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from omegaconf import OmegaConf
import torch
from lib.smplfusion import DDIM, share, scheduler
from .common import *
DOWNLOAD_URL = 'https://huggingface.co/runwayml/stable-diffusion-inpainting/resolve/main/sd-v1-5-inpainting.ckpt?download=true'
MODEL_PATH = f'{MODEL_FOLDER}/sd-1-5-inpainting/sd-v1-5-inpainting.ckpt'
# pre-download
download_file(DOWNLOAD_URL, MODEL_PATH)
def load_model(dtype=torch.float16):
download_file(DOWNLOAD_URL, MODEL_PATH)
state_dict = torch.load(MODEL_PATH)['state_dict']
config = OmegaConf.load(f'{CONFIG_FOLDER}/ddpm/v1.yaml')
print ("Loading model: Stable-Inpainting 1.5")
unet = load_obj(f'{CONFIG_FOLDER}/unet/inpainting/v1.yaml').eval().cuda()
vae = load_obj(f'{CONFIG_FOLDER}/vae.yaml').eval().cuda()
encoder = load_obj(f'{CONFIG_FOLDER}/encoders/clip.yaml').eval().cuda()
extract = lambda state_dict, model: {x[len(model)+1:]:y for x,y in state_dict.items() if model in x}
unet_state = extract(state_dict, 'model.diffusion_model')
encoder_state = extract(state_dict, 'cond_stage_model')
vae_state = extract(state_dict, 'first_stage_model')
unet.load_state_dict(unet_state)
encoder.load_state_dict(encoder_state)
vae.load_state_dict(vae_state)
if dtype == torch.float16:
unet.convert_to_fp16()
vae.to(dtype)
encoder.to(dtype)
unet = unet.requires_grad_(False)
encoder = encoder.requires_grad_(False)
vae = vae.requires_grad_(False)
ddim = DDIM(config, vae, encoder, unet)
share.schedule = scheduler.linear(config.timesteps, config.linear_start, config.linear_end)
return ddim
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