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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=6)
+@spaces.GPU(duration=10)
 def run(content_image, style_name, style_strength, output_quality, progress=gr.Progress(track_tqdm=True)):
     img_size = 1024 if output_quality else 512
     content_img, original_size = preprocess_img(content_image, img_size)
@@ -66,7 +66,7 @@ def run(content_image, style_name, style_strength, output_quality, progress=gr.P
     et = time.time()
     print('TIME TAKEN:', et-st)
     
-    return postprocess_img(generated_img, original_size)
+    yield postprocess_img(generated_img, original_size)
 
 
 def set_slider(value):

inference.py

@@ -30,8 +30,8 @@ def inference(
     alpha=1,
     beta=1
 ):
-    geenrated_image = content_image.clone().requires_grad_(True)
-    optimizer = optim.AdamW([geenrated_image], lr=lr)
+    generated_image = content_image.clone().requires_grad_(True)
+    optimizer = optim.AdamW([generated_image], lr=lr)
 
     with torch.no_grad():
         content_features = model(content_image)
@@ -39,10 +39,10 @@ def inference(
     for _ in tqdm(range(iterations), desc='The magic is happening ✨'):
         optimizer.zero_grad()
 
-        generated_features = model(geenrated_image)
+        generated_features = model(generated_image)
         total_loss = _compute_loss(generated_features, content_features, style_features, alpha, beta)
 
         total_loss.backward()
         optimizer.step()
     
-    return geenrated_image
+    return generated_image