from diffusers import AutoencoderKL, DDPMScheduler, UNet2DConditionModel
from transformers import AutoTokenizer, CLIPTextModel, CLIPTextModelWithProjection
from diffengine.models.editors import StableDiffusionXL
[docs]base_model = "diffusers/stable-diffusion-xl-1.0-inpainting-0.1"
[docs]model = dict(type=StableDiffusionXL,
model=base_model,
tokenizer_one=dict(type=AutoTokenizer.from_pretrained,
subfolder="tokenizer",
use_fast=False),
tokenizer_two=dict(type=AutoTokenizer.from_pretrained,
subfolder="tokenizer_2",
use_fast=False),
scheduler=dict(type=DDPMScheduler.from_pretrained,
subfolder="scheduler"),
text_encoder_one=dict(type=CLIPTextModel.from_pretrained,
subfolder="text_encoder"),
text_encoder_two=dict(type=CLIPTextModelWithProjection.from_pretrained,
subfolder="text_encoder_2"),
vae=dict(
type=AutoencoderKL.from_pretrained,
pretrained_model_name_or_path="madebyollin/sdxl-vae-fp16-fix"),
unet=dict(type=UNet2DConditionModel.from_pretrained,
subfolder="unet"),
gradient_checkpointing=True)