handler.py

import base64
from io import BytesIO
from typing import Dict, List, Any

import torch
from PIL import Image
from diffusers import StableDiffusionPipeline


REPO_ID = "runwayml/stable-diffusion-v1-5"


# helper decoder
def decode_base64_image(image_string):
    base64_image = base64.b64decode(image_string)
    buffer = BytesIO(base64_image)
    return Image.open(buffer)


class EndpointHandler:
    def __init__(self, path=""):
        self.pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16,
            revision="fp16", use_auth_token="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")
        self.pipe = self.pipe.to("cuda")

    def __call__(self, data: Any) -> List[List[Dict[str, float]]]:
        """
        Args:
            data (:obj:):
                includes the input data and the parameters for the inference.
        Return:
            A :obj:`dict`:. base64 encoded image
        """
        prompts = data.pop("inputs", None)
        encoded_image = data.pop("image", None)
        init_image = None
        if encoded_image:
            init_image = decode_base64_image(encoded_image)
            init_image.thumbnail((768, 768))
        image = self.pipe(prompts, init_image=init_image).images[0]

        # encode image as base 64
        buffered = BytesIO()
        image.save(buffered, format="png")

        # post process the prediction
        return {"image": buffered.getvalue()}