Update app.py

app.py

@@ -121,13 +121,13 @@ def run_autoencoder_sandbox(
     progress(0, desc="Preprocessing image...")
     img_tensor, original_resized = preprocess_image(input_image, resolution)
     
-    # Flatten dimensions
+    # Flatten dimensions using .reshape() instead of .view() to avoid layout conflicts
     input_dim = 3 * resolution * resolution
-    flat_clean = img_tensor.view(1, -1)
+    flat_clean = img_tensor.reshape(1, -1)
     
     # 2. Setup Noise (for Denoising mode)
     noisy_img_tensor = add_gaussian_noise(img_tensor, noise_factor)
-    flat_input = noisy_img_tensor.view(1, -1)
+    flat_input = noisy_img_tensor.reshape(1, -1)
     
     # Pre-render the noisy input for user preview
     noisy_preview = postprocess_tensor(noisy_img_tensor, resolution)
@@ -177,8 +177,8 @@ def run_autoencoder_sandbox(
         if epoch % update_every == 0 or epoch == epochs - 1:
             progress((epoch + 1) / epochs, desc=f"Epoch {epoch+1}/{epochs} | Loss: {loss.item():.5f}")
             
-            # Format reconstructed image back to normal shape
-            reconstructed_tensor = reconstructed.view(1, 3, resolution, resolution)
+            # Format reconstructed image back to normal shape using .reshape()
+            reconstructed_tensor = reconstructed.reshape(1, 3, resolution, resolution)
             reconstructed_pil = postprocess_tensor(reconstructed_tensor, resolution)
             
             # Generate loss graph