1079 lines
42 KiB
Python
1079 lines
42 KiB
Python
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import argparse
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import time
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from pathlib import Path
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from typing import Optional
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import mlx.core as mx
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import numpy as np
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from PIL import Image
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from rich.console import Console
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from rich.progress import Progress, SpinnerColumn, TextColumn, BarColumn, TaskProgressColumn, TimeRemainingColumn
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from rich.panel import Panel
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from rich.status import Status
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# Rich console for styled output
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console = Console()
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from mlx_video.models.ltx.config import LTXModelConfig, LTXModelType, LTXRopeType
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from mlx_video.models.ltx.ltx import LTXModel
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from mlx_video.models.ltx.transformer import Modality
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from mlx_video.convert import sanitize_transformer_weights
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from mlx_video.utils import to_denoised, load_image, prepare_image_for_encoding, get_model_path
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from mlx_video.models.ltx.video_vae.decoder import load_vae_decoder
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from mlx_video.models.ltx.video_vae.encoder import load_vae_encoder
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from mlx_video.models.ltx.video_vae.tiling import TilingConfig
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from mlx_video.models.ltx.upsampler import load_upsampler, upsample_latents
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from mlx_video.conditioning import VideoConditionByLatentIndex, apply_conditioning
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from mlx_video.conditioning.latent import LatentState, apply_denoise_mask
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# Distilled sigma schedules
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STAGE_1_SIGMAS = [1.0, 0.99375, 0.9875, 0.98125, 0.975, 0.909375, 0.725, 0.421875, 0.0]
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STAGE_2_SIGMAS = [0.909375, 0.725, 0.421875, 0.0]
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# Audio constants
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AUDIO_SAMPLE_RATE = 24000 # Output audio sample rate
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AUDIO_LATENT_SAMPLE_RATE = 16000 # VAE internal sample rate
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AUDIO_HOP_LENGTH = 160
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AUDIO_LATENT_DOWNSAMPLE_FACTOR = 4
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AUDIO_LATENT_CHANNELS = 8 # Latent channels before patchifying
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AUDIO_MEL_BINS = 16
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AUDIO_LATENTS_PER_SECOND = AUDIO_LATENT_SAMPLE_RATE / AUDIO_HOP_LENGTH / AUDIO_LATENT_DOWNSAMPLE_FACTOR # 25
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def create_position_grid(
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batch_size: int,
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num_frames: int,
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height: int,
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width: int,
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temporal_scale: int = 8,
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spatial_scale: int = 32,
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fps: float = 24.0,
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causal_fix: bool = True,
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) -> mx.array:
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"""Create position grid for RoPE in pixel space.
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Args:
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batch_size: Batch size
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num_frames: Number of frames (latent)
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height: Height (latent)
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width: Width (latent)
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temporal_scale: VAE temporal scale factor (default 8)
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spatial_scale: VAE spatial scale factor (default 32)
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fps: Frames per second (default 24.0)
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causal_fix: Apply causal fix for first frame (default True)
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Returns:
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Position grid of shape (B, 3, num_patches, 2) in pixel space
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where dim 2 is [start, end) bounds for each patch
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"""
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# Patch size is (1, 1, 1) for LTX-2 - no spatial patching
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patch_size_t, patch_size_h, patch_size_w = 1, 1, 1
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# Generate grid coordinates for each dimension (frame, height, width)
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t_coords = np.arange(0, num_frames, patch_size_t)
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h_coords = np.arange(0, height, patch_size_h)
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w_coords = np.arange(0, width, patch_size_w)
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# Create meshgrid with indexing='ij' for (frame, height, width) order
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t_grid, h_grid, w_grid = np.meshgrid(t_coords, h_coords, w_coords, indexing='ij')
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# Stack to get shape (3, grid_t, grid_h, grid_w)
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patch_starts = np.stack([t_grid, h_grid, w_grid], axis=0)
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# Calculate end coordinates (start + patch_size)
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patch_size_delta = np.array([patch_size_t, patch_size_h, patch_size_w]).reshape(3, 1, 1, 1)
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patch_ends = patch_starts + patch_size_delta
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# Stack start and end: shape (3, grid_t, grid_h, grid_w, 2)
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latent_coords = np.stack([patch_starts, patch_ends], axis=-1)
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# Flatten spatial/temporal dims: (3, num_patches, 2)
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num_patches = num_frames * height * width
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latent_coords = latent_coords.reshape(3, num_patches, 2)
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# Broadcast to batch: (batch, 3, num_patches, 2)
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latent_coords = np.tile(latent_coords[np.newaxis, ...], (batch_size, 1, 1, 1))
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# Convert latent coords to pixel coords by scaling with VAE factors
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scale_factors = np.array([temporal_scale, spatial_scale, spatial_scale]).reshape(1, 3, 1, 1)
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pixel_coords = (latent_coords * scale_factors).astype(np.float32)
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# Apply causal fix for first frame temporal axis
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if causal_fix:
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# VAE temporal stride for first frame is 1 instead of temporal_scale
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pixel_coords[:, 0, :, :] = np.clip(
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pixel_coords[:, 0, :, :] + 1 - temporal_scale,
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a_min=0,
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a_max=None
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)
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# Convert temporal to time in seconds by dividing by fps
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pixel_coords[:, 0, :, :] = pixel_coords[:, 0, :, :] / fps
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# Always return float32 for RoPE precision - bfloat16 causes quality degradation
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return mx.array(pixel_coords, dtype=mx.float32)
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def create_audio_position_grid(
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batch_size: int,
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audio_frames: int,
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sample_rate: int = AUDIO_LATENT_SAMPLE_RATE,
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hop_length: int = AUDIO_HOP_LENGTH,
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downsample_factor: int = AUDIO_LATENT_DOWNSAMPLE_FACTOR,
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is_causal: bool = True,
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) -> mx.array:
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"""Create temporal position grid for audio RoPE.
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Audio positions are timestamps in seconds, shape (B, 1, T, 2).
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Matches PyTorch's AudioPatchifier.get_patch_grid_bounds exactly.
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"""
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def get_audio_latent_time_in_sec(start_idx: int, end_idx: int) -> np.ndarray:
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"""Convert latent indices to seconds."""
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latent_frame = np.arange(start_idx, end_idx, dtype=np.float32)
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mel_frame = latent_frame * downsample_factor
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if is_causal:
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mel_frame = np.clip(mel_frame + 1 - downsample_factor, 0, None)
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return mel_frame * hop_length / sample_rate
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start_times = get_audio_latent_time_in_sec(0, audio_frames)
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end_times = get_audio_latent_time_in_sec(1, audio_frames + 1)
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positions = np.stack([start_times, end_times], axis=-1)
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positions = positions[np.newaxis, np.newaxis, :, :] # (1, 1, T, 2)
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positions = np.tile(positions, (batch_size, 1, 1, 1))
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return mx.array(positions, dtype=mx.float32)
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def compute_audio_frames(num_video_frames: int, fps: float) -> int:
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"""Compute number of audio latent frames given video duration."""
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duration = num_video_frames / fps
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return round(duration * AUDIO_LATENTS_PER_SECOND)
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def denoise(
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latents: mx.array,
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positions: mx.array,
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text_embeddings: mx.array,
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transformer: LTXModel,
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sigmas: list,
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verbose: bool = True,
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state: Optional[LatentState] = None,
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# Audio parameters (optional)
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audio_latents: Optional[mx.array] = None,
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audio_positions: Optional[mx.array] = None,
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audio_embeddings: Optional[mx.array] = None,
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) -> tuple[mx.array, Optional[mx.array]]:
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"""Run denoising loop with optional conditioning and optional audio.
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Args:
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latents: Noisy video latent tensor (B, C, F, H, W)
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positions: Video position embeddings
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text_embeddings: Video text conditioning embeddings
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transformer: LTX model
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sigmas: List of sigma values for denoising schedule
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verbose: Whether to show progress bar
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state: Optional LatentState for I2V conditioning
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audio_latents: Optional audio latent tensor (B, C, T, F) for audio generation
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audio_positions: Optional audio position embeddings
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audio_embeddings: Optional audio text embeddings
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Returns:
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Tuple of (video_latents, audio_latents) - audio_latents is None if audio disabled
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"""
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dtype = latents.dtype
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enable_audio = audio_latents is not None
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# If state is provided, use its latent (which may have conditioning applied)
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if state is not None:
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latents = state.latent
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desc = "[cyan]Denoising A/V[/]" if enable_audio else "[cyan]Denoising[/]"
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num_steps = len(sigmas) - 1
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with Progress(
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SpinnerColumn(),
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TextColumn("[progress.description]{task.description}"),
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BarColumn(),
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TaskProgressColumn(),
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TimeRemainingColumn(),
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console=console,
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disable=not verbose,
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) as progress:
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task = progress.add_task(desc, total=num_steps)
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for i in range(num_steps):
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sigma, sigma_next = sigmas[i], sigmas[i + 1]
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b, c, f, h, w = latents.shape
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num_tokens = f * h * w
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latents_flat = mx.transpose(mx.reshape(latents, (b, c, -1)), (0, 2, 1))
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# Compute per-token timesteps
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# For I2V: conditioned tokens get timestep=0 (mask=0), unconditioned get timestep=sigma (mask=1)
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if state is not None:
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# Reshape denoise_mask from (B, 1, F, 1, 1) to (B, num_tokens)
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denoise_mask_flat = mx.reshape(state.denoise_mask, (b, 1, f, 1, 1))
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denoise_mask_flat = mx.broadcast_to(denoise_mask_flat, (b, 1, f, h, w))
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denoise_mask_flat = mx.reshape(denoise_mask_flat, (b, num_tokens))
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# Per-token timesteps: sigma * mask (preserve dtype)
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timesteps = mx.array(sigma, dtype=dtype) * denoise_mask_flat
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else:
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# All tokens get the same timestep (use latent dtype)
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timesteps = mx.full((b, num_tokens), sigma, dtype=dtype)
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video_modality = Modality(
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latent=latents_flat,
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timesteps=timesteps,
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positions=positions,
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context=text_embeddings,
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context_mask=None,
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enabled=True,
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)
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# Prepare audio modality if enabled
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audio_modality = None
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if enable_audio:
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ab, ac, at, af = audio_latents.shape
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audio_flat = mx.transpose(audio_latents, (0, 2, 1, 3)) # (B, T, C, F)
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audio_flat = mx.reshape(audio_flat, (ab, at, ac * af))
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audio_modality = Modality(
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latent=audio_flat,
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timesteps=mx.full((ab, at), sigma, dtype=dtype),
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positions=audio_positions,
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context=audio_embeddings,
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context_mask=None,
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enabled=True,
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)
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velocity, audio_velocity = transformer(video=video_modality, audio=audio_modality)
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mx.eval(velocity)
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if audio_velocity is not None:
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mx.eval(audio_velocity)
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velocity = mx.reshape(mx.transpose(velocity, (0, 2, 1)), (b, c, f, h, w))
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denoised = to_denoised(latents, velocity, sigma)
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# Handle audio velocity if enabled
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audio_denoised = None
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if enable_audio and audio_velocity is not None:
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ab, ac, at, af = audio_latents.shape
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audio_velocity = mx.reshape(audio_velocity, (ab, at, ac, af))
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audio_velocity = mx.transpose(audio_velocity, (0, 2, 1, 3)) # (B, C, T, F)
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audio_denoised = to_denoised(audio_latents, audio_velocity, sigma)
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# Apply conditioning mask if state is provided
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if state is not None:
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denoised = apply_denoise_mask(denoised, state.clean_latent, state.denoise_mask)
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mx.eval(denoised)
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if audio_denoised is not None:
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mx.eval(audio_denoised)
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# Euler step (preserve dtype by converting Python floats to arrays)
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if sigma_next > 0:
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sigma_next_arr = mx.array(sigma_next, dtype=dtype)
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sigma_arr = mx.array(sigma, dtype=dtype)
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latents = denoised + sigma_next_arr * (latents - denoised) / sigma_arr
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if enable_audio and audio_denoised is not None:
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audio_latents = audio_denoised + sigma_next_arr * (audio_latents - audio_denoised) / sigma_arr
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else:
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latents = denoised
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if enable_audio and audio_denoised is not None:
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audio_latents = audio_denoised
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mx.eval(latents)
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if enable_audio:
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mx.eval(audio_latents)
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progress.advance(task)
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return latents, audio_latents if enable_audio else None
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def load_audio_decoder(model_path: Path):
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"""Load audio VAE decoder."""
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from mlx_video.models.ltx.audio_vae import AudioDecoder, CausalityAxis, NormType
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from mlx_video.convert import sanitize_audio_vae_weights
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decoder = AudioDecoder(
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ch=128,
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out_ch=2, # stereo
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ch_mult=(1, 2, 4),
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num_res_blocks=2,
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attn_resolutions=set(),
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resolution=256,
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z_channels=AUDIO_LATENT_CHANNELS,
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norm_type=NormType.PIXEL,
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causality_axis=CausalityAxis.HEIGHT,
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mel_bins=64,
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)
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weight_file = model_path / "ltx-2-19b-distilled.safetensors"
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if weight_file.exists():
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raw_weights = mx.load(str(weight_file))
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sanitized = sanitize_audio_vae_weights(raw_weights)
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if sanitized:
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decoder.load_weights(list(sanitized.items()), strict=False)
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if "per_channel_statistics._mean_of_means" in sanitized:
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decoder.per_channel_statistics._mean_of_means = sanitized["per_channel_statistics._mean_of_means"]
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if "per_channel_statistics._std_of_means" in sanitized:
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decoder.per_channel_statistics._std_of_means = sanitized["per_channel_statistics._std_of_means"]
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return decoder
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def load_vocoder(model_path: Path):
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"""Load vocoder for mel to waveform conversion."""
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from mlx_video.models.ltx.audio_vae import Vocoder
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from mlx_video.convert import sanitize_vocoder_weights
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vocoder = Vocoder(
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resblock_kernel_sizes=[3, 7, 11],
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upsample_rates=[6, 5, 2, 2, 2],
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upsample_kernel_sizes=[16, 15, 8, 4, 4],
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resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5], [1, 3, 5]],
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upsample_initial_channel=1024,
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stereo=True,
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output_sample_rate=AUDIO_SAMPLE_RATE,
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)
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weight_file = model_path / "ltx-2-19b-distilled.safetensors"
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if weight_file.exists():
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raw_weights = mx.load(str(weight_file))
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sanitized = sanitize_vocoder_weights(raw_weights)
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if sanitized:
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vocoder.load_weights(list(sanitized.items()), strict=False)
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return vocoder
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def save_audio(audio: np.ndarray, path: Path, sample_rate: int = AUDIO_SAMPLE_RATE):
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"""Save audio to WAV file."""
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import wave
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if audio.ndim == 2:
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audio = audio.T # (channels, samples) -> (samples, channels)
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audio = np.clip(audio, -1.0, 1.0)
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audio_int16 = (audio * 32767).astype(np.int16)
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with wave.open(str(path), 'wb') as wf:
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wf.setnchannels(2 if audio_int16.ndim == 2 else 1)
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wf.setsampwidth(2)
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wf.setframerate(sample_rate)
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wf.writeframes(audio_int16.tobytes())
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def mux_video_audio(video_path: Path, audio_path: Path, output_path: Path):
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"""Combine video and audio into final output using ffmpeg."""
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import subprocess
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cmd = [
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"ffmpeg", "-y",
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"-i", str(video_path),
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"-i", str(audio_path),
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"-c:v", "copy",
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"-c:a", "aac",
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"-shortest",
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str(output_path)
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]
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try:
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subprocess.run(cmd, check=True, capture_output=True)
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return True
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except subprocess.CalledProcessError as e:
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console.print(f"[red]FFmpeg error: {e.stderr.decode()}[/]")
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return False
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except FileNotFoundError:
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console.print("[red]FFmpeg not found. Please install ffmpeg.[/]")
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return False
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def generate_video(
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model_repo: str,
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text_encoder_repo: str,
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prompt: str,
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height: int = 512,
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width: int = 512,
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num_frames: int = 33,
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seed: int = 42,
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fps: int = 24,
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output_path: str = "output.mp4",
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save_frames: bool = False,
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verbose: bool = True,
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enhance_prompt: bool = False,
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max_tokens: int = 512,
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temperature: float = 0.7,
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image: Optional[str] = None,
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image_strength: float = 1.0,
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image_frame_idx: int = 0,
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tiling: str = "auto",
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stream: bool = False,
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# Audio options
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audio: bool = False,
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output_audio_path: Optional[str] = None,
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):
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"""Generate video from text prompt, optionally conditioned on an image and with audio.
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Args:
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model_repo: Model repository ID
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text_encoder_repo: Text encoder repository ID
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prompt: Text description of the video to generate
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height: Output video height (must be divisible by 64)
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width: Output video width (must be divisible by 64)
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num_frames: Number of frames (must be 1 + 8*k, e.g., 33, 65, 97)
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seed: Random seed for reproducibility
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fps: Frames per second for output video
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output_path: Path to save the output video
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save_frames: Whether to save individual frames as images
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verbose: Whether to print progress
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enhance_prompt: Whether to enhance prompt using Gemma
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max_tokens: Max tokens for prompt enhancement
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temperature: Temperature for prompt enhancement
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image: Path to conditioning image for I2V (Image-to-Video)
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image_strength: Conditioning strength (1.0 = full denoise, 0.0 = keep original)
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image_frame_idx: Frame index to condition (0 = first frame)
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tiling: Tiling mode for VAE decoding. Options:
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- "auto": Automatically determine based on video size (default)
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- "none": Disable tiling
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- "default": 512px spatial, 64 frame temporal
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- "aggressive": 256px spatial, 32 frame temporal (lowest memory)
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- "conservative": 768px spatial, 96 frame temporal (faster)
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- "spatial": Spatial tiling only
|
|
- "temporal": Temporal tiling only
|
|
stream: Stream frames to output as they're decoded (requires tiling)
|
|
audio: Enable synchronized audio generation
|
|
output_audio_path: Path to save audio file (default: same as video with .wav)
|
|
"""
|
|
start_time = time.time()
|
|
|
|
# Validate dimensions
|
|
assert height % 64 == 0, f"Height must be divisible by 64, got {height}"
|
|
assert width % 64 == 0, f"Width must be divisible by 64, got {width}"
|
|
|
|
if num_frames % 8 != 1:
|
|
adjusted_num_frames = round((num_frames - 1) / 8) * 8 + 1
|
|
console.print(f"[yellow]⚠️ Number of frames must be 1 + 8*k. Using nearest valid value: {adjusted_num_frames}[/]")
|
|
num_frames = adjusted_num_frames
|
|
|
|
is_i2v = image is not None
|
|
mode_str = "I2V" if is_i2v else "T2V"
|
|
if audio:
|
|
mode_str += "+Audio"
|
|
|
|
# Display header panel
|
|
header = f"[bold cyan]🎬 [{mode_str}] Generating {width}x{height} video with {num_frames} frames[/]"
|
|
console.print(Panel(header, expand=False))
|
|
console.print(f"[dim]Prompt: {prompt[:80]}{'...' if len(prompt) > 80 else ''}[/]")
|
|
if is_i2v:
|
|
console.print(f"[dim]Image: {image} (strength={image_strength}, frame={image_frame_idx})[/]")
|
|
|
|
# Calculate audio frames if enabled
|
|
audio_frames = None
|
|
if audio:
|
|
audio_frames = compute_audio_frames(num_frames, fps)
|
|
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
|
|
stage1_h, stage1_w = height // 2 // 32, width // 2 // 32
|
|
stage2_h, stage2_w = height // 32, width // 32
|
|
latent_frames = 1 + (num_frames - 1) // 8
|
|
|
|
mx.random.seed(seed)
|
|
|
|
# Load text encoder
|
|
with console.status("[blue]📝 Loading text encoder...[/]", spinner="dots"):
|
|
from mlx_video.models.ltx.text_encoder import LTX2TextEncoder
|
|
text_encoder = LTX2TextEncoder()
|
|
text_encoder.load(model_path=model_path, text_encoder_path=text_encoder_path)
|
|
mx.eval(text_encoder.parameters())
|
|
console.print("[green]✓[/] Text encoder loaded")
|
|
|
|
# Optionally enhance the prompt
|
|
if enhance_prompt:
|
|
with console.status("[magenta]✨ Enhancing prompt...[/]", spinner="dots"):
|
|
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 ''}[/]")
|
|
|
|
# Get embeddings - with audio if enabled
|
|
if audio:
|
|
text_embeddings, audio_embeddings = text_encoder(prompt, return_audio_embeddings=True)
|
|
mx.eval(text_embeddings, audio_embeddings)
|
|
else:
|
|
text_embeddings, _ = text_encoder(prompt, return_audio_embeddings=False)
|
|
audio_embeddings = None
|
|
mx.eval(text_embeddings)
|
|
|
|
model_dtype = text_embeddings.dtype # bfloat16 from text encoder
|
|
|
|
del text_encoder
|
|
mx.clear_cache()
|
|
|
|
# Load transformer
|
|
transformer_desc = "🤖 Loading transformer (A/V mode)..." if audio else "🤖 Loading transformer..."
|
|
with console.status(f"[blue]{transformer_desc}[/]", spinner="dots"):
|
|
raw_weights = mx.load(str(model_path / 'ltx-2-19b-distilled.safetensors'))
|
|
sanitized = sanitize_transformer_weights(raw_weights)
|
|
# Convert transformer weights to bfloat16 for memory efficiency
|
|
sanitized = {k: v.astype(mx.bfloat16) if v.dtype == mx.float32 else v for k, v in sanitized.items()}
|
|
|
|
# Configure model type based on audio flag
|
|
model_type = LTXModelType.AudioVideo if audio else LTXModelType.VideoOnly
|
|
|
|
config_kwargs = dict(
|
|
model_type=model_type,
|
|
num_attention_heads=32,
|
|
attention_head_dim=128,
|
|
in_channels=128,
|
|
out_channels=128,
|
|
num_layers=48,
|
|
cross_attention_dim=4096,
|
|
caption_channels=3840,
|
|
rope_type=LTXRopeType.SPLIT,
|
|
double_precision_rope=True,
|
|
positional_embedding_theta=10000.0,
|
|
positional_embedding_max_pos=[20, 2048, 2048],
|
|
use_middle_indices_grid=True,
|
|
timestep_scale_multiplier=1000,
|
|
)
|
|
|
|
if audio:
|
|
config_kwargs.update(
|
|
audio_num_attention_heads=32,
|
|
audio_attention_head_dim=64,
|
|
audio_in_channels=AUDIO_LATENT_CHANNELS * AUDIO_MEL_BINS, # 8 * 16 = 128
|
|
audio_out_channels=AUDIO_LATENT_CHANNELS * AUDIO_MEL_BINS,
|
|
audio_cross_attention_dim=2048,
|
|
audio_positional_embedding_max_pos=[20],
|
|
)
|
|
|
|
config = LTXModelConfig(**config_kwargs)
|
|
|
|
transformer = LTXModel(config)
|
|
transformer.load_weights(list(sanitized.items()), strict=False)
|
|
mx.eval(transformer.parameters())
|
|
console.print("[green]✓[/] Transformer loaded")
|
|
|
|
# Load VAE encoder and encode image for I2V conditioning
|
|
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 = load_vae_encoder(str(model_path / 'ltx-2-19b-distilled.safetensors'))
|
|
mx.eval(vae_encoder.parameters())
|
|
|
|
# Load and prepare image for stage 1 (half resolution)
|
|
input_image = load_image(image, height=height // 2, width=width // 2, dtype=model_dtype)
|
|
stage1_image_tensor = prepare_image_for_encoding(input_image, height // 2, width // 2, dtype=model_dtype)
|
|
stage1_image_latent = vae_encoder(stage1_image_tensor)
|
|
mx.eval(stage1_image_latent)
|
|
|
|
# Load and prepare image for stage 2 (full resolution)
|
|
input_image = load_image(image, height=height, width=width, dtype=model_dtype)
|
|
stage2_image_tensor = prepare_image_for_encoding(input_image, height, width, dtype=model_dtype)
|
|
stage2_image_latent = vae_encoder(stage2_image_tensor)
|
|
mx.eval(stage2_image_latent)
|
|
|
|
del vae_encoder
|
|
mx.clear_cache()
|
|
console.print("[green]✓[/] VAE encoder loaded and image encoded")
|
|
|
|
# Stage 1: Generate at half resolution
|
|
console.print(f"\n[bold yellow]⚡ Stage 1:[/] Generating at {width//2}x{height//2} (8 steps)")
|
|
mx.random.seed(seed)
|
|
|
|
# Position grids stay float32 for RoPE precision
|
|
positions = create_position_grid(1, latent_frames, stage1_h, stage1_w)
|
|
mx.eval(positions)
|
|
|
|
# Create audio positions if enabled
|
|
audio_positions = None
|
|
audio_latents = None
|
|
if audio:
|
|
audio_positions = create_audio_position_grid(1, audio_frames)
|
|
audio_latents = mx.random.normal((1, AUDIO_LATENT_CHANNELS, audio_frames, AUDIO_MEL_BINS)).astype(model_dtype)
|
|
mx.eval(audio_positions, audio_latents)
|
|
|
|
# Apply I2V conditioning if provided
|
|
state1 = None
|
|
if is_i2v and stage1_image_latent is not None:
|
|
# PyTorch flow: create zeros -> apply conditioning -> apply noiser
|
|
# Create initial state with zeros
|
|
latent_shape = (1, 128, latent_frames, stage1_h, stage1_w)
|
|
state1 = LatentState(
|
|
latent=mx.zeros(latent_shape, dtype=model_dtype),
|
|
clean_latent=mx.zeros(latent_shape, dtype=model_dtype),
|
|
denoise_mask=mx.ones((1, 1, latent_frames, 1, 1), dtype=model_dtype),
|
|
)
|
|
conditioning = VideoConditionByLatentIndex(
|
|
latent=stage1_image_latent,
|
|
frame_idx=image_frame_idx,
|
|
strength=image_strength,
|
|
)
|
|
|
|
state1 = apply_conditioning(state1, [conditioning])
|
|
|
|
# Apply noiser: latent = noise * (mask * noise_scale) + latent * (1 - mask * noise_scale)
|
|
# For Stage 1, noise_scale = 1.0 (first sigma)
|
|
noise = mx.random.normal(latent_shape, dtype=model_dtype)
|
|
noise_scale = mx.array(STAGE_1_SIGMAS[0], dtype=model_dtype) # 1.0
|
|
scaled_mask = state1.denoise_mask * noise_scale
|
|
|
|
state1 = LatentState(
|
|
latent=noise * scaled_mask + state1.latent * (mx.array(1.0, dtype=model_dtype) - scaled_mask),
|
|
clean_latent=state1.clean_latent,
|
|
denoise_mask=state1.denoise_mask,
|
|
)
|
|
latents = state1.latent
|
|
mx.eval(latents)
|
|
else:
|
|
# T2V: just use random noise
|
|
latents = mx.random.normal((1, 128, latent_frames, stage1_h, stage1_w), dtype=model_dtype)
|
|
mx.eval(latents)
|
|
|
|
latents, audio_latents = denoise(
|
|
latents, positions, text_embeddings, transformer, STAGE_1_SIGMAS,
|
|
verbose=verbose, state=state1,
|
|
audio_latents=audio_latents, audio_positions=audio_positions, audio_embeddings=audio_embeddings,
|
|
)
|
|
|
|
# Upsample latents
|
|
with console.status("[magenta]🔍 Upsampling latents 2x...[/]", spinner="dots"):
|
|
upsampler = load_upsampler(str(model_path / 'ltx-2-spatial-upscaler-x2-1.0.safetensors'))
|
|
mx.eval(upsampler.parameters())
|
|
|
|
vae_decoder = load_vae_decoder(
|
|
str(model_path / 'ltx-2-19b-distilled.safetensors'),
|
|
timestep_conditioning=None # Auto-detect from model metadata
|
|
)
|
|
|
|
latents = upsample_latents(latents, upsampler, vae_decoder.latents_mean, vae_decoder.latents_std)
|
|
mx.eval(latents)
|
|
|
|
del upsampler
|
|
mx.clear_cache()
|
|
console.print("[green]✓[/] Latents upsampled")
|
|
|
|
# Stage 2: Refine at full resolution
|
|
console.print(f"\n[bold yellow]⚡ Stage 2:[/] Refining at {width}x{height} (3 steps)")
|
|
# Position grids stay float32 for RoPE precision
|
|
positions = create_position_grid(1, latent_frames, stage2_h, stage2_w)
|
|
mx.eval(positions)
|
|
|
|
# Apply I2V conditioning for stage 2 if provided
|
|
state2 = None
|
|
if is_i2v and stage2_image_latent is not None:
|
|
# PyTorch flow: start with upscaled latent -> apply conditioning -> apply noiser
|
|
state2 = LatentState(
|
|
latent=latents, # Start with upscaled latent
|
|
clean_latent=mx.zeros_like(latents),
|
|
denoise_mask=mx.ones((1, 1, latent_frames, 1, 1), dtype=model_dtype),
|
|
)
|
|
conditioning = VideoConditionByLatentIndex(
|
|
latent=stage2_image_latent,
|
|
frame_idx=image_frame_idx,
|
|
strength=image_strength,
|
|
)
|
|
state2 = apply_conditioning(state2, [conditioning])
|
|
|
|
# Apply noiser: latent = noise * (mask * noise_scale) + latent * (1 - mask * noise_scale)
|
|
# For Stage 2, noise_scale = stage_2_sigmas[0]
|
|
# Conditioned frames (mask=0) keep image latent, unconditioned get partial noise
|
|
noise = mx.random.normal(latents.shape).astype(model_dtype)
|
|
noise_scale = mx.array(STAGE_2_SIGMAS[0], dtype=model_dtype)
|
|
scaled_mask = state2.denoise_mask * noise_scale
|
|
state2 = LatentState(
|
|
latent=noise * scaled_mask + state2.latent * (mx.array(1.0, dtype=model_dtype) - scaled_mask),
|
|
clean_latent=state2.clean_latent,
|
|
denoise_mask=state2.denoise_mask,
|
|
)
|
|
latents = state2.latent
|
|
mx.eval(latents)
|
|
|
|
# Audio also gets noise for stage 2 if enabled
|
|
if audio and audio_latents is not None:
|
|
audio_noise = mx.random.normal(audio_latents.shape).astype(model_dtype)
|
|
one_minus_scale = mx.array(1.0, dtype=model_dtype) - noise_scale
|
|
audio_latents = audio_noise * noise_scale + audio_latents * one_minus_scale
|
|
mx.eval(audio_latents)
|
|
else:
|
|
# T2V: add noise to all frames for refinement
|
|
noise_scale = mx.array(STAGE_2_SIGMAS[0], dtype=model_dtype)
|
|
one_minus_scale = mx.array(1.0 - 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)
|
|
|
|
# Audio also gets noise for stage 2 if enabled
|
|
if audio and audio_latents is not None:
|
|
audio_noise = mx.random.normal(audio_latents.shape).astype(model_dtype)
|
|
audio_latents = audio_noise * noise_scale + audio_latents * one_minus_scale
|
|
mx.eval(audio_latents)
|
|
|
|
latents, audio_latents = denoise(
|
|
latents, positions, text_embeddings, transformer, STAGE_2_SIGMAS,
|
|
verbose=verbose, state=state2,
|
|
audio_latents=audio_latents, audio_positions=audio_positions, audio_embeddings=audio_embeddings,
|
|
)
|
|
|
|
del transformer
|
|
mx.clear_cache()
|
|
|
|
# Decode to video with tiling
|
|
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)
|
|
|
|
# Save outputs
|
|
output_path = Path(output_path)
|
|
output_path.parent.mkdir(parents=True, exist_ok=True)
|
|
|
|
# Stream mode: write frames as they're decoded
|
|
video_writer = None
|
|
stream_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):
|
|
"""Callback to write frames as they're finalized."""
|
|
# frames: (B, 3, num_frames, H, W)
|
|
frames = mx.squeeze(frames, axis=0) # (3, num_frames, H, W)
|
|
frames = mx.transpose(frames, (1, 2, 3, 0)) # (num_frames, H, W, 3)
|
|
frames = mx.clip((frames + 1.0) / 2.0, 0.0, 1.0)
|
|
frames = (frames * 255).astype(mx.uint8)
|
|
frames_np = np.array(frames)
|
|
|
|
for frame in frames_np:
|
|
video_writer.write(cv2.cvtColor(frame, cv2.COLOR_RGB2BGR))
|
|
stream_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 progressive video writer if used
|
|
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}")
|
|
# Still need video_np for save_frames option
|
|
video = mx.squeeze(video, axis=0)
|
|
video = mx.transpose(video, (1, 2, 3, 0))
|
|
video = mx.clip((video + 1.0) / 2.0, 0.0, 1.0)
|
|
video = (video * 255).astype(mx.uint8)
|
|
video_np = np.array(video)
|
|
else:
|
|
# Convert to uint8 frames
|
|
video = mx.squeeze(video, axis=0) # (C, F, H, W)
|
|
video = mx.transpose(video, (1, 2, 3, 0)) # (F, H, W, C)
|
|
video = mx.clip((video + 1.0) / 2.0, 0.0, 1.0)
|
|
video = (video * 255).astype(mx.uint8)
|
|
video_np = np.array(video)
|
|
|
|
# For audio mode, save to temp file first
|
|
if audio:
|
|
temp_video_path = output_path.with_suffix('.temp.mp4')
|
|
save_path = temp_video_path
|
|
else:
|
|
save_path = output_path
|
|
|
|
# Save video
|
|
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
|
|
if audio and audio_latents is not None:
|
|
with console.status("[blue]🔊 Decoding audio...[/]", spinner="dots"):
|
|
audio_decoder = load_audio_decoder(model_path)
|
|
vocoder = load_vocoder(model_path)
|
|
mx.eval(audio_decoder.parameters(), vocoder.parameters())
|
|
|
|
mel_spectrogram = audio_decoder(audio_latents)
|
|
mx.eval(mel_spectrogram)
|
|
|
|
audio_waveform = vocoder(mel_spectrogram)
|
|
mx.eval(audio_waveform)
|
|
|
|
audio_np = np.array(audio_waveform)
|
|
if audio_np.ndim == 3:
|
|
audio_np = audio_np[0]
|
|
|
|
del audio_decoder, vocoder
|
|
mx.clear_cache()
|
|
console.print("[green]✓[/] Audio decoded")
|
|
|
|
# Save audio
|
|
audio_path = Path(output_audio_path) if output_audio_path else output_path.with_suffix('.wav')
|
|
save_audio(audio_np, audio_path, AUDIO_SAMPLE_RATE)
|
|
console.print(f"[green]✅ Saved audio to[/] {audio_path}")
|
|
|
|
# Mux video and audio
|
|
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
|
|
console.print(Panel(
|
|
f"[bold green]🎉 Done![/] Generated in {elapsed:.1f}s ({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 (T2V, I2V, and Audio)",
|
|
formatter_class=argparse.RawDescriptionHelpFormatter,
|
|
epilog="""
|
|
Examples:
|
|
# Text-to-Video (T2V)
|
|
python -m mlx_video.generate --prompt "A cat walking on grass"
|
|
python -m mlx_video.generate --prompt "Ocean waves at sunset" --height 768 --width 768
|
|
python -m mlx_video.generate --prompt "..." --num-frames 65 --seed 123 --output my_video.mp4
|
|
|
|
# Image-to-Video (I2V)
|
|
python -m mlx_video.generate --prompt "A person dancing" --image photo.jpg
|
|
python -m mlx_video.generate --prompt "Waves crashing" --image beach.png --image-strength 0.8
|
|
|
|
# With Audio (T2V+Audio or I2V+Audio)
|
|
python -m mlx_video.generate --prompt "Ocean waves crashing" --audio
|
|
python -m mlx_video.generate --prompt "A jazz band playing" --audio --enhance-prompt
|
|
python -m mlx_video.generate --prompt "Waves crashing" --image beach.png --audio
|
|
"""
|
|
)
|
|
|
|
parser.add_argument(
|
|
"--prompt", "-p",
|
|
type=str,
|
|
required=True,
|
|
help="Text description of the video to generate"
|
|
)
|
|
parser.add_argument(
|
|
"--height", "-H",
|
|
type=int,
|
|
default=512,
|
|
help="Output video height (default: 512, must be divisible by 32)"
|
|
)
|
|
parser.add_argument(
|
|
"--width", "-W",
|
|
type=int,
|
|
default=512,
|
|
help="Output video width (default: 512, must be divisible by 32)"
|
|
)
|
|
parser.add_argument(
|
|
"--num-frames", "-n",
|
|
type=int,
|
|
default=100,
|
|
help="Number of frames (default: 100)"
|
|
)
|
|
parser.add_argument(
|
|
"--seed", "-s",
|
|
type=int,
|
|
default=42,
|
|
help="Random seed for reproducibility (default: 42)"
|
|
)
|
|
parser.add_argument(
|
|
"--fps",
|
|
type=int,
|
|
default=24,
|
|
help="Frames per second for output video (default: 24)"
|
|
)
|
|
parser.add_argument(
|
|
"--output-path", "-o",
|
|
type=str,
|
|
default="output.mp4",
|
|
help="Output video path (default: output.mp4)"
|
|
)
|
|
parser.add_argument(
|
|
"--save-frames",
|
|
action="store_true",
|
|
help="Save individual frames as images"
|
|
)
|
|
parser.add_argument(
|
|
"--model-repo",
|
|
type=str,
|
|
default="Lightricks/LTX-2",
|
|
help="Model repository to use (default: Lightricks/LTX-2)"
|
|
)
|
|
parser.add_argument(
|
|
"--text-encoder-repo",
|
|
type=str,
|
|
default=None,
|
|
help="Text encoder repository to use (default: None)"
|
|
)
|
|
parser.add_argument(
|
|
"--verbose",
|
|
action="store_true",
|
|
help="Verbose output"
|
|
)
|
|
parser.add_argument(
|
|
"--enhance-prompt",
|
|
action="store_true",
|
|
help="Enhance the prompt using Gemma before generation"
|
|
)
|
|
parser.add_argument(
|
|
"--max-tokens",
|
|
type=int,
|
|
default=512,
|
|
help="Maximum number of tokens to generate (default: 512)"
|
|
)
|
|
parser.add_argument(
|
|
"--temperature",
|
|
type=float,
|
|
default=0.7,
|
|
help="Temperature for prompt enhancement (default: 0.7)"
|
|
)
|
|
parser.add_argument(
|
|
"--image", "-i",
|
|
type=str,
|
|
default=None,
|
|
help="Path to conditioning image for I2V (Image-to-Video) generation"
|
|
)
|
|
parser.add_argument(
|
|
"--image-strength",
|
|
type=float,
|
|
default=1.0,
|
|
help="Conditioning strength for I2V (1.0 = full denoise, 0.0 = keep original, default: 1.0)"
|
|
)
|
|
parser.add_argument(
|
|
"--image-frame-idx",
|
|
type=int,
|
|
default=0,
|
|
help="Frame index to condition for I2V (0 = first frame, default: 0)"
|
|
)
|
|
parser.add_argument(
|
|
"--tiling",
|
|
type=str,
|
|
default="auto",
|
|
choices=["auto", "none", "default", "aggressive", "conservative", "spatial", "temporal"],
|
|
help="Tiling mode for VAE decoding (default: auto). "
|
|
"auto=based on video size, none=disabled, default=512px/64f, "
|
|
"aggressive=256px/32f (lowest memory), conservative=768px/96f, spatial=spatial only, temporal=temporal only"
|
|
)
|
|
parser.add_argument(
|
|
"--stream",
|
|
action="store_true",
|
|
help="Stream frames to output file as they're decoded (requires tiling). Allows viewing partial results sooner."
|
|
)
|
|
# Audio options
|
|
parser.add_argument(
|
|
"--audio", "-a",
|
|
action="store_true",
|
|
help="Enable synchronized audio generation"
|
|
)
|
|
parser.add_argument(
|
|
"--output-audio",
|
|
type=str,
|
|
default=None,
|
|
help="Output audio path (default: same as video with .wav)"
|
|
)
|
|
args = parser.parse_args()
|
|
|
|
generate_video(
|
|
model_repo=args.model_repo,
|
|
text_encoder_repo=args.text_encoder_repo,
|
|
prompt=args.prompt,
|
|
height=args.height,
|
|
width=args.width,
|
|
num_frames=args.num_frames,
|
|
seed=args.seed,
|
|
fps=args.fps,
|
|
output_path=args.output_path,
|
|
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,
|
|
)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|