remove noise

app.py

@@ -2,7 +2,6 @@ import gradio as gr
 import torch
 from diffusers import StableDiffusionInpaintPipeline, LCMScheduler
 from PIL import Image
-import numpy as np
 
 # 1. Load the most compatible inpainting model
 model_id = "runwayml/stable-diffusion-inpainting"
@@ -41,27 +40,15 @@ def predict(image_data, prompt):
     base_image = raw_bg.resize((new_w, new_h), Image.LANCZOS)
     mask_image = raw_mask.resize((new_w, new_h), Image.NEAREST)
 
-    # 2. NOISE INJECTION (The Fix)
-    # This manually overwrites the masked area with random colors.
-    # This forces the AI to stop "respecting" the floor and start "creating" the cat.
-    img_array = np.array(base_image)
-    mask_array = np.array(mask_image)
-    # Create random noise
-    noise = np.random.randint(0, 255, img_array.shape, dtype=np.uint8)
-    # Where the mask is white (>128), replace original pixels with noise
-    img_array[mask_array > 128] = noise[mask_array > 128]
-    noised_base = Image.fromarray(img_array)
-
     # return mask_image.resize((orig_w, orig_h), Image.NEAREST)
 
     # 3. RUN THE MODEL
     result = pipe(
         prompt=prompt,
-        image=noised_base, # Use the noised version!
+        image=base_image, # Use the noised version!
         mask_image=mask_image,
         num_inference_steps=20,    # Increased slightly for better detail on CPU
         guidance_scale=8.0,       # Increased to make the "Cat" more likely to appear
-        strength=1.0               # Complete replacement
     ).images[0]