IOPaint/lama_cleaner/model/sdxl.py
2023-12-01 10:15:35 +08:00

107 lines
3.8 KiB
Python

import os
import PIL.Image
import cv2
import numpy as np
import torch
from diffusers import AutoencoderKL
from loguru import logger
from lama_cleaner.model.base import DiffusionInpaintModel
from lama_cleaner.schema import Config
class SDXL(DiffusionInpaintModel):
name = "sdxl"
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
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
if os.path.isfile(self.model_id_or_path):
self.model = StableDiffusionXLInpaintPipeline.from_single_file(
self.model_id_or_path, torch_dtype=torch_dtype
)
else:
vae = AutoencoderKL.from_pretrained(
"madebyollin/sdxl-vae-fp16-fix", torch_dtype=torch.float16
)
self.model = StableDiffusionXLInpaintPipeline.from_pretrained(
self.model_id_or_path,
revision="main",
torch_dtype=torch_dtype,
use_auth_token=kwargs["hf_access_token"],
vae=vae,
)
# https://huggingface.co/docs/diffusers/v0.7.0/en/api/pipelines/stable_diffusion#diffusers.StableDiffusionInpaintPipeline.enable_attention_slicing
self.model.enable_attention_slicing()
# https://huggingface.co/docs/diffusers/v0.7.0/en/optimization/fp16#memory-efficient-attention
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)
if kwargs["sd_cpu_textencoder"]:
logger.warning("Stable Diffusion XL not support run TextEncoder on CPU")
self.callback = kwargs.pop("callback", None)
@staticmethod
def download():
from diffusers import AutoPipelineForInpainting
AutoPipelineForInpainting.from_pretrained(
"diffusers/stable-diffusion-xl-1.0-inpainting-0.1"
)
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
"""
self.set_scheduler(config)
if config.sd_mask_blur != 0:
k = 2 * config.sd_mask_blur + 1
mask = cv2.GaussianBlur(mask, (k, k), 0)[:, :, np.newaxis]
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=self.callback,
height=img_h,
width=img_w,
generator=torch.manual_seed(config.sd_seed),
callback_steps=1,
).images[0]
output = (output * 255).round().astype("uint8")
output = cv2.cvtColor(output, cv2.COLOR_RGB2BGR)
return output
@staticmethod
def is_downloaded() -> bool:
# model will be downloaded when app start, and can't switch in frontend settings
return True