Update app.py

app.py

@@ -25,6 +25,7 @@ image = pipe(prompt).images[0]
 #import cv2
 
 from diffusers import StableDiffusionInpaintPipeline
+
 import numpy as np
 import os
 import shutil
@@ -45,10 +46,10 @@ def load_inpainting_model():
     ).to(device)
     return pipe
 
+"""
 # Load the model once globally to avoid repeated loading
 def load_upscaling_model():
     # Load pipeline
-    """
     device = "cpu"  # Explicitly use CPU
     controlnet = FluxControlNetModel.from_pretrained(
         "jasperai/Flux.1-dev-Controlnet-Upscaler",
@@ -59,21 +60,21 @@ def load_upscaling_model():
         controlnet=controlnet,
         torch_dtype=torch.float32
     ).to(device)
-    """
     pipe = DiffusionPipeline.from_pretrained("jasperai/Flux.1-dev-Controlnet-Upscaler")    
     return pipe
+"""
 
 # Preload the model once
 inpaint_pipeline = load_inpainting_model()
 # Preload the model once
-upscale_pipeline = load_upscaling_model()
+#upscale_pipeline = load_upscaling_model()
 
 # Function to resize image (simpler interpolation method for speed)
 def resize_to_match(input_image, output_image):
 
-    w, h = output_image.size
-    control_image = output_image.resize((w * 4, h * 4))
-   
+    #w, h = output_image.size
+    #control_image = output_image.resize((w * 4, h * 4))
+    """    
     scaled_image = pipe(
         prompt="", 
         control_image=control_image,
@@ -83,13 +84,13 @@ def resize_to_match(input_image, output_image):
         height=control_image.size[1],
         width=control_image.size[0]
     ).images[0]    
-    
-    return scaled_image
+    """
+    #return scaled_image
     
     #torch_img = pil_to_torch(input_image)
     #torch_img_scaled = F.interpolate(torch_img.unsqueeze(0),mode='trilinear').squeeze(0)
     #output_image = torchvision.transforms.functional.to_pil_image(torch_img_scaled, mode=None)
-    #return output_image.resize(input_image.size, Image.BICUBIC)  # Use BILINEAR for faster resizing
+    return output_image.resize(input_image.size, Image.BICUBIC)  # Use BILINEAR for faster resizing
 
 # Function to generate the mask using Florence SAM Masking API (Replicate)
 def generate_mask(image_path, text_prompt="clothing"):
@@ -113,7 +114,7 @@ def inpaint_image(input_image, mask_image):
     prompt = "undress, naked"
     result = inpaint_pipeline(prompt=prompt, image=input_image, mask_image=mask_image)
     inpainted_image = result.images[0]
-    inpainted_image = resize_to_match(input_image, inpainted_image)
+    #inpainted_image = resize_to_match(input_image, inpainted_image)
     return inpainted_image
 
 # Function to process input image and mask