99 lines
3.4 KiB
Python
99 lines
3.4 KiB
Python
import PIL.Image
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import cv2
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import numpy as np
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import torch
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from loguru import logger
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from lama_cleaner.model.base import DiffusionInpaintModel
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from lama_cleaner.schema import Config
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class SDXL(DiffusionInpaintModel):
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name = "sdxl"
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pad_mod = 8
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min_size = 512
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lcm_lora_id = "latent-consistency/lcm-lora-sdxl"
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def init_model(self, device: torch.device, **kwargs):
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from diffusers.pipelines import AutoPipelineForInpainting
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fp16 = not kwargs.get("no_half", False)
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model_kwargs = {
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"local_files_only": kwargs.get("local_files_only", kwargs["sd_run_local"])
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}
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use_gpu = device == torch.device("cuda") and torch.cuda.is_available()
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torch_dtype = torch.float16 if use_gpu and fp16 else torch.float32
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self.model = AutoPipelineForInpainting.from_pretrained(
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"diffusers/stable-diffusion-xl-1.0-inpainting-0.1",
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revision="main",
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torch_dtype=torch_dtype,
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use_auth_token=kwargs["hf_access_token"],
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**model_kwargs,
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)
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# https://huggingface.co/docs/diffusers/v0.7.0/en/api/pipelines/stable_diffusion#diffusers.StableDiffusionInpaintPipeline.enable_attention_slicing
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self.model.enable_attention_slicing()
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# https://huggingface.co/docs/diffusers/v0.7.0/en/optimization/fp16#memory-efficient-attention
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if kwargs.get("enable_xformers", False):
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self.model.enable_xformers_memory_efficient_attention()
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if kwargs.get("cpu_offload", False) and use_gpu:
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logger.info("Enable sequential cpu offload")
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self.model.enable_sequential_cpu_offload(gpu_id=0)
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else:
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self.model = self.model.to(device)
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if kwargs["sd_cpu_textencoder"]:
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logger.warning("Stable Diffusion XL not support run TextEncoder on CPU")
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self.callback = kwargs.pop("callback", None)
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@staticmethod
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def download():
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from diffusers import AutoPipelineForInpainting
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AutoPipelineForInpainting.from_pretrained(
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"diffusers/stable-diffusion-xl-1.0-inpainting-0.1"
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)
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def forward(self, image, mask, config: Config):
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"""Input image and output image have same size
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image: [H, W, C] RGB
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mask: [H, W, 1] 255 means area to repaint
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return: BGR IMAGE
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"""
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self.set_scheduler(config)
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if config.sd_mask_blur != 0:
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k = 2 * config.sd_mask_blur + 1
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mask = cv2.GaussianBlur(mask, (k, k), 0)[:, :, np.newaxis]
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img_h, img_w = image.shape[:2]
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output = self.model(
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image=PIL.Image.fromarray(image),
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prompt=config.prompt,
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negative_prompt=config.negative_prompt,
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mask_image=PIL.Image.fromarray(mask[:, :, -1], mode="L"),
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num_inference_steps=config.sd_steps,
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strength=0.999 if config.sd_strength == 1.0 else config.sd_strength,
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guidance_scale=config.sd_guidance_scale,
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output_type="np",
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callback=self.callback,
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height=img_h,
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width=img_w,
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generator=torch.manual_seed(config.sd_seed),
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callback_steps=1,
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).images[0]
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output = (output * 255).round().astype("uint8")
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output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR)
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return output
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@staticmethod
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def is_downloaded() -> bool:
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# model will be downloaded when app start, and can't switch in frontend settings
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return True
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