualBlock + block = ResidualBlock(8, 16) assert block.shortcut is not None def test_no_shortcut_same_dim(self): - from mlx_video.models.wan.vae22 import ResidualBlock + from mlx_video.models.wan_2.vae22 import ResidualBlock + block = ResidualBlock(8, 8) assert block.shortcut is None @@ -373,7 +408,8 @@ class TestResidualBlockLayers: def test_layer_names_no_underscore_prefix(self): """Layer names must NOT start with underscore (MLX ignores them).""" - from mlx_video.models.wan.vae22 import ResidualBlockLayers + from mlx_video.models.wan_2.vae22 import ResidualBlockLayers + block = ResidualBlockLayers(8, 8) params = dict(block.parameters()) # All param keys should use layer_N, not _layer_N @@ -381,7 +417,8 @@ class TestResidualBlockLayers: assert not key.startswith("_"), f"Parameter {key} starts with underscore" def test_has_expected_layers(self): - from mlx_video.models.wan.vae22 import ResidualBlockLayers + from mlx_video.models.wan_2.vae22 import ResidualBlockLayers + block = ResidualBlockLayers(8, 16) assert hasattr(block, "layer_0") # first RMS_norm assert hasattr(block, "layer_2") # first CausalConv3d @@ -389,7 +426,8 @@ class TestResidualBlockLayers: assert hasattr(block, "layer_6") # second CausalConv3d def test_forward_shape(self): - from mlx_video.models.wan.vae22 import ResidualBlockLayers + from mlx_video.models.wan_2.vae22 import ResidualBlockLayers + block = ResidualBlockLayers(8, 16) x = mx.random.normal((1, 2, 4, 4, 8)) out = block(x) @@ -401,7 +439,8 @@ class TestVAE22AttentionBlock: """Tests for vae22.AttentionBlock (per-frame 2D self-attention).""" def test_output_shape(self): - from mlx_video.models.wan.vae22 import AttentionBlock + from mlx_video.models.wan_2.vae22 import AttentionBlock + block = AttentionBlock(16) block.to_qkv_weight = mx.random.normal(block.to_qkv_weight.shape) * 0.01 block.proj_weight = mx.random.normal(block.proj_weight.shape) * 0.01 @@ -411,7 +450,8 @@ class TestVAE22AttentionBlock: assert out.shape == (1, 2, 4, 4, 16) def test_residual_connection(self): - from mlx_video.models.wan.vae22 import AttentionBlock + from mlx_video.models.wan_2.vae22 import AttentionBlock + block = AttentionBlock(8) block.to_qkv_weight = mx.zeros(block.to_qkv_weight.shape) block.proj_weight = mx.zeros(block.proj_weight.shape) @@ -426,7 +466,8 @@ class TestHead22: """Tests for vae22.Head22 output head.""" def test_output_shape(self): - from mlx_video.models.wan.vae22 import Head22 + from mlx_video.models.wan_2.vae22 import Head22 + head = Head22(16, out_channels=12) x = mx.random.normal((1, 2, 4, 4, 16)) out = head(x) @@ -435,7 +476,8 @@ class TestHead22: def test_layer_names_no_underscore(self): """Head layers must not use underscore prefix.""" - from mlx_video.models.wan.vae22 import Head22 + from mlx_video.models.wan_2.vae22 import Head22 + head = Head22(8) assert hasattr(head, "layer_0") # RMS_norm assert hasattr(head, "layer_2") # CausalConv3d @@ -448,14 +490,16 @@ class TestUnpatchify: """Tests for vae22._unpatchify.""" def test_basic_shape(self): - from mlx_video.models.wan.vae22 import _unpatchify + from mlx_video.models.wan_2.vae22 import _unpatchify + x = mx.random.normal((1, 2, 4, 4, 12)) # 12 = 3 * 2 * 2 out = _unpatchify(x, patch_size=2) mx.eval(out) assert out.shape == (1, 2, 8, 8, 3) def test_patch_size_1_noop(self): - from mlx_video.models.wan.vae22 import _unpatchify + from mlx_video.models.wan_2.vae22 import _unpatchify + x = mx.random.normal((1, 2, 4, 4, 3)) out = _unpatchify(x, patch_size=1) mx.eval(out) @@ -463,7 +507,8 @@ class TestUnpatchify: def test_preserves_content(self): """Unpatchify should be a lossless rearrangement.""" - from mlx_video.models.wan.vae22 import _unpatchify + from mlx_video.models.wan_2.vae22 import _unpatchify + x = mx.arange(48).reshape(1, 1, 2, 2, 12).astype(mx.float32) out = _unpatchify(x, patch_size=2) mx.eval(out) @@ -476,24 +521,32 @@ class TestDenormalizeLatents: """Tests for vae22.denormalize_latents.""" def test_output_shape(self): - from mlx_video.models.wan.vae22 import denormalize_latents + from mlx_video.models.wan_2.vae22 import denormalize_latents + z = mx.random.normal((1, 2, 4, 4, 48)) out = denormalize_latents(z) mx.eval(out) assert out.shape == (1, 2, 4, 4, 48) def test_custom_mean_std(self): - from mlx_video.models.wan.vae22 import denormalize_latents + from mlx_video.models.wan_2.vae22 import denormalize_latents + z = mx.ones((1, 1, 1, 1, 4)) mean = mx.array([1.0, 2.0, 3.0, 4.0]) std = mx.array([0.5, 0.5, 0.5, 0.5]) out = denormalize_latents(z, mean=mean, std=std) mx.eval(out) # z * std + mean = 1*0.5 + [1,2,3,4] = [1.5, 2.5, 3.5, 4.5] - np.testing.assert_allclose(np.array(out).flatten(), [1.5, 2.5, 3.5, 4.5], atol=1e-5) + np.testing.assert_allclose( + np.array(out).flatten(), [1.5, 2.5, 3.5, 4.5], atol=1e-5 + ) def test_uses_default_constants(self): - from mlx_video.models.wan.vae22 import VAE22_MEAN, VAE22_STD, denormalize_latents + from mlx_video.models.wan_2.vae22 import ( + VAE22_MEAN, + denormalize_latents, + ) + # Should not raise with default constants z = mx.zeros((1, 1, 1, 1, 48)) out = denormalize_latents(z) @@ -510,13 +563,15 @@ class TestVAE22NormConstants: """Tests for VAE22_MEAN and VAE22_STD constants.""" def test_dimensions(self): - from mlx_video.models.wan.vae22 import VAE22_MEAN, VAE22_STD + from mlx_video.models.wan_2.vae22 import VAE22_MEAN, VAE22_STD + mx.eval(VAE22_MEAN, VAE22_STD) assert VAE22_MEAN.shape == (48,) assert VAE22_STD.shape == (48,) def test_std_positive(self): - from mlx_video.models.wan.vae22 import VAE22_STD + from mlx_video.models.wan_2.vae22 import VAE22_STD + mx.eval(VAE22_STD) assert (np.array(VAE22_STD) > 0).all() @@ -526,7 +581,8 @@ class TestWan22VAEDecoder: def test_output_shape_small(self): """Tiny decoder should produce correct spatial/temporal output.""" - from mlx_video.models.wan.vae22 import Wan22VAEDecoder + from mlx_video.models.wan_2.vae22 import Wan22VAEDecoder + # Use very small dims to keep test fast dec = Wan22VAEDecoder(z_dim=4, dim=8, dec_dim=8) # Latent: [B=1, T=3, H=2, W=2, C=4] @@ -541,7 +597,8 @@ class TestWan22VAEDecoder: assert np.array(out).max() <= 1.0 def test_output_clipped(self): - from mlx_video.models.wan.vae22 import Wan22VAEDecoder + from mlx_video.models.wan_2.vae22 import Wan22VAEDecoder + dec = Wan22VAEDecoder(z_dim=4, dim=8, dec_dim=8) z = mx.random.normal((1, 2, 2, 2, 4)) * 10.0 # large values out = dec(z) @@ -554,7 +611,8 @@ class TestSanitizeWan22VAEWeights: """Tests for vae22.sanitize_wan22_vae_weights.""" def test_skip_encoder(self): - from mlx_video.models.wan.vae22 import sanitize_wan22_vae_weights + from mlx_video.models.wan_2.vae22 import sanitize_wan22_vae_weights + weights = { "encoder.layer.weight": mx.zeros((4,)), "conv1.weight": mx.zeros((4,)), @@ -566,7 +624,8 @@ class TestSanitizeWan22VAEWeights: assert "decoder.conv1.bias" in out def test_sequential_index_remapping(self): - from mlx_video.models.wan.vae22 import sanitize_wan22_vae_weights + from mlx_video.models.wan_2.vae22 import sanitize_wan22_vae_weights + weights = { "decoder.upsamples.0.upsamples.0.residual.0.gamma": mx.ones((8,)), "decoder.upsamples.0.upsamples.0.residual.6.bias": mx.zeros((8,)), @@ -580,7 +639,8 @@ class TestSanitizeWan22VAEWeights: assert "decoder.head.layer_2.bias" in out def test_resample_conv_remapping(self): - from mlx_video.models.wan.vae22 import sanitize_wan22_vae_weights + from mlx_video.models.wan_2.vae22 import sanitize_wan22_vae_weights + weights = { "decoder.upsamples.1.upsamples.3.resample.1.weight": mx.zeros((8, 8, 3, 3)), "decoder.upsamples.1.upsamples.3.resample.1.bias": mx.zeros((8,)), @@ -590,7 +650,8 @@ class TestSanitizeWan22VAEWeights: assert "decoder.upsamples.1.upsamples.3.resample_bias" in out def test_attention_remapping(self): - from mlx_video.models.wan.vae22 import sanitize_wan22_vae_weights + from mlx_video.models.wan_2.vae22 import sanitize_wan22_vae_weights + weights = { "decoder.middle.1.to_qkv.weight": mx.zeros((24, 8, 1, 1)), "decoder.middle.1.to_qkv.bias": mx.zeros((24,)), @@ -604,7 +665,8 @@ class TestSanitizeWan22VAEWeights: assert "decoder.middle.1.proj_bias" in out def test_conv3d_transpose(self): - from mlx_video.models.wan.vae22 import sanitize_wan22_vae_weights + from mlx_video.models.wan_2.vae22 import sanitize_wan22_vae_weights + # Conv3d weight: [O, I, D, H, W] → [O, D, H, W, I] w = mx.zeros((16, 8, 3, 3, 3)) weights = {"decoder.conv1.weight": w} @@ -612,7 +674,8 @@ class TestSanitizeWan22VAEWeights: assert out["decoder.conv1.weight"].shape == (16, 3, 3, 3, 8) def test_conv2d_transpose(self): - from mlx_video.models.wan.vae22 import sanitize_wan22_vae_weights + from mlx_video.models.wan_2.vae22 import sanitize_wan22_vae_weights + # Conv2d weight: [O, I, H, W] → [O, H, W, I] w = mx.zeros((8, 8, 3, 3)) weights = {"decoder.upsamples.0.upsamples.2.resample.1.weight": w} @@ -621,7 +684,8 @@ class TestSanitizeWan22VAEWeights: assert out[key].shape == (8, 3, 3, 8) def test_gamma_squeeze(self): - from mlx_video.models.wan.vae22 import sanitize_wan22_vae_weights + from mlx_video.models.wan_2.vae22 import sanitize_wan22_vae_weights + # gamma: (dim, 1, 1, 1) → (dim,) w = mx.ones((16, 1, 1, 1)) weights = {"decoder.upsamples.0.upsamples.0.residual.0.gamma": w} @@ -634,8 +698,11 @@ class TestUpResidualBlock: """Tests for vae22.Up_ResidualBlock.""" def test_no_upsample(self): - from mlx_video.models.wan.vae22 import Up_ResidualBlock - block = Up_ResidualBlock(8, 8, num_res_blocks=1, temperal_upsample=False, up_flag=False) + from mlx_video.models.wan_2.vae22 import Up_ResidualBlock + + block = Up_ResidualBlock( + 8, 8, num_res_blocks=1, temperal_upsample=False, up_flag=False + ) x = mx.random.normal((1, 2, 4, 4, 8)) out = block(x) mx.eval(out) @@ -643,8 +710,11 @@ class TestUpResidualBlock: assert out.shape == (1, 2, 4, 4, 8) def test_spatial_upsample(self): - from mlx_video.models.wan.vae22 import Up_ResidualBlock - block = Up_ResidualBlock(8, 4, num_res_blocks=1, temperal_upsample=False, up_flag=True) + from mlx_video.models.wan_2.vae22 import Up_ResidualBlock + + block = Up_ResidualBlock( + 8, 4, num_res_blocks=1, temperal_upsample=False, up_flag=True + ) x = mx.random.normal((1, 2, 4, 4, 8)) out = block(x) mx.eval(out) @@ -652,8 +722,11 @@ class TestUpResidualBlock: assert out.shape == (1, 2, 8, 8, 4) def test_spatial_temporal_upsample(self): - from mlx_video.models.wan.vae22 import Up_ResidualBlock - block = Up_ResidualBlock(8, 4, num_res_blocks=1, temperal_upsample=True, up_flag=True) + from mlx_video.models.wan_2.vae22 import Up_ResidualBlock + + block = Up_ResidualBlock( + 8, 4, num_res_blocks=1, temperal_upsample=True, up_flag=True + ) x = mx.random.normal((1, 2, 4, 4, 8)) out = block(x) mx.eval(out) @@ -665,7 +738,7 @@ class TestPatchify: """Tests for _patchify and _unpatchify round-trip.""" def test_roundtrip(self): - from mlx_video.models.wan.vae22 import _patchify, _unpatchify + from mlx_video.models.wan_2.vae22 import _patchify, _unpatchify x = mx.random.normal((1, 1, 64, 64, 3)) p = _patchify(x, patch_size=2) @@ -675,7 +748,7 @@ class TestPatchify: assert float(mx.abs(x - back).max()) == 0.0 def test_identity_patch_1(self): - from mlx_video.models.wan.vae22 import _patchify, _unpatchify + from mlx_video.models.wan_2.vae22 import _patchify, _unpatchify x = mx.random.normal((1, 2, 8, 8, 3)) assert _patchify(x, patch_size=1).shape == x.shape @@ -686,7 +759,7 @@ class TestAvgDown3D: """Tests for AvgDown3D downsampling.""" def test_spatial_only(self): - from mlx_video.models.wan.vae22 import AvgDown3D + from mlx_video.models.wan_2.vae22 import AvgDown3D down = AvgDown3D(8, 16, factor_t=1, factor_s=2) x = mx.random.normal((1, 2, 8, 8, 8)) @@ -695,7 +768,7 @@ class TestAvgDown3D: assert out.shape == (1, 2, 4, 4, 16) def test_temporal_and_spatial(self): - from mlx_video.models.wan.vae22 import AvgDown3D + from mlx_video.models.wan_2.vae22 import AvgDown3D down = AvgDown3D(8, 16, factor_t=2, factor_s=2) x = mx.random.normal((1, 4, 8, 8, 8)) @@ -704,7 +777,7 @@ class TestAvgDown3D: assert out.shape == (1, 2, 4, 4, 16) def test_single_frame(self): - from mlx_video.models.wan.vae22 import AvgDown3D + from mlx_video.models.wan_2.vae22 import AvgDown3D down = AvgDown3D(8, 8, factor_t=2, factor_s=2) x = mx.random.normal((1, 1, 8, 8, 8)) @@ -718,27 +791,33 @@ class TestDownResidualBlock: """Tests for Down_ResidualBlock.""" def test_no_downsample(self): - from mlx_video.models.wan.vae22 import Down_ResidualBlock + from mlx_video.models.wan_2.vae22 import Down_ResidualBlock - block = Down_ResidualBlock(8, 8, num_res_blocks=1, temperal_downsample=False, down_flag=False) + block = Down_ResidualBlock( + 8, 8, num_res_blocks=1, temperal_downsample=False, down_flag=False + ) x = mx.random.normal((1, 2, 8, 8, 8)) out = block(x) mx.eval(out) assert out.shape == (1, 2, 8, 8, 8) def test_spatial_downsample(self): - from mlx_video.models.wan.vae22 import Down_ResidualBlock + from mlx_video.models.wan_2.vae22 import Down_ResidualBlock - block = Down_ResidualBlock(8, 16, num_res_blocks=1, temperal_downsample=False, down_flag=True) + block = Down_ResidualBlock( + 8, 16, num_res_blocks=1, temperal_downsample=False, down_flag=True + ) x = mx.random.normal((1, 2, 8, 8, 8)) out = block(x) mx.eval(out) assert out.shape == (1, 2, 4, 4, 16) def test_spatial_temporal_downsample(self): - from mlx_video.models.wan.vae22 import Down_ResidualBlock + from mlx_video.models.wan_2.vae22 import Down_ResidualBlock - block = Down_ResidualBlock(8, 16, num_res_blocks=1, temperal_downsample=True, down_flag=True) + block = Down_ResidualBlock( + 8, 16, num_res_blocks=1, temperal_downsample=True, down_flag=True + ) x = mx.random.normal((1, 4, 8, 8, 8)) out = block(x) mx.eval(out) @@ -749,7 +828,7 @@ class TestEncoder3d: """Tests for Encoder3d.""" def test_output_shape(self): - from mlx_video.models.wan.vae22 import Encoder3d + from mlx_video.models.wan_2.vae22 import Encoder3d enc = Encoder3d(dim=16, z_dim=8) x = mx.random.normal((1, 1, 16, 16, 12)) @@ -760,7 +839,7 @@ class TestEncoder3d: assert out.shape == (1, 1, 2, 2, 8) def test_multi_frame(self): - from mlx_video.models.wan.vae22 import Encoder3d + from mlx_video.models.wan_2.vae22 import Encoder3d enc = Encoder3d(dim=16, z_dim=8, temperal_downsample=(True, True, False)) x = mx.random.normal((1, 5, 16, 16, 12)) @@ -775,7 +854,7 @@ class TestWan22VAEEncoder: """Tests for Wan22VAEEncoder wrapper.""" def test_output_shape(self): - from mlx_video.models.wan.vae22 import Wan22VAEEncoder + from mlx_video.models.wan_2.vae22 import Wan22VAEEncoder enc = Wan22VAEEncoder(z_dim=48, dim=16) # Input: single image 32×32 (patchify÷2 → 16×16, then 3 spatial ÷8 → 2×2) @@ -786,7 +865,7 @@ class TestWan22VAEEncoder: assert z.shape == (1, 1, 2, 2, 48) def test_full_dim(self): - from mlx_video.models.wan.vae22 import Wan22VAEEncoder + from mlx_video.models.wan_2.vae22 import Wan22VAEEncoder enc = Wan22VAEEncoder(z_dim=48, dim=160) img = mx.random.normal((1, 1, 64, 64, 3)) @@ -801,7 +880,7 @@ class TestNormalizeLatents: """Tests for normalize/denormalize latent roundtrip.""" def test_roundtrip(self): - from mlx_video.models.wan.vae22 import denormalize_latents, normalize_latents + from mlx_video.models.wan_2.vae22 import denormalize_latents, normalize_latents z = mx.random.normal((1, 2, 4, 4, 48)) z_norm = normalize_latents(z) @@ -816,7 +895,8 @@ class TestVAEEncoderTemporalOrder: def test_encoder_temporal_downsample_pattern(self): """Encoder3d with (False, True, True): T=5→5→3→2.""" - from mlx_video.models.wan.vae22 import Encoder3d + from mlx_video.models.wan_2.vae22 import Encoder3d + enc = Encoder3d(dim=16, z_dim=8, temperal_downsample=(False, True, True)) x = mx.random.normal((1, 5, 16, 16, 12)) mx.eval(enc.parameters()) @@ -826,7 +906,8 @@ class TestVAEEncoderTemporalOrder: def test_wrapper_uses_correct_pattern(self): """Wan22VAEEncoder should use (False, True, True) temporal downsample.""" - from mlx_video.models.wan.vae22 import Wan22VAEEncoder, Resample + from mlx_video.models.wan_2.vae22 import Resample, Wan22VAEEncoder + enc = Wan22VAEEncoder(z_dim=48, dim=16) down_blocks = enc.encoder.downsamples found_modes = [] @@ -840,7 +921,8 @@ class TestVAEEncoderTemporalOrder: def test_single_frame_encoder(self): """Single frame (T=1) should work with (False, True, True) pattern.""" - from mlx_video.models.wan.vae22 import Wan22VAEEncoder + from mlx_video.models.wan_2.vae22 import Wan22VAEEncoder + enc = Wan22VAEEncoder(z_dim=48, dim=16) img = mx.random.normal((1, 1, 32, 32, 3)) mx.eval(enc.parameters()) @@ -851,8 +933,11 @@ class TestVAEEncoderTemporalOrder: def test_wrong_order_gives_different_result(self): """(True, True, False) vs (False, True, True) produce different outputs.""" - from mlx_video.models.wan.vae22 import Encoder3d - enc_correct = Encoder3d(dim=16, z_dim=8, temperal_downsample=(False, True, True)) + from mlx_video.models.wan_2.vae22 import Encoder3d + + enc_correct = Encoder3d( + dim=16, z_dim=8, temperal_downsample=(False, True, True) + ) enc_wrong = Encoder3d(dim=16, z_dim=8, temperal_downsample=(True, True, False)) x = mx.random.normal((1, 5, 16, 16, 12)) @@ -878,17 +963,13 @@ class TestVAE21RoundTrip: def test_encode_decode_shape_and_values(self): """Encoder3d → Decoder3d: output shape matches input, values are finite.""" - from mlx_video.models.wan.vae import Decoder3d, Encoder3d + from mlx_video.models.wan_2.vae import Decoder3d, Encoder3d z_dim = 4 dim = 8 # No temporal up/downsampling to keep the test simple - enc = Encoder3d( - dim=dim, z_dim=z_dim, temporal_downsample=[False, False, False] - ) - dec = Decoder3d( - dim=dim, z_dim=z_dim, temporal_upsample=[False, False, False] - ) + enc = Encoder3d(dim=dim, z_dim=z_dim, temporal_downsample=[False, False, False]) + dec = Decoder3d(dim=dim, z_dim=z_dim, temporal_upsample=[False, False, False]) mx.eval(enc.parameters(), dec.parameters()) # [B=1, C=3, T=1, H=8, W=8] @@ -914,7 +995,7 @@ class TestVAE22RoundTrip: def test_encode_decode_shape_and_values(self): """Wan22VAEEncoder → Wan22VAEDecoder: shapes consistent, values in range.""" - from mlx_video.models.wan.vae22 import ( + from mlx_video.models.wan_2.vae22 import ( Wan22VAEDecoder, Wan22VAEEncoder, denormalize_latents, @@ -937,15 +1018,12 @@ class TestVAE22RoundTrip: mx.eval(out) # 3 spatial upsamples(×8) + unpatchify(×2) = ×16 - assert out.shape[0] == 1 # batch - 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