Update app.py

app.py

@@ -35,17 +35,17 @@ def predict_clothing(images):
         with torch.no_grad():
             output = model.generate(**inputs, max_new_tokens=32)
 
-        # .<|eot_id|>
-        output_reponse = str(processor.decode(output[0])).split('\n')[-1]
-        output_texts.append(output_reponse[:-11])
+        
+        output_reponse = str(processor.decode(output[0])).split('\n')[-1] 
+        output_texts.append(output_reponse[:-11])   # without .<|eot_id|>
         
     print(output_texts)
     return output_texts
     
 
-@spaces.GPU(duration=180)
+@spaces.GPU()
 def generate_image(category, img1, img2, img3, height, width, img_guidance_scale, inference_steps, seed, separate_cfg_infer, offload_model,
-                   use_input_image_size_as_output, max_input_image_size, randomize_seed, guidance_scale=3):
+                   use_input_image_size_as_output, max_input_image_size, randomize_seed, guidance_scale=2.6):
 
     print()
     input_images = [img1, img2, img3]
@@ -95,8 +95,7 @@ def get_example():
     ]
     return case
 
-def run_for_examples(img1, img2, img3, height, width, img_guidance_scale, seed, max_input_image_size, randomize_seed,
-                     use_input_image_size_as_output, guidance_scale=3):    
+def run_for_examples(img1, img2, img3, height, width, img_guidance_scale, seed, max_input_image_size, randomize_seed, use_input_image_size_as_output,):    
     # Check the internal configuration of the function
     inference_steps = 50
     separate_cfg_infer = True
@@ -110,17 +109,16 @@ def run_for_examples(img1, img2, img3, height, width, img_guidance_scale, seed,
 description = """
 This is a Virtual Try-On Platform.
 
-Usage:
+### Usage:
 - First upload your own image as the first image, also tagged 'Person'
 - Then upload you 'Top-wear' and 'Bottom-wear' images
 - If its a single dress, and/or you don't have a Topwear and Bottomwear as separate images upload that single image under 'Topwear'
 
-Tips:
-- For image editing task and controlnet task, we recommend setting the height and width of output image as the same as input image. For example, if you want to edit a 512x512 image, you should set the height and width of output image as 512x512. You also can set the `use_input_image_size_as_output` to automatically set the height and width of output image as the same as input image.
+### Tips:
 - For out-of-memory or time cost, you can set `offload_model=True` or refer to [./docs/inference.md#requiremented-resources](https://github.com/VectorSpaceLab/OmniGen/blob/main/docs/inference.md#requiremented-resources) to select a appropriate setting.
 - If inference time is too long when inputting multiple images, please try to reduce the `max_input_image_size`. For more details please refer to [./docs/inference.md#requiremented-resources](https://github.com/VectorSpaceLab/OmniGen/blob/main/docs/inference.md#requiremented-resources).
 
-**HF Spaces often encounter errors due to quota limitations, so recommend to run it locally.**
+**Please note that HF Spaces often encounter errors due to GPU quota or other limitations, so please try lowering the image sizes and inference steps to manage the generation.**
 """
 
 Credits = """**Credits**