import gradio as gr import numpy as np import time def normalize(image): # different methods to map image values to [0, 1] # scale to [0, 1] using min-max normalization # image = (image - np.min(image, keepdims=True)) / (np.max(image, keepdims=True) - np.min(image, keepdims=True)) # standardize to zero mean and unit variance, then scale to [0, 1] # image = (image - np.mean(image, keepdims=True)) / (np.std(image, keepdims=True) + 1e-8) # Avoid division by zero # image = (image + 1) / 2 # Scale to [0, 1] # just clip to [0, 1] return np.clip(image, 0, 1) # Renamed for clarity and consistency def fake_diffusion_denoise(image, steps): original_image = image.astype(np.float32) / 255.0 # Add initial noise noisy_start_image = original_image + np.random.normal(0, 0.7, original_image.shape) noisy_start_image = normalize(noisy_start_image) for i in range(steps): time.sleep(0.2) # Simulate denoising: gradually revert to the original image (linear progress) progress = (i + 1) / steps denoised_step = (1 - progress) * noisy_start_image + progress * original_image denoised_step = normalize(denoised_step) yield (denoised_step * 255).astype(np.uint8) yield (original_image * 255).astype(np.uint8) # Ensure final image is clean def real_diffusion_add_noise(image, steps): base_image = image.astype(np.float32) / 255.0 max_noise_std = 0.8 # Maximum noise level to reach for i in range(steps): time.sleep(0.2) # Increase noise progressively current_noise_std = max_noise_std * ((i + 1) / steps) noise = np.random.normal(0, current_noise_std, base_image.shape) noisy_step = base_image + noise noisy_step = normalize(noisy_step) yield (noisy_step * 255).astype(np.uint8) # Yield the most noisy version as the final step final_noise = np.random.normal(0, max_noise_std, base_image.shape) final_noisy_image = normalize(base_image + final_noise) yield (final_noisy_image * 255).astype(np.uint8) def flow_matching_denoise(image, steps): original_image = image.astype(np.float32) / 255.0 # Start with a significantly noisy image very_noisy_image = original_image + np.random.normal(0, 1.0, original_image.shape) # High initial noise very_noisy_image = normalize(very_noisy_image) for i in range(steps): time.sleep(0.2) # Non-linear progress using a sigmoid-like curve for smoother transition p_norm = (i + 1) / steps # Normalized progress 0 to 1 # Transform p_norm to a range like -5 to 5 for sigmoid sigmoid_input = 10 * (p_norm - 0.5) flow_progress = 1 / (1 + np.exp(-sigmoid_input)) denoised_step = (1 - flow_progress) * very_noisy_image + flow_progress * original_image denoised_step = normalize(denoised_step) yield (denoised_step * 255).astype(np.uint8) yield (original_image * 255).astype(np.uint8) # Ensure final image is clean # Main processing function that routes to different methods def process_image_selected_method(method_selection, input_image, num_steps): if input_image is None: yield np.zeros((200, 200, 3), dtype=np.uint8) return if method_selection == "Fake Diffusion (Denoise)": yield from fake_diffusion_denoise(input_image, num_steps) elif method_selection == "Real Diffusion (Add Noise)": yield from real_diffusion_add_noise(input_image, num_steps) elif method_selection == "Flow Matching (Denoise)": yield from flow_matching_denoise(input_image, num_steps) else: yield input_image method_choices = ["Fake Diffusion (Denoise)", "Real Diffusion (Add Noise)", "Flow Matching (Denoise)"] with gr.Blocks() as demo: gr.Markdown("# Diffusion Processing Demo") gr.Markdown("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.") with gr.Row(): method_selection = gr.Dropdown(choices=method_choices, label="Select Method", value="Fake Diffusion (Denoise)") num_steps = gr.Slider(minimum=1, maximum=30, value=10, step=1, label="Processing Steps") with gr.Row(): input_image = gr.Image(type="numpy", label="Input Image", value="https://gradio-builds.s3.amazonaws.com/diffusion_image/cute_dog.jpg") output_image = gr.Image(type="numpy", label="Processed Image") process_button = gr.Button("Process Image") process_button.click( fn=process_image_selected_method, inputs=[method_selection, input_image, num_steps], outputs=output_image ) # define queue - required for generators demo.queue() demo.launch()