handler.py

from typing import  Dict, List, Any
import base64
from PIL import Image
from io import BytesIO
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel
#from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, StableDiffusionSafetyChecker
# import Safety Checker
from diffusers.pipelines.stable_diffusion import StableDiffusionSafetyChecker

import torch


import numpy as np
import cv2
import controlnet_hinter

# set device
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
if device.type != 'cuda':
    raise ValueError("need to run on GPU")
# set mixed precision dtype
dtype = torch.bfloat16 if torch.cuda.get_device_capability()[0] == 8 else torch.float16
    
# controlnet mapping for controlnet id and control hinter
CONTROLNET_MAPPING = {
    "canny_edge": {
        "model_id": "lllyasviel/sd-controlnet-canny",
        "hinter": controlnet_hinter.hint_canny
    },
    "pose": {
        "model_id": "lllyasviel/sd-controlnet-openpose",
        "hinter": controlnet_hinter.hint_openpose
    },
    "depth": {
        "model_id": "lllyasviel/sd-controlnet-depth",
        "hinter": controlnet_hinter.hint_depth
    },
    "scribble": {
        "model_id": "lllyasviel/sd-controlnet-scribble",
        "hinter": controlnet_hinter.hint_scribble,
    },
    "segmentation": {
        "model_id": "lllyasviel/sd-controlnet-seg",
        "hinter": controlnet_hinter.hint_segmentation,
    },
    "normal": {
        "model_id": "lllyasviel/sd-controlnet-normal",
        "hinter": controlnet_hinter.hint_normal,
    },
    "hed": {
        "model_id": "lllyasviel/sd-controlnet-hed",
        "hinter": controlnet_hinter.hint_hed,
    },
    "hough": {
        "model_id": "lllyasviel/sd-controlnet-mlsd",
        "hinter": controlnet_hinter.hint_hough,
    }
}


class EndpointHandler():
    def __init__(self, path=""):
        # define default controlnet id and load controlnet
        self.control_type = "depth"
        self.controlnet = ControlNetModel.from_pretrained(CONTROLNET_MAPPING[self.control_type]["model_id"],torch_dtype=dtype).to(device)

        #processor = AutoProcessor.from_pretrained("CompVis/stable-diffusion-safety-checker")

        
        # Load StableDiffusionControlNetPipeline 
        #self.stable_diffusion_id = "runwayml/stable-diffusion-v1-5"
        self.stable_diffusion_id = "Lykon/dreamshaper-8"
#        self.pipe = StableDiffusionControlNetPipeline.from_pretrained(self.stable_diffusion_id, 
#                                                                      controlnet=self.controlnet, 
#                                                                      torch_dtype=dtype,
#                                                                      #safety_checker=None).to(device)
#                                                                      #processor = AutoProcessor.from_pretrained("CompVis/stable-diffusion-safety-checker")
#                                                                      #safety_checker = SafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker")
#                                                                      safety_checker = StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker")

#        self.pipe = StableDiffusionControlNetPipeline.from_pretrained(
#            self.stable_diffusion_id, 
#            controlnet=self.controlnet, 
#            torch_dtype=dtype,
#            safety_checker = SafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker")
#        ).to(device)


        self.pipe = StableDiffusionControlNetPipeline.from_pretrained(self.stable_diffusion_id,
                                                                      controlnet=self.controlnet, 
                                                                      torch_dtype=dtype,
                                                                      safety_checker=StableDiffusionSafetyChecker.from_pretrained("CompVis/stable-diffusion-safety-checker", torch_dtype=dtype)).to("cuda")
        # Define Generator with seed
        self.generator = torch.Generator(device=device.type).manual_seed(3)

    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.
        """
        prompt = data.pop("inputs", None)
        image = data.pop("image", None)
        controlnet_type = data.pop("controlnet_type", None)
        
        # Check if neither prompt nor image is provided
        if prompt is None and image is None:
            return {"error": "Please provide a prompt and base64 encoded image."}
        
        # Check if a new controlnet is provided
        if controlnet_type is not None and controlnet_type != self.control_type:
            print(f"changing controlnet from {self.control_type} to {controlnet_type} using {CONTROLNET_MAPPING[controlnet_type]['model_id']} model")
            self.control_type = controlnet_type
            self.controlnet = ControlNetModel.from_pretrained(CONTROLNET_MAPPING[self.control_type]["model_id"],
                                                              torch_dtype=dtype).to(device)
            self.pipe.controlnet = self.controlnet
        
        
        # hyperparamters
        negatice_prompt = data.pop("negative_prompt", None)
        num_inference_steps = data.pop("num_inference_steps", 30)
        guidance_scale = data.pop("guidance_scale", 7.0)
        negative_prompt = data.pop("negative_prompt", None)
        height = data.pop("height", None)
        width = data.pop("width", None)
        controlnet_conditioning_scale = data.pop("controlnet_conditioning_scale", 1.2)
        
        # process image
        image = self.decode_base64_image(image)
        #control_image = CONTROLNET_MAPPING[self.control_type]["hinter"](image)

        # run inference pipeline
        out = self.pipe(
            prompt=prompt, 
            negative_prompt=negative_prompt,
            #image=control_image,
            image=image,
            num_inference_steps=num_inference_steps, 
            guidance_scale=guidance_scale,
            num_images_per_prompt=1,
            height=height,
            width=width,
            controlnet_conditioning_scale=controlnet_conditioning_scale,
            generator=self.generator
        )

        
        # return first generate PIL image
        return out.images[0]
    
    # 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