Update app.py

app.py

@@ -39,8 +39,8 @@ def infer(prompt_input, negative_prompt_input, seed, width, height, guidance_sca
         
     with torch.inference_mode():
         consume_time_list.append(time.time() - start_time - sum(consume_time_list))
-        # sample_width, sample_height = clamp_size(width, height)
-        sample_width, sample_height = width, height
+        sample_width, sample_height = clamp_size(width, height)
+        # sample_width, sample_height = width, height
         emptylatentimage_5 = emptylatentimage.generate(
             width=sample_width, height=sample_height, batch_size=1
         )
@@ -118,15 +118,15 @@ def infer(prompt_input, negative_prompt_input, seed, width, height, guidance_sca
         consume_time_list.append(time.time() - start_time - sum(consume_time_list))
 
 
-        if width <= 1024 and height <= 1024:
-            image_tensor = get_value_at_index(ksampler_efficient_23, 5)[0]
-            image_tensor = torch.clamp(image_tensor * 255.0, 0, 255)  # calc to 255 on gpu
-            image_uint8 = image_tensor.cpu().numpy().astype(np.uint8)
-            # pillow_img = Image.fromarray(image_uint8)
-            consume_time_list.append(time.time() - start_time - sum(consume_time_list))
-            consume_time = time.time() - start_time
-            print(f"[{datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}] consume：{consume_time:.1f}s ({[f'{t:.1f}' for t in consume_time_list]})")
-            return image_uint8
+        # if width <= 1024 and height <= 1024:
+        #     image_tensor = get_value_at_index(ksampler_efficient_23, 5)[0]
+        #     image_tensor = torch.clamp(image_tensor * 255.0, 0, 255)  # calc to 255 on gpu
+        #     image_uint8 = image_tensor.cpu().numpy().astype(np.uint8)
+        #     # pillow_img = Image.fromarray(image_uint8)
+        #     consume_time_list.append(time.time() - start_time - sum(consume_time_list))
+        #     consume_time = time.time() - start_time
+        #     print(f"[{datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}] consume：{consume_time:.1f}s ({[f'{t:.1f}' for t in consume_time_list]})")
+        #     return image_uint8
 
             
         imagescaleby_17 = imagescaleby.upscale(