Update app.py

app.py

@@ -8,34 +8,29 @@ import time
 
 # --- Model Loading (CPU Version) ---
 print("Loading model on CPU... This may take several minutes.")
+# This is the corrected line that fixes the error.
 pipe = AutoPipelineForInpainting.from_pretrained(
-    "runwayml/stable-diffusion-inpainting" # Using the slightly smaller 1.5 model for better CPU performance
+    "stabilityai/stable-diffusion-2-inpainting",
+    torch_dtype=torch.float32, # Use float32 for broad CPU compatibility
+    safety_checker=None        # Explicitly disable the safety checker to prevent loading errors
 )
 print("Model loaded successfully.")
 
+
 # --- Default "Magic" Prompts ---
-# These will be used if the user doesn't provide their own prompt.
 DEFAULT_PROMPT = "photorealistic, 4k, ultra high quality, sharp focus, masterpiece, high detail, professional photo"
 DEFAULT_NEGATIVE_PROMPT = "blurry, pixelated, distorted, deformed, ugly, disfigured, cartoon, anime, low quality, watermark, text"
 
 # --- The Inpainting Function ---
-# It now handles the logic for an optional user prompt.
 def inpaint_image(input_dict, user_prompt, guidance_scale, num_steps, progress=gr.Progress()):
-    """
-    Performs inpainting. Uses a default prompt if the user_prompt is empty.
-    """
     image = input_dict["image"].convert("RGB")
     mask_image = input_dict["mask"].convert("RGB")
 
-    # --- This is the core logic for the hybrid approach ---
     if user_prompt and user_prompt.strip():
-        # If the user provided a prompt, use it.
         prompt = user_prompt
-        # For custom prompts, a general negative prompt is still useful.
         negative_prompt = DEFAULT_NEGATIVE_PROMPT 
         print(f"Using custom prompt: '{prompt}'")
     else:
-        # If the user left the prompt box empty, use our high-quality defaults.
         prompt = DEFAULT_PROMPT
         negative_prompt = DEFAULT_NEGATIVE_PROMPT
         print(f"User prompt is empty. Using default 'General Fix' prompt.")
@@ -43,11 +38,9 @@ def inpaint_image(input_dict, user_prompt, guidance_scale, num_steps, progress=g
     print(f"Starting inpainting on CPU...")
     start_time = time.time()
     
-    # Callback to update the progress bar in the UI
     def progress_callback(step, timestep, latents):
         progress(step / int(num_steps), desc=f"Running step {step}/{int(num_steps)}")
 
-    # Run the pipeline
     result_image = pipe(
         prompt=prompt,
         image=image,
@@ -61,7 +54,6 @@ def inpaint_image(input_dict, user_prompt, guidance_scale, num_steps, progress=g
     
     end_time = time.time()
     print(f"Inpainting finished in {end_time - start_time:.2f} seconds.")
-
     return result_image
 
 
@@ -70,7 +62,6 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
     gr.Markdown(
         """
         # 🎨 AI Image Fixer
-        
         **How to use:**
         1. Upload an image.
         2. Use the brush to **paint over the area you want to fix**.
@@ -82,31 +73,17 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
     
     gr.Warning(
         "⚠️ This Space is running on a free CPU. "
-        "Image generation will be VERY SLOW (expect 5-15 minutes). "
+        "Image generation will be VERY SLOW (expect 5-20 minutes). "
         "A progress bar will show the status below the button. Please be patient!"
     )
     
     with gr.Row():
-        # Input Column
         with gr.Column(scale=2):
-            input_image = gr.Image(
-                label="1. Upload & Mask Image",
-                source="upload",
-                tool="brush",
-                type="pil"
-            )
-            
-            # The prompt textbox is back, but now it's optional!
-            prompt_textbox = gr.Textbox(
-                label="2. Describe Your Fix (Optional)",
-                placeholder="Leave empty for a general fix, or type e.g., 'a perfect human hand'"
-            )
-            
+            input_image = gr.Image(label="1. Upload & Mask Image", source="upload", tool="brush", type="pil")
+            prompt_textbox = gr.Textbox(label="2. Describe Your Fix (Optional)", placeholder="Leave empty for a general fix, or type e.g., 'a perfect human hand'")
             with gr.Accordion("Advanced Settings", open=False):
                 guidance_scale = gr.Slider(minimum=0, maximum=20, value=8.0, label="Guidance Scale")
                 num_steps = gr.Slider(minimum=10, maximum=50, step=1, value=25, label="Inference Steps")
-        
-        # Output Column
         with gr.Column(scale=1):
             output_image = gr.Image(label="Result", type="pil")