handler.py

from typing import  Dict, List, Any
import torch
from diffusers import StableDiffusionXLImg2ImgPipeline
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=""):

        self.smooth_pipe = StableDiffusionXLImg2ImgPipeline.from_pretrained(
          "stabilityai/stable-diffusion-xl-refiner-1.0", torch_dtype=torch.float16
        )
        self.smooth_pipe.to("cuda")
        


    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)
        
        prompt = data.pop("prompt", "")


        if encoded_image is not None:
            image = self.decode_base64_image(encoded_image)
            self.smooth_pipe.enable_xformers_memory_efficient_attention()
            out = self.smooth_pipe(prompt, image=image, ).images[0]

            return out
    
    # 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