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Runtime error
Update app.py
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app.py
CHANGED
@@ -31,10 +31,15 @@ def generate(slider_x, slider_y, prompt, seed, iterations, steps,
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print("x_concept_1", x_concept_1, "x_concept_2", x_concept_2)
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if not sorted(slider_x) == sorted([x_concept_1, x_concept_2]):
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avg_diff = clip_slider.find_latent_direction(slider_x[0], slider_x[1], num_iterations=iterations)
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x_concept_1, x_concept_2 = slider_x[0], slider_x[1]
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print("avg_diff[0].dtype", avg_diff[0].dtype)
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if not sorted(slider_y) == sorted([y_concept_1, y_concept_2]):
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avg_diff_2nd = clip_slider.find_latent_direction(slider_y[0], slider_y[1], num_iterations=iterations)
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y_concept_1, y_concept_2 = slider_y[0], slider_y[1]
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end_time = time.time()
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print(f"direction time: {end_time - start_time:.2f} ms")
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@@ -119,11 +124,7 @@ with gr.Blocks(css=css) as demo:
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steps = gr.Slider(label = "num inference steps", minimum=1, value=8, maximum=30)
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seed = gr.Slider(minimum=0, maximum=np.iinfo(np.int32).max, label="Seed", interactive=True, randomize=True)
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inputs=[slider_x, slider_y, prompt, seed, iterations, steps, x_concept_1, x_concept_2, y_concept_1, y_concept_2, avg_diff_x_1, avg_diff_x_2, avg_diff_y_1, avg_diff_y_2],
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outputs=[x, y, x_concept_1, x_concept_2, y_concept_1, y_concept_2, avg_diff_x_1, avg_diff_x_2, avg_diff_y_1, avg_diff_y_2, output_image])
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x.change(fn=update_x, inputs=[x,y, prompt, seed, steps, avg_diff_x_1, avg_diff_x_2, avg_diff_y_1, avg_diff_y_2], outputs=[output_image])
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y.change(fn=update_y, inputs=[x,y, prompt, seed, steps, avg_diff_x_1, avg_diff_x_2, avg_diff_y_1, avg_diff_y_2], outputs=[output_image])
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with gr.Tab(label="image2image"):
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with gr.Row():
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with gr.Column():
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@@ -142,11 +143,16 @@ with gr.Blocks(css=css) as demo:
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steps_a = gr.Slider(label = "num inference steps", minimum=1, value=8, maximum=30)
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seed_a = gr.Slider(minimum=0, maximum=np.iinfo(np.int32).max, label="Seed", interactive=True, randomize=True)
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inputs=[slider_x_a, slider_y_a, prompt_a, seed_a, iterations_a, steps_a, x_concept_1, x_concept_2, y_concept_1, y_concept_2, avg_diff_x_1, avg_diff_x_2, avg_diff_y_1, avg_diff_y_2],
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outputs=[x_a, y_a, x_concept_1, x_concept_2, y_concept_1, y_concept_2, avg_diff_x_1, avg_diff_x_2, avg_diff_y_1, avg_diff_y_2, output_image_a])
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if __name__ == "__main__":
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print("x_concept_1", x_concept_1, "x_concept_2", x_concept_2)
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if not sorted(slider_x) == sorted([x_concept_1, x_concept_2]):
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avg_diff = clip_slider.find_latent_direction(slider_x[0], slider_x[1], num_iterations=iterations)
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avg_diff[0].to(torch.float16)
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avg_diff[1].to(torch.float16)
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x_concept_1, x_concept_2 = slider_x[0], slider_x[1]
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print("avg_diff[0].dtype", avg_diff[0].dtype)
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if not sorted(slider_y) == sorted([y_concept_1, y_concept_2]):
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avg_diff_2nd = clip_slider.find_latent_direction(slider_y[0], slider_y[1], num_iterations=iterations)
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avg_diff_2nd[0].to(torch.float16)
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avg_diff_2nd[1].to(torch.float16)
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y_concept_1, y_concept_2 = slider_y[0], slider_y[1]
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end_time = time.time()
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print(f"direction time: {end_time - start_time:.2f} ms")
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steps = gr.Slider(label = "num inference steps", minimum=1, value=8, maximum=30)
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seed = gr.Slider(minimum=0, maximum=np.iinfo(np.int32).max, label="Seed", interactive=True, randomize=True)
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with gr.Tab(label="image2image"):
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with gr.Row():
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with gr.Column():
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steps_a = gr.Slider(label = "num inference steps", minimum=1, value=8, maximum=30)
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seed_a = gr.Slider(minimum=0, maximum=np.iinfo(np.int32).max, label="Seed", interactive=True, randomize=True)
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submit.click(fn=generate,
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inputs=[slider_x, slider_y, prompt, seed, iterations, steps, x_concept_1, x_concept_2, y_concept_1, y_concept_2, avg_diff_x_1, avg_diff_x_2, avg_diff_y_1, avg_diff_y_2],
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outputs=[x, y, x_concept_1, x_concept_2, y_concept_1, y_concept_2, avg_diff_x_1, avg_diff_x_2, avg_diff_y_1, avg_diff_y_2, output_image])
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x.change(fn=update_x, inputs=[x,y, prompt, seed, steps, avg_diff_x_1, avg_diff_x_2, avg_diff_y_1, avg_diff_y_2], outputs=[output_image])
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y.change(fn=update_y, inputs=[x,y, prompt, seed, steps, avg_diff_x_1, avg_diff_x_2, avg_diff_y_1, avg_diff_y_2], outputs=[output_image])
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submit_a.click(fn=generate,
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inputs=[slider_x_a, slider_y_a, prompt_a, seed_a, iterations_a, steps_a, x_concept_1, x_concept_2, y_concept_1, y_concept_2, avg_diff_x_1, avg_diff_x_2, avg_diff_y_1, avg_diff_y_2],
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outputs=[x_a, y_a, x_concept_1, x_concept_2, y_concept_1, y_concept_2, avg_diff_x_1, avg_diff_x_2, avg_diff_y_1, avg_diff_y_2, output_image_a])
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x_a.change(fn=update_x, inputs=[x_a,y_a, prompt_a, seed_a, steps_a, avg_diff_x_1, avg_diff_x_2, avg_diff_y_1, avg_diff_y_2], outputs=[output_image_a])
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y_a.change(fn=update_y, inputs=[x_a,y_a, prompt, seed_a, steps_a, avg_diff_x_1, avg_diff_x_2, avg_diff_y_1, avg_diff_y_2], outputs=[output_image_a])
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if __name__ == "__main__":
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