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InvokeAI/invokeai/backend/config/invokeai_configure.py
Lincoln Stein ef8cf83b28 migrate to new HF diffusers cache location
2023-03-05 08:20:24 -05:00

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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
#
import sys
print("Loading Python libraries...\n",file=sys.stderr)
import argparse
import io
import os
import re
import shutil
import traceback
import warnings
from argparse import Namespace
from pathlib import Path
from shutil import get_terminal_size
from urllib import request
import npyscreen
import torch
import transformers
from diffusers import AutoencoderKL
from huggingface_hub import HfFolder
from huggingface_hub import login as hf_hub_login
from omegaconf import OmegaConf
from tqdm import tqdm
from transformers import (
AutoProcessor,
CLIPSegForImageSegmentation,
CLIPTextModel,
CLIPTokenizer,
)
import invokeai.configs as configs
from ...frontend.install.model_install import addModelsForm, process_and_execute
from ...frontend.install.widgets import (
CenteredButtonPress,
IntTitleSlider,
set_min_terminal_size,
)
from ..args import PRECISION_CHOICES, Args
from ..globals import Globals, global_cache_dir, global_config_dir, global_config_file
from .model_install_backend import (
default_dataset,
download_from_hf,
hf_download_with_resume,
recommended_datasets,
)
warnings.filterwarnings("ignore")
transformers.logging.set_verbosity_error()
# --------------------------globals-----------------------
Model_dir = "models"
Weights_dir = "ldm/stable-diffusion-v1/"
# the initial "configs" dir is now bundled in the `invokeai.configs` package
Dataset_path = Path(configs.__path__[0]) / "INITIAL_MODELS.yaml"
Default_config_file = Path(global_config_dir()) / "models.yaml"
SD_Configs = Path(global_config_dir()) / "stable-diffusion"
Datasets = OmegaConf.load(Dataset_path)
# minimum size for the UI
MIN_COLS = 135
MIN_LINES = 45
INIT_FILE_PREAMBLE = """# 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 invokeai-configure again.
# Place frequently-used startup commands here, one or more per line.
# Examples:
# --outdir=D:\data\images
# --no-nsfw_checker
# --web --host=0.0.0.0
# --steps=20
# -Ak_euler_a -C10.0
"""
# --------------------------------------------
def postscript(errors: None):
if not any(errors):
message = f"""
** INVOKEAI INSTALLATION SUCCESSFUL **
If you installed manually from source or with 'pip install': activate the virtual environment
then run one of the following commands to start InvokeAI.
Web UI:
invokeai --web # (connect to http://localhost:9090)
invokeai --web --host 0.0.0.0 # (connect to http://your-lan-ip:9090 from another computer on the local network)
Command-line interface:
invokeai
If you installed using an installation script, run:
{Globals.root}/invoke.{"bat" if sys.platform == "win32" else "sh"}
Add the '--help' argument to see all of the command-line switches available for use.
"""
else:
message = "\n** There were errors during installation. It is possible some of the models were not fully downloaded.\n"
for err in errors:
message += f"\t - {err}\n"
message += "Please check the logs above and correct any issues."
print(message)
# ---------------------------------------------
def yes_or_no(prompt: str, default_yes=True):
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 HfLogin(access_token) -> str:
"""
Helper for logging in to Huggingface
The stdout capture is needed to hide the irrelevant "git credential helper" warning
"""
capture = io.StringIO()
sys.stdout = capture
try:
hf_hub_login(token=access_token, add_to_git_credential=False)
sys.stdout = sys.__stdout__
except Exception as exc:
sys.stdout = sys.__stdout__
print(exc)
raise exc
# -------------------------------------
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 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)
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", file=sys.stderr)
print(f"Error downloading {label} model", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
# ---------------------------------------------
# this will preload the Bert tokenizer fles
def download_bert():
print("Installing bert tokenizer...", file=sys.stderr)
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=DeprecationWarning)
from transformers import BertTokenizerFast
download_from_hf(BertTokenizerFast, "bert-base-uncased")
# ---------------------------------------------
def download_sd1_clip():
print("Installing SD1 clip model...", file=sys.stderr)
version = "openai/clip-vit-large-patch14"
download_from_hf(CLIPTokenizer, version)
download_from_hf(CLIPTextModel, version)
# ---------------------------------------------
def download_sd2_clip():
version = "stabilityai/stable-diffusion-2"
print("Installing SD2 clip model...", file=sys.stderr)
download_from_hf(CLIPTokenizer, version, subfolder="tokenizer")
download_from_hf(CLIPTextModel, version, subfolder="text_encoder")
# ---------------------------------------------
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"
wdn_model_url = "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.5.0/realesr-general-wdn-x4v3.pth"
model_dest = os.path.join(
Globals.root, "models/realesrgan/realesr-general-x4v3.pth"
)
wdn_model_dest = os.path.join(
Globals.root, "models/realesrgan/realesr-general-wdn-x4v3.pth"
)
download_with_progress_bar(model_url, model_dest, "RealESRGAN")
download_with_progress_bar(wdn_model_url, wdn_model_dest, "RealESRGANwdn")
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...", file=sys.stderr)
CLIPSEG_MODEL = "CIDAS/clipseg-rd64-refined"
try:
download_from_hf(AutoProcessor, CLIPSEG_MODEL)
download_from_hf(CLIPSegForImageSegmentation, CLIPSEG_MODEL)
except Exception:
print("Error installing clipseg model:")
print(traceback.format_exc())
# -------------------------------------
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 transformers import AutoFeatureExtractor
except ModuleNotFoundError:
print("Error installing NSFW checker model:")
print(traceback.format_exc())
return
safety_model_id = "CompVis/stable-diffusion-safety-checker"
print("AutoFeatureExtractor...", file=sys.stderr)
download_from_hf(AutoFeatureExtractor, safety_model_id)
print("StableDiffusionSafetyChecker...", file=sys.stderr)
download_from_hf(StableDiffusionSafetyChecker, safety_model_id)
# -------------------------------------
def download_vaes():
print("Installing stabilityai VAE...", file=sys.stderr)
try:
# first the diffusers version
repo_id = "stabilityai/sd-vae-ft-mse"
args = dict(
cache_dir=global_cache_dir("hub"),
)
if not AutoencoderKL.from_pretrained(repo_id, **args):
raise Exception(f"download of {repo_id} failed")
repo_id = "stabilityai/sd-vae-ft-mse-original"
model_name = "vae-ft-mse-840000-ema-pruned.ckpt"
# next the legacy checkpoint version
if not hf_download_with_resume(
repo_id=repo_id,
model_name=model_name,
model_dir=str(Globals.root / Model_dir / Weights_dir),
):
raise Exception(f"download of {model_name} failed")
except Exception as e:
print(f"Error downloading StabilityAI standard VAE: {str(e)}", file=sys.stderr)
print(traceback.format_exc(), file=sys.stderr)
# -------------------------------------
def get_root(root: str = None) -> str:
if root:
return root
elif os.environ.get("INVOKEAI_ROOT"):
return os.environ.get("INVOKEAI_ROOT")
else:
return Globals.root
# -------------------------------------
class editOptsForm(npyscreen.FormMultiPage):
# for responsive resizing - disabled
# FIX_MINIMUM_SIZE_WHEN_CREATED = False
def create(self):
program_opts = self.parentApp.program_opts
old_opts = self.parentApp.invokeai_opts
first_time = not (Globals.root / Globals.initfile).exists()
access_token = HfFolder.get_token()
window_width, window_height = get_terminal_size()
for i in [
"Configure startup settings. You can come back and change these later.",
"Use ctrl-N and ctrl-P to move to the <N>ext and <P>revious fields.",
"Use cursor arrows to make a checkbox selection, and space to toggle.",
]:
self.add_widget_intelligent(
npyscreen.FixedText,
value=i,
editable=False,
color="CONTROL",
)
self.nextrely += 1
self.add_widget_intelligent(
npyscreen.TitleFixedText,
name="== BASIC OPTIONS ==",
begin_entry_at=0,
editable=False,
color="CONTROL",
scroll_exit=True,
)
self.nextrely -= 1
self.add_widget_intelligent(
npyscreen.FixedText,
value="Select an output directory for images:",
editable=False,
color="CONTROL",
)
self.outdir = self.add_widget_intelligent(
npyscreen.TitleFilename,
name="(<tab> autocompletes, ctrl-N advances):",
value=old_opts.outdir or str(default_output_dir()),
select_dir=True,
must_exist=False,
use_two_lines=False,
labelColor="GOOD",
begin_entry_at=40,
scroll_exit=True,
)
self.nextrely += 1
self.add_widget_intelligent(
npyscreen.FixedText,
value="Activate the NSFW checker to blur images showing potential sexual imagery:",
editable=False,
color="CONTROL",
)
self.safety_checker = self.add_widget_intelligent(
npyscreen.Checkbox,
name="NSFW checker",
value=old_opts.safety_checker,
relx=5,
scroll_exit=True,
)
self.nextrely += 1
for i in [
"If you have an account at HuggingFace you may paste your access token here",
'to allow InvokeAI to download styles & subjects from the "Concept Library".',
"See https://huggingface.co/settings/tokens",
]:
self.add_widget_intelligent(
npyscreen.FixedText,
value=i,
editable=False,
color="CONTROL",
)
self.hf_token = self.add_widget_intelligent(
npyscreen.TitlePassword,
name="Access Token (ctrl-shift-V pastes):",
value=access_token,
begin_entry_at=42,
use_two_lines=False,
scroll_exit=True,
)
self.nextrely += 1
self.add_widget_intelligent(
npyscreen.TitleFixedText,
name="== ADVANCED OPTIONS ==",
begin_entry_at=0,
editable=False,
color="CONTROL",
scroll_exit=True,
)
self.nextrely -= 1
self.add_widget_intelligent(
npyscreen.TitleFixedText,
name="GPU Management",
begin_entry_at=0,
editable=False,
color="CONTROL",
scroll_exit=True,
)
self.nextrely -= 1
self.free_gpu_mem = self.add_widget_intelligent(
npyscreen.Checkbox,
name="Free GPU memory after each generation",
value=old_opts.free_gpu_mem,
relx=5,
scroll_exit=True,
)
self.xformers = self.add_widget_intelligent(
npyscreen.Checkbox,
name="Enable xformers support if available",
value=old_opts.xformers,
relx=5,
scroll_exit=True,
)
self.ckpt_convert = self.add_widget_intelligent(
npyscreen.Checkbox,
name="Load legacy checkpoint models into memory as diffusers models",
value=old_opts.ckpt_convert,
relx=5,
scroll_exit=True,
)
self.always_use_cpu = self.add_widget_intelligent(
npyscreen.Checkbox,
name="Force CPU to be used on GPU systems",
value=old_opts.always_use_cpu,
relx=5,
scroll_exit=True,
)
precision = old_opts.precision or (
"float32" if program_opts.full_precision else "auto"
)
self.precision = self.add_widget_intelligent(
npyscreen.TitleSelectOne,
name="Precision",
values=PRECISION_CHOICES,
value=PRECISION_CHOICES.index(precision),
begin_entry_at=3,
max_height=len(PRECISION_CHOICES) + 1,
scroll_exit=True,
)
self.max_loaded_models = self.add_widget_intelligent(
IntTitleSlider,
name="Number of models to cache in CPU memory (each will use 2-4 GB!)",
value=old_opts.max_loaded_models,
out_of=10,
lowest=1,
begin_entry_at=4,
scroll_exit=True,
)
self.nextrely += 1
self.add_widget_intelligent(
npyscreen.FixedText,
value="Directory containing embedding/textual inversion files:",
editable=False,
color="CONTROL",
)
self.embedding_path = self.add_widget_intelligent(
npyscreen.TitleFilename,
name="(<tab> autocompletes, ctrl-N advances):",
value=str(default_embedding_dir()),
select_dir=True,
must_exist=False,
use_two_lines=False,
labelColor="GOOD",
begin_entry_at=40,
scroll_exit=True,
)
self.nextrely += 1
self.add_widget_intelligent(
npyscreen.TitleFixedText,
name="== LICENSE ==",
begin_entry_at=0,
editable=False,
color="CONTROL",
scroll_exit=True,
)
self.nextrely -= 1
for i in [
"BY DOWNLOADING THE STABLE DIFFUSION WEIGHT FILES, YOU AGREE TO HAVE READ",
"AND ACCEPTED THE CREATIVEML RESPONSIBLE AI LICENSE LOCATED AT",
"https://huggingface.co/spaces/CompVis/stable-diffusion-license",
]:
self.add_widget_intelligent(
npyscreen.FixedText,
value=i,
editable=False,
color="CONTROL",
)
self.license_acceptance = self.add_widget_intelligent(
npyscreen.Checkbox,
name="I accept the CreativeML Responsible AI License",
value=not first_time,
relx=2,
scroll_exit=True,
)
self.nextrely += 1
label = (
"DONE"
if program_opts.skip_sd_weights or program_opts.default_only
else "NEXT"
)
self.ok_button = self.add_widget_intelligent(
CenteredButtonPress,
name=label,
relx=(window_width - len(label)) // 2,
rely=-3,
when_pressed_function=self.on_ok,
)
def on_ok(self):
options = self.marshall_arguments()
if self.validate_field_values(options):
self.parentApp.new_opts = options
if hasattr(self.parentApp, "model_select"):
self.parentApp.setNextForm("MODELS")
else:
self.parentApp.setNextForm(None)
self.editing = False
else:
self.editing = True
def validate_field_values(self, opt: Namespace) -> bool:
bad_fields = []
if not opt.license_acceptance:
bad_fields.append(
"Please accept the license terms before proceeding to model downloads"
)
if not Path(opt.outdir).parent.exists():
bad_fields.append(
f"The output directory does not seem to be valid. Please check that {str(Path(opt.outdir).parent)} is an existing directory."
)
if not Path(opt.embedding_path).parent.exists():
bad_fields.append(
f"The embedding directory does not seem to be valid. Please check that {str(Path(opt.embedding_path).parent)} is an existing directory."
)
if len(bad_fields) > 0:
message = "The following problems were detected and must be corrected:\n"
for problem in bad_fields:
message += f"* {problem}\n"
npyscreen.notify_confirm(message)
return False
else:
return True
def marshall_arguments(self):
new_opts = Namespace()
for attr in [
"outdir",
"safety_checker",
"free_gpu_mem",
"max_loaded_models",
"xformers",
"always_use_cpu",
"embedding_path",
"ckpt_convert",
]:
setattr(new_opts, attr, getattr(self, attr).value)
new_opts.hf_token = self.hf_token.value
new_opts.license_acceptance = self.license_acceptance.value
new_opts.precision = PRECISION_CHOICES[self.precision.value[0]]
return new_opts
class EditOptApplication(npyscreen.NPSAppManaged):
def __init__(self, program_opts: Namespace, invokeai_opts: Namespace):
super().__init__()
self.program_opts = program_opts
self.invokeai_opts = invokeai_opts
self.user_cancelled = False
self.user_selections = default_user_selections(program_opts)
def onStart(self):
npyscreen.setTheme(npyscreen.Themes.DefaultTheme)
self.options = self.addForm(
"MAIN",
editOptsForm,
name="InvokeAI Startup Options",
)
if not (self.program_opts.skip_sd_weights or self.program_opts.default_only):
self.model_select = self.addForm(
"MODELS",
addModelsForm,
name="Install Stable Diffusion Models",
multipage=True,
)
def new_opts(self):
return self.options.marshall_arguments()
def edit_opts(program_opts: Namespace, invokeai_opts: Namespace) -> argparse.Namespace:
editApp = EditOptApplication(program_opts, invokeai_opts)
editApp.run()
return editApp.new_opts()
def default_startup_options(init_file: Path) -> Namespace:
opts = Args().parse_args([])
outdir = Path(opts.outdir)
if not outdir.is_absolute():
opts.outdir = str(Globals.root / opts.outdir)
if not init_file.exists():
opts.safety_checker = True
return opts
def default_user_selections(program_opts: Namespace) -> Namespace:
return Namespace(
starter_models=default_dataset()
if program_opts.default_only
else recommended_datasets()
if program_opts.yes_to_all
else dict(),
purge_deleted_models=False,
scan_directory=None,
autoscan_on_startup=None,
import_model_paths=None,
convert_to_diffusers=None,
)
# -------------------------------------
def initialize_rootdir(root: str, yes_to_all: bool = False):
print("** INITIALIZING INVOKEAI RUNTIME DIRECTORY **")
for name in (
"models",
"configs",
"embeddings",
"text-inversion-output",
"text-inversion-training-data",
):
os.makedirs(os.path.join(root, name), exist_ok=True)
configs_src = Path(configs.__path__[0])
configs_dest = Path(root) / "configs"
if not os.path.samefile(configs_src, configs_dest):
shutil.copytree(configs_src, configs_dest, dirs_exist_ok=True)
# -------------------------------------
def run_console_ui(
program_opts: Namespace, initfile: Path = None
) -> (Namespace, Namespace):
# parse_args() will read from init file if present
invokeai_opts = default_startup_options(initfile)
set_min_terminal_size(MIN_COLS, MIN_LINES)
editApp = EditOptApplication(program_opts, invokeai_opts)
editApp.run()
if editApp.user_cancelled:
return (None, None)
else:
return (editApp.new_opts, editApp.user_selections)
# -------------------------------------
def write_opts(opts: Namespace, init_file: Path):
"""
Update the invokeai.init file with values from opts Namespace
"""
# touch file if it doesn't exist
if not init_file.exists():
with open(init_file, "w") as f:
f.write(INIT_FILE_PREAMBLE)
# We want to write in the changed arguments without clobbering
# any other initialization values the user has entered. There is
# no good way to do this because of the one-way nature of
# argparse: i.e. --outdir could be --outdir, --out, or -o
# initfile needs to be replaced with a fully structured format
# such as yaml; this is a hack that will work much of the time
args_to_skip = re.compile(
"^--?(o|out|no-xformer|xformer|no-ckpt|ckpt|free|no-nsfw|nsfw|prec|max_load|embed|always|ckpt|free_gpu)"
)
# fix windows paths
opts.outdir = opts.outdir.replace("\\", "/")
opts.embedding_path = opts.embedding_path.replace("\\", "/")
new_file = f"{init_file}.new"
try:
lines = [x.strip() for x in open(init_file, "r").readlines()]
with open(new_file, "w") as out_file:
for line in lines:
if len(line) > 0 and not args_to_skip.match(line):
out_file.write(line + "\n")
out_file.write(
f"""
--outdir={opts.outdir}
--embedding_path={opts.embedding_path}
--precision={opts.precision}
--max_loaded_models={int(opts.max_loaded_models)}
--{'no-' if not opts.safety_checker else ''}nsfw_checker
--{'no-' if not opts.xformers else ''}xformers
--{'no-' if not opts.ckpt_convert else ''}ckpt_convert
{'--free_gpu_mem' if opts.free_gpu_mem else ''}
{'--always_use_cpu' if opts.always_use_cpu else ''}
"""
)
except OSError as e:
print(f"** An error occurred while writing the init file: {str(e)}")
os.replace(new_file, init_file)
if opts.hf_token:
HfLogin(opts.hf_token)
# -------------------------------------
def default_output_dir() -> Path:
return Globals.root / "outputs"
# -------------------------------------
def default_embedding_dir() -> Path:
return Globals.root / "embeddings"
# -------------------------------------
def write_default_options(program_opts: Namespace, initfile: Path):
opt = default_startup_options(initfile)
opt.hf_token = HfFolder.get_token()
write_opts(opt, initfile)
# -------------------------------------
def main():
parser = argparse.ArgumentParser(description="InvokeAI model downloader")
parser.add_argument(
"--skip-sd-weights",
dest="skip_sd_weights",
action=argparse.BooleanOptionalAction,
default=False,
help="skip downloading the large Stable Diffusion weight files",
)
parser.add_argument(
"--skip-support-models",
dest="skip_support_models",
action=argparse.BooleanOptionalAction,
default=False,
help="skip downloading the support models",
)
parser.add_argument(
"--full-precision",
dest="full_precision",
action=argparse.BooleanOptionalAction,
type=bool,
default=False,
help="use 32-bit weights instead of faster 16-bit weights",
)
parser.add_argument(
"--yes",
"-y",
dest="yes_to_all",
action="store_true",
help='answer "yes" to all prompts',
)
parser.add_argument(
"--default_only",
action="store_true",
help="when --yes specified, only install the default model",
)
parser.add_argument(
"--config_file",
"-c",
dest="config_file",
type=str,
default=None,
help="path to configuration file to create",
)
parser.add_argument(
"--root_dir",
dest="root",
type=str,
default=None,
help="path to root of install directory",
)
opt = parser.parse_args()
# setting a global here
Globals.root = Path(os.path.expanduser(get_root(opt.root) or ""))
errors = set()
try:
models_to_download = default_user_selections(opt)
# We check for to see if the runtime directory is correctly initialized.
init_file = Path(Globals.root, Globals.initfile)
if not init_file.exists() or not global_config_file().exists():
initialize_rootdir(Globals.root, opt.yes_to_all)
if opt.yes_to_all:
write_default_options(opt, init_file)
init_options = Namespace(
precision="float32" if opt.full_precision else "float16"
)
else:
init_options, models_to_download = run_console_ui(opt, init_file)
if init_options:
write_opts(init_options, init_file)
else:
print(
'\n** CANCELLED AT USER\'S REQUEST. USE THE "invoke.sh" LAUNCHER TO RUN LATER **\n'
)
sys.exit(0)
if opt.skip_support_models:
print("\n** SKIPPING SUPPORT MODEL DOWNLOADS PER USER REQUEST **")
else:
print("\n** DOWNLOADING SUPPORT MODELS **")
download_bert()
download_sd1_clip()
download_sd2_clip()
download_realesrgan()
download_gfpgan()
download_codeformer()
download_clipseg()
download_safety_checker()
download_vaes()
if opt.skip_sd_weights:
print("\n** SKIPPING DIFFUSION WEIGHTS DOWNLOAD PER USER REQUEST **")
elif models_to_download:
print("\n** DOWNLOADING DIFFUSION WEIGHTS **")
process_and_execute(opt, models_to_download)
postscript(errors=errors)
except KeyboardInterrupt:
print("\nGoodbye! Come back soon.")
# -------------------------------------
if __name__ == "__main__":
main()
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