91 lines
3.1 KiB
Python
91 lines
3.1 KiB
Python
import os
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import PIL.Image
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import cv2
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import torch
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from diffusers import AutoencoderKL
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from loguru import logger
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from iopaint.schema import InpaintRequest, ModelType
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from .base import DiffusionInpaintModel
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from .utils import handle_from_pretrained_exceptions
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class SDXL(DiffusionInpaintModel):
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name = "diffusers/stable-diffusion-xl-1.0-inpainting-0.1"
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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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model_id_or_path = "diffusers/stable-diffusion-xl-1.0-inpainting-0.1"
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def init_model(self, device: torch.device, **kwargs):
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from diffusers.pipelines import StableDiffusionXLInpaintPipeline
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fp16 = not kwargs.get("no_half", False)
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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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if self.model_info.model_type == ModelType.DIFFUSERS_SDXL:
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num_in_channels = 4
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else:
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num_in_channels = 9
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if os.path.isfile(self.model_id_or_path):
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self.model = StableDiffusionXLInpaintPipeline.from_single_file(
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self.model_id_or_path,
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dtype=torch_dtype,
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num_in_channels=num_in_channels,
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)
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else:
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vae = AutoencoderKL.from_pretrained(
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"madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch_dtype
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)
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self.model = handle_from_pretrained_exceptions(
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StableDiffusionXLInpaintPipeline.from_pretrained,
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pretrained_model_name_or_path=self.model_id_or_path,
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torch_dtype=torch_dtype,
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vae=vae,
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variant="fp16",
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)
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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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def forward(self, image, mask, config: InpaintRequest):
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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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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_on_step_end=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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).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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