handler.py

from typing import  Dict, List, Any
import torch
from torch import autocast
from diffusers import StableDiffusionPipeline
import base64
from io import BytesIO
import os
import random
from os import path
from contextlib import nullcontext
import time
from sys import platform
import torch
from diffusers import AutoPipelineForImage2Image, LCMScheduler
from diffusers.utils import load_image

# 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 the optimized model
        self.pipe = StableDiffusionPipeline.from_pretrained(path, torch_dtype=torch.float16)
        self.pipe = self.pipe.to(device)

        lcm_lora_id = "latent-consistency/lcm-lora-sdv1-5"
        self.pipe.scheduler = LCMScheduler.from_config(self.pipe.scheduler.config)

        self.pipe.load_lora_weights(lcm_lora_id)
        self.pipe.fuse_lora()


    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
        """
        inputs = data.pop("inputs", data)
        
        # run inference pipeline
        with torch.inference_mode():
          with autocast(device.type):
              image = self.pipe(inputs, guidance_scale=1, num_inference_steps=4).images[0]  
            
        # encode image as base 64
        buffered = BytesIO()
        image.save(buffered, format="JPEG")
        img_str = base64.b64encode(buffered.getvalue())

        # postprocess the prediction
        return {"image": img_str.decode()}