two tab
Browse files
app.py
CHANGED
@@ -268,6 +268,15 @@ def infer(
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# "A delicious ceviche cheesecake slice",
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# ]
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examples = [
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["A robot cooking dinner in the kitchen", "An orange cat wearing sunglasses on a ship"],
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]
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@@ -283,170 +292,229 @@ with gr.Blocks(css=css) as demo:
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gr.Markdown("# CrossFlow")
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gr.Markdown("[CrossFlow](https://cross-flow.github.io/) directly transforms text representations into images for text-to-image generation, without the need for both the noise distribution and conditioning mechanism.")
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gr.Markdown("This direct mapping enables meaningful 'Linear Interpolation' and 'Arithmetic Operations' in the text latent space, as demonstrated here.")
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if __name__ == "__main__":
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# "A delicious ceviche cheesecake slice",
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# ]
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def infer_tab1(prompt1, prompt2, seed, randomize_seed, guidance_scale, num_inference_steps, num_of_interpolation):
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default_op = "Addition"
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return infer(prompt1, prompt2, seed, randomize_seed, guidance_scale, num_inference_steps, num_of_interpolation, default_op)
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# Wrapper for Tab 2: Uses operation_mode and fixes num_of_interpolation to 3.
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def infer_tab2(prompt1, prompt2, seed, randomize_seed, guidance_scale, num_inference_steps, operation_mode):
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default_interpolation = 3
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return infer(prompt1, prompt2, seed, randomize_seed, guidance_scale, num_inference_steps, default_interpolation, operation_mode)
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+
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examples = [
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["A robot cooking dinner in the kitchen", "An orange cat wearing sunglasses on a ship"],
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]
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gr.Markdown("# CrossFlow")
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gr.Markdown("[CrossFlow](https://cross-flow.github.io/) directly transforms text representations into images for text-to-image generation, without the need for both the noise distribution and conditioning mechanism.")
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gr.Markdown("This direct mapping enables meaningful 'Linear Interpolation' and 'Arithmetic Operations' in the text latent space, as demonstrated here.")
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# with gr.Tabs():
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# with gr.Tab("Linear Interpolation"):
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# gr.Markdown("This demo uses 256px images, 25 sampling steps (instead of 50), and 10 interpolations (instead of 50) to conserve GPU memory. For better results, see the original [code](https://github.com/qihao067/CrossFlow). (You may adjust them in Advanced Settings, but doing so may trigger OOM errors.)")
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# # gr.Markdown("CrossFlow directly transforms text representations into images for text-to-image generation, enabling interpolation in the input text latent space.")
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# with gr.Row():
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# prompt1 = gr.Text(
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# label="Prompt_1",
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# show_label=False,
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# max_lines=1,
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# placeholder="Enter your prompt for the first image",
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# container=False,
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# )
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# with gr.Row():
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# prompt2 = gr.Text(
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# label="Prompt_2",
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# show_label=False,
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# max_lines=1,
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# placeholder="Enter your prompt for the second image",
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# container=False,
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# )
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# with gr.Row():
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# run_button = gr.Button("Run", scale=0, variant="primary")
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# # Create separate outputs for the first image, last image, and the animated GIF
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# first_image_output = gr.Image(label="Image of the first prompt", show_label=True)
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# last_image_output = gr.Image(label="Image of the second prompt", show_label=True)
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# gif_output = gr.Image(label="Linear interpolation", show_label=True)
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# with gr.Accordion("Advanced Settings", open=False):
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# seed = gr.Slider(
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# label="Seed",
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# minimum=0,
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# maximum=MAX_SEED,
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# step=1,
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# value=0,
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# )
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# randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
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# with gr.Row():
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# guidance_scale = gr.Slider(
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# label="Guidance scale",
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# minimum=0.0,
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# maximum=10.0,
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# step=0.1,
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# value=7.0, # Replace with defaults that work for your model
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# )
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# with gr.Row():
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# num_inference_steps = gr.Slider(
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# label="Number of inference steps - 50 inference steps are recommended; but you can reduce to 20 if the demo fails.",
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# minimum=1,
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# maximum=50,
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# step=1,
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# value=25, # Replace with defaults that work for your model
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# )
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# with gr.Row():
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# num_of_interpolation = gr.Slider(
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# label="Number of images for interpolation - More images yield smoother transitions but require more resources and may fail.",
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# minimum=5,
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# maximum=50,
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# step=1,
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# value=10, # Replace with defaults that work for your model
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# )
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# gr.Examples(examples=examples, inputs=[prompt1, prompt2])
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# with gr.Tab("Arithmetic Operations"):
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# # The second tab is currently empty. You can add more components later.
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# gr.Markdown("This demo only supports addition or subtraction between two text latents ('Prompt_1 + Prompt_2' or 'Prompt_1 - Prompt_2'). For the other arithmetic operations, see the original [code](https://github.com/qihao067/CrossFlow).")
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# with gr.Row():
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# prompt1 = gr.Text(
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# label="Prompt_1",
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# show_label=False,
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# max_lines=1,
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# placeholder="Enter your prompt for the first image",
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# container=False,
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# )
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# with gr.Row():
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# prompt2 = gr.Text(
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# label="Prompt_2",
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# show_label=False,
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# max_lines=1,
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# placeholder="Enter your prompt for the second image",
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# container=False,
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# )
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# with gr.Row():
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# operation_mode = gr.Radio(
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# choices=["Addition", "Subtraction"],
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# label="Operation Mode",
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# value="Addition",
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# )
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# with gr.Row():
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# run_button = gr.Button("Run", scale=0, variant="primary")
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# # Create separate outputs for the first image, last image, and the animated GIF
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# first_image_output = gr.Image(label="Image of the first prompt", show_label=True)
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# last_image_output = gr.Image(label="Image of the second prompt", show_label=True)
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# gif_output = gr.Image(label="Linear interpolation", show_label=True)
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# with gr.Accordion("Advanced Settings", open=False):
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# seed = gr.Slider(
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# label="Seed",
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# minimum=0,
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# maximum=MAX_SEED,
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# step=1,
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# value=0,
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# )
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# randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
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# with gr.Row():
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# guidance_scale = gr.Slider(
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# label="Guidance scale",
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# minimum=0.0,
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# maximum=10.0,
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# step=0.1,
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# value=7.0, # Replace with defaults that work for your model
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# )
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# # with gr.Row():
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# # num_inference_steps = gr.Slider(
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# # label="Number of inference steps - 50 inference steps are recommended; but you can reduce to 20 if the demo fails.",
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# # minimum=1,
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# # maximum=50,
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# # step=1,
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# # value=55, # Replace with defaults that work for your model
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# # )
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# with gr.Row():
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# num_of_interpolation = gr.Slider(
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# label="Number of images for interpolation - More images yield smoother transitions but require more resources and may fail.",
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# minimum=5,
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# maximum=50,
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# step=1,
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# value=50, # Replace with defaults that work for your model
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# )
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# gr.Examples(examples=examples, inputs=[prompt1, prompt2])
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# gr.on(
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# triggers=[run_button.click, prompt1.submit, prompt2.submit],
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# fn=infer,
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# inputs=[
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# prompt1,
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# prompt2,
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# seed,
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# randomize_seed,
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# guidance_scale,
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# num_inference_steps,
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# num_of_interpolation,
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# ],
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# outputs=[first_image_output, last_image_output, gif_output, seed],
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# # outputs=[first_image_output, last_image_output, seed],
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# )
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with gr.Blocks(css=css) as demo:
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with gr.Tabs():
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# --- Tab 1: Interpolation Mode (no operation_mode) ---
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with gr.Tab("Tab 1: Interpolation Mode"):
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gr.Markdown("**Tab 1:** This tab uses a slider for the number of interpolated images. The operation mode is fixed to *Addition* by default.")
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prompt1_tab1 = gr.Text(placeholder="Prompt for first image", label="Prompt 1")
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prompt2_tab1 = gr.Text(placeholder="Prompt for second image", label="Prompt 2")
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seed_tab1 = gr.Slider(minimum=0, maximum=MAX_SEED, step=1, value=0, label="Seed")
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randomize_seed_tab1 = gr.Checkbox(label="Randomize seed", value=True)
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guidance_scale_tab1 = gr.Slider(minimum=0.0, maximum=10.0, step=0.1, value=7.0, label="Guidance Scale")
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num_inference_steps_tab1 = gr.Slider(minimum=1, maximum=50, step=1, value=25, label="Number of Inference Steps")
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num_of_interpolation_tab1 = gr.Slider(minimum=5, maximum=50, step=1, value=10, label="Number of Images for Interpolation")
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run_button_tab1 = gr.Button("Run")
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first_image_output_tab1 = gr.Image(label="Image of the first prompt")
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last_image_output_tab1 = gr.Image(label="Image of the second prompt")
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gif_output_tab1 = gr.Image(label="Linear interpolation")
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run_button_tab1.click(
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fn=infer_tab1,
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inputs=[
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prompt1_tab1,
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prompt2_tab1,
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seed_tab1,
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randomize_seed_tab1,
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guidance_scale_tab1,
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num_inference_steps_tab1,
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num_of_interpolation_tab1
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],
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outputs=[first_image_output_tab1, last_image_output_tab1, gif_output_tab1, seed_tab1]
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)
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# --- Tab 2: Operation Mode (no num_of_interpolation) ---
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with gr.Tab("Tab 2: Operation Mode"):
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gr.Markdown("**Tab 2:** This tab lets you choose the operation mode (Addition or Subtraction) while fixing the number of interpolations to 3.")
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prompt1_tab2 = gr.Text(placeholder="Prompt for first image", label="Prompt 1")
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prompt2_tab2 = gr.Text(placeholder="Prompt for second image", label="Prompt 2")
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seed_tab2 = gr.Slider(minimum=0, maximum=MAX_SEED, step=1, value=0, label="Seed")
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randomize_seed_tab2 = gr.Checkbox(label="Randomize seed", value=True)
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guidance_scale_tab2 = gr.Slider(minimum=0.0, maximum=10.0, step=0.1, value=7.0, label="Guidance Scale")
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num_inference_steps_tab2 = gr.Slider(minimum=1, maximum=50, step=1, value=25, label="Number of Inference Steps")
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operation_mode_tab2 = gr.Radio(choices=["Addition", "Subtraction"], label="Operation Mode", value="Addition")
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run_button_tab2 = gr.Button("Run")
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first_image_output_tab2 = gr.Image(label="Image of the first prompt")
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last_image_output_tab2 = gr.Image(label="Image of the second prompt")
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gif_output_tab2 = gr.Image(label="Linear interpolation")
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run_button_tab2.click(
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fn=infer_tab2,
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inputs=[
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prompt1_tab2,
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prompt2_tab2,
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seed_tab2,
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randomize_seed_tab2,
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guidance_scale_tab2,
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num_inference_steps_tab2,
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operation_mode_tab2
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],
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outputs=[first_image_output_tab2, last_image_output_tab2, gif_output_tab2, seed_tab2]
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)
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if __name__ == "__main__":
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