Update app.py

app.py

@@ -4,7 +4,7 @@ import torch
 from PIL import Image
 
 model_id = 'Norod78/sd2-simpsons-blip'
-prefix = ''
+prefix = None
      
 scheduler = DPMSolverMultistepScheduler(
     beta_start=0.00085,
@@ -37,10 +37,9 @@ def error_str(error, title="Error"):
     return f"""#### {title}
             {error}"""  if error else ""
 
-def inference(prompt, guidance, steps, width=512, height=512, seed=0, img=None, strength=0.5, neg_prompt="", auto_prefix=False):
+def inference(prompt, guidance, steps, width=512, height=512, seed=0, img=None, strength=0.5, neg_prompt=""):
 
   generator = torch.Generator('cuda').manual_seed(seed) if seed != 0 else None
-  prompt = f"{prefix} {prompt}" if auto_prefix else prompt
 
   try:
     if img is not None:
@@ -110,7 +109,7 @@ with gr.Blocks(css=css) as demo:
         with gr.Column(scale=55):
           with gr.Group():
               with gr.Row():
-                prompt = gr.Textbox(label="Prompt", show_label=False, max_lines=2,placeholder=f"{prefix} [your prompt]").style(container=False)
+                prompt = gr.Textbox(label="Prompt", show_label=False, max_lines=2,placeholder="[your prompt]").style(container=False)
                 generate = gr.Button(value="Generate").style(rounded=(False, True, True, False))
 
               image_out = gr.Image(height=512)
@@ -119,8 +118,7 @@ with gr.Blocks(css=css) as demo:
         with gr.Column(scale=45):
           with gr.Tab("Options"):
             with gr.Group():
-              neg_prompt = gr.Textbox(label="Negative prompt", placeholder="What to exclude from the image")
-              auto_prefix = gr.Checkbox(label="Prefix styling tokens automatically ()", value=prefix, visible=prefix)
+              neg_prompt = gr.Textbox(label="Negative prompt", placeholder="What to exclude from the image")              
 
               with gr.Row():
                 guidance = gr.Slider(label="Guidance scale", value=7.5, maximum=15)
@@ -137,9 +135,7 @@ with gr.Blocks(css=css) as demo:
                 image = gr.Image(label="Image", height=256, tool="editor", type="pil")
                 strength = gr.Slider(label="Transformation strength", minimum=0, maximum=1, step=0.01, value=0.5)
 
-    auto_prefix.change(lambda x: gr.update(placeholder=f"{prefix} [your prompt]" if x else "[Your prompt]"), inputs=auto_prefix, outputs=prompt, queue=False)
-
-    inputs = [prompt, guidance, steps, width, height, seed, image, strength, neg_prompt, auto_prefix]
+    inputs = [prompt, guidance, steps, width, height, seed, image, strength, neg_prompt]
     outputs = [image_out, error_output]
     prompt.submit(inference, inputs=inputs, outputs=outputs)
     generate.click(inference, inputs=inputs, outputs=outputs)