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import os |
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import pathlib |
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import tempfile |
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import gradio as gr |
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import torch |
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from huggingface_hub import snapshot_download |
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from modelscope.outputs import OutputKeys |
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from modelscope.pipelines import pipeline |
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DESCRIPTION = "# ModelScope-Image2Video" |
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if not torch.cuda.is_available(): |
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DESCRIPTION += "\n<p>Running on CPU 🥶 This demo does not work on CPU.</p>" |
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if torch.cuda.is_available(): |
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model_cache_dir = os.getenv("MODEL_CACHE_DIR", "./models") |
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model_dir = pathlib.Path(model_cache_dir) / "MS-Image2Video" |
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snapshot_download(repo_id="damo-vilab/MS-Image2Video", repo_type="model", local_dir=model_dir) |
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pipe = pipeline(task="image-to-video", model=model_dir.as_posix(), model_revision="v1.1.0", device="cuda:0") |
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else: |
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pipe = None |
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def image_to_video(image_path: str) -> str: |
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output_file = tempfile.NamedTemporaryFile(suffix=".mp4", delete=False) |
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pipe(image_path, output_video=output_file.name)[OutputKeys.OUTPUT_VIDEO] |
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return output_file.name |
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with gr.Blocks(css="style.css") as demo: |
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gr.Markdown(DESCRIPTION) |
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gr.DuplicateButton( |
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value="Duplicate Space for private use", |
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elem_id="duplicate-button", |
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visible=os.getenv("SHOW_DUPLICATE_BUTTON") == "1", |
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) |
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with gr.Group(): |
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input_image = gr.Image(label="Input image", type="filepath") |
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run_button = gr.Button() |
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output_video = gr.Video(label="Output video") |
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run_button.click( |
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fn=image_to_video, |
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inputs=input_image, |
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outputs=output_video, |
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api_name="run", |
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) |
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if __name__ == "__main__": |
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demo.queue(max_size=10).launch() |
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