# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project import argparse import os import time from pathlib import Path import numpy as np import torch from vllm_omni.diffusion.data import DiffusionParallelConfig from vllm_omni.entrypoints.omni import Omni from vllm_omni.inputs.data import OmniDiffusionSamplingParams from vllm_omni.outputs import OmniRequestOutput from vllm_omni.platforms import current_omni_platform def parse_args() -> argparse.Namespace: parser = argparse.ArgumentParser(description="Generate a video with Wan2.2 T2V.") parser.add_argument( "--model", default="Wan-AI/Wan2.2-T2V-A14B-Diffusers", help="Diffusers Wan2.2 model ID or local path.", ) parser.add_argument("--prompt", default="A serene lakeside sunrise with mist over the water.", help="Text prompt.") parser.add_argument("--negative_prompt", default="", help="Negative prompt.") parser.add_argument("--seed", type=int, default=42, help="Random seed.") parser.add_argument("--guidance_scale", type=float, default=4.0, help="CFG scale (applied to low/high).") parser.add_argument("--guidance_scale_high", type=float, default=None, help="Optional separate CFG for high-noise.") parser.add_argument("--height", type=int, default=720, help="Video height.") parser.add_argument("--width", type=int, default=1280, help="Video width.") parser.add_argument("--num_frames", type=int, default=81, help="Number of frames (Wan default is 81).") parser.add_argument("--num_inference_steps", type=int, default=40, help="Sampling steps.") parser.add_argument( "--boundary_ratio", type=float, default=0.875, help="Boundary split ratio for low/high DiT. Default 0.875 uses both transformers for best quality. Set to 1.0 to load only the low-noise transformer (saves noticeable memory with good quality, recommended if memory is limited).", ) parser.add_argument( "--flow_shift", type=float, default=5.0, help="Scheduler flow_shift (5.0 for 720p, 12.0 for 480p)." ) parser.add_argument( "--cache_backend", type=str, default=None, choices=["cache_dit"], help=( "Cache backend to use for acceleration. " "Options: 'cache_dit' (DBCache + SCM + TaylorSeer). " "Default: None (no cache acceleration)." ), ) parser.add_argument( "--enable-cache-dit-summary", action="store_true", help="Enable cache-dit summary logging after diffusion forward passes.", ) parser.add_argument("--output", type=str, default="wan22_output.mp4", help="Path to save the video (mp4).") parser.add_argument("--fps", type=int, default=24, help="Frames per second for the output video.") parser.add_argument( "--vae_use_slicing", action="store_true", help="Enable VAE slicing for memory optimization.", ) parser.add_argument( "--vae_use_tiling", action="store_true", help="Enable VAE tiling for memory optimization.", ) parser.add_argument( "--enforce_eager", action="store_true", help="Disable torch.compile and force eager execution.", ) parser.add_argument( "--enable-cpu-offload", action="store_true", help="Enable CPU offloading for diffusion models.", ) parser.add_argument( "--ulysses_degree", type=int, default=1, help="Number of GPUs used for ulysses sequence parallelism.", ) parser.add_argument( "--ring_degree", type=int, default=1, help="Number of GPUs used for ring sequence parallelism.", ) return parser.parse_args() def main(): args = parse_args() generator = torch.Generator(device=current_omni_platform.device_type).manual_seed(args.seed) # Wan2.2 cache-dit tuning (from cache-dit examples and cache_alignment). cache_config = None if args.cache_backend == "cache_dit": cache_config = { # DBCache parameters [cache-dit only] "Fn_compute_blocks": 1, # Optimized for single-transformer models "Bn_compute_blocks": 0, # Number of backward compute blocks "max_warmup_steps": 4, # Maximum warmup steps (works for few-step models) "max_cached_steps": 20, "residual_diff_threshold": 0.24, # Higher threshold for more aggressive caching "max_continuous_cached_steps": 3, # Limit to prevent precision degradation # TaylorSeer parameters [cache-dit only] "enable_taylorseer": False, # Disabled by default (not suitable for few-step models) "taylorseer_order": 1, # TaylorSeer polynomial order # SCM (Step Computation Masking) parameters [cache-dit only] "scm_steps_mask_policy": None, # SCM mask policy: None (disabled), "slow", "medium", "fast", "ultra" "scm_steps_policy": "dynamic", # SCM steps policy: "dynamic" or "static" } # Configure parallel settings (only SP is supported for Wan) # Note: cfg_parallel and tensor_parallel are not implemented for Wan models parallel_config = DiffusionParallelConfig( ulysses_degree=args.ulysses_degree, ring_degree=args.ring_degree, ) # Check if profiling is requested via environment variable profiler_enabled = bool(os.getenv("VLLM_TORCH_PROFILER_DIR")) omni = Omni( model=args.model, vae_use_slicing=args.vae_use_slicing, vae_use_tiling=args.vae_use_tiling, boundary_ratio=args.boundary_ratio, flow_shift=args.flow_shift, cache_backend=args.cache_backend, cache_config=cache_config, enable_cache_dit_summary=args.enable_cache_dit_summary, enable_cpu_offload=args.enable_cpu_offload, parallel_config=parallel_config, enforce_eager=args.enforce_eager, ) if profiler_enabled: print("[Profiler] Starting profiling...") omni.start_profile() # Print generation configuration print(f"\n{'=' * 60}") print("Generation Configuration:") print(f" Model: {args.model}") print(f" Inference steps: {args.num_inference_steps}") print(f" Frames: {args.num_frames}") print(f" Parallel configuration: ulysses_degree={args.ulysses_degree}, ring_degree={args.ring_degree}") print(f" Video size: {args.width}x{args.height}") print(f"{'=' * 60}\n") generation_start = time.perf_counter() frames = omni.generate( { "prompt": args.prompt, "negative_prompt": args.negative_prompt, }, OmniDiffusionSamplingParams( height=args.height, width=args.width, generator=generator, guidance_scale=args.guidance_scale, guidance_scale_2=args.guidance_scale_high, num_inference_steps=args.num_inference_steps, num_frames=args.num_frames, ), ) generation_end = time.perf_counter() generation_time = generation_end - generation_start # Print profiling results print(f"Total generation time: {generation_time:.4f} seconds ({generation_time * 1000:.2f} ms)") # Extract video frames from OmniRequestOutput if isinstance(frames, list) and len(frames) > 0: first_item = frames[0] # Check if it's an OmniRequestOutput if hasattr(first_item, "final_output_type"): if first_item.final_output_type != "image": raise ValueError( f"Unexpected output type '{first_item.final_output_type}', expected 'image' for video generation." ) # Pipeline mode: extract from nested request_output if hasattr(first_item, "is_pipeline_output") and first_item.is_pipeline_output: if isinstance(first_item.request_output, list) and len(first_item.request_output) > 0: inner_output = first_item.request_output[0] if isinstance(inner_output, OmniRequestOutput) and hasattr(inner_output, "images"): frames = inner_output.images[0] if inner_output.images else None if frames is None: raise ValueError("No video frames found in output.") # Diffusion mode: use direct images field elif hasattr(first_item, "images") and first_item.images: frames = first_item.images else: raise ValueError("No video frames found in OmniRequestOutput.") output_path = Path(args.output) output_path.parent.mkdir(parents=True, exist_ok=True) try: from diffusers.utils import export_to_video except ImportError: raise ImportError("diffusers is required for export_to_video.") # frames may be np.ndarray (preferred) or torch.Tensor # export_to_video expects a list of frames with values in [0, 1] if isinstance(frames, torch.Tensor): video_tensor = frames.detach().cpu() if video_tensor.dim() == 5: # [B, C, F, H, W] or [B, F, H, W, C] if video_tensor.shape[1] in (3, 4): video_tensor = video_tensor[0].permute(1, 2, 3, 0) else: video_tensor = video_tensor[0] elif video_tensor.dim() == 4 and video_tensor.shape[0] in (3, 4): video_tensor = video_tensor.permute(1, 2, 3, 0) # If float, assume [-1,1] and normalize to [0,1] if video_tensor.is_floating_point(): video_tensor = video_tensor.clamp(-1, 1) * 0.5 + 0.5 video_array = video_tensor.float().numpy() else: video_array = frames if hasattr(video_array, "shape") and video_array.ndim == 5: video_array = video_array[0] # Convert 4D array (frames, H, W, C) to list of frames for export_to_video if isinstance(video_array, np.ndarray) and video_array.ndim == 4: video_array = list(video_array) export_to_video(video_array, str(output_path), fps=args.fps) print(f"Saved generated video to {output_path}") if profiler_enabled: print("\n[Profiler] Stopping profiler and collecting results...") profile_results = omni.stop_profile() if profile_results and isinstance(profile_results, dict): traces = profile_results.get("traces", []) print("\n" + "=" * 60) print("PROFILING RESULTS:") for rank, trace in enumerate(traces): print(f"\nRank {rank}:") if trace: print(f" • Trace: {trace}") if not traces: print(" No traces collected.") print("=" * 60) else: print("[Profiler] No valid profiling data returned.") if __name__ == "__main__": main()