Update app.py

app.py

@@ -321,7 +321,7 @@ def _generate_image(prompt_embeds, image_list, width, height, num_inference_step
         }
         return None, error_info
 
-def infer(prompt, input_images=None, seed=42, randomize_seed=False, width=1024, height=1024, num_inference_steps=50, guidance_scale=2.5, prompt_upsampling=False, progress=gr.Progress()):
+def infer(prompt, input_images=None, seed=42, randomize_seed=False, width=1024, height=1024, num_inference_steps=50, guidance_scale=2.5, progress=gr.Progress()):
     
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
@@ -338,13 +338,6 @@ def infer(prompt, input_images=None, seed=42, randomize_seed=False, width=1024,
 
     # 1. Upsampling (Network bound - No GPU needed)
     final_prompt = prompt
-    # 强制不优化提示词
-    # if prompt_upsampling:
-    #     progress(0.05, desc="Upsampling prompt...")
-    #     final_prompt = upsample_prompt_logic(prompt, image_list)
-    #     print(f"Original Prompt: {prompt}")
-    #     print(f"Upsampled Prompt: {final_prompt}")
-
     # 2. Text Encoding (Network bound - No GPU needed)
     progress(0.1, desc="Encoding prompt...")
     # This returns CPU tensors
@@ -422,11 +415,6 @@ with gr.Blocks() as demo:
                     )
                 
                 with gr.Accordion("Advanced Settings", open=False):
-                    prompt_upsampling = gr.Checkbox(
-                        label="Prompt Upsampling",
-                        value=False,
-                        info="Automatically enhance the prompt using a VLM"
-                    )
         
                     seed = gr.Slider(
                         label="Seed",
@@ -500,7 +488,7 @@ with gr.Blocks() as demo:
     gr.on(
         triggers=[run_button.click, prompt.submit],
         fn=infer,
-        inputs=[prompt, input_images, seed, randomize_seed, width, height, num_inference_steps, guidance_scale, prompt_upsampling],
+        inputs=[prompt, input_images, seed, randomize_seed, width, height, num_inference_steps, guidance_scale],
         outputs=[result, seed]
     )