IOPaint/iopaint/model/sdxl.py

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import os
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import PIL.Image
import cv2
import torch
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from diffusers import AutoencoderKL
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from loguru import logger
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from iopaint.model.base import DiffusionInpaintModel
from iopaint.model.utils import handle_from_pretrained_exceptions
from iopaint.schema import InpaintRequest, ModelType
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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
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)
use_gpu = device == torch.device("cuda") and torch.cuda.is_available()
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:
num_in_channels = 4
else:
num_in_channels = 9
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if os.path.isfile(self.model_id_or_path):
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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)
else:
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(
StableDiffusionXLInpaintPipeline.from_pretrained,
pretrained_model_name_or_path=self.model_id_or_path,
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torch_dtype=torch_dtype,
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:
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.warning("Stable Diffusion XL not support run TextEncoder on CPU")
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
image: [H, W, C] RGB
mask: [H, W, 1] 255 means area to repaint
return: BGR IMAGE
"""
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self.set_scheduler(config)
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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=0.999 if config.sd_strength == 1.0 else config.sd_strength,
guidance_scale=config.sd_guidance_scale,
output_type="np",
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callback_on_step_end=self.callback,
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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