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import gradio as gr |
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from evolution import random_walk |
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from generate import generate |
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def process_random_walk(img): |
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img1, _ = random_walk(img) |
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return img1 |
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def process_first_generation(img1, model_path="pretrain_weights/b2m/unet_ema"): |
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generated_images = generate(img1, model_path) |
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return generated_images[0] |
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def process_second_generation(img1, model_path="pretrain_weights/m2i/unet_ema"): |
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generated_images = generate(img1, model_path) |
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return generated_images[0] |
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with gr.Blocks() as app: |
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with gr.Row(): |
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with gr.Column(): |
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input_image = gr.Image(value="figs/4.png", image_mode='L', type='numpy', label="Upload Grayscale Image") |
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process_button_1 = gr.Button("1. Process Evolution") |
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with gr.Column(): |
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output_image_1 = gr.Image(value="figs/4_1.png", image_mode='L', type="numpy", label="After Evolution Image",sources=[]) |
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process_button_2 = gr.Button("2. Generate Masks") |
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with gr.Row(): |
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with gr.Column(): |
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output_image_3 = gr.Image(value="figs/4_1_mask.png", image_mode='L', type="numpy", label="Generated Mask Image",sources=[]) |
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process_button_3 = gr.Button("3. Generate Images") |
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with gr.Column(): |
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output_image_5 = gr.Image(value="figs/4_1.jpg", type="numpy", image_mode='RGB', label="Final Generated Image 1",sources=[]) |
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process_button_1.click( |
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process_random_walk, |
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inputs=[input_image], |
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outputs=[output_image_1] |
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) |
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process_button_2.click( |
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process_first_generation, |
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inputs=[output_image_1], |
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outputs=[output_image_3] |
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) |
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process_button_3.click( |
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process_second_generation, |
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inputs=[output_image_3], |
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outputs=[output_image_5] |
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) |
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app.launch() |
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