mirror of
https://github.com/invoke-ai/InvokeAI
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90cd791e76
- NEVER overwrite user's existing models.yaml - Instead, merge its contents into new config file, and rename original to models.yaml.orig (with message) - models.yaml has been removed from repository and renamed models.yaml.example
551 lines
20 KiB
Python
551 lines
20 KiB
Python
#!/usr/bin/env python3
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# Copyright (c) 2022 Lincoln D. Stein (https://github.com/lstein)
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# Before running stable-diffusion on an internet-isolated machine,
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# run this script from one with internet connectivity. The
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# two machines must share a common .cache directory.
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#
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# Coauthor: Kevin Turner http://github.com/keturn
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#
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print('Loading Python libraries...\n')
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import argparse
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import clip
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import sys
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import transformers
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import os
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import warnings
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import torch
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import zipfile
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import traceback
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import getpass
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import requests
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from urllib import request
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from tqdm import tqdm
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from omegaconf import OmegaConf
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from pathlib import Path
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from transformers import CLIPTokenizer, CLIPTextModel
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from transformers import BertTokenizerFast, AutoFeatureExtractor
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from huggingface_hub import hf_hub_download, HfFolder, hf_hub_url
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transformers.logging.set_verbosity_error()
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#--------------------------globals--
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Model_dir = './models/ldm/stable-diffusion-v1/'
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Config_file = './configs/models.yaml'
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SD_Configs = './configs/stable-diffusion'
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Datasets = {
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'stable-diffusion-1.5': {
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'description': 'The newest Stable Diffusion version 1.5 weight file (4.27 GB)',
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'repo_id': 'runwayml/stable-diffusion-v1-5',
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'config': 'v1-inference.yaml',
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'file': 'v1-5-pruned-emaonly.ckpt',
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'recommended': True,
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'width': 512,
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'height': 512,
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},
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'inpainting-1.5': {
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'description': 'RunwayML SD 1.5 model optimized for inpainting (4.27 GB)',
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'repo_id': 'runwayml/stable-diffusion-inpainting',
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'config': 'v1-inpainting-inference.yaml',
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'file': 'sd-v1-5-inpainting.ckpt',
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'recommended': True,
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'width': 512,
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'height': 512,
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},
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'stable-diffusion-1.4': {
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'description': 'The original Stable Diffusion version 1.4 weight file (4.27 GB)',
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'repo_id': 'CompVis/stable-diffusion-v-1-4-original',
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'config': 'v1-inference.yaml',
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'file': 'sd-v1-4.ckpt',
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'recommended': False,
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'width': 512,
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'height': 512,
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},
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'waifu-diffusion-1.3': {
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'description': 'Stable Diffusion 1.4 fine tuned on anime-styled images (4.27)',
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'repo_id': 'hakurei/waifu-diffusion-v1-3',
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'config': 'v1-inference.yaml',
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'file': 'model-epoch09-float32.ckpt',
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'recommended': False,
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'width': 512,
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'height': 512,
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},
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'ft-mse-improved-autoencoder-840000': {
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'description': 'StabilityAI improved autoencoder fine-tuned for human faces (recommended; 335 MB)',
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'repo_id': 'stabilityai/sd-vae-ft-mse-original',
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'config': 'VAE',
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'file': 'vae-ft-mse-840000-ema-pruned.ckpt',
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'recommended': True,
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'width': 512,
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'height': 512,
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},
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}
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Config_preamble = '''# This file describes the alternative machine learning models
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# available to InvokeAI script.
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#
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# To add a new model, follow the examples below. Each
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# model requires a model config file, a weights file,
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# and the width and height of the images it
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# was trained on.
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'''
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#---------------------------------------------
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def introduction():
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print(
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'''Welcome to InvokeAI. This script will help download the Stable Diffusion weight files
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and other large models that are needed for text to image generation. At any point you may interrupt
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this program and resume later.\n'''
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)
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#--------------------------------------------
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def postscript():
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print(
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'''You're all set! You may now launch InvokeAI using one of these two commands:
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Web version:
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python scripts/invoke.py --web (connect to http://localhost:9090)
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Command-line version:
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python scripts/invoke.py
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Have fun!
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'''
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)
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#---------------------------------------------
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def yes_or_no(prompt:str, default_yes=True):
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default = "y" if default_yes else 'n'
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response = input(f'{prompt} [{default}] ') or default
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if default_yes:
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return response[0] not in ('n','N')
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else:
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return response[0] in ('y','Y')
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#---------------------------------------------
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def user_wants_to_download_weights()->str:
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'''
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Returns one of "skip", "recommended" or "customized"
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'''
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print('''You can download and configure the weights files manually or let this
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script do it for you. Manual installation is described at:
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https://github.com/invoke-ai/InvokeAI/blob/main/docs/installation/INSTALLING_MODELS.md
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You may download the recommended models (about 10GB total), select a customized set, or
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completely skip this step.
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'''
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)
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selection = None
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while selection is None:
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choice = input('Download <r>ecommended models, <c>ustomize the list, or <s>kip this step? [r]: ')
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if choice.startswith(('r','R')) or len(choice)==0:
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selection = 'recommended'
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elif choice.startswith(('c','C')):
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selection = 'customized'
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elif choice.startswith(('s','S')):
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selection = 'skip'
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return selection
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#---------------------------------------------
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def select_datasets(action:str):
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done = False
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while not done:
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datasets = dict()
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dflt = None # the first model selected will be the default; TODO let user change
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counter = 1
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if action == 'customized':
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print('''
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Choose the weight file(s) you wish to download. Before downloading you
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will be given the option to view and change your selections.
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'''
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)
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for ds in Datasets.keys():
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recommended = '(recommended)' if Datasets[ds]['recommended'] else ''
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print(f'[{counter}] {ds}:\n {Datasets[ds]["description"]} {recommended}')
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if yes_or_no(' Download?',default_yes=Datasets[ds]['recommended']):
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datasets[ds]=counter
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counter += 1
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else:
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for ds in Datasets.keys():
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if Datasets[ds]['recommended']:
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datasets[ds]=counter
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counter += 1
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print('The following weight files will be downloaded:')
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for ds in datasets:
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dflt = '*' if dflt is None else ''
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print(f' [{datasets[ds]}] {ds}{dflt}')
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print("*default")
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ok_to_download = yes_or_no('Ok to download?')
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if not ok_to_download:
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if yes_or_no('Change your selection?'):
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action = 'customized'
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pass
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else:
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done = True
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else:
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done = True
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return datasets if ok_to_download else None
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#-------------------------------Authenticate against Hugging Face
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def authenticate():
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print('''
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To download the Stable Diffusion weight files from the official Hugging Face
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repository, you need to read and accept the CreativeML Responsible AI license.
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This involves a few easy steps.
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1. If you have not already done so, create an account on Hugging Face's web site
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using the "Sign Up" button:
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https://huggingface.co/join
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You will need to verify your email address as part of the HuggingFace
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registration process.
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2. Log into your Hugging Face account:
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https://huggingface.co/login
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3. Accept the license terms located here:
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https://huggingface.co/runwayml/stable-diffusion-v1-5
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and here:
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https://huggingface.co/runwayml/stable-diffusion-inpainting
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(Yes, you have to accept two slightly different license agreements)
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'''
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)
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input('Press <enter> when you are ready to continue:')
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access_token = HfFolder.get_token()
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if access_token is None:
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print('''
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4. Thank you! The last step is to enter your HuggingFace access token so that
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this script is authorized to initiate the download. Go to the access tokens
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page of your Hugging Face account and create a token by clicking the
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"New token" button:
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https://huggingface.co/settings/tokens
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(You can enter anything you like in the token creation field marked "Name".
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"Role" should be "read").
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Now copy the token to your clipboard and paste it here: '''
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)
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access_token = getpass.getpass()
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HfFolder.save_token(access_token)
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return access_token
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#---------------------------------------------
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# look for legacy model.ckpt in models directory and offer to
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# normalize its name
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def migrate_models_ckpt():
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if not os.path.exists(os.path.join(Model_dir,'model.ckpt')):
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return
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new_name = Datasets['stable-diffusion-1.4']['file']
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print('You seem to have the Stable Diffusion v4.1 "model.ckpt" already installed.')
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rename = yes_or_no(f'Ok to rename it to "{new_name}" for future reference?')
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if rename:
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print(f'model.ckpt => {new_name}')
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os.rename(os.path.join(Model_dir,'model.ckpt'),os.path.join(Model_dir,new_name))
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#---------------------------------------------
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def download_weight_datasets(models:dict, access_token:str):
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migrate_models_ckpt()
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successful = dict()
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for mod in models.keys():
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repo_id = Datasets[mod]['repo_id']
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filename = Datasets[mod]['file']
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success = download_with_resume(
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repo_id=repo_id,
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model_name=filename,
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access_token=access_token
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)
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if success:
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successful[mod] = True
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keys = ', '.join(successful.keys())
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print(f'Successfully installed {keys}')
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return successful
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#---------------------------------------------
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def download_with_resume(repo_id:str, model_name:str, access_token:str)->bool:
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model_dest = os.path.join(Model_dir, model_name)
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os.makedirs(os.path.dirname(model_dest), exist_ok=True)
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url = hf_hub_url(repo_id, model_name)
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header = {"Authorization": f'Bearer {access_token}'}
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open_mode = 'wb'
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exist_size = 0
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if os.path.exists(model_dest):
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exist_size = os.path.getsize(model_dest)
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header['Range'] = f'bytes={exist_size}-'
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open_mode = 'ab'
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resp = requests.get(url, headers=header, stream=True)
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total = int(resp.headers.get('content-length', 0))
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if resp.status_code==416: # "range not satisfiable", which means nothing to return
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print(f'* {model_name}: complete file found. Skipping.')
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return True
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elif exist_size > 0:
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print(f'* {model_name}: partial file found. Resuming...')
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else:
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print(f'* {model_name}: Downloading...')
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try:
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if total < 2000:
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print(f'* {model_name}: {resp.text}')
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return False
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with open(model_dest, open_mode) as file, tqdm(
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desc=model_name,
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initial=exist_size,
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total=total+exist_size,
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unit='iB',
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unit_scale=True,
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unit_divisor=1000,
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) as bar:
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for data in resp.iter_content(chunk_size=1024):
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size = file.write(data)
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bar.update(size)
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except Exception as e:
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print(f'An error occurred while downloading {model_name}: {str(e)}')
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return False
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return True
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#---------------------------------------------
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def update_config_file(successfully_downloaded:dict):
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yaml = new_config_file_contents(successfully_downloaded)
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try:
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if os.path.exists(Config_file):
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print(f'** {Config_file} exists. Renaming to {Config_file}.orig')
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os.rename(Config_file,f'{Config_file}.orig')
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tmpfile = os.path.join(os.path.dirname(Config_file),'new_config.tmp')
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with open(tmpfile, 'w') as outfile:
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outfile.write(Config_preamble)
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outfile.write(yaml)
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os.rename(tmpfile,Config_file)
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except Exception as e:
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print(f'**Error creating config file {Config_file}: {str(e)} **')
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return
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print(f'Successfully created new configuration file {Config_file}')
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#---------------------------------------------
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def new_config_file_contents(successfully_downloaded:dict)->str:
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if os.path.exists(Config_file):
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conf = OmegaConf.load(Config_file)
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else:
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conf = OmegaConf.create()
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# find the VAE file, if there is one
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vae = None
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default_selected = False
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for model in successfully_downloaded:
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if Datasets[model]['config'] == 'VAE':
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vae = Datasets[model]['file']
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for model in successfully_downloaded:
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if Datasets[model]['config'] == 'VAE': # skip VAE entries
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continue
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stanza = conf[model] if model in conf else { }
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stanza['description'] = Datasets[model]['description']
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stanza['weights'] = os.path.join(Model_dir,Datasets[model]['file'])
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stanza['config'] =os.path.join(SD_Configs, Datasets[model]['config'])
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stanza['width'] = Datasets[model]['width']
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stanza['height'] = Datasets[model]['height']
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stanza.pop('default',None) # this will be set later
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if vae:
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stanza['vae'] = os.path.join(Model_dir,vae)
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# BUG - the first stanza is always the default. User should select.
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if not default_selected:
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stanza['default'] = True
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default_selected = True
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conf[model] = stanza
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return OmegaConf.to_yaml(conf)
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#---------------------------------------------
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# this will preload the Bert tokenizer fles
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def download_bert():
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print('Installing bert tokenizer (ignore deprecation errors)...', end='')
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with warnings.catch_warnings():
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warnings.filterwarnings('ignore', category=DeprecationWarning)
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tokenizer = BertTokenizerFast.from_pretrained('bert-base-uncased')
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print('...success')
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sys.stdout.flush()
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#---------------------------------------------
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# this will download requirements for Kornia
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def download_kornia():
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print('Installing Kornia requirements...', end='')
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with warnings.catch_warnings():
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warnings.filterwarnings('ignore', category=DeprecationWarning)
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import kornia
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print('...success')
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#---------------------------------------------
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def download_clip():
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version = 'openai/clip-vit-large-patch14'
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sys.stdout.flush()
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print('Loading CLIP model...',end='')
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tokenizer = CLIPTokenizer.from_pretrained(version)
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transformer = CLIPTextModel.from_pretrained(version)
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print('...success')
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#---------------------------------------------
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def download_gfpgan():
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print('Installing models from RealESRGAN and facexlib...',end='')
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try:
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from realesrgan import RealESRGANer
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from realesrgan.archs.srvgg_arch import SRVGGNetCompact
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from facexlib.utils.face_restoration_helper import FaceRestoreHelper
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RealESRGANer(
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scale=4,
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model_path='https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth',
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model = SRVGGNetCompact(num_in_ch=3, num_out_ch=3, num_feat=64, num_conv=32, upscale=4, act_type='prelu')
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)
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FaceRestoreHelper(1, det_model='retinaface_resnet50')
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print('...success')
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except Exception:
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print('Error loading ESRGAN:')
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print(traceback.format_exc())
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print('Loading models from GFPGAN')
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for model in (
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[
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'https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth',
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'src/gfpgan/experiments/pretrained_models/GFPGANv1.4.pth'
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],
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[
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'https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_Resnet50_Final.pth',
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'./gfpgan/weights/detection_Resnet50_Final.pth'
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],
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[
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'https://github.com/xinntao/facexlib/releases/download/v0.2.2/parsing_parsenet.pth',
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'./gfpgan/weights/parsing_parsenet.pth'
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],
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):
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model_url,model_dest = model
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try:
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if not os.path.exists(model_dest):
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print(f'Downloading gfpgan model file {model_url}...',end='')
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os.makedirs(os.path.dirname(model_dest), exist_ok=True)
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request.urlretrieve(model_url,model_dest)
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print('...success')
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except Exception:
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print('Error loading GFPGAN:')
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print(traceback.format_exc())
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#---------------------------------------------
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def download_codeformer():
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print('Installing CodeFormer model file...',end='')
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try:
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model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth'
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model_dest = 'ldm/invoke/restoration/codeformer/weights/codeformer.pth'
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if not os.path.exists(model_dest):
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print('Downloading codeformer model file...')
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os.makedirs(os.path.dirname(model_dest), exist_ok=True)
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request.urlretrieve(model_url,model_dest)
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except Exception:
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print('Error loading CodeFormer:')
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print(traceback.format_exc())
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print('...success')
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#---------------------------------------------
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def download_clipseg():
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print('Installing clipseg model for text-based masking...',end='')
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try:
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model_url = 'https://owncloud.gwdg.de/index.php/s/ioHbRzFx6th32hn/download'
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model_dest = 'src/clipseg/clipseg_weights.zip'
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weights_dir = 'src/clipseg/weights'
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if not os.path.exists(weights_dir):
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os.makedirs(os.path.dirname(model_dest), exist_ok=True)
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request.urlretrieve(model_url,model_dest)
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with zipfile.ZipFile(model_dest,'r') as zip:
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zip.extractall('src/clipseg')
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os.rename('src/clipseg/clipseg_weights','src/clipseg/weights')
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os.remove(model_dest)
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from clipseg_models.clipseg import CLIPDensePredT
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model = CLIPDensePredT(version='ViT-B/16', reduce_dim=64, )
|
|
model.eval()
|
|
model.load_state_dict(
|
|
torch.load(
|
|
'src/clipseg/weights/rd64-uni-refined.pth',
|
|
map_location=torch.device('cpu')
|
|
),
|
|
strict=False,
|
|
)
|
|
except Exception:
|
|
print('Error installing clipseg model:')
|
|
print(traceback.format_exc())
|
|
print('...success')
|
|
|
|
#-------------------------------------
|
|
def download_safety_checker():
|
|
print('Installing safety model for NSFW content detection...',end='')
|
|
try:
|
|
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
|
|
except ModuleNotFoundError:
|
|
print('Error installing safety checker model:')
|
|
print(traceback.format_exc())
|
|
return
|
|
safety_model_id = "CompVis/stable-diffusion-safety-checker"
|
|
safety_feature_extractor = AutoFeatureExtractor.from_pretrained(safety_model_id)
|
|
safety_checker = StableDiffusionSafetyChecker.from_pretrained(safety_model_id)
|
|
print('...success')
|
|
|
|
#-------------------------------------
|
|
if __name__ == '__main__':
|
|
parser = argparse.ArgumentParser(description='InvokeAI model downloader')
|
|
parser.add_argument('--interactive',
|
|
dest='interactive',
|
|
action=argparse.BooleanOptionalAction,
|
|
default=True,
|
|
help='run in interactive mode (default)')
|
|
opt = parser.parse_args()
|
|
|
|
try:
|
|
if opt.interactive:
|
|
introduction()
|
|
print('** WEIGHT SELECTION **')
|
|
choice = user_wants_to_download_weights()
|
|
if choice != 'skip':
|
|
models = select_datasets(choice)
|
|
if models is None:
|
|
if yes_or_no('Quit?',default_yes=False):
|
|
sys.exit(0)
|
|
print('** LICENSE AGREEMENT FOR WEIGHT FILES **')
|
|
access_token = authenticate()
|
|
print('\n** DOWNLOADING WEIGHTS **')
|
|
successfully_downloaded = download_weight_datasets(models, access_token)
|
|
update_config_file(successfully_downloaded)
|
|
print('\n** DOWNLOADING SUPPORT MODELS **')
|
|
download_bert()
|
|
download_kornia()
|
|
download_clip()
|
|
download_gfpgan()
|
|
download_codeformer()
|
|
download_clipseg()
|
|
download_safety_checker()
|
|
postscript()
|
|
except KeyboardInterrupt:
|
|
print('\nGoodbye! Come back soon.')
|
|
except Exception as e:
|
|
print(f'\nA problem occurred during download.\nThe error was: "{str(e)}"')
|
|
|
|
|
|
|