Update app.py

app.py

@@ -25,13 +25,13 @@ if st.button("Generate Image") and text:
     image_features = clip_processor(images=example_image, return_tensors="pt", padding=True)
 
     # Ensure the dimensions of pixel_values are the same for text and image features
-    max_len = max(text_features.pixel_values.shape[1], image_features.pixel_values.shape[1])
-    text_features.pixel_values = torch.nn.functional.pad(text_features.pixel_values, (0, max_len - text_features.pixel_values.shape[1]))
-    image_features.pixel_values = torch.nn.functional.pad(image_features.pixel_values, (0, max_len - image_features.pixel_values.shape[1]))
+    max_len = max(text_features['pixel_values'].shape[1], image_features['pixel_values'].shape[1])
+    text_features['pixel_values'] = torch.nn.functional.pad(text_features['pixel_values'], (0, max_len - text_features['pixel_values'].shape[1]))
+    image_features['pixel_values'] = torch.nn.functional.pad(image_features['pixel_values'], (0, max_len - image_features['pixel_values'].shape[1]))
 
     # Concatenate text and image features
     combined_features = {
-        "pixel_values": torch.cat([text_features.pixel_values, image_features.pixel_values], dim=1)
+        "pixel_values": torch.cat([text_features['pixel_values'], image_features['pixel_values']], dim=1)
     }
 
     # Forward pass through CLIP
@@ -41,5 +41,5 @@ if st.button("Generate Image") and text:
     image_array = image_representation.squeeze().cpu().numpy()
     generated_image = Image.fromarray((image_array * 255).astype('uint8'))
 
-    
+    # Display the generated image
     st.image(generated_image, caption="Generated Image", use_column_width=True)