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linkdom
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ba8d1a5
1
Parent(s):
147d42f
add three methods
Browse files
app.py
CHANGED
@@ -2,29 +2,100 @@ import gradio as gr
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import numpy as np
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import time
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#
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def
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original_image = image.astype(np.float32) / 255.0
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for i in range(steps):
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time.sleep(0.2)
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# Simulate denoising: gradually revert to the original image
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progress = (i + 1) / steps
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denoised_step = (1 - progress) *
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denoised_step = np.clip(denoised_step, 0, 1)
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yield (denoised_step * 255).astype(np.uint8)
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demo = gr.Interface(
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inputs=[
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gr.Image(type="numpy", label="Input Image", value="https://gradio-builds.s3.amazonaws.com/diffusion_image/cute_dog.jpg"),
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gr.Slider(1,
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],
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outputs=gr.Image(type="numpy", label="
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title="
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description="
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)
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# define queue - required for generators
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import numpy as np
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import time
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# Renamed for clarity and consistency
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def fake_diffusion_denoise(image, steps):
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if image is None:
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yield np.zeros((100, 100, 3), dtype=np.uint8)
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return
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original_image = image.astype(np.float32) / 255.0
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# Add initial noise
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noisy_start_image = original_image + np.random.normal(0, 0.7, original_image.shape)
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noisy_start_image = np.clip(noisy_start_image, 0, 1)
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for i in range(steps):
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time.sleep(0.2)
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# Simulate denoising: gradually revert to the original image (linear progress)
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progress = (i + 1) / steps
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denoised_step = (1 - progress) * noisy_start_image + progress * original_image
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denoised_step = np.clip(denoised_step, 0, 1)
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yield (denoised_step * 255).astype(np.uint8)
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yield (original_image * 255).astype(np.uint8) # Ensure final image is clean
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def real_diffusion_add_noise(image, steps):
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if image is None:
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yield np.zeros((100, 100, 3), dtype=np.uint8)
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return
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base_image = image.astype(np.float32) / 255.0
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max_noise_std = 0.8 # Maximum noise level to reach
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for i in range(steps):
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time.sleep(0.2)
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# Increase noise progressively
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current_noise_std = max_noise_std * ((i + 1) / steps)
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noise = np.random.normal(0, current_noise_std, base_image.shape)
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noisy_step = base_image + noise
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noisy_step = np.clip(noisy_step, 0, 1)
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yield (noisy_step * 255).astype(np.uint8)
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# Yield the most noisy version as the final step
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final_noise = np.random.normal(0, max_noise_std, base_image.shape)
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final_noisy_image = np.clip(base_image + final_noise, 0, 1)
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yield (final_noisy_image * 255).astype(np.uint8)
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def flow_matching_denoise(image, steps):
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if image is None:
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yield np.zeros((100, 100, 3), dtype=np.uint8)
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return
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original_image = image.astype(np.float32) / 255.0
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# Start with a significantly noisy image
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very_noisy_image = original_image + np.random.normal(0, 1.0, original_image.shape) # High initial noise
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very_noisy_image = np.clip(very_noisy_image, 0, 1)
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for i in range(steps):
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time.sleep(0.2)
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# Non-linear progress using a sigmoid-like curve for smoother transition
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p_norm = (i + 1) / steps # Normalized progress 0 to 1
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# Transform p_norm to a range like -5 to 5 for sigmoid
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sigmoid_input = 10 * (p_norm - 0.5)
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flow_progress = 1 / (1 + np.exp(-sigmoid_input))
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denoised_step = (1 - flow_progress) * very_noisy_image + flow_progress * original_image
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denoised_step = np.clip(denoised_step, 0, 1)
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yield (denoised_step * 255).astype(np.uint8)
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yield (original_image * 255).astype(np.uint8) # Ensure final image is clean
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# Main processing function that routes to different methods
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def process_image_selected_method(method_selection, input_image, num_steps):
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if input_image is None:
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# This case should ideally be handled by Gradio if a default image URL is provided
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# or prevented by making the image input mandatory.
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# Yielding a placeholder if it somehow becomes None during processing.
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yield np.zeros((200, 200, 3), dtype=np.uint8)
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return
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if method_selection == "Fake Diffusion (Denoise)":
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yield from fake_diffusion_denoise(input_image, num_steps)
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elif method_selection == "Real Diffusion (Add Noise)":
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yield from real_diffusion_add_noise(input_image, num_steps)
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elif method_selection == "Flow Matching (Denoise)":
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yield from flow_matching_denoise(input_image, num_steps)
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else:
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# Default behavior: return the original image as is, or an error image
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yield input_image
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method_choices = ["Fake Diffusion (Denoise)", "Real Diffusion (Add Noise)", "Flow Matching (Denoise)"]
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demo = gr.Interface(
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fn=process_image_selected_method, # Use the router function
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inputs=[
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gr.Dropdown(choices=method_choices, label="Select Method", value="Fake Diffusion (Denoise)"),
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gr.Image(type="numpy", label="Input Image", value="https://gradio-builds.s3.amazonaws.com/diffusion_image/cute_dog.jpg"),
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gr.Slider(minimum=1, maximum=30, value=10, step=1, label="Processing Steps")
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],
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outputs=gr.Image(type="numpy", label="Processed Image"),
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title="Diffusion Processing Demo",
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description="Select a method: 'Fake Diffusion (Denoise)' and 'Flow Matching (Denoise)' will denoise an image. 'Real Diffusion (Add Noise)' will progressively add noise to the image. Adjust steps for granularity."
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)
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# define queue - required for generators
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