Update app.py

app.py

@@ -2,7 +2,7 @@ import gradio as gr
 import numpy as np
 import random
 
-import spaces #[uncomment to use ZeroGPU]
+import spaces  # [uncomment to use ZeroGPU]
 from diffusers import DiffusionPipeline
 import torch
 
@@ -20,8 +20,11 @@ pipe = pipe.to(device)
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 1024
 
+# Specific prefixes for the prompt and negative prompt
+prompt_prefix = "score_9, score_8_up, score_7_up, source_anime"
+negative_prompt_prefix = "score_6, score_5, score_4, source_cartoon, 3d, (censor), monochrome, blurry, lowres, watermark"
 
-@spaces.GPU #[uncomment to use ZeroGPU]
+@spaces.GPU  # [uncomment to use ZeroGPU]
 def infer(
     prompt,
     negative_prompt,
@@ -31,17 +34,20 @@ def infer(
     height,
     guidance_scale,
     num_inference_steps,
-    # progress=gr.Progress(track_tqdm=True),
-    progress=None,
+    progress=gr.Progress(track_tqdm=True),
 ):
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
 
     generator = torch.Generator().manual_seed(seed)
 
+    # Prepend the specific terms to the prompt and negative prompt
+    full_prompt = f"{prompt_prefix}, {prompt}"
+    full_negative_prompt = f"{negative_prompt_prefix}, {negative_prompt}"
+
     image = pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
+        prompt=full_prompt,
+        negative_prompt=full_negative_prompt,
         guidance_scale=guidance_scale,
         num_inference_steps=num_inference_steps,
         width=width,
@@ -87,7 +93,7 @@ with gr.Blocks(css=css) as demo:
                 label="Negative prompt",
                 max_lines=1,
                 placeholder="Enter a negative prompt",
-                visible=False,
+                visible=True,
             )
 
             seed = gr.Slider(