app.py

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()