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Update app.py
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app.py
CHANGED
@@ -28,7 +28,7 @@ def inference(input_img, num_inference_steps, guidance_scale, seed ):
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#result = pipeline(cond, num_inference_steps=75).images[0]
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result = pipeline(cond, num_inference_steps=num_inference_steps,
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guidance_scale=guidance_scale,
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generator=torch.Generator(pipeline.device).manual_seed(seed)).images[0]
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# for general real and synthetic images of general objects
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# usually it is enough to have around 28 inference steps
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@@ -49,7 +49,7 @@ import gradio as gr
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with gr.Blocks() as demo:
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gr.Markdown("<h1><center> Zero123++ Demo</center></h1>")
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with gr.Column():
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input_img = gr.Image(label='Input Image',
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with gr.Column():
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output_img = gr.Image(label='Zero123++ Output')
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with gr.Accordion("Advanced options:", open=False):
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@@ -73,7 +73,7 @@ with gr.Blocks() as demo:
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],
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inputs=[input_img, num_inference_steps, guidance_scale, seed],
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outputs=output_img,
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fn=
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cache_examples=True,
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)
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#result = pipeline(cond, num_inference_steps=75).images[0]
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result = pipeline(cond, num_inference_steps=num_inference_steps,
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guidance_scale=guidance_scale,
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generator=torch.Generator(pipeline.device).manual_seed(int(seed))).images[0]
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# for general real and synthetic images of general objects
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# usually it is enough to have around 28 inference steps
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with gr.Blocks() as demo:
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gr.Markdown("<h1><center> Zero123++ Demo</center></h1>")
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with gr.Column():
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input_img = gr.Image(label='Input Image', type='filepath')
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with gr.Column():
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output_img = gr.Image(label='Zero123++ Output')
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with gr.Accordion("Advanced options:", open=False):
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],
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inputs=[input_img, num_inference_steps, guidance_scale, seed],
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outputs=output_img,
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fn=inference,
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cache_examples=True,
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)
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