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app.py
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import numpy as np
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from PIL import Image
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import torch
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from torchvision import transforms
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import gradio as gr
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from src.image_prep import canny_from_pil
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from src.pix2pix_turbo import Pix2Pix_Turbo
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model = Pix2Pix_Turbo("edge_to_image")
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def process(input_image, prompt, low_threshold, high_threshold):
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# resize to be a multiple of 8
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new_width = input_image.width - input_image.width % 8
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new_height = input_image.height - input_image.height % 8
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input_image = input_image.resize((new_width, new_height))
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canny = canny_from_pil(input_image, low_threshold, high_threshold)
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with torch.no_grad():
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c_t = transforms.ToTensor()(canny).unsqueeze(0).cuda()
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output_image = model(c_t, prompt)
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output_pil = transforms.ToPILImage()(output_image[0].cpu() * 0.5 + 0.5)
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# flippy canny values, map all 0s to 1s and 1s to 0s
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canny_viz = 1 - (np.array(canny) / 255)
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canny_viz = Image.fromarray((canny_viz * 255).astype(np.uint8))
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return canny_viz, output_pil
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if __name__ == "__main__":
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# load the model
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with gr.Blocks() as demo:
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gr.Markdown("# Pix2pix-Turbo: **Canny Edge -> Image**")
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with gr.Row():
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with gr.Column():
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input_image = gr.Image(sources="upload", type="pil")
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prompt = gr.Textbox(label="Prompt")
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low_threshold = gr.Slider(
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label="Canny low threshold",
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minimum=1,
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maximum=255,
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value=100,
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step=10,
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)
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high_threshold = gr.Slider(
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label="Canny high threshold",
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minimum=1,
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maximum=255,
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value=200,
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step=10,
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)
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run_button = gr.Button(value="Run")
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with gr.Column():
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result_canny = gr.Image(type="pil")
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with gr.Column():
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result_output = gr.Image(type="pil")
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prompt.submit(
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fn=process,
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inputs=[input_image, prompt, low_threshold, high_threshold],
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outputs=[result_canny, result_output],
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)
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low_threshold.change(
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fn=process,
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inputs=[input_image, prompt, low_threshold, high_threshold],
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outputs=[result_canny, result_output],
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)
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high_threshold.change(
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fn=process,
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inputs=[input_image, prompt, low_threshold, high_threshold],
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outputs=[result_canny, result_output],
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)
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run_button.click(
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fn=process,
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inputs=[input_image, prompt, low_threshold, high_threshold],
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outputs=[result_canny, result_output],
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)
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demo.queue()
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demo.launch(debug=True, share=False)
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