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app.py

@@ -40,7 +40,7 @@ for style_name, style_img_path in style_options.items():
         style_features = (model(style_img_512), model(style_img_1024))
     cached_style_features[style_name] = style_features 
 
-@spaces.GPU(duration=10)
+@spaces.GPU(duration=15)
 def run(content_image, style_name, style_strength, output_quality, progress=gr.Progress(track_tqdm=True)):
     yield None
     img_size = 1024 if output_quality else 512

inference.py

@@ -33,18 +33,21 @@ def inference(
 ):
     generated_image = content_image.clone().requires_grad_(True)
     optimizer = optim_caller([generated_image], lr=lr)
+    min_losses = [[]] * iterations
 
     with torch.no_grad():
         content_features = model(content_image)
         
-    def closure():
+    def closure(iter):
         optimizer.zero_grad()
         generated_features = model(generated_image)
         total_loss = _compute_loss(generated_features, content_features, style_features, alpha, beta)
         total_loss.backward()
+        min_losses[iter] = min(min_losses[iter], total_loss.item())
         return total_loss
     
-    for _ in tqdm(range(iterations), desc='The magic is happening ✨'):
+    for iter in tqdm(range(iterations), desc='The magic is happening ✨'):
         optimizer.step(closure)
+        print(f'Loss ({iter+1}):', min_losses[iter])
     
     return generated_image