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 from iopaint.schema import InpaintRequest, ModelType class SD(DiffusionInpaintModel): pad_mod = 8 min_size = 512 lcm_lora_id = "latent-consistency/lcm-lora-sdv1-5" def init_model(self, device: torch.device, **kwargs): from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline fp16 = not 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, ) ) use_gpu = device == torch.device("cuda") and torch.cuda.is_available() torch_dtype = torch.float16 if use_gpu and fp16 else torch.float32 if self.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 = StableDiffusionInpaintPipeline.from_single_file( self.model_id_or_path, dtype=torch_dtype, **model_kwargs ) else: self.model = handle_from_pretrained_exceptions( StableDiffusionInpaintPipeline.from_pretrained, pretrained_model_name_or_path=self.model_id_or_path, variant="fp16", dtype=torch_dtype, **model_kwargs, ) if torch.backends.mps.is_available(): # MPS: Recommended RAM < 64 GB https://huggingface.co/docs/diffusers/optimization/mps # CUDA: Don't enable attention slicing if you're already using `scaled_dot_product_attention` (SDPA) from PyTorch 2.0 or xFormers. https://huggingface.co/docs/diffusers/v0.25.0/en/api/pipelines/stable_diffusion/image_variation#diffusers.StableDiffusionImageVariationPipeline.enable_attention_slicing self.model.enable_attention_slicing() 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] output = self.model( image=PIL.Image.fromarray(image), prompt=config.prompt, negative_prompt=config.negative_prompt, 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_on_step_end=self.callback, height=img_h, width=img_w, generator=torch.manual_seed(config.sd_seed), ).images[0] output = (output * 255).round().astype("uint8") output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR) return output class SD15(SD): name = "runwayml/stable-diffusion-inpainting" model_id_or_path = "runwayml/stable-diffusion-inpainting" class Anything4(SD): name = "Sanster/anything-4.0-inpainting" model_id_or_path = "Sanster/anything-4.0-inpainting" class RealisticVision14(SD): name = "Sanster/Realistic_Vision_V1.4-inpainting" model_id_or_path = "Sanster/Realistic_Vision_V1.4-inpainting" class SD2(SD): name = "stabilityai/stable-diffusion-2-inpainting" model_id_or_path = "stabilityai/stable-diffusion-2-inpainting"