Update app.py

app.py

@@ -2,11 +2,11 @@ import tensorflow as tf
 import huggingface_hub as hf_hub
 import gradio as gr
 
-num_rows = 3
-num_cols = 3
+num_rows = 4
+num_cols = 2
 num_images = num_rows * num_cols
 image_size = 64
-plot_image_size = 256
+plot_image_size = 128
 
 model = hf_hub.from_pretrained_keras("beresandras/denoising-diffusion-models")
 
@@ -23,7 +23,7 @@ def diffusion_schedule(diffusion_times, min_signal_rate, max_signal_rate):
 
 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), seed=42)
+    initial_noise = tf.random.normal(shape=(num_images, image_size, image_size, 3))
     
     # reverse diffusion
     noisy_images = initial_noise
@@ -64,7 +64,7 @@ inputs = [
     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 = [[2, 0.0, 0.02, 0.95], [10, 0.0, 0.02, 0.95], [20, 1.0, 0.02, 0.95]]
+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(