Update handler.py

handler.py

@@ -12,16 +12,6 @@ device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 if device.type != 'cuda':
     raise ValueError("need to run on GPU")
 
-def make_inpaint_condition(image, image_mask):
-    image = np.array(image.convert("RGB")).astype(np.float32) / 255.0
-    image_mask = np.array(image_mask.convert("L")).astype(np.float32) / 255.0
-
-    assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
-    image[image_mask > 0.5] = -1.0  # set as masked pixel
-    image = np.expand_dims(image, 0).transpose(0, 3, 1, 2)
-    image = torch.from_numpy(image)
-    return image
-
 class EndpointHandler():
     def __init__(self, path=""):
 
@@ -42,7 +32,7 @@ class EndpointHandler():
             "runwayml/stable-diffusion-v1-5", controlnet=self.controlnet, torch_dtype=torch.float16
         )
 
-        self.pipe.scheduler = DDIMScheduler.from_config(pipe.scheduler.config)
+        self.pipe.scheduler = DDIMScheduler.from_config(self.pipe.scheduler.config)
         self.pipe.enable_model_cpu_offload()
         self.pipe.enable_xformers_memory_efficient_attention()
 
@@ -161,7 +151,7 @@ class EndpointHandler():
         return image2
         """
 
-        control_image = make_inpaint_condition(image, mask_image)
+        control_image = self.make_inpaint_condition(image, mask_image)
 
         # generate image
         image = pipe(
@@ -184,3 +174,13 @@ class EndpointHandler():
         buffer = BytesIO(base64_image)
         image = Image.open(buffer)
         return image
+
+    def make_inpaint_condition(self, image, image_mask):
+        image = np.array(image.convert("RGB")).astype(np.float32) / 255.0
+        image_mask = np.array(image_mask.convert("L")).astype(np.float32) / 255.0
+    
+        assert image.shape[0:1] == image_mask.shape[0:1], "image and image_mask must have the same image size"
+        image[image_mask > 0.5] = -1.0  # set as masked pixel
+        image = np.expand_dims(image, 0).transpose(0, 3, 1, 2)
+        image = torch.from_numpy(image)
+        return image