Update app.py

app.py

@@ -78,7 +78,6 @@ def get_size(init_image):
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
-
 # Load, init model
 controlnet = ControlNetModel().from_pretrained("briaai/DEV-ControlNetInpaintingFast", torch_dtype=torch.float16)
 vae = AutoencoderKL.from_pretrained("madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16)
@@ -96,10 +95,6 @@ generator = torch.Generator(device='cuda').manual_seed(123456)
 vae = pipe.vae
 
 pipe.enable_model_cpu_offload() 
-# pipe.force_zeros_for_empty_prompt = False
-
-# default_negative_prompt= "" #"Logo,Watermark,Text,Ugly,Morbid,Extra fingers,Poorly drawn hands,Mutation,Blurry,Extra limbs,Gross proportions,Missing arms,Mutated hands,Long neck,Duplicate,Mutilated,Mutilated hands,Poorly drawn face,Deformed,Bad anatomy,Cloned face,Malformed limbs,Missing legs,Too many fingers"
-
 
 def read_content(file_path: str) -> str:
     """read the content of target file
@@ -113,21 +108,14 @@ def predict(dict, prompt="", negative_prompt = default_negative_prompt, guidance
     if negative_prompt == "":
         negative_prompt = None
 
-    
-    # init_image = dict["image"].convert("RGB").resize((1024, 1024))
-    # mask = dict["mask"].convert("L").resize((1024, 1024))
+    init_image = dict["image"].convert("RGB")#.resize((1024, 1024))
+    mask = dict["mask"].convert("L")#.resize((1024, 1024))
 
     width, height = get_size(init_image)
 
     init_image = init_image.resize((width, height))
     mask = mask.resize((width, height))
     
-    # Resize to nearest ratio ?
-    
-    # mask = np.array(mask)
-    # mask[mask>0]=255
-    # mask = Image.fromarray(mask)
-    
 
     masked_image, image_mask, masked_image_to_present = get_masked_image(init_image, mask, width, height)
     masked_image_tensor = image_transforms(masked_image)