fixing

handler.py

@@ -9,10 +9,10 @@ device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 
 class EndpointHandler():
     def __init__(self, path=""):
-        model_id = "timbrooks/instruct-pix2pix"
-        self.pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32, safety_checker=None)
-        self.pipe.to(device)
-        self.pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(self.pipe.scheduler.config)
+        #model_id = "timbrooks/instruct-pix2pix"
+        #self.pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32, safety_checker=None)
+        #self.pipe.to(device)
+        #self.pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(self.pipe.scheduler.config)
 
     def __call__(self, data: Dict[str, Any]) -> List[Dict[str, Any]]:
         """
@@ -23,6 +23,12 @@ class EndpointHandler():
             A :obj:`string`:. Base64 encoded image string
         """
 
+        model_id = "timbrooks/instruct-pix2pix"
+        self.pipe = StableDiffusionInstructPix2PixPipeline.from_pretrained(model_id, torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32, safety_checker=None)
+        self.pipe.to(device)
+        self.pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(self.pipe.scheduler.config)
+
+
         image_data = data.pop("inputs", data)
         # decode base64 image to PIL
         image = Image.open(BytesIO(base64.b64decode(image_data)))