import argparse import time from pathlib import Path from typing import Optional import mlx.core as mx import mlx.nn as nn import numpy as np from PIL import Image from tqdm import tqdm class Colors: """ANSI color codes for terminal output.""" CYAN = "\033[96m" BLUE = "\033[94m" GREEN = "\033[92m" YELLOW = "\033[93m" RED = "\033[91m" MAGENTA = "\033[95m" BOLD = "\033[1m" DIM = "\033[2m" RESET = "\033[0m" from mlx_video.models.ltx.config import LTXModelConfig, LTXModelType, LTXRopeType from mlx_video.models.ltx.ltx import LTXModel from mlx_video.models.ltx.transformer import Modality from mlx_video.convert import sanitize_transformer_weights, sanitize_vae_encoder_weights from mlx_video.utils import to_denoised, load_image, prepare_image_for_encoding from mlx_video.models.ltx.video_vae.decoder import load_vae_decoder from mlx_video.models.ltx.video_vae.encoder import load_vae_encoder from mlx_video.models.ltx.video_vae.tiling import TilingConfig from mlx_video.models.ltx.upsampler import load_upsampler, upsample_latents from mlx_video.conditioning import VideoConditionByLatentIndex, apply_conditioning from mlx_video.conditioning.latent import LatentState, create_initial_state, apply_denoise_mask, add_noise_with_state from mlx_video.utils import get_model_path # Distilled sigma schedules STAGE_1_SIGMAS = [1.0, 0.99375, 0.9875, 0.98125, 0.975, 0.909375, 0.725, 0.421875, 0.0] STAGE_2_SIGMAS = [0.909375, 0.725, 0.421875, 0.0] def create_position_grid( batch_size: int, num_frames: int, height: int, width: int, temporal_scale: int = 8, spatial_scale: int = 32, fps: float = 24.0, causal_fix: bool = True, ) -> mx.array: """Create position grid for RoPE in pixel space. Args: batch_size: Batch size num_frames: Number of frames (latent) height: Height (latent) width: Width (latent) temporal_scale: VAE temporal scale factor (default 8) spatial_scale: VAE spatial scale factor (default 32) fps: Frames per second (default 24.0) causal_fix: Apply causal fix for first frame (default True) Returns: Position grid of shape (B, 3, num_patches, 2) in pixel space where dim 2 is [start, end) bounds for each patch """ # Patch size is (1, 1, 1) for LTX-2 - no spatial patching patch_size_t, patch_size_h, patch_size_w = 1, 1, 1 # Generate grid coordinates for each dimension (frame, height, width) t_coords = np.arange(0, num_frames, patch_size_t) h_coords = np.arange(0, height, patch_size_h) w_coords = np.arange(0, width, patch_size_w) # Create meshgrid with indexing='ij' for (frame, height, width) order t_grid, h_grid, w_grid = np.meshgrid(t_coords, h_coords, w_coords, indexing='ij') # Stack to get shape (3, grid_t, grid_h, grid_w) patch_starts = np.stack([t_grid, h_grid, w_grid], axis=0) # Calculate end coordinates (start + patch_size) patch_size_delta = np.array([patch_size_t, patch_size_h, patch_size_w]).reshape(3, 1, 1, 1) patch_ends = patch_starts + patch_size_delta # Stack start and end: shape (3, grid_t, grid_h, grid_w, 2) latent_coords = np.stack([patch_starts, patch_ends], axis=-1) # Flatten spatial/temporal dims: (3, num_patches, 2) num_patches = num_frames * height * width latent_coords = latent_coords.reshape(3, num_patches, 2) # Broadcast to batch: (batch, 3, num_patches, 2) latent_coords = np.tile(latent_coords[np.newaxis, ...], (batch_size, 1, 1, 1)) # Convert latent coords to pixel coords by scaling with VAE factors scale_factors = np.array([temporal_scale, spatial_scale, spatial_scale]).reshape(1, 3, 1, 1) pixel_coords = (latent_coords * scale_factors).astype(np.float32) # Apply causal fix for first frame temporal axis if causal_fix: # VAE temporal stride for first frame is 1 instead of temporal_scale pixel_coords[:, 0, :, :] = np.clip( pixel_coords[:, 0, :, :] + 1 - temporal_scale, a_min=0, a_max=None ) # Convert temporal to time in seconds by dividing by fps pixel_coords[:, 0, :, :] = pixel_coords[:, 0, :, :] / fps # Always return float32 for RoPE precision - bfloat16 causes quality degradation return mx.array(pixel_coords, dtype=mx.float32) def denoise( latents: mx.array, positions: mx.array, text_embeddings: mx.array, transformer: LTXModel, sigmas: list, verbose: bool = True, state: Optional[LatentState] = None, ) -> mx.array: """Run denoising loop with optional conditioning. Args: latents: Noisy latent tensor (B, C, F, H, W) positions: Position embeddings text_embeddings: Text conditioning embeddings transformer: LTX model sigmas: List of sigma values for denoising schedule verbose: Whether to show progress bar state: Optional LatentState for I2V conditioning Returns: Denoised latent tensor """ # If state is provided, use its latent (which may have conditioning applied) dtype = latents.dtype if state is not None: latents = state.latent for i in tqdm(range(len(sigmas) - 1), desc="Denoising", disable=not verbose): sigma, sigma_next = sigmas[i], sigmas[i + 1] b, c, f, h, w = latents.shape num_tokens = f * h * w latents_flat = mx.transpose(mx.reshape(latents, (b, c, -1)), (0, 2, 1)) # Compute per-token timesteps # For I2V: conditioned tokens get timestep=0 (mask=0), unconditioned get timestep=sigma (mask=1) if state is not None: # Reshape denoise_mask from (B, 1, F, 1, 1) to (B, num_tokens) denoise_mask_flat = mx.reshape(state.denoise_mask, (b, 1, f, 1, 1)) denoise_mask_flat = mx.broadcast_to(denoise_mask_flat, (b, 1, f, h, w)) denoise_mask_flat = mx.reshape(denoise_mask_flat, (b, num_tokens)) # Per-token timesteps: sigma * mask (preserve dtype) timesteps = mx.array(sigma, dtype=dtype) * denoise_mask_flat else: # All tokens get the same timestep (use latent dtype) timesteps = mx.full((b, num_tokens), sigma, dtype=dtype) video_modality = Modality( latent=latents_flat, timesteps=timesteps, positions=positions, context=text_embeddings, context_mask=None, enabled=True, ) velocity, _ = transformer(video=video_modality, audio=None) mx.eval(velocity) velocity = mx.reshape(mx.transpose(velocity, (0, 2, 1)), (b, c, f, h, w)) denoised = to_denoised(latents, velocity, sigma) # Apply conditioning mask if state is provided if state is not None: denoised = apply_denoise_mask(denoised, state.clean_latent, state.denoise_mask) mx.eval(denoised) # Euler step (preserve dtype by converting Python floats to arrays) if sigma_next > 0: sigma_next_arr = mx.array(sigma_next, dtype=dtype) sigma_arr = mx.array(sigma, dtype=dtype) latents = denoised + sigma_next_arr * (latents - denoised) / sigma_arr else: latents = denoised mx.eval(latents) return latents def generate_video( model_repo: str, text_encoder_repo: str, prompt: str, height: int = 512, width: int = 512, num_frames: int = 33, seed: int = 42, fps: int = 24, output_path: str = "output.mp4", save_frames: bool = False, verbose: bool = True, enhance_prompt: bool = False, max_tokens: int = 512, temperature: float = 0.7, image: Optional[str] = None, image_strength: float = 1.0, image_frame_idx: int = 0, tiling: str = "auto", stream: bool = False, ): """Generate video from text prompt, optionally conditioned on an image. Args: model_repo: Model repository ID text_encoder_repo: Text encoder repository ID prompt: Text description of the video to generate height: Output video height (must be divisible by 64) width: Output video width (must be divisible by 64) num_frames: Number of frames (must be 1 + 8*k, e.g., 33, 65, 97) seed: Random seed for reproducibility fps: Frames per second for output video output_path: Path to save the output video save_frames: Whether to save individual frames as images verbose: Whether to print progress enhance_prompt: Whether to enhance prompt using Gemma max_tokens: Max tokens for prompt enhancement temperature: Temperature for prompt enhancement image: Path to conditioning image for I2V (Image-to-Video) image_strength: Conditioning strength (1.0 = full denoise, 0.0 = keep original) image_frame_idx: Frame index to condition (0 = first frame) tiling: Tiling mode for VAE decoding. Options: - "auto": Automatically determine based on video size (default) - "none": Disable tiling - "default": 512px spatial, 64 frame temporal - "aggressive": 256px spatial, 32 frame temporal (lowest memory) - "conservative": 768px spatial, 96 frame temporal (faster) - "spatial": Spatial tiling only - "temporal": Temporal tiling only """ start_time = time.time() # Validate dimensions assert height % 64 == 0, f"Height must be divisible by 64, got {height}" assert width % 64 == 0, f"Width must be divisible by 64, got {width}" if num_frames % 8 != 1: adjusted_num_frames = round((num_frames - 1) / 8) * 8 + 1 print(f"{Colors.YELLOW}⚠️ Number of frames must be 1 + 8*k. Using nearest valid value: {adjusted_num_frames}{Colors.RESET}") num_frames = adjusted_num_frames is_i2v = image is not None mode_str = "I2V" if is_i2v else "T2V" print(f"{Colors.BOLD}{Colors.CYAN}🎬 [{mode_str}] Generating {width}x{height} video with {num_frames} frames{Colors.RESET}") print(f"{Colors.DIM}Prompt: {prompt[:80]}{'...' if len(prompt) > 80 else ''}{Colors.RESET}") if is_i2v: print(f"{Colors.DIM}Image: {image} (strength={image_strength}, frame={image_frame_idx}){Colors.RESET}") # Get model path model_path = get_model_path(model_repo) text_encoder_path = model_path if text_encoder_repo is None else get_model_path(text_encoder_repo) # Calculate latent dimensions stage1_h, stage1_w = height // 2 // 32, width // 2 // 32 stage2_h, stage2_w = height // 32, width // 32 latent_frames = 1 + (num_frames - 1) // 8 mx.random.seed(seed) # Load text encoder print(f"{Colors.BLUE}📝 Loading text encoder...{Colors.RESET}") from mlx_video.models.ltx.text_encoder import LTX2TextEncoder text_encoder = LTX2TextEncoder() text_encoder.load(model_path=model_path, text_encoder_path=text_encoder_path) mx.eval(text_encoder.parameters()) # Optionally enhance the prompt if enhance_prompt: print(f"{Colors.MAGENTA}✨ Enhancing prompt...{Colors.RESET}") prompt = text_encoder.enhance_t2v(prompt, max_tokens=max_tokens, temperature=temperature, seed=seed, verbose=verbose) print(f"{Colors.DIM}Enhanced: {prompt[:150]}{'...' if len(prompt) > 150 else ''}{Colors.RESET}") text_embeddings, _ = text_encoder(prompt, return_audio_embeddings=False) model_dtype = text_embeddings.dtype # bfloat16 from text encoder mx.eval(text_embeddings) del text_encoder mx.clear_cache() # Load transformer print(f"{Colors.BLUE}🤖 Loading transformer...{Colors.RESET}") raw_weights = mx.load(str(model_path / 'ltx-2-19b-distilled.safetensors')) sanitized = sanitize_transformer_weights(raw_weights) # Convert transformer weights to bfloat16 for memory efficiency sanitized = {k: v.astype(mx.bfloat16) if v.dtype == mx.float32 else v for k, v in sanitized.items()} config = LTXModelConfig( model_type=LTXModelType.VideoOnly, num_attention_heads=32, attention_head_dim=128, in_channels=128, out_channels=128, num_layers=48, cross_attention_dim=4096, caption_channels=3840, rope_type=LTXRopeType.SPLIT, double_precision_rope=True, positional_embedding_theta=10000.0, positional_embedding_max_pos=[20, 2048, 2048], use_middle_indices_grid=True, timestep_scale_multiplier=1000, ) transformer = LTXModel(config) transformer.load_weights(list(sanitized.items()), strict=False) mx.eval(transformer.parameters()) # Load VAE encoder and encode image for I2V conditioning stage1_image_latent = None stage2_image_latent = None if is_i2v: print(f"{Colors.BLUE}🖼️ Loading VAE encoder and encoding image...{Colors.RESET}") vae_encoder = load_vae_encoder(str(model_path / 'ltx-2-19b-distilled.safetensors')) mx.eval(vae_encoder.parameters()) # Load and prepare image for stage 1 (half resolution) input_image = load_image(image, height=height // 2, width=width // 2, dtype=model_dtype) stage1_image_tensor = prepare_image_for_encoding(input_image, height // 2, width // 2, dtype=model_dtype) stage1_image_latent = vae_encoder(stage1_image_tensor) mx.eval(stage1_image_latent) print(f" Stage 1 image latent: {stage1_image_latent.shape}") # Load and prepare image for stage 2 (full resolution) input_image = load_image(image, height=height, width=width, dtype=model_dtype) stage2_image_tensor = prepare_image_for_encoding(input_image, height, width, dtype=model_dtype) stage2_image_latent = vae_encoder(stage2_image_tensor) mx.eval(stage2_image_latent) print(f" Stage 2 image latent: {stage2_image_latent.shape}") del vae_encoder mx.clear_cache() # Stage 1: Generate at half resolution print(f"{Colors.YELLOW}⚡ Stage 1: Generating at {width//2}x{height//2} (8 steps)...{Colors.RESET}") mx.random.seed(seed) # Position grids stay float32 for RoPE precision positions = create_position_grid(1, latent_frames, stage1_h, stage1_w) mx.eval(positions) # Apply I2V conditioning if provided state1 = None if is_i2v and stage1_image_latent is not None: # PyTorch flow: create zeros -> apply conditioning -> apply noiser # Create initial state with zeros latent_shape = (1, 128, latent_frames, stage1_h, stage1_w) state1 = LatentState( latent=mx.zeros(latent_shape, dtype=model_dtype), clean_latent=mx.zeros(latent_shape, dtype=model_dtype), denoise_mask=mx.ones((1, 1, latent_frames, 1, 1), dtype=model_dtype), ) conditioning = VideoConditionByLatentIndex( latent=stage1_image_latent, frame_idx=image_frame_idx, strength=image_strength, ) state1 = apply_conditioning(state1, [conditioning]) # Apply noiser: latent = noise * (mask * noise_scale) + latent * (1 - mask * noise_scale) # For Stage 1, noise_scale = 1.0 (first sigma) noise = mx.random.normal(latent_shape, dtype=model_dtype) noise_scale = mx.array(STAGE_1_SIGMAS[0], dtype=model_dtype) # 1.0 scaled_mask = state1.denoise_mask * noise_scale state1 = LatentState( latent=noise * scaled_mask + state1.latent * (mx.array(1.0, dtype=model_dtype) - scaled_mask), clean_latent=state1.clean_latent, denoise_mask=state1.denoise_mask, ) latents = state1.latent mx.eval(latents) else: # T2V: just use random noise latents = mx.random.normal((1, 128, latent_frames, stage1_h, stage1_w), dtype=model_dtype) mx.eval(latents) latents = denoise(latents, positions, text_embeddings, transformer, STAGE_1_SIGMAS, verbose=verbose, state=state1) # Upsample latents print(f"{Colors.MAGENTA}🔍 Upsampling latents 2x...{Colors.RESET}") upsampler = load_upsampler(str(model_path / 'ltx-2-spatial-upscaler-x2-1.0.safetensors')) mx.eval(upsampler.parameters()) vae_decoder = load_vae_decoder( str(model_path / 'ltx-2-19b-distilled.safetensors'), timestep_conditioning=None # Auto-detect from model metadata ) latents = upsample_latents(latents, upsampler, vae_decoder.latents_mean, vae_decoder.latents_std) mx.eval(latents) del upsampler mx.clear_cache() # Stage 2: Refine at full resolution print(f"{Colors.YELLOW}⚡ Stage 2: Refining at {width}x{height} (3 steps)...{Colors.RESET}") # Position grids stay float32 for RoPE precision positions = create_position_grid(1, latent_frames, stage2_h, stage2_w) mx.eval(positions) # Apply I2V conditioning for stage 2 if provided state2 = None if is_i2v and stage2_image_latent is not None: # PyTorch flow: start with upscaled latent -> apply conditioning -> apply noiser state2 = LatentState( latent=latents, # Start with upscaled latent clean_latent=mx.zeros_like(latents), denoise_mask=mx.ones((1, 1, latent_frames, 1, 1), dtype=model_dtype), ) conditioning = VideoConditionByLatentIndex( latent=stage2_image_latent, frame_idx=image_frame_idx, strength=image_strength, ) state2 = apply_conditioning(state2, [conditioning]) # Apply noiser: latent = noise * (mask * noise_scale) + latent * (1 - mask * noise_scale) # For Stage 2, noise_scale = stage_2_sigmas[0] # Conditioned frames (mask=0) keep image latent, unconditioned get partial noise noise = mx.random.normal(latents.shape).astype(model_dtype) noise_scale = mx.array(STAGE_2_SIGMAS[0], dtype=model_dtype) scaled_mask = state2.denoise_mask * noise_scale state2 = LatentState( latent=noise * scaled_mask + state2.latent * (mx.array(1.0, dtype=model_dtype) - scaled_mask), clean_latent=state2.clean_latent, denoise_mask=state2.denoise_mask, ) latents = state2.latent mx.eval(latents) else: # T2V: add noise to all frames for refinement noise_scale = mx.array(STAGE_2_SIGMAS[0], dtype=model_dtype) one_minus_scale = mx.array(1.0 - STAGE_2_SIGMAS[0], dtype=model_dtype) noise = mx.random.normal(latents.shape).astype(model_dtype) latents = noise * noise_scale + latents * one_minus_scale mx.eval(latents) latents = denoise(latents, positions, text_embeddings, transformer, STAGE_2_SIGMAS, verbose=verbose, state=state2) del transformer mx.clear_cache() # Decode to video with tiling print(f"{Colors.BLUE}🎞️ Decoding video...{Colors.RESET}") # Select tiling configuration if tiling == "none": tiling_config = None elif tiling == "auto": tiling_config = TilingConfig.auto(height, width, num_frames) elif tiling == "default": tiling_config = TilingConfig.default() elif tiling == "aggressive": tiling_config = TilingConfig.aggressive() elif tiling == "conservative": tiling_config = TilingConfig.conservative() elif tiling == "spatial": tiling_config = TilingConfig.spatial_only() elif tiling == "temporal": tiling_config = TilingConfig.temporal_only() else: print(f"{Colors.YELLOW} Unknown tiling mode '{tiling}', using auto{Colors.RESET}") tiling_config = TilingConfig.auto(height, width, num_frames) # Save outputs output_path = Path(output_path) output_path.parent.mkdir(parents=True, exist_ok=True) # Stream mode: write frames as they're decoded video_writer = None stream_pbar = None if stream and tiling_config is not None: import cv2 fourcc = cv2.VideoWriter_fourcc(*'avc1') video_writer = cv2.VideoWriter(str(output_path), fourcc, fps, (width, height)) stream_pbar = tqdm(total=num_frames, desc="Streaming", unit="frame") def on_frames_ready(frames: mx.array, start_idx: int): """Callback to write frames as they're finalized.""" # frames: (B, 3, num_frames, H, W) frames = mx.squeeze(frames, axis=0) # (3, num_frames, H, W) frames = mx.transpose(frames, (1, 2, 3, 0)) # (num_frames, H, W, 3) frames = mx.clip((frames + 1.0) / 2.0, 0.0, 1.0) frames = (frames * 255).astype(mx.uint8) frames_np = np.array(frames) for frame in frames_np: video_writer.write(cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)) stream_pbar.update(1) else: on_frames_ready = None if tiling_config is not None: spatial_info = f"{tiling_config.spatial_config.tile_size_in_pixels}px" if tiling_config.spatial_config else "none" temporal_info = f"{tiling_config.temporal_config.tile_size_in_frames}f" if tiling_config.temporal_config else "none" print(f"{Colors.DIM} Tiling ({tiling}): spatial={spatial_info}, temporal={temporal_info}{Colors.RESET}") video = vae_decoder.decode_tiled(latents, tiling_config=tiling_config, tiling_mode=tiling, debug=verbose, on_frames_ready=on_frames_ready) else: print(f"{Colors.DIM} Tiling: disabled{Colors.RESET}") video = vae_decoder(latents) mx.eval(video) mx.clear_cache() # Close progressive video writer if used if video_writer is not None: video_writer.release() if stream_pbar is not None: stream_pbar.close() print(f"{Colors.GREEN}✅ Streamed video to{Colors.RESET} {output_path}") # Still need video_np for save_frames option video = mx.squeeze(video, axis=0) video = mx.transpose(video, (1, 2, 3, 0)) video = mx.clip((video + 1.0) / 2.0, 0.0, 1.0) video = (video * 255).astype(mx.uint8) video_np = np.array(video) else: # Convert to uint8 frames video = mx.squeeze(video, axis=0) # (C, F, H, W) video = mx.transpose(video, (1, 2, 3, 0)) # (F, H, W, C) video = mx.clip((video + 1.0) / 2.0, 0.0, 1.0) video = (video * 255).astype(mx.uint8) video_np = np.array(video) # Save video normally try: import cv2 h, w = video_np.shape[1], video_np.shape[2] fourcc = cv2.VideoWriter_fourcc(*'avc1') out = cv2.VideoWriter(str(output_path), fourcc, fps, (w, h)) for frame in video_np: out.write(cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)) out.release() print(f"{Colors.GREEN}✅ Saved video to{Colors.RESET} {output_path}") except Exception as e: print(f"{Colors.RED}❌ Could not save video: {e}{Colors.RESET}") if save_frames: frames_dir = output_path.parent / f"{output_path.stem}_frames" frames_dir.mkdir(exist_ok=True) for i, frame in enumerate(video_np): Image.fromarray(frame).save(frames_dir / f"frame_{i:04d}.png") print(f"{Colors.GREEN}✅ Saved {len(video_np)} frames to {frames_dir}{Colors.RESET}") elapsed = time.time() - start_time print(f"{Colors.BOLD}{Colors.GREEN}🎉 Done! Generated in {elapsed:.1f}s ({elapsed/num_frames:.2f}s/frame){Colors.RESET}") print(f"{Colors.BOLD}{Colors.GREEN}✨ Peak memory: {mx.get_peak_memory() / (1024 ** 3):.2f}GB{Colors.RESET}") return video_np def main(): parser = argparse.ArgumentParser( description="Generate videos with MLX LTX-2 (T2V and I2V)", formatter_class=argparse.RawDescriptionHelpFormatter, epilog=""" Examples: # Text-to-Video (T2V) python -m mlx_video.generate --prompt "A cat walking on grass" python -m mlx_video.generate --prompt "Ocean waves at sunset" --height 768 --width 768 python -m mlx_video.generate --prompt "..." --num-frames 65 --seed 123 --output my_video.mp4 # Image-to-Video (I2V) python -m mlx_video.generate --prompt "A person dancing" --image photo.jpg python -m mlx_video.generate --prompt "Waves crashing" --image beach.png --image-strength 0.8 """ ) parser.add_argument( "--prompt", "-p", type=str, required=True, help="Text description of the video to generate" ) parser.add_argument( "--height", "-H", type=int, default=512, help="Output video height (default: 512, must be divisible by 32)" ) parser.add_argument( "--width", "-W", type=int, default=512, help="Output video width (default: 512, must be divisible by 32)" ) parser.add_argument( "--num-frames", "-n", type=int, default=100, help="Number of frames (default: 100)" ) parser.add_argument( "--seed", "-s", type=int, default=42, help="Random seed for reproducibility (default: 42)" ) parser.add_argument( "--fps", type=int, default=24, help="Frames per second for output video (default: 24)" ) parser.add_argument( "--output-path", type=str, default="output.mp4", help="Output video path (default: output.mp4)" ) parser.add_argument( "--save-frames", action="store_true", help="Save individual frames as images" ) parser.add_argument( "--model-repo", type=str, default="Lightricks/LTX-2", help="Model repository to use (default: Lightricks/LTX-2)" ) parser.add_argument( "--text-encoder-repo", type=str, default=None, help="Text encoder repository to use (default: None)" ) parser.add_argument( "--verbose", action="store_true", help="Verbose output" ) parser.add_argument( "--enhance-prompt", action="store_true", help="Enhance the prompt using Gemma before generation" ) parser.add_argument( "--max-tokens", type=int, default=512, help="Maximum number of tokens to generate (default: 512)" ) parser.add_argument( "--temperature", type=float, default=0.7, help="Temperature for prompt enhancement (default: 0.7)" ) parser.add_argument( "--image", "-i", type=str, default=None, help="Path to conditioning image for I2V (Image-to-Video) generation" ) parser.add_argument( "--image-strength", type=float, default=1.0, help="Conditioning strength for I2V (1.0 = full denoise, 0.0 = keep original, default: 1.0)" ) parser.add_argument( "--image-frame-idx", type=int, default=0, help="Frame index to condition for I2V (0 = first frame, default: 0)" ) parser.add_argument( "--tiling", type=str, default="auto", choices=["auto", "none", "default", "aggressive", "conservative", "spatial", "temporal"], help="Tiling mode for VAE decoding (default: auto). " "auto=based on video size, none=disabled, default=512px/64f, " "aggressive=256px/32f (lowest memory), conservative=768px/96f, spatial=spatial only, temporal=temporal only" ) parser.add_argument( "--stream", action="store_true", help="Stream frames to output file as they're decoded (requires tiling). Allows viewing partial results sooner." ) args = parser.parse_args() generate_video( **vars(args) ) if __name__ == "__main__": main()