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import os |
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import sys |
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import shutil |
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import uuid |
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import subprocess |
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import gradio as gr |
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import shutil |
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from glob import glob |
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from huggingface_hub import snapshot_download, hf_hub_download |
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os.makedirs("pretrained_weights", exist_ok=True) |
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subfolders = [ |
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"stable-video-diffusion-img2vid-xt" |
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] |
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for subfolder in subfolders: |
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os.makedirs(os.path.join("pretrained_weights", subfolder), exist_ok=True) |
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snapshot_download( |
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repo_id = "stabilityai/stable-video-diffusion-img2vid", |
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local_dir = "./pretrained_weights/stable-video-diffusion-img2vid-xt" |
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) |
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snapshot_download( |
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repo_id = "Yhmeng1106/anidoc", |
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local_dir = "./pretrained_weights" |
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) |
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hf_hub_download( |
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repo_id = "facebook/cotracker", |
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filename = "cotracker2.pth", |
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local_dir = "./pretrained_weights" |
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) |
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def generate(control_sequence, ref_image): |
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control_image = control_sequence |
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ref_image = ref_image |
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unique_id = str(uuid.uuid4()) |
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output_dir = f"results_{unique_id}" |
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try: |
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subprocess.run( |
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[ |
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"python", "scripts_infer/anidoc_inference.py", |
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"--all_sketch", |
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"--matching", |
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"--tracking", |
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"--control_image", f"{control_image}", |
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"--ref_image", f"{ref_image}", |
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"--output_dir", f"{output_dir}", |
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"--max_point", "10", |
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], |
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check=True |
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) |
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output_video = glob(os.path.join(output_dir,"*.mp4")) |
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print(output_video) |
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if output_video: |
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output_video_path = output_video[0] |
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else: |
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output_video_path = None |
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print(output_video_path) |
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return output_video_path |
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except subprocess.CalledProcessError as e: |
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raise gr.Error(f"Error during inference: {str(e)}") |
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css=""" |
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div#col-container{ |
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margin: 0 auto; |
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max-width: 982px; |
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} |
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""" |
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with gr.Blocks(css=css) as demo: |
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with gr.Column(elem_id="col-container"): |
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gr.Markdown("# AniDoc: Animation Creation Made Easier") |
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gr.Markdown("AniDoc colorizes a sequence of sketches based on a character design reference with high fidelity, even when the sketches significantly differ in pose and scale.") |
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gr.HTML(""" |
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<div style="display:flex;column-gap:4px;"> |
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<a href="https://github.com/yihao-meng/AniDoc"> |
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<img src='https://img.shields.io/badge/GitHub-Repo-blue'> |
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</a> |
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<a href="https://yihao-meng.github.io/AniDoc_demo/"> |
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<img src='https://img.shields.io/badge/Project-Page-green'> |
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</a> |
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<a href="https://arxiv.org/pdf/2412.14173"> |
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<img src='https://img.shields.io/badge/ArXiv-Paper-red'> |
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</a> |
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<a href="https://huggingface.co/spaces/fffiloni/AniDoc?duplicate=true"> |
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<img src="https://huggingface.co/datasets/huggingface/badges/resolve/main/duplicate-this-space-sm.svg" alt="Duplicate this Space"> |
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</a> |
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<a href="https://huggingface.co/fffiloni"> |
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<img src="https://huggingface.co/datasets/huggingface/badges/resolve/main/follow-me-on-HF-sm-dark.svg" alt="Follow me on HF"> |
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</a> |
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</div> |
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""") |
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with gr.Row(): |
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with gr.Column(): |
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control_sequence = gr.Video(label="Control Sequence", format="mp4") |
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ref_image = gr.Image(label="Reference Image", type="filepath") |
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submit_btn = gr.Button("Submit") |
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with gr.Column(): |
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video_result = gr.Video(label="Result") |
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gr.Examples( |
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examples = [ |
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["data_test/sample1.mp4", "data_test/sample1.png"], |
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["data_test/sample2.mp4", "data_test/sample2.png"], |
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["data_test/sample3.mp4", "data_test/sample3.png"], |
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["data_test/sample4.mp4", "data_test/sample4.png"] |
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], |
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inputs = [control_sequence, ref_image] |
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) |
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submit_btn.click( |
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fn = generate, |
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inputs = [control_sequence, ref_image], |
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outputs = [video_result] |
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) |
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demo.queue().launch(show_api=False, show_error=True, share = True) |
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