multimodalart HF staff commited on
Commit
b2a6e83
1 Parent(s): 4254e9c

Update app.py

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Files changed (1) hide show
  1. app.py +0 -44
app.py CHANGED
@@ -101,39 +101,12 @@ def generate_video(prompt, image=None, duration=5, guidance_scale=9, video_guida
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  export_to_video(frames, output_path, fps=8)
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  return output_path
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- # Image-to-video generation function
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- #@spaces.GPU(duration=240)
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- #def generate_video_from_image(image, prompt, duration, video_guidance_scale):
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- # temp = int(duration * 2.4) # Convert seconds to temp value (assuming 24 FPS)
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- # torch_dtype = torch.bfloat16 if MODEL_DTYPE == "bf16" else torch.float32
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- #
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- # target_size = (1280, 720)
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- # cropped_image = center_crop(image, 1280, 720)
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- # resized_image = cropped_image.resize((1280, 720))
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- #
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- # with torch.no_grad(), torch.cuda.amp.autocast(enabled=True, dtype=torch_dtype):
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- # frames = model.generate_i2v(
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- # prompt=prompt,
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- # input_image=resized_image,
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- # num_inference_steps=[10, 10, 10],
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- # temp=temp,
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- # guidance_scale=7.0,
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- # video_guidance_scale=video_guidance_scale,
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- # output_type="pil",
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- # save_memory=True,
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- # )
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-
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- output_path = "output_video_i2v.mp4"
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- export_to_video(frames, output_path, fps=24)
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- return output_path
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-
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  # Gradio interface
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  with gr.Blocks() as demo:
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  gr.Markdown("# Pyramid Flow")
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  gr.Markdown("Pyramid Flow is a training-efficient Autoregressive Video Generation model based on Flow Matching. It is trained only on open-source datasets within 20.7k A100 GPU hours")
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  gr.Markdown("[[Paper](https://arxiv.org/pdf/2410.05954)], [[Model](https://huggingface.co/rain1011/pyramid-flow-sd3)], [[Code](https://github.com/jy0205/Pyramid-Flow)]")
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- #with gr.Tab("Text-to-Video"):
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  with gr.Row():
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  with gr.Column():
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  with gr.Accordion("Image to Video (optional)", open=False):
@@ -171,22 +144,5 @@ with gr.Blocks() as demo:
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  inputs=[t2v_prompt, i2v_image, t2v_duration, t2v_guidance_scale, t2v_video_guidance_scale],
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  outputs=t2v_output
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  )
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-
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- #with gr.Tab("Image-to-Video"):
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- # with gr.Row():
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- # with gr.Column():
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-
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- # i2v_prompt = gr.Textbox(label="Prompt")
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- # i2v_duration = gr.Slider(minimum=1, maximum=10, value=5, step=1, label="Duration (seconds)")
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- # i2v_video_guidance_scale = gr.Slider(minimum=1, maximum=15, value=4, step=0.1, label="Video Guidance Scale")
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- # i2v_generate_btn = gr.Button("Generate Video")
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- # with gr.Column():
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- # i2v_output = gr.Video(label="Generated Video")
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-
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- #i2v_generate_btn.click(
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- # generate_video_from_image,
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- # inputs=[i2v_image, i2v_prompt, i2v_duration, i2v_video_guidance_scale],
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- # outputs=i2v_output
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- #)
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  demo.launch()
 
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  export_to_video(frames, output_path, fps=8)
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  return output_path
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  # Gradio interface
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  with gr.Blocks() as demo:
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  gr.Markdown("# Pyramid Flow")
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  gr.Markdown("Pyramid Flow is a training-efficient Autoregressive Video Generation model based on Flow Matching. It is trained only on open-source datasets within 20.7k A100 GPU hours")
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  gr.Markdown("[[Paper](https://arxiv.org/pdf/2410.05954)], [[Model](https://huggingface.co/rain1011/pyramid-flow-sd3)], [[Code](https://github.com/jy0205/Pyramid-Flow)]")
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  with gr.Row():
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  with gr.Column():
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  with gr.Accordion("Image to Video (optional)", open=False):
 
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  inputs=[t2v_prompt, i2v_image, t2v_duration, t2v_guidance_scale, t2v_video_guidance_scale],
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  outputs=t2v_output
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  )
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  demo.launch()