handler.py

from typing import Dict, List, Any
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
import torch
import os
from huggingface_hub import HfApi
from pathlib import Path
from diffusers.utils import load_image
from PIL import Image
import numpy as np
from controlnet_aux import PidiNetDetector, HEDdetector
from diffusers import (
    ControlNetModel,
    StableDiffusionControlNetPipeline,
    UniPCMultistepScheduler,
)
from io import BytesIO
import base64
import warnings
warnings.filterwarnings('ignore', category=UserWarning, message='TypedStorage is deprecated')

checkpoint = "lllyasviel/control_v11p_sd15_scribble"


class EndpointHandler:
    def __init__(self, path=""):
        # load model and processor from path
        self.model =  ControlNetModel.from_pretrained(checkpoint, torch_dtype=torch.float16)
        self.processor = HEDdetector.from_pretrained('lllyasviel/Annotators')


    def __call__(self, data: Dict[str, Any]) -> Dict[str, str]:
        """
        Args:
            data (:dict:):
                The payload with the text prompt and generation parameters.
        """
        # process input
        inputs = data.pop("inputs", data)
        image_base64 = inputs["image_base64"]
        prompt = inputs["prompt"]

        # preprocess
        image = Image.open(BytesIO(base64.b64decode(image_base64)))
        control_image = self.processor(image, scribble=True)
        pipe = StableDiffusionControlNetPipeline.from_pretrained(
    "runwayml/stable-diffusion-v1-5", controlnet=self.model, torch_dtype=torch.float16
)
        pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
        pipe.enable_model_cpu_offload()

        generator = torch.manual_seed(0)
        image = pipe(prompt, num_inference_steps=30, generator=generator, image=control_image).images[0]

        # postprocess the prediction
        buffered = BytesIO()
        image.save(buffered, format="JPEG")
        img_str = base64.b64encode(buffered.getvalue())

        return {"result": img_str.decode()}