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| import tensorflow as tf | |
| import huggingface_hub as hf_hub | |
| import gradio as gr | |
| num_rows = 2 | |
| num_cols = 4 | |
| num_images = num_rows * num_cols | |
| image_size = 64 | |
| plot_image_size = 128 | |
| model = hf_hub.from_pretrained_keras("keras-io/denoising-diffusion-implicit-models") | |
| def diffusion_schedule(diffusion_times, min_signal_rate, max_signal_rate): | |
| start_angle = tf.acos(max_signal_rate) | |
| end_angle = tf.acos(min_signal_rate) | |
| diffusion_angles = start_angle + diffusion_times * (end_angle - start_angle) | |
| signal_rates = tf.cos(diffusion_angles) | |
| noise_rates = tf.sin(diffusion_angles) | |
| return noise_rates, signal_rates | |
| def generate_images(diffusion_steps, stochasticity, min_signal_rate, max_signal_rate): | |
| step_size = 1.0 / diffusion_steps | |
| initial_noise = tf.random.normal(shape=(num_images, image_size, image_size, 3)) | |
| # reverse diffusion | |
| noisy_images = initial_noise | |
| for step in range(diffusion_steps): | |
| diffusion_times = tf.ones((num_images, 1, 1, 1)) - step * step_size | |
| next_diffusion_times = diffusion_times - step_size | |
| noise_rates, signal_rates = diffusion_schedule(diffusion_times, min_signal_rate, max_signal_rate) | |
| next_noise_rates, next_signal_rates = diffusion_schedule(next_diffusion_times, min_signal_rate, max_signal_rate) | |
| sample_noises = tf.random.normal(shape=(num_images, image_size, image_size, 3)) | |
| sample_noise_rates = stochasticity * (1.0 - (signal_rates / next_signal_rates)**2)**0.5 * (next_noise_rates / noise_rates) | |
| pred_noises, pred_images = model([noisy_images, noise_rates, signal_rates]) | |
| noisy_images = ( | |
| next_signal_rates * pred_images | |
| + (next_noise_rates**2 - sample_noise_rates**2)**0.5 * pred_noises | |
| + sample_noise_rates * sample_noises | |
| ) | |
| # denormalize | |
| data_mean = tf.constant([[[[0.4705, 0.3943, 0.3033]]]]) | |
| data_std_dev = tf.constant([[[[0.2892, 0.2364, 0.2680]]]]) | |
| generated_images = data_mean + pred_images * data_std_dev | |
| generated_images = tf.clip_by_value(generated_images, 0.0, 1.0) | |
| # make grid | |
| generated_images = tf.image.resize(generated_images, (plot_image_size, plot_image_size), method="nearest") | |
| generated_images = tf.reshape(generated_images, (num_rows, num_cols, plot_image_size, plot_image_size, 3)) | |
| generated_images = tf.transpose(generated_images, (0, 2, 1, 3, 4)) | |
| generated_images = tf.reshape(generated_images, (num_rows * plot_image_size, num_cols * plot_image_size, 3)) | |
| return generated_images.numpy() | |
| inputs = [ | |
| gr.inputs.Slider(1, 20, step=1, default=10, label="Diffusion steps"), | |
| gr.inputs.Slider(0.0, 1.0, step=0.05, default=0.0, label="Stochasticity (η in the paper)"), | |
| gr.inputs.Slider(0.02, 0.10, step=0.01, default=0.02, label="Minimal signal rate"), | |
| gr.inputs.Slider(0.80, 0.95, step=0.01, default=0.95, label="Maximal signal rate"), | |
| ] | |
| output = gr.outputs.Image(label="Generated images") | |
| examples = [[3, 0.0, 0.02, 0.95], [10, 0.0, 0.02, 0.95], [20, 1.0, 0.02, 0.95]] | |
| title = "Denoising Diffusion Implicit Models 🌹💨" | |
| description = "Generating images with a denoising diffusion implicit model, trained on the Oxford Flowers dataset. <br/> For details, check out the corresponding <a href='https://keras.io/examples/generative/ddim/' target='_blank'>Keras code example</a>, and the <a href='https://github.com/beresandras/clear-diffusion-keras' target='_blank'>code repository</a> that was used for ablations, with additional features." | |
| article = "<div style='text-align: center;'><a href='https://keras.io/examples/generative/ddim/' target='_blank'>Keras code example</a> and demo by <a href='https://www.linkedin.com/in/andras-beres-789190210' target='_blank'>András Béres</a></div>" | |
| gr.Interface( | |
| generate_images, | |
| inputs=inputs, | |
| outputs=output, | |
| examples=examples, | |
| title=title, | |
| description=description, | |
| article=article, | |
| ).launch() |