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| import os | |
| import sys | |
| import uuid | |
| import shutil | |
| import time | |
| import gradio as gr | |
| import torch | |
| from diffusers import StableVideoDiffusionPipeline | |
| from PIL import Image | |
| import numpy as np | |
| import cv2 | |
| import subprocess | |
| import tempfile | |
| class WanAnimateApp: | |
| def __init__(self): | |
| model_name = "stabilityai/stable-video-diffusion-img2vid-xt" | |
| self.pipe = StableVideoDiffusionPipeline.from_pretrained( | |
| model_name, | |
| torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32, | |
| variant="fp16", | |
| device_map="cpu" | |
| ) | |
| def predict( | |
| self, | |
| ref_img, | |
| video, | |
| model_id, | |
| model, | |
| ): | |
| if ref_img is None or video is None: | |
| return None, "Upload both image and video." | |
| try: | |
| # Local processing — PIL directly (no open for PIL type) | |
| if isinstance(ref_img, Image.Image): | |
| ref_image = ref_img.convert("RGB").resize((576, 320)) | |
| else: | |
| ref_image = Image.open(ref_img).convert("RGB").resize((576, 320)) | |
| cap = cv2.VideoCapture(video) | |
| frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) | |
| cap.release() | |
| motion_hint = f" with dynamic motion from {frame_count} frames" | |
| # Prompt based on mode | |
| if model_id == "wan2.2-animate-move": | |
| prompt = f"Animate the character in the reference image{motion_hint}, high quality, smooth movements." | |
| else: | |
| prompt = f"Replace the character in the video with the reference image{motion_hint}, seamless, detailed." | |
| # Parameters | |
| num_frames = 25 if model == "wan-pro" else 14 | |
| num_steps = 25 if model == "wan-pro" else 15 | |
| # Local generation | |
| generator = torch.Generator(device="cpu").manual_seed(42) | |
| output = self.pipe( | |
| ref_image, | |
| num_inference_steps=num_steps, | |
| num_frames=num_frames, | |
| generator=generator, | |
| decode_chunk_size=2 | |
| ).frames[0] | |
| # Save MP4 with ffmpeg | |
| temp_dir = tempfile.mkdtemp() | |
| for i, frame in enumerate(output): | |
| frame.save(f"{temp_dir}/frame_{i:04d}.png") | |
| temp_video = f"/tmp/output_{uuid.uuid4()}.mp4" | |
| subprocess.run([ | |
| 'ffmpeg', '-y', '-framerate', '7', '-i', f"{temp_dir}/frame_%04d.png", | |
| '-c:v', 'libx264', '-pix_fmt', 'yuv420p', temp_video | |
| ], check=True) | |
| shutil.rmtree(temp_dir) | |
| return temp_video, "SUCCEEDED" | |
| except Exception as e: | |
| return None, f"Failed: {str(e)}" | |
| def start_app(): | |
| app = WanAnimateApp() | |
| with gr.Blocks(title="Wan2.2-Animate (Local No API)") as demo: | |
| gr.HTML(""" | |
| <div style="padding: 2rem; text-align: center; max-width: 1200px; margin: 0 auto; font-family: Arial, sans-serif;"> | |
| <h1 style="font-size: 2.5rem; font-weight: bold; margin-bottom: 0.5rem; color: #333;"> | |
| Wan2.2-Animate: Unified Character Animation and Replacement with Holistic Replication | |
| </h1> | |
| <h3 style="font-size: 1.5rem; font-weight: bold; margin-bottom: 0.5rem; color: #333;"> | |
| Local version without API (SVD Proxy) | |
| </h3> | |
| <div style="font-size: 1.25rem; margin-bottom: 1.5rem; color: #555;"> | |
| Tongyi Lab, Alibaba | |
| </div> | |
| <div style="display: flex; flex-wrap: wrap; justify-content: center; gap: 1rem; margin-bottom: 1.5rem;"> | |
| <a href="https://arxiv.org/abs/2509.14055" target="_blank" style="display: inline-flex; align-items: center; padding: 0.5rem 1rem; background-color: #f0f0f0; color: #333; text-decoration: none; border-radius: 9999px; font-weight: 500;"> | |
| <span style="margin-right: 0.5rem;">📄</span>Paper | |
| </a> | |
| <a href="https://github.com/Wan-Video/Wan2.2" target="_blank" style="display: inline-flex; align-items: center; padding: 0.5rem 1rem; background-color: #f0f0f0; color: #333; text-decoration: none; border-radius: 9999px; font-weight: 500;"> | |
| <span style="margin-right: 0.5rem;">💻</span>GitHub | |
| </a> | |
| <a href="https://huggingface.co/Wan-AI/Wan2.2-Animate-14B" target="_blank" style="display: inline-flex; align-items: center; padding: 0.5rem 1rem; background-color: #f0f0f0; color: #333; text-decoration: none; border-radius: 9999px; font-weight: 500;"> | |
| <span style="margin-right: 0.5rem;">🤗</span>HF Model | |
| </a> | |
| </div> | |
| </div> | |
| """) | |
| gr.HTML(""" | |
| <details> | |
| <summary>‼️Usage (使用说明)</summary> | |
| Wan-Animate supports two modes: | |
| <ul> | |
| <li>Move Mode: animate the character in input image with movements from the input video</li> | |
| <li>Mix Mode: replace the character in input video with the character in input image</li> | |
| </ul> | |
| Wan-Animate supports two modes: | |
| <ul> | |
| <li>Move Mode: Use the movements extracted from the input video to drive the character in the input image</li> | |
| <li>Mix Mode: Use the character in the input image to replace the character in the input video</li> | |
| </ul> | |
| Currently, the following restrictions apply to inputs: | |
| <ul> | |
| <li>Video file size: Less than 200MB</li> | |
| <li>Video resolution: The shorter side must be greater than 200, and the longer side must be less than 2048</li> | |
| <li>Video duration: 2s to 30s</li> | |
| <li>Video aspect ratio: 1:3 to 3:1</li> | |
| <li>Video formats: mp4, avi, mov</li> | |
| <li>Image file size: Less than 5MB</li> | |
| <li>Image resolution: The shorter side must be greater than 200, and the longer side must be less than 4096</li> | |
| <li>Image formats: jpg, png, jpeg, webp, bmp</li> | |
| </ul> | |
| Current, the inference quality has two variants. You can use our open-source code for more flexible configuration. | |
| <ul> | |
| <li>wan-pro: 25fps, 720p</li> | |
| <li>wan-std: 15fps, 720p</li> | |
| </ul> | |
| </details> | |
| """) | |
| with gr.Row(): | |
| with gr.Column(): | |
| ref_img = gr.Image( | |
| label="Reference Image(参考图像)", | |
| type="pil", # PIL type to fix FileNotFound | |
| sources=["upload"], | |
| ) | |
| video = gr.Video( | |
| label="Template Video(模版视频)", | |
| sources=["upload"], | |
| ) | |
| with gr.Row(): | |
| model_id = gr.Dropdown( | |
| label="Mode(模式)", | |
| choices=["wan2.2-animate-move", "wan2.2-animate-mix"], | |
| value="wan2.2-animate-move", | |
| info="" | |
| ) | |
| model = gr.Dropdown( | |
| label="Inference Quality", | |
| choices=["wan-pro", "wan-std"], | |
| value="wan-pro", | |
| ) | |
| run_button = gr.Button("Generate Video(生成视频)") | |
| with gr.Column(): | |
| output_video = gr.Video(label="Output Video(输出视频)") | |
| output_status = gr.Textbox(label="Status(状态)") | |
| run_button.click( | |
| fn=app.predict, | |
| inputs=[ | |
| ref_img, | |
| video, | |
| model_id, | |
| model, | |
| ], | |
| outputs=[output_video, output_status], | |
| ) | |
| gr.Examples( | |
| examples=[["examples/mov/1/1.jpeg", "examples/mov/1/1.mp4", "wan2.2-animate-move", "wan-pro"]], | |
| inputs=[ref_img, video, model_id, model], | |
| outputs=[output_video, output_status], | |
| fn=app.predict, | |
| cache_examples=False, | |
| ) | |
| demo.queue(default_concurrency_limit=1) | |
| demo.launch( | |
| server_name="0.0.0.0", | |
| server_port=7860 | |
| ) | |
| if __name__ == "__main__": | |
| start_app() |