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0381a853b5
InvokeAI/scripts/configure_invokeai.py
Lincoln Stein 0381a853b5 add interactive configuration to the model loader 
- Loader is renamed `configure_invokeai.py`, but `preload_models.py` is retained
  (as a shell) for backward compatibility

- At startup, if no runtime root directory exists and no `.invokeai` startup file is
  present, user will be prompted to select the runtime and outputs directories.

- Also expanded the number of initial models offered to the user to include the
  most "liked" ones from HuggingFace, including the two trinart models, the
  PaperCut model, and the VoxelArt model.

- Created a configuration file for initial models to be offered to the user, at
  configs/INITIAL_MODELS.yaml
2022-11-19 19:20:28 +00:00

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#!/usr/bin/env python
# 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.
#
# Coauthor: Kevin Turner http://github.com/keturn
#
print('Loading Python libraries...\n')
import argparse
import sys
import os
import re
import warnings
import shutil
from urllib import request
from tqdm import tqdm
from omegaconf import OmegaConf
from huggingface_hub import HfFolder, hf_hub_url
from pathlib import Path
from getpass_asterisk import getpass_asterisk
from transformers import CLIPTokenizer, CLIPTextModel
from ldm.invoke.globals import Globals
from ldm.invoke.readline import generic_completer
import traceback
import requests
import clip
import transformers
import warnings
warnings.filterwarnings('ignore')
import torch
transformers.logging.set_verbosity_error()
#--------------------------globals-----------------------
Model_dir = 'models'
Weights_dir = 'ldm/stable-diffusion-v1/'
Dataset_path = './configs/INITIAL_MODELS.yaml'
Default_config_file = './configs/models.yaml'
SD_Configs = './configs/stable-diffusion'
Datasets = OmegaConf.load(Dataset_path)
completer = generic_completer(['yes','no'])
Config_preamble = '''# This file describes the alternative machine learning models
# available to InvokeAI script.
#
# To add a new model, follow the examples below. Each
# model requires a model config file, a weights file,
# and the width and height of the images it
# was trained on.
'''
#---------------------------------------------
def introduction():
print(
'''Welcome to InvokeAI. This script will help download the Stable Diffusion weight files
and other large models that are needed for text to image generation. At any point you may interrupt
this program and resume later.\n'''
)
#--------------------------------------------
def postscript():
print(
'''\n** Model Installation Successful **\nYou're all set! You may now launch InvokeAI using one of these two commands:
Web version:
python scripts/invoke.py --web (connect to http://localhost:9090)
Command-line version:
python scripts/invoke.py
Remember to activate that 'invokeai' environment before running invoke.py.
Or, if you used one of the automated installers, execute "invoke.sh" (Linux/Mac)
or "invoke.bat" (Windows) to start the script.
Have fun!
'''
)
#---------------------------------------------
def yes_or_no(prompt:str, default_yes=True):
completer.set_options(['yes','no'])
completer.complete_extensions(None) # turn off path-completion mode
default = "y" if default_yes else 'n'
response = input(f'{prompt} [{default}] ') or default
if default_yes:
return response[0] not in ('n','N')
else:
return response[0] in ('y','Y')
#---------------------------------------------
def user_wants_to_download_weights()->str:
'''
Returns one of "skip", "recommended" or "customized"
'''
print('''You can download and configure the weights files manually or let this
script do it for you. Manual installation is described at:
https://github.com/invoke-ai/InvokeAI/blob/main/docs/installation/INSTALLING_MODELS.md
You may download the recommended models (about 10GB total), select a customized set, or
completely skip this step.
'''
)
completer.set_options(['recommended','customized','skip'])
completer.complete_extensions(None) # turn off path-completion mode
selection = None
while selection is None:
choice = input('Download <r>ecommended models, <c>ustomize the list, or <s>kip this step? [r]: ')
if choice.startswith(('r','R')) or len(choice)==0:
selection = 'recommended'
elif choice.startswith(('c','C')):
selection = 'customized'
elif choice.startswith(('s','S')):
selection = 'skip'
return selection
#---------------------------------------------
def select_datasets(action:str):
done = False
while not done:
datasets = dict()
dflt = None # the first model selected will be the default; TODO let user change
counter = 1
if action == 'customized':
print('''
Choose the weight file(s) you wish to download. Before downloading you
will be given the option to view and change your selections.
'''
)
for ds in Datasets.keys():
recommended = '(recommended)' if Datasets[ds]['recommended'] else ''
print(f'[{counter}] {ds}:\n {Datasets[ds]["description"]} {recommended}')
if yes_or_no(' Download?',default_yes=Datasets[ds]['recommended']):
datasets[ds]=counter
counter += 1
else:
for ds in Datasets.keys():
if Datasets[ds]['recommended']:
datasets[ds]=counter
counter += 1
print('The following weight files will be downloaded:')
for ds in datasets:
dflt = '*' if dflt is None else ''
print(f' [{datasets[ds]}] {ds}{dflt}')
print("*default")
ok_to_download = yes_or_no('Ok to download?')
if not ok_to_download:
if yes_or_no('Change your selection?'):
action = 'customized'
pass
else:
done = True
else:
done = True
return datasets if ok_to_download else None
#---------------------------------------------
def recommended_datasets()->dict:
datasets = dict()
for ds in Datasets.keys():
if Datasets[ds]['recommended']:
datasets[ds]=True
return datasets
#-------------------------------Authenticate against Hugging Face
def authenticate():
print('''
To download the Stable Diffusion weight files from the official Hugging Face
repository, you need to read and accept the CreativeML Responsible AI license.
This involves a few easy steps.
1. If you have not already done so, create an account on Hugging Face's web site
using the "Sign Up" button:
https://huggingface.co/join
You will need to verify your email address as part of the HuggingFace
registration process.
2. Log into your Hugging Face account:
https://huggingface.co/login
3. Accept the license terms located here:
https://huggingface.co/runwayml/stable-diffusion-v1-5
and here:
https://huggingface.co/runwayml/stable-diffusion-inpainting
(Yes, you have to accept two slightly different license agreements)
'''
)
input('Press <enter> when you are ready to continue:')
print('(Fetching Hugging Face token from cache...',end='')
access_token = HfFolder.get_token()
if access_token is not None:
print('found')
if access_token is None:
print('not found')
print('''
4. Thank you! The last step is to enter your HuggingFace access token so that
this script is authorized to initiate the download. Go to the access tokens
page of your Hugging Face account and create a token by clicking the
"New token" button:
https://huggingface.co/settings/tokens
(You can enter anything you like in the token creation field marked "Name".
"Role" should be "read").
Now copy the token to your clipboard and paste it here: '''
)
access_token = getpass_asterisk.getpass_asterisk()
return access_token
#---------------------------------------------
# look for legacy model.ckpt in models directory and offer to
# normalize its name
def migrate_models_ckpt():
model_path = os.path.join(Globals.root,Model_dir,Weights_dir)
if not os.path.exists(os.path.join(model_path,'model.ckpt')):
return
new_name = Datasets['stable-diffusion-1.4']['file']
print('You seem to have the Stable Diffusion v4.1 "model.ckpt" already installed.')
rename = yes_or_no(f'Ok to rename it to "{new_name}" for future reference?')
if rename:
print(f'model.ckpt => {new_name}')
os.rename(os.path.join(model_path,'model.ckpt'),os.path.join(model_path,new_name))
#---------------------------------------------
def download_weight_datasets(models:dict, access_token:str):
migrate_models_ckpt()
successful = dict()
for mod in models.keys():
repo_id = Datasets[mod]['repo_id']
filename = Datasets[mod]['file']
print(os.path.join(Globals.root,Model_dir,Weights_dir), file=sys.stderr)
success = hf_download_with_resume(
repo_id=repo_id,
model_dir=os.path.join(Globals.root,Model_dir,Weights_dir),
model_name=filename,
access_token=access_token
)
if success:
successful[mod] = True
if len(successful) < len(models):
print(f'\n\n** There were errors downloading one or more files. **')
print('Please double-check your license agreements, and your access token.')
HfFolder.delete_token()
print('Press any key to try again. Type ^C to quit.\n')
input()
return None
HfFolder.save_token(access_token)
keys = ', '.join(successful.keys())
print(f'Successfully installed {keys}')
return successful
#---------------------------------------------
def hf_download_with_resume(repo_id:str, model_dir:str, model_name:str, access_token:str=None)->bool:
model_dest = os.path.join(model_dir, model_name)
os.makedirs(model_dir, exist_ok=True)
url = hf_hub_url(repo_id, model_name)
header = {"Authorization": f'Bearer {access_token}'} if access_token else {}
open_mode = 'wb'
exist_size = 0
if os.path.exists(model_dest):
exist_size = os.path.getsize(model_dest)
header['Range'] = f'bytes={exist_size}-'
open_mode = 'ab'
resp = requests.get(url, headers=header, stream=True)
total = int(resp.headers.get('content-length', 0))
if resp.status_code==416: # "range not satisfiable", which means nothing to return
print(f'* {model_name}: complete file found. Skipping.')
return True
elif resp.status_code != 200:
print(f'** An error occurred during downloading {model_name}: {resp.reason}')
elif exist_size > 0:
print(f'* {model_name}: partial file found. Resuming...')
else:
print(f'* {model_name}: Downloading...')
try:
if total < 2000:
print(f'*** ERROR DOWNLOADING {model_name}: {resp.text}')
return False
with open(model_dest, open_mode) as file, tqdm(
desc=model_name,
initial=exist_size,
total=total+exist_size,
unit='iB',
unit_scale=True,
unit_divisor=1000,
) as bar:
for data in resp.iter_content(chunk_size=1024):
size = file.write(data)
bar.update(size)
except Exception as e:
print(f'An error occurred while downloading {model_name}: {str(e)}')
return False
return True
#---------------------------------------------
def download_with_progress_bar(model_url:str, model_dest:str, label:str='the'):
try:
print(f'Installing {label} model file {model_url}...',end='',file=sys.stderr)
if not os.path.exists(model_dest):
os.makedirs(os.path.dirname(model_dest), exist_ok=True)
print('',file=sys.stderr)
request.urlretrieve(model_url,model_dest,ProgressBar(os.path.basename(model_dest)))
print('...downloaded successfully', file=sys.stderr)
else:
print('...exists', file=sys.stderr)
except Exception:
print('...download failed')
print(f'Error downloading {label} model')
print(traceback.format_exc())
#---------------------------------------------
def update_config_file(successfully_downloaded:dict,opt:dict):
config_file = opt.config_file or Default_config_file
config_file = os.path.normpath(os.path.join(Globals.root,config_file))
yaml = new_config_file_contents(successfully_downloaded,config_file)
try:
if os.path.exists(config_file):
print(f'** {config_file} exists. Renaming to {config_file}.orig')
os.rename(config_file,f'{config_file}.orig')
tmpfile = os.path.join(os.path.dirname(config_file),'new_config.tmp')
with open(tmpfile, 'w') as outfile:
outfile.write(Config_preamble)
outfile.write(yaml)
os.rename(tmpfile,config_file)
except Exception as e:
print(f'**Error creating config file {config_file}: {str(e)} **')
return
print(f'Successfully created new configuration file {config_file}')
#---------------------------------------------
def new_config_file_contents(successfully_downloaded:dict, config_file:str)->str:
if os.path.exists(config_file):
conf = OmegaConf.load(config_file)
else:
conf = OmegaConf.create()
# find the VAE file, if there is one
vaes = {}
default_selected = False
for model in successfully_downloaded:
a = Datasets[model]['config'].split('/')
if a[0] != 'VAE':
continue
vae_target = a[1] if len(a)>1 else 'default'
vaes[vae_target] = Datasets[model]['file']
for model in successfully_downloaded:
if Datasets[model]['config'].startswith('VAE'): # skip VAE entries
continue
stanza = conf[model] if model in conf else { }
stanza['description'] = Datasets[model]['description']
stanza['weights'] = os.path.join(Model_dir,Weights_dir,Datasets[model]['file'])
stanza['config'] = os.path.normpath(os.path.join(SD_Configs, Datasets[model]['config']))
stanza['width'] = Datasets[model]['width']
stanza['height'] = Datasets[model]['height']
stanza.pop('default',None) # this will be set later
if vaes:
for target in vaes:
if re.search(target, model, flags=re.IGNORECASE):
stanza['vae'] = os.path.normpath(os.path.join(Model_dir,Weights_dir,vaes[target]))
else:
stanza['vae'] = os.path.normpath(os.path.join(Model_dir,Weights_dir,vaes['default']))
# BUG - the first stanza is always the default. User should select.
if not default_selected:
stanza['default'] = True
default_selected = True
conf[model] = stanza
return OmegaConf.to_yaml(conf)
#---------------------------------------------
# this will preload the Bert tokenizer fles
def download_bert():
print('Installing bert tokenizer (ignore deprecation errors)...', end='',file=sys.stderr)
with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=DeprecationWarning)
from transformers import BertTokenizerFast, AutoFeatureExtractor
download_from_hf(BertTokenizerFast,'bert-base-uncased')
print('...success',file=sys.stderr)
#---------------------------------------------
def download_from_hf(model_class:object, model_name:str):
print('',file=sys.stderr) # to prevent tqdm from overwriting
return model_class.from_pretrained(model_name,
cache_dir=os.path.join(Globals.root,Model_dir,model_name),
resume_download=True
)
#---------------------------------------------
def download_clip():
print('Installing CLIP model (ignore deprecation errors)...',file=sys.stderr)
version = 'openai/clip-vit-large-patch14'
print('Tokenizer...',file=sys.stderr, end='')
download_from_hf(CLIPTokenizer,version)
print('Text model...',file=sys.stderr, end='')
download_from_hf(CLIPTextModel,version)
print('...success',file=sys.stderr)
#---------------------------------------------
def download_realesrgan():
print('Installing models from RealESRGAN...',file=sys.stderr)
model_url = 'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-x4v3.pth'
model_dest = os.path.join(Globals.root,'models/realesrgan/realesr-general-x4v3.pth')
download_with_progress_bar(model_url, model_dest, 'RealESRGAN')
def download_gfpgan():
print('Installing GFPGAN models...',file=sys.stderr)
for model in (
[
'https://github.com/TencentARC/GFPGAN/releases/download/v1.3.0/GFPGANv1.4.pth',
'./models/gfpgan/GFPGANv1.4.pth'
],
[
'https://github.com/xinntao/facexlib/releases/download/v0.1.0/detection_Resnet50_Final.pth',
'./models/gfpgan/weights/detection_Resnet50_Final.pth'
],
[
'https://github.com/xinntao/facexlib/releases/download/v0.2.2/parsing_parsenet.pth',
'./models/gfpgan/weights/parsing_parsenet.pth'
],
):
model_url,model_dest = model[0],os.path.join(Globals.root,model[1])
download_with_progress_bar(model_url, model_dest, 'GFPGAN weights')
#---------------------------------------------
def download_codeformer():
print('Installing CodeFormer model file...',file=sys.stderr)
model_url = 'https://github.com/sczhou/CodeFormer/releases/download/v0.1.0/codeformer.pth'
model_dest = os.path.join(Globals.root,'models/codeformer/codeformer.pth')
download_with_progress_bar(model_url, model_dest, 'CodeFormer')
#---------------------------------------------
def download_clipseg():
print('Installing clipseg model for text-based masking...',end='', file=sys.stderr)
import zipfile
try:
model_url = 'https://owncloud.gwdg.de/index.php/s/ioHbRzFx6th32hn/download'
model_dest = os.path.join(Globals.root,'models/clipseg/clipseg_weights')
weights_zip = 'models/clipseg/weights.zip'
if not os.path.exists(model_dest):
os.makedirs(os.path.dirname(model_dest), exist_ok=True)
if not os.path.exists(f'{model_dest}/rd64-uni-refined.pth'):
dest = os.path.join(Globals.root,weights_zip)
request.urlretrieve(model_url,dest)
with zipfile.ZipFile(dest,'r') as zip:
zip.extractall(os.path.join(Globals.root,'models/clipseg'))
os.remove(dest)
from clipseg.clipseg import CLIPDensePredT
model = CLIPDensePredT(version='ViT-B/16', reduce_dim=64, )
model.eval()
model.load_state_dict(
torch.load(
os.path.join(Globals.root,'models/clipseg/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',file=sys.stderr)
#-------------------------------------
def download_safety_checker():
print('Installing safety model for NSFW content detection...',file=sys.stderr)
try:
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from transformers import AutoFeatureExtractor
except ModuleNotFoundError:
print('Error installing safety checker model:')
print(traceback.format_exc())
return
safety_model_id = "CompVis/stable-diffusion-safety-checker"
print('AutoFeatureExtractor...', end='',file=sys.stderr)
download_from_hf(AutoFeatureExtractor,safety_model_id)
print('StableDiffusionSafetyChecker...', end='',file=sys.stderr)
download_from_hf(StableDiffusionSafetyChecker,safety_model_id)
print('...success',file=sys.stderr)
#-------------------------------------
def download_weights(opt:dict):
if opt.yes_to_all:
models = recommended_datasets()
access_token = HfFolder.get_token()
if len(models)>0 and access_token is not None:
successfully_downloaded = download_weight_datasets(models, access_token)
update_config_file(successfully_downloaded,opt)
return
else:
print('** Cannot download models because no Hugging Face access token could be found. Please re-run without --yes')
else:
choice = user_wants_to_download_weights()
if choice == 'recommended':
models = recommended_datasets()
elif choice == 'customized':
models = select_datasets(choice)
if models is None and yes_or_no('Quit?',default_yes=False):
sys.exit(0)
else: # 'skip'
return
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,opt)
#-------------------------------------
def get_root(root:str=None)->str:
if root:
return root
elif os.environ.get('INVOKEAI_ROOT'):
return os.environ.get('INVOKEAI_ROOT')
else:
init_file = os.path.expanduser(Globals.initfile)
if not os.path.exists(init_file):
return None
# if we get here, then we read from initfile
root = None
with open(init_file, 'r') as infile:
lines = infile.readlines()
for l in lines:
if re.search('\s*#',l): # ignore comments
continue
match = re.search('--root\s*=?\s*"?([^"]+)"?',l)
if match:
root = match.groups()[0]
root = root.strip()
return root
#-------------------------------------
def select_root(yes_to_all:bool=False):
default = os.path.expanduser('~/invokeai')
if (yes_to_all):
return default
completer.set_default_dir(default)
completer.complete_extensions(())
completer.set_line(default)
return input(f"Select a directory in which to install InvokeAI's models and configuration files [{default}]: ")
#-------------------------------------
def select_outputs(root:str,yes_to_all:bool=False):
default = os.path.normpath(os.path.join(root,'outputs'))
if (yes_to_all):
return default
completer.set_default_dir(os.path.expanduser('~'))
completer.complete_extensions(())
completer.set_line(default)
return input('Select the default directory for image outputs [{default}]: ')
#-------------------------------------
def initialize_rootdir(root:str,yes_to_all:bool=False):
assert os.path.exists('./configs'),'Run this script from within the top level of the InvokeAI source code directory, "InvokeAI"'
print(f'** INITIALIZING INVOKEAI RUNTIME DIRECTORY **')
root = root or select_root(yes_to_all)
outputs = select_outputs(root,yes_to_all)
Globals.root = root
print(f'InvokeAI models and configuration files will be placed into {root} and image outputs will be placed into {outputs}.')
print(f'\nYou may change these values at any time by editing the --root and --output_dir options in "{Globals.initfile}",')
print(f'You may also change the runtime directory by setting the environment variable INVOKEAI_ROOT.\n')
for name in ('models','configs','scripts','frontend/dist'):
os.makedirs(os.path.join(root,name), exist_ok=True)
for src in ('configs','scripts','frontend/dist'):
dest = os.path.join(root,src)
if not os.path.samefile(src,dest):
shutil.copytree(src,dest,dirs_exist_ok=True)
os.makedirs(outputs)
init_file = os.path.expanduser(Globals.initfile)
if not os.path.exists(init_file):
print(f'Creating the initialization file at "{init_file}".\n')
with open(init_file,'w') as f:
f.write(f'''# InvokeAI initialization file
# This is the InvokeAI initialization file, which contains command-line default values.
# Feel free to edit. If anything goes wrong, you can re-initialize this file by deleting
# or renaming it and then running configure_invokeai.py again.
# The --root option below points to the folder in which InvokeAI stores its models, configs and outputs.
--root="{root}"
# the --outdir option controls the default location of image files.
--outdir="{outputs}"
# You may place other frequently-used startup commands here, one or more per line.
# Examples:
# --web --host=0.0.0.0
# --steps=20
# -Ak_euler_a -C10.0
#
'''
)
#-------------------------------------
class ProgressBar():
def __init__(self,model_name='file'):
self.pbar = None
self.name = model_name
def __call__(self, block_num, block_size, total_size):
if not self.pbar:
self.pbar=tqdm(desc=self.name,
initial=0,
unit='iB',
unit_scale=True,
unit_divisor=1000,
total=total_size)
self.pbar.update(block_size)
#-------------------------------------
def 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)')
parser.add_argument('--yes','-y',
dest='yes_to_all',
action='store_true',
help='answer "yes" to all prompts')
parser.add_argument('--config_file',
'-c',
dest='config_file',
type=str,
default='./configs/models.yaml',
help='path to configuration file to create')
parser.add_argument('--root',
dest='root',
type=str,
default=None,
help='path to root of install directory')
opt = parser.parse_args()
# setting a global here
Globals.root = os.path.expanduser(get_root(opt.root) or '')
try:
introduction()
# We check for two files to see if the runtime directory is correctly initialized.
# 1. a key stable diffusion config file
# 2. the web front end static files
if Globals.root == '' \
or not os.path.exists(os.path.join(Globals.root,'configs/stable-diffusion/v1-inference.yaml')) \
or not os.path.exists(os.path.join(Globals.root,'frontend/dist')):
initialize_rootdir(Globals.root,opt.yes_to_all)
print(f'(Initializing with runtime root {Globals.root})\n')
if opt.interactive:
print('** DOWNLOADING DIFFUSION WEIGHTS **')
download_weights(opt)
print('\n** DOWNLOADING SUPPORT MODELS **')
download_bert()
download_clip()
download_realesrgan()
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)}"')
#-------------------------------------
if __name__ == '__main__':
main()
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