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Running
on
A10G
Update app.py
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app.py
CHANGED
@@ -7,7 +7,7 @@ pipe = DiffusionPipeline.from_pretrained("cerspense/zeroscope_v2_576w", torch_dt
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pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
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pipe.enable_model_cpu_offload()
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def infer(prompt
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#prompt = "Darth Vader is surfing on waves"
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video_frames = pipe(prompt, num_inference_steps=40, height=320, width=576, num_frames=24).frames
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video_path = export_to_video(video_frames)
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@@ -21,33 +21,24 @@ a {text-decoration-line: underline; font-weight: 600;}
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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.
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Zeroscope Text-to-Video
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</h1>
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</div>
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<p style="margin-bottom: 10px; font-size: 94%">
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A watermark-free Modelscope-based video model optimized for producing high-quality 16:9 compositions and a smooth video output. <br />
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This model was trained using 9,923 clips and 29,769 tagged frames at 24 frames, 576x320 resolution.
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[![Duplicate this Space](https://huggingface.co/datasets/huggingface/badges/raw/main/duplicate-this-space-sm.svg)](https://huggingface.co/spaces/fffiloni/zeroscope?duplicate=true)
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</p>
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</div>""")
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prompt_in = gr.Textbox(label="Prompt", placeholder="Darth Vader is surfing on waves")
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inference_steps = gr.Slider(label="Inference Steps", minimum=10, maximum=100, step=1, value=40, interactive=False)
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submit_btn = gr.Button("Submit")
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video_result = gr.Video(label="Video Output")
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submit_btn.click(fn=infer,
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inputs=[prompt_in
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outputs=[video_result])
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demo.queue(max_size=12).launch()
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pipe.scheduler = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config)
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pipe.enable_model_cpu_offload()
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def infer(prompt):
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#prompt = "Darth Vader is surfing on waves"
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video_frames = pipe(prompt, num_inference_steps=40, height=320, width=576, num_frames=24).frames
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video_path = export_to_video(video_frames)
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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.Mardown(
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"""
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# Zeroscope Text-to-Video
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A watermark-free Modelscope-based video model optimized for producing high-quality 16:9 compositions and a smooth video output. <br />
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This model was trained using 9,923 clips and 29,769 tagged frames at 24 frames, 576x320 resolution.
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[![Duplicate this Space](https://huggingface.co/datasets/huggingface/badges/raw/main/duplicate-this-space-sm.svg)](https://huggingface.co/spaces/fffiloni/zeroscope?duplicate=true)
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"""
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)
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prompt_in = gr.Textbox(label="Prompt", placeholder="Darth Vader is surfing on waves")
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#inference_steps = gr.Slider(label="Inference Steps", minimum=10, maximum=100, step=1, value=40, interactive=False)
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submit_btn = gr.Button("Submit")
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video_result = gr.Video(label="Video Output")
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submit_btn.click(fn=infer,
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inputs=[prompt_in],
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outputs=[video_result])
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demo.queue(max_size=12).launch()
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