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