mirror of
https://github.com/invoke-ai/InvokeAI
synced 2024-08-30 20:32:17 +00:00
127 lines
4.0 KiB
Python
127 lines
4.0 KiB
Python
"""
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invokeai.backend.globals defines a small number of global variables that would
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otherwise have to be passed through long and complex call chains.
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It defines a Namespace object named "Globals" that contains
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the attributes:
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- root - the root directory under which "models" and "outputs" can be found
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- initfile - path to the initialization file
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- try_patchmatch - option to globally disable loading of 'patchmatch' module
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- always_use_cpu - force use of CPU even if GPU is available
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"""
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import os
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import os.path as osp
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from argparse import Namespace
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from pathlib import Path
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from typing import Union
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Globals = Namespace()
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# Where to look for the initialization file and other key components
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Globals.initfile = "invokeai.init"
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Globals.models_file = "models.yaml"
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Globals.models_dir = "models"
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Globals.config_dir = "configs"
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Globals.autoscan_dir = "weights"
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Globals.converted_ckpts_dir = "converted_ckpts"
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# Set the default root directory. This can be overwritten by explicitly
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# passing the `--root <directory>` argument on the command line.
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# logic is:
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# 1) use INVOKEAI_ROOT environment variable (no check for this being a valid directory)
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# 2) use VIRTUAL_ENV environment variable, with a check for initfile being there
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# 3) use ~/invokeai
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if os.environ.get("INVOKEAI_ROOT"):
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Globals.root = osp.abspath(os.environ.get("INVOKEAI_ROOT"))
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elif (
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os.environ.get("VIRTUAL_ENV")
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and Path(os.environ.get("VIRTUAL_ENV"), "..", Globals.initfile).exists()
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):
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Globals.root = osp.abspath(osp.join(os.environ.get("VIRTUAL_ENV"), ".."))
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else:
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Globals.root = osp.abspath(osp.expanduser("~/invokeai"))
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# Try loading patchmatch
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Globals.try_patchmatch = True
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# Use CPU even if GPU is available (main use case is for debugging MPS issues)
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Globals.always_use_cpu = False
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# Whether the internet is reachable for dynamic downloads
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# The CLI will test connectivity at startup time.
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Globals.internet_available = True
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# Whether to disable xformers
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Globals.disable_xformers = False
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# Low-memory tradeoff for guidance calculations.
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Globals.sequential_guidance = False
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# whether we are forcing full precision
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Globals.full_precision = False
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# whether we should convert ckpt files into diffusers models on the fly
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Globals.ckpt_convert = True
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# logging tokenization everywhere
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Globals.log_tokenization = False
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def global_config_file() -> Path:
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return Path(Globals.root, Globals.config_dir, Globals.models_file)
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def global_config_dir() -> Path:
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return Path(Globals.root, Globals.config_dir)
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def global_models_dir() -> Path:
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return Path(Globals.root, Globals.models_dir)
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def global_autoscan_dir() -> Path:
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return Path(Globals.root, Globals.autoscan_dir)
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def global_converted_ckpts_dir() -> Path:
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return Path(global_models_dir(), Globals.converted_ckpts_dir)
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def global_set_root(root_dir: Union[str, Path]):
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Globals.root = root_dir
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def global_resolve_path(path: Union[str,Path]):
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if path is None:
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return None
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return Path(Globals.root,path).resolve()
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def global_cache_dir(subdir: Union[str, Path] = "") -> Path:
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"""
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Returns Path to the model cache directory. If a subdirectory
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is provided, it will be appended to the end of the path, allowing
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for Hugging Face-style conventions. Currently, Hugging Face has
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moved all models into the "hub" subfolder, so for any pretrained
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HF model, use:
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global_cache_dir('hub')
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The legacy location for transformers used to be global_cache_dir('transformers')
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and global_cache_dir('diffusers') for diffusers.
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"""
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home: str = os.getenv("HF_HOME")
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if home is None:
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home = os.getenv("XDG_CACHE_HOME")
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if home is not None:
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# Set `home` to $XDG_CACHE_HOME/huggingface, which is the default location mentioned in Hugging Face Hub Client Library.
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# See: https://huggingface.co/docs/huggingface_hub/main/en/package_reference/environment_variables#xdgcachehome
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home += os.sep + "huggingface"
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if home is not None:
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return Path(home, subdir)
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else:
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return Path(Globals.root, "models", subdir)
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