InvokeAI/invokeai/backend/globals.py
2023-03-03 01:02:00 -05:00

126 lines
4.1 KiB
Python

"""
invokeai.backend.globals defines a small number of global variables that would
otherwise have to be passed through long and complex call chains.
It defines a Namespace object named "Globals" that contains
the attributes:
- root - the root directory under which "models" and "outputs" can be found
- initfile - path to the initialization file
- try_patchmatch - option to globally disable loading of 'patchmatch' module
- always_use_cpu - force use of CPU even if GPU is available
"""
import os
import os.path as osp
from argparse import Namespace
from pathlib import Path
from typing import Union
Globals = Namespace()
# Where to look for the initialization file and other key components
Globals.initfile = "invokeai.init"
Globals.models_file = "models.yaml"
Globals.models_dir = "models"
Globals.config_dir = "configs"
Globals.autoscan_dir = "weights"
Globals.converted_ckpts_dir = "converted_ckpts"
# Set the default root directory. This can be overwritten by explicitly
# passing the `--root <directory>` argument on the command line.
# logic is:
# 1) use INVOKEAI_ROOT environment variable (no check for this being a valid directory)
# 2) use VIRTUAL_ENV environment variable, with a check for initfile being there
# 3) use ~/invokeai
if os.environ.get("INVOKEAI_ROOT"):
Globals.root = osp.abspath(os.environ.get("INVOKEAI_ROOT"))
elif (
os.environ.get("VIRTUAL_ENV")
and Path(os.environ.get("VIRTUAL_ENV"), "..", Globals.initfile).exists()
):
Globals.root = osp.abspath(osp.join(os.environ.get("VIRTUAL_ENV"), ".."))
else:
Globals.root = osp.abspath(osp.expanduser("~/invokeai"))
# Try loading patchmatch
Globals.try_patchmatch = True
# Use CPU even if GPU is available (main use case is for debugging MPS issues)
Globals.always_use_cpu = False
# Whether the internet is reachable for dynamic downloads
# The CLI will test connectivity at startup time.
Globals.internet_available = True
# Whether to disable xformers
Globals.disable_xformers = False
# Low-memory tradeoff for guidance calculations.
Globals.sequential_guidance = False
# whether we are forcing full precision
Globals.full_precision = False
# whether we should convert ckpt files into diffusers models on the fly
Globals.ckpt_convert = True
# logging tokenization everywhere
Globals.log_tokenization = False
def global_config_file() -> Path:
return Path(Globals.root, Globals.config_dir, Globals.models_file)
def global_config_dir() -> Path:
return Path(Globals.root, Globals.config_dir)
def global_models_dir() -> Path:
return Path(Globals.root, Globals.models_dir)
def global_autoscan_dir() -> Path:
return Path(Globals.root, Globals.autoscan_dir)
def global_converted_ckpts_dir() -> Path:
return Path(global_models_dir(), Globals.converted_ckpts_dir)
def global_set_root(root_dir: Union[str, Path]):
Globals.root = root_dir
def global_cache_dir(subdir: Union[str, Path] = "") -> Path:
"""
Returns Path to the model cache directory. If a subdirectory
is provided, it will be appended to the end of the path, allowing
for huggingface-style conventions:
global_cache_dir('diffusers')
global_cache_dir('hub')
Current HuggingFace documentation (mid-Jan 2023) indicates that
transformers models will be cached into a "transformers" subdirectory,
but in practice they seem to go into "hub". But if needed:
global_cache_dir('transformers')
One other caveat is that HuggingFace is moving some diffusers models
into the "hub" subdirectory as well, so this will need to be revisited
from time to time.
"""
home: str = os.getenv("HF_HOME")
if home is None:
home = os.getenv("XDG_CACHE_HOME")
if home is not None:
# Set `home` to $XDG_CACHE_HOME/huggingface, which is the default location mentioned in HuggingFace Hub Client Library.
# See: https://huggingface.co/docs/huggingface_hub/main/en/package_reference/environment_variables#xdgcachehome
home += os.sep + "huggingface"
if home is not None:
return Path(home, subdir)
else:
return Path(Globals.root, "models", subdir)