From 3a0da19adbc6a71913866d062372e877135d56fa Mon Sep 17 00:00:00 2001 From: Prince Canuma Date: Mon, 16 Mar 2026 14:50:01 +0100 Subject: [PATCH] Refactor LTX-2 model structure --- mlx_video/__init__.py | 6 +- mlx_video/conditioning/__init__.py | 3 - mlx_video/convert.py | 8 +- mlx_video/generate.py | 2565 +--------------- mlx_video/models/__init__.py | 2 +- mlx_video/models/ltx/__init__.py | 8 - mlx_video/models/ltx/video_vae/__init__.py | 8 - mlx_video/models/ltx_2/__init__.py | 8 + mlx_video/models/{ltx => ltx_2}/adaln.py | 0 mlx_video/models/{ltx => ltx_2}/attention.py | 4 +- .../{ltx => ltx_2}/audio_vae/__init__.py | 0 .../{ltx => ltx_2}/audio_vae/attention.py | 0 .../audio_vae/audio_processor.py | 0 .../{ltx => ltx_2}/audio_vae/audio_vae.py | 4 +- .../audio_vae/causal_conv_2d.py | 0 .../{ltx => ltx_2}/audio_vae/downsample.py | 0 .../{ltx => ltx_2}/audio_vae/normalization.py | 0 .../models/{ltx => ltx_2}/audio_vae/ops.py | 0 .../models/{ltx => ltx_2}/audio_vae/resnet.py | 0 .../{ltx => ltx_2}/audio_vae/upsample.py | 0 .../{ltx => ltx_2}/audio_vae/vocoder.py | 0 .../models/ltx_2/conditioning/__init__.py | 3 + .../{ => models/ltx_2}/conditioning/latent.py | 0 mlx_video/models/{ltx => ltx_2}/config.py | 6 +- mlx_video/models/{ltx => ltx_2}/convert.py | 8 +- .../models/{ltx => ltx_2}/feed_forward.py | 0 mlx_video/models/ltx_2/generate.py | 2566 +++++++++++++++++ mlx_video/models/{ltx => ltx_2}/ltx.py | 10 +- mlx_video/{ => models/ltx_2}/postprocess.py | 0 .../prompts/gemma_i2v_system_prompt.txt | 0 .../prompts/gemma_t2v_system_prompt.txt | 0 mlx_video/models/{ltx => ltx_2}/rope.py | 2 +- mlx_video/{ => models/ltx_2}/samplers.py | 0 .../models/{ltx => ltx_2}/text_encoder.py | 2 +- .../models/{ltx => ltx_2}/text_projection.py | 0 .../models/{ltx => ltx_2}/transformer.py | 6 +- mlx_video/models/{ltx => ltx_2}/upsampler.py | 0 mlx_video/models/ltx_2/video_vae/__init__.py | 8 + .../{ltx => ltx_2}/video_vae/convolution.py | 0 .../{ltx => ltx_2}/video_vae/decoder.py | 8 +- .../{ltx => ltx_2}/video_vae/encoder.py | 2 +- .../models/{ltx => ltx_2}/video_vae/ops.py | 0 .../models/{ltx => ltx_2}/video_vae/resnet.py | 2 +- .../{ltx => ltx_2}/video_vae/sampling.py | 2 +- .../models/{ltx => ltx_2}/video_vae/tiling.py | 0 .../{ltx => ltx_2}/video_vae/video_vae.py | 12 +- mlx_video/text_projection.py | 32 - tests/test_rope.py | 4 +- tests/test_vae_streaming.py | 4 +- uv.lock | 1954 ++++++++----- 50 files changed, 3882 insertions(+), 3365 deletions(-) delete mode 100644 mlx_video/conditioning/__init__.py delete mode 100644 mlx_video/models/ltx/__init__.py delete mode 100644 mlx_video/models/ltx/video_vae/__init__.py create mode 100644 mlx_video/models/ltx_2/__init__.py rename mlx_video/models/{ltx => ltx_2}/adaln.py (100%) rename mlx_video/models/{ltx => ltx_2}/attention.py (97%) rename mlx_video/models/{ltx => ltx_2}/audio_vae/__init__.py (100%) rename mlx_video/models/{ltx => ltx_2}/audio_vae/attention.py (100%) rename mlx_video/models/{ltx => ltx_2}/audio_vae/audio_processor.py (100%) rename mlx_video/models/{ltx => ltx_2}/audio_vae/audio_vae.py (99%) rename mlx_video/models/{ltx => ltx_2}/audio_vae/causal_conv_2d.py (100%) rename mlx_video/models/{ltx => ltx_2}/audio_vae/downsample.py (100%) rename mlx_video/models/{ltx => ltx_2}/audio_vae/normalization.py (100%) rename mlx_video/models/{ltx => ltx_2}/audio_vae/ops.py (100%) rename mlx_video/models/{ltx => ltx_2}/audio_vae/resnet.py (100%) rename mlx_video/models/{ltx => ltx_2}/audio_vae/upsample.py (100%) rename mlx_video/models/{ltx => ltx_2}/audio_vae/vocoder.py (100%) create mode 100644 mlx_video/models/ltx_2/conditioning/__init__.py rename mlx_video/{ => models/ltx_2}/conditioning/latent.py (100%) rename mlx_video/models/{ltx => ltx_2}/config.py (98%) rename mlx_video/models/{ltx => ltx_2}/convert.py (98%) rename mlx_video/models/{ltx => ltx_2}/feed_forward.py (100%) create mode 100644 mlx_video/models/ltx_2/generate.py rename mlx_video/models/{ltx => ltx_2}/ltx.py (98%) rename mlx_video/{ => models/ltx_2}/postprocess.py (100%) rename mlx_video/models/{ltx => ltx_2}/prompts/gemma_i2v_system_prompt.txt (100%) rename mlx_video/models/{ltx => ltx_2}/prompts/gemma_t2v_system_prompt.txt (100%) rename mlx_video/models/{ltx => ltx_2}/rope.py (99%) rename mlx_video/{ => models/ltx_2}/samplers.py (100%) rename mlx_video/models/{ltx => ltx_2}/text_encoder.py (99%) rename mlx_video/models/{ltx => ltx_2}/text_projection.py (100%) rename mlx_video/models/{ltx => ltx_2}/transformer.py (98%) rename mlx_video/models/{ltx => ltx_2}/upsampler.py (100%) create mode 100644 mlx_video/models/ltx_2/video_vae/__init__.py rename mlx_video/models/{ltx => ltx_2}/video_vae/convolution.py (100%) rename mlx_video/models/{ltx => ltx_2}/video_vae/decoder.py (98%) rename mlx_video/models/{ltx => ltx_2}/video_vae/encoder.py (94%) rename mlx_video/models/{ltx => ltx_2}/video_vae/ops.py (100%) rename mlx_video/models/{ltx => ltx_2}/video_vae/resnet.py (98%) rename mlx_video/models/{ltx => ltx_2}/video_vae/sampling.py (99%) rename mlx_video/models/{ltx => ltx_2}/video_vae/tiling.py (100%) rename mlx_video/models/{ltx => ltx_2}/video_vae/video_vae.py (97%) delete mode 100644 mlx_video/text_projection.py diff --git a/mlx_video/__init__.py b/mlx_video/__init__.py index 0256f7b..cea80ec 100644 --- a/mlx_video/__init__.py +++ b/mlx_video/__init__.py @@ -1,4 +1,4 @@ -from mlx_video.models.ltx import LTXModel, LTXModelConfig +from mlx_video.models.ltx_2 import LTXModel, LTXModelConfig from mlx_video.convert import ( load_transformer_weights, load_vae_weights, @@ -9,7 +9,7 @@ from mlx_video.convert import ( ) # Audio VAE components -from mlx_video.models.ltx.audio_vae import ( +from mlx_video.models.ltx_2.audio_vae import ( AudioDecoder, Vocoder, decode_audio, @@ -19,7 +19,7 @@ from mlx_video.models.ltx.audio_vae import ( ) # Conditioning -from mlx_video.conditioning import ( +from mlx_video.models.ltx_2.conditioning import ( VideoConditionByLatentIndex, ) 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 index 1efc97f..2a8d463 100644 --- a/mlx_video/convert.py +++ b/mlx_video/convert.py @@ -7,8 +7,8 @@ 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 +from mlx_video.models.ltx_2.config import LTXModelConfig, LTXModelType +from mlx_video.models.ltx_2.ltx import LTXModel def get_model_path( @@ -639,8 +639,8 @@ def convert_audio_encoder( raw_weights = mx.load(vae_path) # Extract encoder weights and per-channel statistics - from mlx_video.models.ltx.audio_vae import AudioEncoder - from mlx_video.models.ltx.config import AudioEncoderModelConfig + 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" / "config.json" diff --git a/mlx_video/generate.py b/mlx_video/generate.py index d4b415c..fe2c5d7 100644 --- a/mlx_video/generate.py +++ b/mlx_video/generate.py @@ -1,2566 +1,5 @@ -"""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.progress import Progress, SpinnerColumn, TextColumn, BarColumn, TaskProgressColumn, TimeRemainingColumn -from rich.panel import Panel - -# Rich console for styled output -console = Console() - - -from mlx_video.models.ltx.ltx import LTXModel -from mlx_video.models.ltx.transformer import Modality - -from mlx_video.utils import to_denoised, load_image, prepare_image_for_encoding, get_model_path -from mlx_video.models.ltx.video_vae.decoder import VideoDecoder -from mlx_video.models.ltx.video_vae import VideoEncoder -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 - - -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.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.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.rope import precompute_freqs_cis - from mlx_video.samplers import get_res2s_coefficients, sde_noise_step, get_new_noise - - 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.audio_vae import AudioDecoder - - decoder = AudioDecoder.from_pretrained(model_path / "audio_vae") - - 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.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 = 0.0, - stg_blocks: Optional[list] = None, - modality_scale: float = 1.0, - 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, -): - """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) - - # Calculate latent dimensions - if is_two_stage: - stage1_h, stage1_w = height // 2 // 32, width // 2 // 32 - stage2_h, stage2_w = height // 32, width // 32 - 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.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.audio_vae.audio_processor import load_audio, ensure_stereo, waveform_to_mel - from mlx_video.convert import convert_audio_encoder - from mlx_video.models.ltx.audio_vae import AudioEncoder - - 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") - - 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) - - 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() - console.print("[green]✓[/] VAE encoder loaded and image encoded") - - # Stage 1 - console.print(f"\n[bold yellow]⚡ Stage 1:[/] Generating at {width//2}x{height//2} (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("[magenta]🔍 Upsampling latents 2x...[/]", spinner="dots"): - upscaler_files = sorted(model_path.glob("*spatial-upscaler-x2*.safetensors")) - if not upscaler_files: - raise FileNotFoundError(f"No spatial upscaler found in {model_path}") - upsampler = load_upsampler(str(upscaler_files[0])) - 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 {width}x{height} (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") - - 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) - - 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() - console.print("[green]✓[/] VAE encoder loaded and image encoded") - - # Stage 1: Dev denoising at half 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 {width//2}x{height//2} ({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 2x - with console.status("[magenta]🔍 Upsampling latents 2x...[/]", spinner="dots"): - upscaler_files = sorted(model_path.glob("*spatial-upscaler-x2*.safetensors")) - if not upscaler_files: - raise FileNotFoundError(f"No spatial upscaler found in {model_path}") - upsampler = load_upsampler(str(upscaler_files[0])) - 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 - 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 - - # 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") - - 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) - - 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() - 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 half 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 {width//2}x{height//2} ({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=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 2x - with console.status("[magenta]Upsampling latents 2x...[/]", spinner="dots"): - upscaler_files = sorted(model_path.glob("*spatial-upscaler-x2*.safetensors")) - if not upscaler_files: - raise FileNotFoundError(f"No spatial upscaler found in {model_path}") - upsampler = load_upsampler(str(upscaler_files[0])) - 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 {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 - 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=0.0, help="STG (Spatiotemporal Guidance) scale (default 0.0 = disabled, PyTorch default: 1.0)") - 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=1.0, help="Cross-modal guidance scale (default 1.0 = disabled, PyTorch default: 3.0)") - 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)") - 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, - ) - +"""Entry point stub — delegates to mlx_video.models.ltx_2.generate.""" +from mlx_video.models.ltx_2.generate import main, generate_video if __name__ == "__main__": main() diff --git a/mlx_video/models/__init__.py b/mlx_video/models/__init__.py index 923325a..1d811e5 100644 --- a/mlx_video/models/__init__.py +++ b/mlx_video/models/__init__.py @@ -1,2 +1,2 @@ -from mlx_video.models.ltx import LTXModel, LTXModelConfig +from mlx_video.models.ltx_2 import LTXModel, LTXModelConfig 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/video_vae/__init__.py b/mlx_video/models/ltx/video_vae/__init__.py deleted file mode 100644 index 3233b75..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 -from mlx_video.models.ltx.video_vae.encoder import encode_image -from mlx_video.models.ltx.video_vae.decoder import LTX2VideoDecoder, VideoDecoder -from mlx_video.models.ltx.video_vae.tiling import ( - TilingConfig, - SpatialTilingConfig, - TemporalTilingConfig, -) diff --git a/mlx_video/models/ltx_2/__init__.py b/mlx_video/models/ltx_2/__init__.py new file mode 100644 index 0000000..7e58251 --- /dev/null +++ b/mlx_video/models/ltx_2/__init__.py @@ -0,0 +1,8 @@ + +from mlx_video.models.ltx_2.config import ( + LTXModelConfig, + TransformerConfig, + LTXModelType, +) +from mlx_video.models.ltx_2.ltx import LTXModel, X0Model +from mlx_video.models.ltx_2.audio_vae import AudioDecoder, Vocoder, decode_audio diff --git a/mlx_video/models/ltx/adaln.py b/mlx_video/models/ltx_2/adaln.py similarity index 100% rename from mlx_video/models/ltx/adaln.py rename to mlx_video/models/ltx_2/adaln.py diff --git a/mlx_video/models/ltx/attention.py b/mlx_video/models/ltx_2/attention.py similarity index 97% rename from mlx_video/models/ltx/attention.py rename to mlx_video/models/ltx_2/attention.py index 99e249c..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( diff --git a/mlx_video/models/ltx/audio_vae/__init__.py b/mlx_video/models/ltx_2/audio_vae/__init__.py similarity index 100% rename from mlx_video/models/ltx/audio_vae/__init__.py rename to mlx_video/models/ltx_2/audio_vae/__init__.py diff --git a/mlx_video/models/ltx/audio_vae/attention.py b/mlx_video/models/ltx_2/audio_vae/attention.py similarity index 100% rename from mlx_video/models/ltx/audio_vae/attention.py rename to mlx_video/models/ltx_2/audio_vae/attention.py diff --git a/mlx_video/models/ltx/audio_vae/audio_processor.py b/mlx_video/models/ltx_2/audio_vae/audio_processor.py similarity index 100% rename from mlx_video/models/ltx/audio_vae/audio_processor.py rename to mlx_video/models/ltx_2/audio_vae/audio_processor.py diff --git a/mlx_video/models/ltx/audio_vae/audio_vae.py b/mlx_video/models/ltx_2/audio_vae/audio_vae.py similarity index 99% rename from mlx_video/models/ltx/audio_vae/audio_vae.py rename to mlx_video/models/ltx_2/audio_vae/audio_vae.py index 29eb7e3..e9954ed 100644 --- a/mlx_video/models/ltx/audio_vae/audio_vae.py +++ b/mlx_video/models/ltx_2/audio_vae/audio_vae.py @@ -168,7 +168,7 @@ class AudioEncoder(nn.Module): @classmethod def from_pretrained(cls, model_path: Path) -> "AudioEncoder": """Load audio encoder from pretrained weights.""" - from mlx_video.models.ltx.config import AudioEncoderModelConfig + from mlx_video.models.ltx_2.config import AudioEncoderModelConfig import json model_path = Path(model_path) @@ -380,7 +380,7 @@ class AudioDecoder(nn.Module): @classmethod def from_pretrained(cls, model_path: Path) -> "AudioDecoder": """Load audio VAE decoder from pretrained model.""" - from mlx_video.models.ltx.config import AudioDecoderModelConfig + from mlx_video.models.ltx_2.config import AudioDecoderModelConfig import json config = AudioDecoderModelConfig.from_dict(json.load(open(model_path / "config.json"))) 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 100% 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 diff --git a/mlx_video/models/ltx/audio_vae/downsample.py b/mlx_video/models/ltx_2/audio_vae/downsample.py similarity index 100% rename from mlx_video/models/ltx/audio_vae/downsample.py rename to mlx_video/models/ltx_2/audio_vae/downsample.py diff --git a/mlx_video/models/ltx/audio_vae/normalization.py b/mlx_video/models/ltx_2/audio_vae/normalization.py similarity index 100% rename from mlx_video/models/ltx/audio_vae/normalization.py rename to mlx_video/models/ltx_2/audio_vae/normalization.py diff --git a/mlx_video/models/ltx/audio_vae/ops.py b/mlx_video/models/ltx_2/audio_vae/ops.py similarity index 100% rename from mlx_video/models/ltx/audio_vae/ops.py rename to mlx_video/models/ltx_2/audio_vae/ops.py diff --git a/mlx_video/models/ltx/audio_vae/resnet.py b/mlx_video/models/ltx_2/audio_vae/resnet.py similarity index 100% rename from mlx_video/models/ltx/audio_vae/resnet.py rename to mlx_video/models/ltx_2/audio_vae/resnet.py diff --git a/mlx_video/models/ltx/audio_vae/upsample.py b/mlx_video/models/ltx_2/audio_vae/upsample.py similarity index 100% rename from mlx_video/models/ltx/audio_vae/upsample.py rename to mlx_video/models/ltx_2/audio_vae/upsample.py diff --git a/mlx_video/models/ltx/audio_vae/vocoder.py b/mlx_video/models/ltx_2/audio_vae/vocoder.py similarity index 100% rename from mlx_video/models/ltx/audio_vae/vocoder.py rename to mlx_video/models/ltx_2/audio_vae/vocoder.py 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..3f8516e --- /dev/null +++ b/mlx_video/models/ltx_2/conditioning/__init__.py @@ -0,0 +1,3 @@ +"""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 100% rename from mlx_video/conditioning/latent.py rename to mlx_video/models/ltx_2/conditioning/latent.py diff --git a/mlx_video/models/ltx/config.py b/mlx_video/models/ltx_2/config.py similarity index 98% rename from mlx_video/models/ltx/config.py rename to mlx_video/models/ltx_2/config.py index 57c7f46..4692d45 100644 --- a/mlx_video/models/ltx/config.py +++ b/mlx_video/models/ltx_2/config.py @@ -355,9 +355,9 @@ class VideoEncoderModelConfig(BaseModelConfig): ]) def __post_init__(self): - from mlx_video.models.ltx.video_vae.resnet import NormLayerType - from mlx_video.models.ltx.video_vae.video_vae import LogVarianceType - from mlx_video.models.ltx.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 + from mlx_video.models.ltx_2.video_vae.convolution import PaddingModeType if self.norm_layer is None: self.norm_layer = NormLayerType.PIXEL_NORM diff --git a/mlx_video/models/ltx/convert.py b/mlx_video/models/ltx_2/convert.py similarity index 98% rename from mlx_video/models/ltx/convert.py rename to mlx_video/models/ltx_2/convert.py index eb4c532..dadbcdd 100644 --- a/mlx_video/models/ltx/convert.py +++ b/mlx_video/models/ltx_2/convert.py @@ -26,14 +26,14 @@ or Lightricks/LTX-2.3/ltx-2.3-22b-distilled.safetensors) to the modular director Usage: # From HF repo ID - python -m mlx_video.models.ltx.convert --source Lightricks/LTX-2 --output LTX-2-distilled --variant distilled - python -m mlx_video.models.ltx.convert --source Lightricks/LTX-2.3 --output LTX-2.3-distilled --variant distilled + 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.convert --source ./Lightricks-LTX-2/ --output LTX-2-distilled --variant distilled + 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.convert --source ./ltx-2-19b-distilled.safetensors --output LTX-2-distilled --variant distilled + python -m mlx_video.models.ltx_2.convert --source ./ltx-2-19b-distilled.safetensors --output LTX-2-distilled --variant distilled """ import argparse 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..2ef7da3 --- /dev/null +++ b/mlx_video/models/ltx_2/generate.py @@ -0,0 +1,2566 @@ +"""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.progress import Progress, SpinnerColumn, TextColumn, BarColumn, TaskProgressColumn, TimeRemainingColumn +from rich.panel import Panel + +# Rich console for styled output +console = Console() + + +from mlx_video.models.ltx_2.ltx import LTXModel +from mlx_video.models.ltx_2.transformer import Modality + +from mlx_video.utils import to_denoised, load_image, prepare_image_for_encoding, get_model_path +from mlx_video.models.ltx_2.video_vae.decoder import VideoDecoder +from mlx_video.models.ltx_2.video_vae import VideoEncoder +from mlx_video.models.ltx_2.video_vae.tiling import TilingConfig +from mlx_video.models.ltx_2.upsampler import load_upsampler, upsample_latents +from mlx_video.models.ltx_2.conditioning import VideoConditionByLatentIndex, apply_conditioning +from mlx_video.models.ltx_2.conditioning.latent import LatentState, apply_denoise_mask + + +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_res2s_coefficients, sde_noise_step, get_new_noise + + 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") + + 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 = 0.0, + stg_blocks: Optional[list] = None, + modality_scale: float = 1.0, + 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, +): + """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) + + # Calculate latent dimensions + if is_two_stage: + stage1_h, stage1_w = height // 2 // 32, width // 2 // 32 + stage2_h, stage2_w = height // 32, width // 32 + 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.audio_processor import load_audio, ensure_stereo, waveform_to_mel + from mlx_video.convert import convert_audio_encoder + from mlx_video.models.ltx_2.audio_vae import AudioEncoder + + 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") + + 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) + + 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() + console.print("[green]✓[/] VAE encoder loaded and image encoded") + + # Stage 1 + console.print(f"\n[bold yellow]⚡ Stage 1:[/] Generating at {width//2}x{height//2} (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("[magenta]🔍 Upsampling latents 2x...[/]", spinner="dots"): + upscaler_files = sorted(model_path.glob("*spatial-upscaler-x2*.safetensors")) + if not upscaler_files: + raise FileNotFoundError(f"No spatial upscaler found in {model_path}") + upsampler = load_upsampler(str(upscaler_files[0])) + 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 {width}x{height} (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") + + 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) + + 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() + console.print("[green]✓[/] VAE encoder loaded and image encoded") + + # Stage 1: Dev denoising at half 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 {width//2}x{height//2} ({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 2x + with console.status("[magenta]🔍 Upsampling latents 2x...[/]", spinner="dots"): + upscaler_files = sorted(model_path.glob("*spatial-upscaler-x2*.safetensors")) + if not upscaler_files: + raise FileNotFoundError(f"No spatial upscaler found in {model_path}") + upsampler = load_upsampler(str(upscaler_files[0])) + 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 + 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 + + # 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") + + 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) + + 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() + 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 half 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 {width//2}x{height//2} ({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=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 2x + with console.status("[magenta]Upsampling latents 2x...[/]", spinner="dots"): + upscaler_files = sorted(model_path.glob("*spatial-upscaler-x2*.safetensors")) + if not upscaler_files: + raise FileNotFoundError(f"No spatial upscaler found in {model_path}") + upsampler = load_upsampler(str(upscaler_files[0])) + 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 {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 + 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=0.0, help="STG (Spatiotemporal Guidance) scale (default 0.0 = disabled, PyTorch default: 1.0)") + 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=1.0, help="Cross-modal guidance scale (default 1.0 = disabled, PyTorch default: 3.0)") + 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)") + 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, + ) + + +if __name__ == "__main__": + main() diff --git a/mlx_video/models/ltx/ltx.py b/mlx_video/models/ltx_2/ltx.py similarity index 98% rename from mlx_video/models/ltx/ltx.py rename to mlx_video/models/ltx_2/ltx.py index 527e523..18496b8 100644 --- a/mlx_video/models/ltx/ltx.py +++ b/mlx_video/models/ltx_2/ltx.py @@ -3,16 +3,16 @@ from typing import List, Optional, Tuple import mlx.core as mx import mlx.nn as nn from pathlib import Path -from mlx_video.models.ltx.config import ( +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.adaln import AdaLayerNormSingle +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, diff --git a/mlx_video/postprocess.py b/mlx_video/models/ltx_2/postprocess.py similarity index 100% rename from mlx_video/postprocess.py rename to mlx_video/models/ltx_2/postprocess.py 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 99% rename from mlx_video/models/ltx/rope.py rename to mlx_video/models/ltx_2/rope.py index cd2bda4..21de1d4 100644 --- a/mlx_video/models/ltx/rope.py +++ b/mlx_video/models/ltx_2/rope.py @@ -4,7 +4,7 @@ from typing import List, Optional, Tuple import mlx.core as mx -from mlx_video.models.ltx.config import LTXRopeType +from mlx_video.models.ltx_2.config import LTXRopeType def apply_rotary_emb( diff --git a/mlx_video/samplers.py b/mlx_video/models/ltx_2/samplers.py similarity index 100% rename from mlx_video/samplers.py rename to mlx_video/models/ltx_2/samplers.py diff --git a/mlx_video/models/ltx/text_encoder.py b/mlx_video/models/ltx_2/text_encoder.py similarity index 99% rename from mlx_video/models/ltx/text_encoder.py rename to mlx_video/models/ltx_2/text_encoder.py index de95504..c5d7aff 100644 --- a/mlx_video/models/ltx/text_encoder.py +++ b/mlx_video/models/ltx_2/text_encoder.py @@ -15,7 +15,7 @@ from rich.console import Console from rich.progress import Progress, SpinnerColumn, TextColumn, BarColumn, TaskProgressColumn, TimeRemainingColumn from mlx_video.utils import rms_norm, apply_quantization -from mlx_video.models.ltx.rope import apply_interleaved_rotary_emb +from mlx_video.models.ltx_2.rope import apply_interleaved_rotary_emb from mlx_vlm.models.gemma3.language import Gemma3Model from mlx_vlm.models.gemma3.config import TextConfig diff --git a/mlx_video/models/ltx/text_projection.py b/mlx_video/models/ltx_2/text_projection.py similarity index 100% rename from mlx_video/models/ltx/text_projection.py rename to mlx_video/models/ltx_2/text_projection.py diff --git a/mlx_video/models/ltx/transformer.py b/mlx_video/models/ltx_2/transformer.py similarity index 98% rename from mlx_video/models/ltx/transformer.py rename to mlx_video/models/ltx_2/transformer.py index e4355b0..2144acf 100644 --- a/mlx_video/models/ltx/transformer.py +++ b/mlx_video/models/ltx_2/transformer.py @@ -4,9 +4,9 @@ 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.config import LTXRopeType, TransformerConfig +from mlx_video.models.ltx_2.attention import Attention +from mlx_video.models.ltx_2.feed_forward import FeedForward from mlx_video.utils import rms_norm diff --git a/mlx_video/models/ltx/upsampler.py b/mlx_video/models/ltx_2/upsampler.py similarity index 100% rename from mlx_video/models/ltx/upsampler.py rename to mlx_video/models/ltx_2/upsampler.py 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..c154eea --- /dev/null +++ b/mlx_video/models/ltx_2/video_vae/__init__.py @@ -0,0 +1,8 @@ +from mlx_video.models.ltx_2.video_vae.video_vae import VideoEncoder +from mlx_video.models.ltx_2.video_vae.encoder import encode_image +from mlx_video.models.ltx_2.video_vae.decoder import LTX2VideoDecoder, VideoDecoder +from mlx_video.models.ltx_2.video_vae.tiling import ( + TilingConfig, + SpatialTilingConfig, + TemporalTilingConfig, +) diff --git a/mlx_video/models/ltx/video_vae/convolution.py b/mlx_video/models/ltx_2/video_vae/convolution.py similarity index 100% rename from mlx_video/models/ltx/video_vae/convolution.py rename to mlx_video/models/ltx_2/video_vae/convolution.py diff --git a/mlx_video/models/ltx/video_vae/decoder.py b/mlx_video/models/ltx_2/video_vae/decoder.py similarity index 98% rename from mlx_video/models/ltx/video_vae/decoder.py rename to mlx_video/models/ltx_2/video_vae/decoder.py index be4e794..0da4a61 100644 --- a/mlx_video/models/ltx/video_vae/decoder.py +++ b/mlx_video/models/ltx_2/video_vae/decoder.py @@ -21,10 +21,10 @@ from pathlib import Path 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, PerChannelStatistics -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 unpatchify, PerChannelStatistics +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( diff --git a/mlx_video/models/ltx/video_vae/encoder.py b/mlx_video/models/ltx_2/video_vae/encoder.py similarity index 94% rename from mlx_video/models/ltx/video_vae/encoder.py rename to mlx_video/models/ltx_2/video_vae/encoder.py index ed4dcc4..a605da0 100644 --- a/mlx_video/models/ltx/video_vae/encoder.py +++ b/mlx_video/models/ltx_2/video_vae/encoder.py @@ -6,7 +6,7 @@ to latent space, which can then be used to condition video generation. """ import mlx.core as mx -from mlx_video.models.ltx.video_vae.video_vae import VideoEncoder +from mlx_video.models.ltx_2.video_vae.video_vae import VideoEncoder diff --git a/mlx_video/models/ltx/video_vae/ops.py b/mlx_video/models/ltx_2/video_vae/ops.py similarity index 100% rename from mlx_video/models/ltx/video_vae/ops.py rename to mlx_video/models/ltx_2/video_vae/ops.py diff --git a/mlx_video/models/ltx/video_vae/resnet.py b/mlx_video/models/ltx_2/video_vae/resnet.py similarity index 98% rename from mlx_video/models/ltx/video_vae/resnet.py rename to mlx_video/models/ltx_2/video_vae/resnet.py index d93754c..686636d 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 diff --git a/mlx_video/models/ltx/video_vae/sampling.py b/mlx_video/models/ltx_2/video_vae/sampling.py similarity index 99% rename from mlx_video/models/ltx/video_vae/sampling.py rename to mlx_video/models/ltx_2/video_vae/sampling.py index 76a96bf..034c5a6 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): diff --git a/mlx_video/models/ltx/video_vae/tiling.py b/mlx_video/models/ltx_2/video_vae/tiling.py similarity index 100% rename from mlx_video/models/ltx/video_vae/tiling.py rename to mlx_video/models/ltx_2/video_vae/tiling.py 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 97% rename from mlx_video/models/ltx/video_vae/video_vae.py rename to mlx_video/models/ltx_2/video_vae/video_vae.py index 1b40b1f..45a447d 100644 --- a/mlx_video/models/ltx/video_vae/video_vae.py +++ b/mlx_video/models/ltx_2/video_vae/video_vae.py @@ -7,15 +7,15 @@ 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, ) @@ -229,7 +229,7 @@ class VideoEncoder(nn.Module): config: VideoEncoderModelConfig with encoder parameters """ super().__init__() - from mlx_video.models.ltx.config import VideoEncoderModelConfig + from mlx_video.models.ltx_2.config import VideoEncoderModelConfig self.patch_size = config.patch_size self.norm_layer = config.norm_layer @@ -409,7 +409,7 @@ class VideoEncoder(nn.Module): Loaded VideoEncoder instance """ import json - from mlx_video.models.ltx.config import VideoEncoderModelConfig + from mlx_video.models.ltx_2.config import VideoEncoderModelConfig # Load config config_path = model_path / "config.json" 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/tests/test_rope.py b/tests/test_rope.py index 7406cf2..8590963 100644 --- a/tests/test_rope.py +++ b/tests/test_rope.py @@ -2,10 +2,10 @@ import pytest import mlx.core as mx import numpy as np -from mlx_video.models.ltx.rope import ( +from mlx_video.models.ltx_2.rope import ( precompute_freqs_cis, ) -from mlx_video.models.ltx.config import LTXModelConfig, LTXRopeType +from mlx_video.models.ltx_2.config import LTXModelConfig, LTXRopeType def create_video_position_grid( diff --git a/tests/test_vae_streaming.py b/tests/test_vae_streaming.py index be29d00..0f3abd8 100644 --- a/tests/test_vae_streaming.py +++ b/tests/test_vae_streaming.py @@ -4,8 +4,8 @@ import pytest import mlx.core as mx import numpy as np -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, diff --git a/uv.lock b/uv.lock index 65e21f1..66cf6c8 100644 --- a/uv.lock +++ b/uv.lock @@ -2,8 +2,15 @@ version = 1 revision = 3 requires-python = ">=3.11" resolution-markers = [ - "python_full_version >= '3.12'", - "python_full_version < '3.12'", + "python_full_version >= '3.14' and sys_platform == 'win32'", + "python_full_version >= '3.14' and sys_platform == 'emscripten'", + "python_full_version >= '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version == '3.13.*' and sys_platform == 'win32'", + "python_full_version == '3.13.*' and sys_platform == 'emscripten'", + "python_full_version == '3.13.*' and sys_platform != 'emscripten' and sys_platform != 'win32'", + "python_full_version < '3.13' and sys_platform == 'win32'", + "python_full_version < '3.13' and sys_platform == 'emscripten'", + "python_full_version < '3.13' and sys_platform != 'emscripten' and sys_platform != 'win32'", ] [[package]] @@ -171,12 +178,81 @@ wheels = [ ] [[package]] -name = "certifi" -version = "2026.1.4" +name = "audioop-lts" +version = "0.2.2" source = { registry = "https://pypi.org/simple" } -sdist = { url = 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