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Build error
Update app.py
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app.py
CHANGED
@@ -54,12 +54,12 @@ def resize_image(input_path, output_path, target_height):
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return output_path
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def infer(image_in, prompt):
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n_prompt = 'NSFW, nude, naked, porn, ugly'
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image_to_canny = load_image(image_in)
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image_to_canny = np.array(image_to_canny)
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image_to_canny = cv2.Canny(image_to_canny, 100, 200)
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image_to_canny = image_to_canny[:, :, None]
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@@ -71,38 +71,53 @@ def infer(image_in, prompt):
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dict(
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control_index=0,
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control_image=image_to_canny,
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control_weight=
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control_pooled_projections='zeros'
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)
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]
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# infer
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image = pipe(
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prompt=prompt,
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negative_prompt=n_prompt,
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controlnet_conditioning=controlnet_conditioning,
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num_inference_steps=
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guidance_scale=
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height=1024,
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width=1024,
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).images[0]
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return image
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gr.Markdown("""
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# SD3 ControlNet
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""")
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submit_btn.click(
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fn = infer,
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inputs = [image_in, prompt],
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outputs = [result],
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show_api=False
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)
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demo.queue().launch()
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return output_path
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def infer(image_in, prompt, inference_steps, guidance_scale, control_weight):
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n_prompt = 'NSFW, nude, naked, porn, ugly'
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# Canny preprocessing
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image_to_canny = load_image(image_in)
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image_to_canny = np.array(image_to_canny)
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image_to_canny = cv2.Canny(image_to_canny, 100, 200)
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image_to_canny = image_to_canny[:, :, None]
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dict(
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control_index=0,
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control_image=image_to_canny,
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control_weight=control_weight,
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control_pooled_projections='zeros'
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)
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]
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# infer
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image = pipe(
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prompt=prompt,
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negative_prompt=n_prompt,
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controlnet_conditioning=controlnet_conditioning,
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num_inference_steps=inference_steps,
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guidance_scale=guidance_scale,
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).images[0]
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return image, image_to_canny
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css="""
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#col-container{
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margin: 0 auto;
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max-width: 1080px;
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}
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"""
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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.Markdown("""
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# SD3 ControlNet
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""")
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with gr.Row():
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with gr.Column():
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image_in = gr.Image(label="Image reference", sources=["upload"], type="filepath")
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prompt = gr.Textbox(label="Prompt")
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with gr.Accordion("Advanced settings", open=False):
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with gr.Column():
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with gr.Row():
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inference_steps = gr.Slider(label="Inference steps", minimum=1, maximum=50, step=1, value=25)
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guidance_scale = gr.Slider(label="Guidance scale", minimum=1.0, maximum=10.0, step=0.1, value=7.0)
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control_weight = gr.Slider(label="Control Weight", minimum=0.0, maximum=1.0, step=0.01, value=0.7)
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submit_btn = gr.Button("Submit")
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with gr.Column():
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result = gr.Image(label="Result")
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canny_used = gr.Image(label="Preprocessed Canny")
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submit_btn.click(
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fn = infer,
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inputs = [image_in, prompt, inference_steps, guidance_scale, control_weight],
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outputs = [result, canny_used],
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show_api=False
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)
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demo.queue().launch()
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