Update app.py

app.py

@@ -255,9 +255,8 @@ with gr.Blocks(css=css, theme=theme) as app:
             with gr.Row():
                 with gr.Column(scale=4, min_width=300):
                     input_img = gr.Image(label="Input Picture", type="filepath")
-                    
                 with gr.Column(scale=3):
-                    output_text = gr.Textbox(label="Flux Prompt", lines=2, scale=6, show_copy_button = True)
+                    output_text = gr.Textbox(label="Prompt", lines=2, scale=6, show_copy_button = True)
                     submit_btn = gr.Button(value="Generate Pompt", scale=4, variant='primary')
                     clear_prompt =gr.Button("Clear 🗑️",variant="primary", elem_id="clear_button")
                     clear_prompt.click(lambda: (None, None), None, [input_img, output_text], queue=False, show_api=False)
@@ -265,21 +264,28 @@ with gr.Blocks(css=css, theme=theme) as app:
     submit_btn.click(feifeichat, [input_img], [output_text])
 
     with gr.Tab("Basic Settings"):
-        with gr.Row():
-            prompt = gr.Textbox(placeholder="Enter the image description...", show_label=True, label='Image Prompt ✍️', lines=3, show_copy_button = True)
-        with gr.Row():
-            task = gr.Radio(interactive=True, value="Stable Diffusion XL 1.0", show_label=True, label="Model of neural network ❄️ ", choices=['Stable Diffusion XL 1.0', 'Crystal Clear XL', 
-                                                                                                              'Juggernaut XL', 'DreamShaper XL',
-                                                                                                              'SDXL Niji', 'Cinemax SDXL', 'NightVision XL'])
+        with gr.Column(scale=3):
+            with gr.Row():
+                prompt = gr.Textbox(placeholder="Enter the image description...", show_label=True, label='Image Prompt ✍️', lines=3, show_copy_button = True)
+            with gr.Row():
+                task = gr.Radio(interactive=True, value="Stable Diffusion XL 1.0", show_label=True, label="Model of neural network ❄️ ", choices=['Stable Diffusion XL 1.0', 'Crystal Clear XL', 
+                                                                                                                  'Juggernaut XL', 'DreamShaper XL',
+                                                                                                                  'SDXL Niji', 'Cinemax SDXL', 'NightVision XL'])
     with gr.Accordion("Advanced Options", open=True):
         with gr.Row():
             negative_prompt = gr.Textbox(placeholder="Negative Prompt", show_label=True, label='Negative Prompt:', lines=3, value="[deformed | disfigured], poorly drawn, [bad : wrong] anatomy, [extra | missing | floating | disconnected] limb, (mutated hands and fingers), blurry")
+        with gr.Row():    
             sampler = gr.Dropdown(value="DPM++ 2M Karras", show_label=True, label="Sampling Method:", choices=[
                 "DPM++ 2M Karras", "DPM++ 2S a Karras", "DPM2 a Karras", "DPM2 Karras", "DPM++ SDE Karras", "DEIS", "LMS", "DPM Adaptive", "DPM++ 2M", "DPM2 Ancestral", "DPM++ S", "DPM++ SDE", "DDPM", "DPM Fast", "dpmpp_2s_ancestral", "Euler", "Euler CFG PP", "Euler a", "Euler Ancestral", "Euler+beta", "Heun", "Heun PP2", "DDIM", "LMS Karras", "PLMS", "UniPC", "UniPC BH2"])
+        with gr.Row():
             steps = gr.Slider(show_label=True, label="Sampling Steps:", minimum=1, maximum=50, value=35, step=1)
+        with gr.Row():    
             cfg_scale = gr.Slider(show_label=True, label="CFG Scale:", minimum=1, maximum=20, value=7, step=1)
+        with gr.Row():    
             seed = gr.Number(show_label=True, label="Seed:", minimum=-1, maximum=1000000, value=-1, step=1)
+        with gr.Row():    
             width = gr.Slider(label="Width", minimum=512, maximum=2048, step=8, value=896, interactive=True,)
+        with gr.Row():    
             height = gr.Slider(label="Height", minimum=512, maximum=2048, step=8, value=1152,interactive=True,)
         with gr.Column():
             with gr.Row():