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import gradio as gr |
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from modelscope.pipelines import pipeline |
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from modelscope.outputs import OutputKeys |
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pipe = pipeline(task='image-to-video', model='damo/Image-to-Video', model_revision='v1.1.0') |
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def infer (image_in): |
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IMG_PATH = image_in |
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output_video_path = pipe(IMG_PATH, output_video='output.mp4')[OutputKeys.OUTPUT_VIDEO] |
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print(output_video_path) |
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return output_video_path |
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with gr.Blocks() as demo: |
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with gr.Column(elem_id="col-container"): |
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gr.Markdown(""" |
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<p> |
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You are currently viewing a micro-service API meant to be used by robots.<br/> |
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For the human UI, please check out the <a href="https://huggingface.co/spaces/fffiloni/MS-Image2Video">original Space by Sylvain Filoni</a>. |
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</p> |
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""") |
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image_in = gr.Image( |
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label = "Source Image", |
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source = "upload", |
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type = "filepath", |
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elem_id = "image-in" |
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) |
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with gr.Row(): |
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submit_btn = gr.Button( |
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"Submit" |
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) |
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video_out = gr.Video( |
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label = "Video Result", |
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elem_id = "video-out" |
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) |
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with gr.Row(): |
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submit_btn.click( |
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fn = infer, |
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inputs = [ |
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image_in |
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], |
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outputs = [ |
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video_out, |
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] |
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) |
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demo.queue(max_size=6).launch() |
