Update app.py

app.py

@@ -8,7 +8,7 @@ num_images = num_rows * num_cols
 image_size = 64
 plot_image_size = 256
 
-model = hf_hub.from_pretrained_keras("beresandras/denoising-diffusion-model")
+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)
@@ -37,8 +37,7 @@ def generate_images(diffusion_steps, stochasticity, min_signal_rate, max_signal_
         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 = model([noisy_images, noise_rates])
-        pred_images = (noisy_images - noise_rates * pred_noises) / signal_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