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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("beresandras/denoising-diffusion-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"
article = "<div style='text-align: center;'>Keras code example 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,
article=article,
).launch() |