import PIL.Image import cv2 import torch from diffusers import ControlNetModel from loguru import logger from iopaint.schema import InpaintRequest, ModelType from .base import DiffusionInpaintModel from .helper.controlnet_preprocess import ( make_canny_control_image, make_openpose_control_image, make_depth_control_image, make_inpaint_control_image, ) from .helper.cpu_text_encoder import CPUTextEncoderWrapper from .original_sd_configs import get_config_files from .utils import ( get_scheduler, handle_from_pretrained_exceptions, get_torch_dtype, enable_low_mem, is_local_files_only, ) class ControlNet(DiffusionInpaintModel): name = "controlnet" pad_mod = 8 min_size = 512 @property def lcm_lora_id(self): if self.model_info.model_type in [ ModelType.DIFFUSERS_SD, ModelType.DIFFUSERS_SD_INPAINT, ]: return "latent-consistency/lcm-lora-sdv1-5" if self.model_info.model_type in [ ModelType.DIFFUSERS_SDXL, ModelType.DIFFUSERS_SDXL_INPAINT, ]: return "latent-consistency/lcm-lora-sdxl" raise NotImplementedError(f"Unsupported controlnet lcm model {self.model_info}") def init_model(self, device: torch.device, **kwargs): model_info = kwargs["model_info"] controlnet_method = kwargs["controlnet_method"] self.model_info = model_info self.controlnet_method = controlnet_method model_kwargs = { **kwargs.get("pipe_components", {}), "local_files_only": is_local_files_only(**kwargs), } self.local_files_only = model_kwargs["local_files_only"] disable_nsfw_checker = kwargs["disable_nsfw"] or kwargs.get( "cpu_offload", False ) if disable_nsfw_checker: logger.info("Disable Stable Diffusion Model NSFW checker") model_kwargs.update( dict( safety_checker=None, feature_extractor=None, requires_safety_checker=False, ) ) use_gpu, torch_dtype = get_torch_dtype(device, kwargs.get("no_half", False)) self.torch_dtype = torch_dtype original_config_file_name = "v1" if model_info.model_type in [ ModelType.DIFFUSERS_SD, ModelType.DIFFUSERS_SD_INPAINT, ]: from diffusers import ( StableDiffusionControlNetInpaintPipeline as PipeClass, ) original_config_file_name = "v1" elif model_info.model_type in [ ModelType.DIFFUSERS_SDXL, ModelType.DIFFUSERS_SDXL_INPAINT, ]: from diffusers import ( StableDiffusionXLControlNetInpaintPipeline as PipeClass, ) original_config_file_name = "xl" controlnet = ControlNetModel.from_pretrained( pretrained_model_name_or_path=controlnet_method, resume_download=True, local_files_only=model_kwargs["local_files_only"], torch_dtype=self.torch_dtype, ) if model_info.is_single_file_diffusers: if self.model_info.model_type == ModelType.DIFFUSERS_SD: model_kwargs["num_in_channels"] = 4 else: model_kwargs["num_in_channels"] = 9 self.model = PipeClass.from_single_file( model_info.path, controlnet=controlnet, load_safety_checker=not disable_nsfw_checker, torch_dtype=torch_dtype, original_config_file=get_config_files()[original_config_file_name], **model_kwargs, ) else: self.model = handle_from_pretrained_exceptions( PipeClass.from_pretrained, pretrained_model_name_or_path=model_info.path, controlnet=controlnet, variant="fp16", torch_dtype=torch_dtype, **model_kwargs, ) enable_low_mem(self.model, kwargs.get("low_mem", False)) if kwargs.get("cpu_offload", False) and use_gpu: logger.info("Enable sequential cpu offload") self.model.enable_sequential_cpu_offload(gpu_id=0) else: self.model = self.model.to(device) if kwargs["sd_cpu_textencoder"]: logger.info("Run Stable Diffusion TextEncoder on CPU") self.model.text_encoder = CPUTextEncoderWrapper( self.model.text_encoder, torch_dtype ) self.callback = kwargs.pop("callback", None) def switch_controlnet_method(self, new_method: str): self.controlnet_method = new_method controlnet = ControlNetModel.from_pretrained( new_method, resume_download=True, local_files_only=self.local_files_only, torch_dtype=self.torch_dtype, ).to(self.model.device) self.model.controlnet = controlnet def _get_control_image(self, image, mask): if "canny" in self.controlnet_method: control_image = make_canny_control_image(image) elif "openpose" in self.controlnet_method: control_image = make_openpose_control_image(image) elif "depth" in self.controlnet_method: control_image = make_depth_control_image(image) elif "inpaint" in self.controlnet_method: control_image = make_inpaint_control_image(image, mask) else: raise NotImplementedError(f"{self.controlnet_method} not implemented") return control_image def forward(self, image, mask, config: InpaintRequest): """Input image and output image have same size image: [H, W, C] RGB mask: [H, W, 1] 255 means area to repaint return: BGR IMAGE """ scheduler_config = self.model.scheduler.config scheduler = get_scheduler(config.sd_sampler, scheduler_config) self.model.scheduler = scheduler img_h, img_w = image.shape[:2] control_image = self._get_control_image(image, mask) mask_image = PIL.Image.fromarray(mask[:, :, -1], mode="L") image = PIL.Image.fromarray(image) output = self.model( image=image, mask_image=mask_image, control_image=control_image, prompt=config.prompt, negative_prompt=config.negative_prompt, num_inference_steps=config.sd_steps, guidance_scale=config.sd_guidance_scale, output_type="np", callback_on_step_end=self.callback, height=img_h, width=img_w, generator=torch.manual_seed(config.sd_seed), controlnet_conditioning_scale=config.controlnet_conditioning_scale, ).images[0] output = (output * 255).round().astype("uint8") output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR) return output