from PIL import Image import PIL.Image import cv2 import torch from loguru import logger from ..base import DiffusionInpaintModel from ..helper.cpu_text_encoder import CPUTextEncoderWrapper from ..utils import handle_from_pretrained_exceptions, get_torch_dtype, enable_low_mem from iopaint.schema import InpaintRequest from .powerpaint_tokenizer import add_task_to_prompt from ...const import POWERPAINT_NAME class PowerPaint(DiffusionInpaintModel): name = POWERPAINT_NAME pad_mod = 8 min_size = 512 lcm_lora_id = "latent-consistency/lcm-lora-sdv1-5" def init_model(self, device: torch.device, **kwargs): from .pipeline_powerpaint import StableDiffusionInpaintPipeline from .powerpaint_tokenizer import PowerPaintTokenizer use_gpu, torch_dtype = get_torch_dtype(device, kwargs.get("no_half", False)) model_kwargs = {} if kwargs["disable_nsfw"] or kwargs.get("cpu_offload", False): logger.info("Disable Stable Diffusion Model NSFW checker") model_kwargs.update( dict( safety_checker=None, feature_extractor=None, requires_safety_checker=False, ) ) self.model = handle_from_pretrained_exceptions( StableDiffusionInpaintPipeline.from_pretrained, pretrained_model_name_or_path=self.name, variant="fp16", torch_dtype=torch_dtype, **model_kwargs, ) self.model.tokenizer = PowerPaintTokenizer(self.model.tokenizer) 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 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 """ self.set_scheduler(config) img_h, img_w = image.shape[:2] promptA, promptB, negative_promptA, negative_promptB = add_task_to_prompt( config.prompt, config.negative_prompt, config.powerpaint_task ) output = self.model( image=PIL.Image.fromarray(image), promptA=promptA, promptB=promptB, tradoff=config.fitting_degree, tradoff_nag=config.fitting_degree, negative_promptA=negative_promptA, negative_promptB=negative_promptB, mask_image=PIL.Image.fromarray(mask[:, :, -1], mode="L"), num_inference_steps=config.sd_steps, strength=config.sd_strength, guidance_scale=config.sd_guidance_scale, output_type="np", callback=self.callback, height=img_h, width=img_w, generator=torch.manual_seed(config.sd_seed), callback_steps=1, ).images[0] output = (output * 255).round().astype("uint8") output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR) return output