Update app.py

app.py

@@ -21,13 +21,14 @@ pipe = pipe.to(device)
 
 MAX_SEED = np.iinfo(np.int32).max
 MAX_IMAGE_SIZE = 2048
+from PIL import Image
 def safe_infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_steps):
-    # check if prompt contains any banned words
-    if any(word in prompt.lower() for word in targets):
+    clean = ''.join(c for c in prompt.lower() if c.isalnum() or c.isspace())
+    if any(word in clean for word in targets):
         print("Found at least one banned word!")
-        return "Refused due to safety.", seed
+        blank = Image.new("RGB", (width, height), (0, 0, 0))
+        return blank, seed
 
-    # otherwise run normal inference
     return infer(prompt, negative_prompt, seed, randomize_seed, width, height, guidance_scale, num_steps)
 
 # @spaces.GPU #[uncomment to use ZeroGPU]
@@ -130,7 +131,7 @@ with gr.Blocks(css=css) as demo:
                 guidance_scale = gr.Slider(
                     label="Guidance scale",
                     minimum=0.0,
-                    maximum=10.0,
+                    maximum=20.0,
                     step=0.1,
                     value=3.6,  # Replace with defaults that work for your model
                 )
@@ -138,7 +139,7 @@ with gr.Blocks(css=css) as demo:
                 num_inference_steps = gr.Slider(
                     label="Number of inference steps",
                     minimum=1,
-                    maximum=50,
+                    maximum=80,
                     step=1,
                     value=15,  # Replace with defaults that work for your model
                 )
@@ -146,8 +147,6 @@ with gr.Blocks(css=css) as demo:
         gr.Examples(examples=examples, inputs=[prompt])
     gr.on(
         triggers=[run_button.click, prompt.submit],
-        if any(word in prompt for word in targets):
-            print("Found at least one!")
         fn=safe_infer,
         inputs=[
             prompt,