90 lines
3.1 KiB
Python
90 lines
3.1 KiB
Python
import os
|
|
|
|
import PIL.Image
|
|
import cv2
|
|
import torch
|
|
from diffusers import AutoencoderKL
|
|
from loguru import logger
|
|
|
|
from iopaint.schema import InpaintRequest, ModelType
|
|
|
|
from .base import DiffusionInpaintModel
|
|
from .utils import handle_from_pretrained_exceptions, get_torch_dtype, enable_low_mem
|
|
|
|
|
|
class SDXL(DiffusionInpaintModel):
|
|
name = "diffusers/stable-diffusion-xl-1.0-inpainting-0.1"
|
|
pad_mod = 8
|
|
min_size = 512
|
|
lcm_lora_id = "latent-consistency/lcm-lora-sdxl"
|
|
model_id_or_path = "diffusers/stable-diffusion-xl-1.0-inpainting-0.1"
|
|
|
|
def init_model(self, device: torch.device, **kwargs):
|
|
from diffusers.pipelines import StableDiffusionXLInpaintPipeline
|
|
|
|
use_gpu, torch_dtype = get_torch_dtype(device, kwargs.get("no_half", False))
|
|
|
|
if self.model_info.model_type == ModelType.DIFFUSERS_SDXL:
|
|
num_in_channels = 4
|
|
else:
|
|
num_in_channels = 9
|
|
|
|
if os.path.isfile(self.model_id_or_path):
|
|
self.model = StableDiffusionXLInpaintPipeline.from_single_file(
|
|
self.model_id_or_path,
|
|
dtype=torch_dtype,
|
|
num_in_channels=num_in_channels,
|
|
)
|
|
else:
|
|
vae = AutoencoderKL.from_pretrained(
|
|
"madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch_dtype
|
|
)
|
|
self.model = handle_from_pretrained_exceptions(
|
|
StableDiffusionXLInpaintPipeline.from_pretrained,
|
|
pretrained_model_name_or_path=self.model_id_or_path,
|
|
torch_dtype=torch_dtype,
|
|
vae=vae,
|
|
variant="fp16",
|
|
)
|
|
|
|
enable_low_mem(self.model, kwargs.get("low_mem", False))
|
|
|
|
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)
|
|
|
|
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=0.999 if config.sd_strength == 1.0 else 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
|