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add new console frontend to initial model selection, and other improvements

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1. The invokeai-configure script has now been refactored. The work of
   selecting and downloading initial models at install time is now done
   by a script named invokeai-initial-models (module
   name is ldm.invoke.config.initial_model_select)

   The calling arguments for invokeai-configure have not changed, so
   nothing should break. After initializing the root directory, the
   script calls invokeai-initial-models to let the user select the
   starting models to install.

2. invokeai-initial-models puts up a console GUI with checkboxes to
   indicate which models to install. It respects the --default_only
   and --yes arguments so that CI will continue to work.

3. User can now edit the VAE assigned to diffusers models in the CLI.

4. Fixed a bug that caused a crash during model loading when the VAE
   is set to None, rather than being empty.
This commit is contained in:
Lincoln Stein
2023-02-12 23:52:44 -05:00
parent 3dd7393984
commit 714fff39ba
7 changed files with 85 additions and 508 deletions
Download Patch File Download Diff File Expand all files Collapse all files

507
ldm/invoke/config/invokeai_configure.py
View File

@ -10,37 +10,36 @@ print("Loading Python libraries...\n")
import argparse
import io
import os
import re
import shutil
import sys
import traceback
import warnings
from pathlib import Path
from tempfile import TemporaryFile
from typing import Union
from urllib import request
import requests
import transformers
from diffusers import AutoencoderKL
from getpass_asterisk import getpass_asterisk
from huggingface_hub import HfFolder, hf_hub_url
from huggingface_hub import HfFolder
from huggingface_hub import login as hf_hub_login
from omegaconf import OmegaConf
from omegaconf.dictconfig import DictConfig
from tqdm import tqdm
from transformers import (AutoProcessor, CLIPSegForImageSegmentation,
CLIPTextModel, CLIPTokenizer)
from transformers import (
AutoProcessor,
CLIPSegForImageSegmentation,
CLIPTextModel,
CLIPTokenizer,
)
import invokeai.configs as configs
from ldm.invoke.devices import choose_precision, choose_torch_device
from ldm.invoke.generator.diffusers_pipeline import \
StableDiffusionGeneratorPipeline
from ldm.invoke.globals import Globals, global_cache_dir, global_config_dir
from ldm.invoke.config.initial_model_select import (
download_from_hf,
select_and_download_models,
yes_or_no,
)
from ldm.invoke.globals import Globals, global_config_dir
from ldm.invoke.readline import generic_completer
warnings.filterwarnings("ignore")
import torch
transformers.logging.set_verbosity_error()
@ -104,125 +103,6 @@ Have fun!
print(message)
# ---------------------------------------------
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://invoke-ai.github.io/InvokeAI/installation/020_INSTALL_MANUAL/
You may download the recommended models (about 15GB total), install all models (40 GB!!)
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, <a>ll models, <c>ustomized 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(("a", "A")):
selection = "all"
elif choice.startswith(("s", "S")):
selection = "skip"
return selection
# ---------------------------------------------
def select_datasets(action: str):
done = False
default_datasets = default_dataset()
while not done:
datasets = dict()
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 = Datasets[ds].get("recommended", False)
r_str = "(recommended)" if recommended else ""
print(f'[{counter}] {ds}:\n {Datasets[ds]["description"]} {r_str}')
if yes_or_no(" Download?", default_yes=recommended):
datasets[ds] = True
counter += 1
else:
for ds in Datasets.keys():
if Datasets[ds].get("recommended", False):
datasets[ds] = True
counter += 1
print("The following weight files will be downloaded:")
counter = 1
for ds in datasets:
dflt = "*" if ds in default_datasets else ""
print(f" [{counter}] {ds}{dflt}")
counter += 1
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].get("recommended", False):
datasets[ds] = True
return datasets
# ---------------------------------------------
def default_dataset() -> dict:
datasets = dict()
for ds in Datasets.keys():
if Datasets[ds].get("default", False):
datasets[ds] = True
return datasets
# ---------------------------------------------
def all_datasets() -> dict:
datasets = dict()
for ds in Datasets.keys():
datasets[ds] = True
return datasets
# ---------------------------------------------
def HfLogin(access_token) -> str:
"""
@ -242,7 +122,7 @@ def HfLogin(access_token) -> str:
# -------------------------------Authenticate against Hugging Face
def authenticate(yes_to_all=False):
def save_hf_token(yes_to_all=False):
print("** LICENSE AGREEMENT FOR WEIGHT FILES **")
print("=" * shutil.get_terminal_size()[0])
print(
@ -356,149 +236,6 @@ You may re-run the configuration script again in the future if you do not wish t
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.replace(
os.path.join(model_path, "model.ckpt"), os.path.join(model_path, new_name)
)
# ---------------------------------------------
def download_weight_datasets(
models: dict, access_token: str, precision: str = "float32"
):
migrate_models_ckpt()
successful = dict()
for mod in models.keys():
print(f"Downloading {mod}:")
successful[mod] = _download_repo_or_file(
Datasets[mod], access_token, precision=precision
)
return successful
def _download_repo_or_file(
mconfig: DictConfig, access_token: str, precision: str = "float32"
) -> Path:
path = None
if mconfig["format"] == "ckpt":
path = _download_ckpt_weights(mconfig, access_token)
else:
path = _download_diffusion_weights(mconfig, access_token, precision=precision)
if "vae" in mconfig and "repo_id" in mconfig["vae"]:
_download_diffusion_weights(
mconfig["vae"], access_token, precision=precision
)
return path
def _download_ckpt_weights(mconfig: DictConfig, access_token: str) -> Path:
repo_id = mconfig["repo_id"]
filename = mconfig["file"]
cache_dir = os.path.join(Globals.root, Model_dir, Weights_dir)
return hf_download_with_resume(
repo_id=repo_id,
model_dir=cache_dir,
model_name=filename,
access_token=access_token,
)
def _download_diffusion_weights(
mconfig: DictConfig, access_token: str, precision: str = "float32"
):
repo_id = mconfig["repo_id"]
model_class = (
StableDiffusionGeneratorPipeline
if mconfig.get("format", None) == "diffusers"
else AutoencoderKL
)
extra_arg_list = [{"revision": "fp16"}, {}] if precision == "float16" else [{}]
path = None
for extra_args in extra_arg_list:
try:
path = download_from_hf(
model_class,
repo_id,
cache_subdir="diffusers",
safety_checker=None,
**extra_args,
)
except OSError as e:
if str(e).startswith("fp16 is not a valid"):
pass
else:
print(f"An unexpected error occurred while downloading the model: {e})")
if path:
break
return path
# ---------------------------------------------
def hf_download_with_resume(
repo_id: str, model_dir: str, model_name: str, access_token: str = None
) -> Path:
model_dest = Path(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 model_dest
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 None
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 None
return model_dest
# ---------------------------------------------
def download_with_progress_bar(model_url: str, model_dest: str, label: str = "the"):
try:
@ -517,125 +254,6 @@ def download_with_progress_bar(model_url: str, model_dest: str, label: str = "th
print(f"Error downloading {label} model")
print(traceback.format_exc())
# ---------------------------------------------
def update_config_file(successfully_downloaded: dict, opt: dict):
config_file = (
Path(opt.config_file) if opt.config_file is not None else Default_config_file
)
# In some cases (incomplete setup, etc), the default configs directory might be missing.
# Create it if it doesn't exist.
# this check is ignored if opt.config_file is specified - user is assumed to know what they
# are doing if they are passing a custom config file from elsewhere.
if config_file is Default_config_file and not config_file.parent.exists():
configs_src = Dataset_path.parent
configs_dest = Default_config_file.parent
shutil.copytree(configs_src, configs_dest, dirs_exist_ok=True)
yaml = new_config_file_contents(successfully_downloaded, config_file, opt)
try:
backup = None
if os.path.exists(config_file):
print(
f"** {config_file.name} exists. Renaming to {config_file.stem}.yaml.orig"
)
backup = config_file.with_suffix(".yaml.orig")
## Ugh. Windows is unable to overwrite an existing backup file, raises a WinError 183
if sys.platform == "win32" and backup.is_file():
backup.unlink()
config_file.rename(backup)
with TemporaryFile() as tmp:
tmp.write(Config_preamble.encode())
tmp.write(yaml.encode())
with open(str(config_file.expanduser().resolve()), "wb") as new_config:
tmp.seek(0)
new_config.write(tmp.read())
except Exception as e:
print(f"**Error creating config file {config_file}: {str(e)} **")
if backup is not None:
print("restoring previous config file")
## workaround, for WinError 183, see above
if sys.platform == "win32" and config_file.is_file():
config_file.unlink()
backup.rename(config_file)
return
print(f"Successfully created new configuration file {config_file}")
# ---------------------------------------------
def new_config_file_contents(successfully_downloaded: dict, config_file: Path, opt: dict) -> str:
if config_file.exists():
conf = OmegaConf.load(str(config_file.expanduser().resolve()))
else:
conf = OmegaConf.create()
default_selected = None
for model in successfully_downloaded:
# a bit hacky - what we are doing here is seeing whether a checkpoint
# version of the model was previously defined, and whether the current
# model is a diffusers (indicated with a path)
if conf.get(model) and Path(successfully_downloaded[model]).is_dir():
offer_to_delete_weights(model, conf[model], opt.yes_to_all)
stanza = {}
mod = Datasets[model]
stanza["description"] = mod["description"]
stanza["repo_id"] = mod["repo_id"]
stanza["format"] = mod["format"]
# diffusers don't need width and height (probably .ckpt doesn't either)
# so we no longer require these in INITIAL_MODELS.yaml
if "width" in mod:
stanza["width"] = mod["width"]
if "height" in mod:
stanza["height"] = mod["height"]
if "file" in mod:
stanza["weights"] = os.path.relpath(
successfully_downloaded[model], start=Globals.root
)
stanza["config"] = os.path.normpath(os.path.join(SD_Configs, mod["config"]))
if "vae" in mod:
if "file" in mod["vae"]:
stanza["vae"] = os.path.normpath(
os.path.join(Model_dir, Weights_dir, mod["vae"]["file"])
)
else:
stanza["vae"] = mod["vae"]
if mod.get("default", False):
stanza["default"] = True
default_selected = True
conf[model] = stanza
# if no default model was chosen, then we select the first
# one in the list
if not default_selected:
conf[list(successfully_downloaded.keys())[0]]["default"] = True
return OmegaConf.to_yaml(conf)
# ---------------------------------------------
def offer_to_delete_weights(model_name: str, conf_stanza: dict, yes_to_all: bool):
if not (weights := conf_stanza.get('weights')):
return
if re.match('/VAE/',conf_stanza.get('config')):
return
if yes_to_all or \
yes_or_no(f'\n** The checkpoint version of {model_name} is superseded by the diffusers version. Delete the original file {weights}?', default_yes=False):
weights = Path(weights)
if not weights.is_absolute():
weights = Path(Globals.root) / weights
try:
weights.unlink()
except OSError as e:
print(str(e))
# ---------------------------------------------
# this will preload the Bert tokenizer fles
def download_bert():
@ -652,22 +270,6 @@ def download_bert():
print("...success", file=sys.stderr)
# ---------------------------------------------
def download_from_hf(
model_class: object, model_name: str, cache_subdir: Path = Path("hub"), **kwargs
):
print("", file=sys.stderr) # to prevent tqdm from overwriting
path = global_cache_dir(cache_subdir)
model = model_class.from_pretrained(
model_name,
cache_dir=path,
resume_download=True,
**kwargs,
)
model_name = '--'.join(('models',*model_name.split('/')))
return path / model_name if model else None
# ---------------------------------------------
def download_clip():
print("Installing CLIP model (ignore deprecation errors)...", file=sys.stderr)
@ -744,8 +346,9 @@ def download_clipseg():
def download_safety_checker():
print("Installing model for NSFW content detection...", file=sys.stderr)
try:
from diffusers.pipelines.stable_diffusion.safety_checker import \
StableDiffusionSafetyChecker
from diffusers.pipelines.stable_diffusion.safety_checker import (
StableDiffusionSafetyChecker,
)
from transformers import AutoFeatureExtractor
except ModuleNotFoundError:
print("Error installing NSFW checker model:")
@ -759,52 +362,6 @@ def download_safety_checker():
print("...success", file=sys.stderr)
# -------------------------------------
def download_weights(opt: dict) -> Union[str, None]:
precision = (
"float32"
if opt.full_precision
else choose_precision(torch.device(choose_torch_device()))
)
if opt.yes_to_all:
models = default_dataset() if opt.default_only else recommended_datasets()
access_token = authenticate(opt.yes_to_all)
if len(models) > 0:
successfully_downloaded = download_weight_datasets(
models, access_token, precision=precision
)
update_config_file(successfully_downloaded, opt)
return
else:
choice = user_wants_to_download_weights()
if choice == "recommended":
models = recommended_datasets()
elif choice == "all":
models = all_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
access_token = authenticate()
if access_token is not None:
HfFolder.save_token(access_token)
print("\n** DOWNLOADING WEIGHTS **")
successfully_downloaded = download_weight_datasets(
models, access_token, precision=precision
)
update_config_file(successfully_downloaded, opt)
if len(successfully_downloaded) < len(models):
return "some of the model weights downloads were not successful"
# -------------------------------------
def get_root(root: str = None) -> str:
if root:
@ -951,13 +508,6 @@ class ProgressBar:
# -------------------------------------
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) - DEPRECATED",
)
parser.add_argument(
"--skip-sd-weights",
dest="skip_sd_weights",
@ -1005,26 +555,16 @@ def main():
# setting a global here
Globals.root = os.path.expanduser(get_root(opt.root) or "")
errors = set()
try:
# We check for to see if the runtime directory is correctly initialized.
if Globals.root == "" or not os.path.exists(
os.path.join(Globals.root, "invokeai.init")
):
initialize_rootdir(Globals.root, opt.yes_to_all)
save_hf_token(opt.yes_to_all)
# Optimistically try to download all required assets. If any errors occur, add them and proceed anyway.
errors = set()
if not opt.interactive:
print(
"WARNING: The --(no)-interactive argument is deprecated and will be removed. Use --skip-sd-weights."
)
opt.skip_sd_weights = True
if opt.skip_sd_weights:
print("** SKIPPING DIFFUSION WEIGHTS DOWNLOAD PER USER REQUEST **")
else:
print("** DOWNLOADING DIFFUSION WEIGHTS **")
errors.add(download_weights(opt))
print("\n** DOWNLOADING SUPPORT MODELS **")
download_bert()
download_clip()
@ -1033,6 +573,13 @@ def main():
download_codeformer()
download_clipseg()
download_safety_checker()
if opt.skip_sd_weights:
print("** SKIPPING DIFFUSION WEIGHTS DOWNLOAD PER USER REQUEST **")
else:
print("** DOWNLOADING DIFFUSION WEIGHTS **")
errors.add(select_and_download_models(opt))
postscript(errors=errors)
except KeyboardInterrupt:
print("\nGoodbye! Come back soon.")