handler.py

from typing import  Dict, List, Any
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionInpaintPipeline, EulerAncestralDiscreteScheduler
from PIL import Image
import base64
from io import BytesIO
import numpy as np
# from RealESRGAN import RealESRGAN

# 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(path, torch_dtype=torch.float16)
        # use EulerAncestralDiscreteScheduler
        self.pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(self.pipe.scheduler.config)
        # pipe.enable_sequential_cpu_offload()
        # move to device
        self.pipe.to(device)
        self.pipe.enable_xformers_memory_efficient_attention()

        # self.upscaler = RealESRGAN(device, scale=4)
        # self.upscaler.load_weights('weights/RealESRGAN_x4.pth', download=True)


    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.
        """
        inputs = data.pop("inputs", data)
        encoded_image = data.pop("image", None)
        encoded_mask_image = data.pop("mask_image", None)
        num_images = data.pop("num_images", None)
        print(f"num_image {num_images}")
        if num_images > 4 or num_images < 1:
          return {"Invalid Request": "Number of generated images must be >= 1 and <=4"}

        # hyperparamters
        num_inference_steps = data.pop("num_inference_steps", 50)
        guidance_scale = data.pop("guidance_scale", 7.5)
        negative_prompt = data.pop("negative_prompt", None)
        height = data.pop("height", None)
        width = data.pop("width", None)

        # 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(inputs,
                        image=image,
                        mask_image=mask_image,
                        num_inference_steps=num_inference_steps,
                        guidance_scale=guidance_scale,
                        num_images_per_prompt=num_images,
                        negative_prompt=negative_prompt,
                        height=height,
                        width=width
        ).images

        # for i in range(len(out)):
        #   gen_img = Image.composite(out[i], image.resize(out[i].size), mask_image.resize(out[i].size))
        #   gen_img = self.upscaler.predict(gen_img)
        #   gen_img = Image.composite(gen_img, image.resize(gen_img.size), mask_image.resize(gen_img.size))
        #   out[i] = gen_img

        # return first generate PIL image
        json_imgs = {}
        for i in range(len(out)):
          buffered = BytesIO()
          out[i].save(buffered, format="PNG")
          img_str = base64.b64encode(buffered.getvalue())
          json_imgs[f"{i}"] = img_str.decode()
        return json_imgs

    # 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