Upload handler.py

handler.py

@@ -0,0 +1,58 @@
+from typing import  Dict, List, Any
+import torch
+from diffusers import DPMSolverMultistepScheduler, StableDiffusionInpaintPipeline
+from PIL import Image
+import base64
+from io import BytesIO
+
+
+# set device
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+if device.type != 'cuda':
+    raise ValueError("need to run on GPU")
+
+class EndpointHandler():
+    def __init__(self, path=""):
+        # load StableDiffusionInpaintPipeline pipeline
+        self.pipe = StableDiffusionInpaintPipeline.from_pretrained(
+            "runwayml/stable-diffusion-inpainting",
+            revision="fp16",
+            torch_dtype=torch.float16,
+        )
+        # use DPMSolverMultistepScheduler
+        self.pipe.scheduler = DPMSolverMultistepScheduler.from_config(self.pipe.scheduler.config)
+        # move to device        
+        self.pipe = self.pipe.to(device)
+
+
+    def __call__(self, data: Any) -> List[List[Dict[str, float]]]:
+        """
+        :param data: A dictionary contains `inputs` and optional `image` field.
+        :return: A dictionary with `image` field contains image in base64.
+        """
+        encoded_image = data.pop("image", None)
+        encoded_mask_image = data.pop("mask_image", None)
+        
+        prompt = data.pop("prompt", "")
+        
+        # process image
+        if encoded_image is not None and encoded_mask_image is not None:
+            image = self.decode_base64_image(encoded_image)
+            mask_image = self.decode_base64_image(encoded_mask_image)
+        else:
+            image = None
+            mask_image = None 
+        
+        # run inference pipeline
+        out = self.pipe(prompt=prompt, image=image, mask_image=mask_image)
+            
+        # return first generate PIL image
+        return out.images[0]
+    
+    # helper to decode input image
+    def decode_base64_image(self, image_string):
+        base64_image = base64.b64decode(image_string)
+        buffer = BytesIO(base64_image)
+        image = Image.open(buffer)
+        return image