# SPDX-License-Identifier: Apache-2.0 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project import argparse from pathlib import Path import numpy as np import torch from vllm_omni.entrypoints.omni import Omni from vllm_omni.outputs import OmniRequestOutput from vllm_omni.utils.platform_utils import detect_device_type, is_npu 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.") 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("--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.") return parser.parse_args() def main(): args = parse_args() device = detect_device_type() generator = torch.Generator(device=device).manual_seed(args.seed) # Enable VAE memory optimizations on NPU vae_use_slicing = is_npu() vae_use_tiling = is_npu() omni = Omni( model=args.model, vae_use_slicing=vae_use_slicing, vae_use_tiling=vae_use_tiling, boundary_ratio=args.boundary_ratio, flow_shift=args.flow_shift, ) # omni.generate() returns Generator[OmniRequestOutput, None, None] frames = omni.generate( args.prompt, negative_prompt=args.negative_prompt, 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, ) # Extract video frames from OmniRequestOutput frames = list(frames) 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.") # 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, dict) and "images" in inner_output: 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.") # 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 __name__ == "__main__": main()