Update app.py

app.py

@@ -24,16 +24,25 @@ def reduce_seeds(seed):
     group_size = 100  # Cyclic group of size 100
     return seed % group_size
 
-# Fourier-based optimization
+# Fourier-based optimization with proper tensor conversion
 def fft_convolution(image):
+    # Convert the image to a PyTorch tensor
+    tensor_image = torch.tensor(image).permute(2, 0, 1).float()  # Convert to tensor and adjust channels
+
     # Convert to frequency domain using FFT
-    freq_image = fftn(image)
-    # Apply some transformation in the frequency domain (e.g., filtering, smoothing)
-    # This is a placeholder; you can implement any frequency domain operation here
-    transformed_freq_image = freq_image * np.exp(-np.abs(freq_image))  # Example operation
+    freq_image = fftn(tensor_image)
+
+    # Apply some transformation in the frequency domain (example: filtering, smoothing)
+    # This is a placeholder; implement any frequency domain operation here
+    transformed_freq_image = freq_image * torch.exp(-torch.abs(freq_image))  # Example operation
+
     # Convert back to spatial domain using inverse FFT
     transformed_image = ifftn(transformed_freq_image).real
-    return transformed_image
+
+    # Convert back to NumPy array and adjust channels back to original shape
+    result_image = transformed_image.permute(1, 2, 0).numpy()
+    
+    return result_image
 
 def infer(prompt_part1, color, dress_type, design, prompt_part5, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_inference_steps):
     prompt = f"{prompt_part1} {color} colored plain {dress_type} with {design} design, {prompt_part5}"