lama_cleaner/model/instruct_pix2pix.py

import PIL.Image
import cv2
import torch
from loguru import logger

from lama_cleaner.model.base import DiffusionInpaintModel
from lama_cleaner.model.utils import set_seed
from lama_cleaner.schema import Config


class InstructPix2Pix(DiffusionInpaintModel):
    name = "instruct_pix2pix"
    pad_mod = 8
    min_size = 512

    def init_model(self, device: torch.device, **kwargs):
        from diffusers import StableDiffusionInstructPix2PixPipeline
        fp16 = not kwargs.get('no_half', False)

        model_kwargs = {"local_files_only": kwargs.get('local_files_only', False)}
        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
        self.model = StableDiffusionInstructPix2PixPipeline.from_pretrained(
            "timbrooks/instruct-pix2pix",
            revision="fp16" if use_gpu and fp16 else "main",
            torch_dtype=torch_dtype,
            **model_kwargs
        )

        self.model.enable_attention_slicing()
        if kwargs.get('enable_xformers', False):
            self.model.enable_xformers_memory_efficient_attention()

        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)

    def forward(self, image, mask, config: Config):
        """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
        edit = pipe(prompt, image=image, num_inference_steps=20, image_guidance_scale=1.5, guidance_scale=7).images[0]
        """
        output = self.model(
            image=PIL.Image.fromarray(image),
            prompt=config.prompt,
            negative_prompt=config.negative_prompt,
            num_inference_steps=config.p2p_steps,
            image_guidance_scale=config.p2p_image_guidance_scale,
            guidance_scale=config.p2p_guidance_scale,
            output_type="np.array",
            generator=torch.manual_seed(config.sd_seed)
        ).images[0]

        output = (output * 255).round().astype("uint8")
        output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR)
        return output

    #
    # def forward_post_process(self, result, image, mask, config):
    #     if config.sd_match_histograms:
    #         result = self._match_histograms(result, image[:, :, ::-1], mask)
    #
    #     if config.sd_mask_blur != 0:
    #         k = 2 * config.sd_mask_blur + 1
    #         mask = cv2.GaussianBlur(mask, (k, k), 0)
    #     return result, image, mask

    @staticmethod
    def is_downloaded() -> bool:
        # model will be downloaded when app start, and can't switch in frontend settings
        return True