#!/usr/bin/env python3 # Copyright (c) 2022 Lincoln D. Stein (https://github.com/lstein) # Before running stable-diffusion on an internet-isolated machine, # run this script from one with internet connectivity. The # two machines must share a common .cache directory. from transformers import CLIPTokenizer, CLIPTextModel import clip from transformers import BertTokenizerFast import sys import transformers import os import warnings transformers.logging.set_verbosity_error() # this will preload the Bert tokenizer fles print('preloading bert tokenizer...') tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased') print('...success') # this will download requirements for Kornia print('preloading Kornia requirements (ignore the deprecation warnings)...') with warnings.catch_warnings(): warnings.filterwarnings('ignore', category=DeprecationWarning) import kornia print('...success') version = 'openai/clip-vit-large-patch14' print('preloading CLIP model (Ignore the deprecation warnings)...') sys.stdout.flush() tokenizer = CLIPTokenizer.from_pretrained(version) transformer = CLIPTextModel.from_pretrained(version) print('\n\n...success') # In the event that the user has installed GFPGAN and also elected to use # RealESRGAN, this will attempt to download the model needed by RealESRGANer gfpgan = False try: from realesrgan import RealESRGANer gfpgan = True except ModuleNotFoundError: pass if gfpgan: print('Loading models from RealESRGAN and facexlib') try: from basicsr.archs.rrdbnet_arch import RRDBNet from facexlib.utils.face_restoration_helper import FaceRestoreHelper RealESRGANer( scale=2, model_path='https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth', model=RRDBNet( num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=2, ), ) RealESRGANer( scale=4, model_path='https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth', model=RRDBNet( num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32, scale=4, ), ) FaceRestoreHelper(1, det_model='retinaface_resnet50') print('...success') except Exception: import traceback print('Error loading GFPGAN:') print(traceback.format_exc())