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Merge remote-tracking branch 'origin/main' into feat/dev_reload

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Kevin Turner 2023-08-22 18:15:59 -07:00
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25
docs/features/CONFIGURATION.md
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@ -175,22 +175,27 @@ These configuration settings allow you to enable and disable various InvokeAI fe
| `internet_available` | `true` | When a resource is not available locally, try to fetch it via the internet |
| `log_tokenization` | `false` | Before each text2image generation, print a color-coded representation of the prompt to the console; this can help understand why a prompt is not working as expected |
| `patchmatch` | `true` | Activate the "patchmatch" algorithm for improved inpainting |
| `restore` | `true` | Activate the facial restoration features (DEPRECATED; restoration features will be removed in 3.0.0) |
### Memory/Performance
### Generation
These options tune InvokeAI's memory and performance characteristics.
| Setting | Default Value | Description |
|----------|----------------|--------------|
| `always_use_cpu` | `false` | Use the CPU to generate images, even if a GPU is available |
| `free_gpu_mem` | `false` | Aggressively free up GPU memory after each operation; this will allow you to run in low-VRAM environments with some performance penalties |
| `max_cache_size` | `6` | Amount of CPU RAM (in GB) to reserve for caching models in memory; more cache allows you to keep models in memory and switch among them quickly |
| `max_vram_cache_size` | `2.75` | Amount of GPU VRAM (in GB) to reserve for caching models in VRAM; more cache speeds up generation but reduces the size of the images that can be generated. This can be set to zero to maximize the amount of memory available for generation. |
| `precision` | `auto` | Floating point precision. One of `auto`, `float16` or `float32`. `float16` will consume half the memory of `float32` but produce slightly lower-quality images. The `auto` setting will guess the proper precision based on your video card and operating system |
|-----------------------|---------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| `sequential_guidance` | `false` | Calculate guidance in serial rather than in parallel, lowering memory requirements at the cost of some performance loss |
| `xformers_enabled` | `true` | If the x-formers memory-efficient attention module is installed, activate it for better memory usage and generation speed|
| `tiled_decode` | `false` | If true, then during the VAE decoding phase the image will be decoded a section at a time, reducing memory consumption at the cost of a performance hit |
| `attention_type` | `auto` | Select the type of attention to use. One of `auto`,`normal`,`xformers`,`sliced`, or `torch-sdp` |
| `attention_slice_size` | `auto` | When "sliced" attention is selected, set the slice size. One of `auto`, `balanced`, `max` or the integers 1-8|
| `force_tiled_decode` | `false` | Force the VAE step to decode in tiles, reducing memory consumption at the cost of performance |
### Device
These options configure the generation execution device.
| Setting | Default Value | Description |
|-----------------------|---------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| `device` | `auto` | Preferred execution device. One of `auto`, `cpu`, `cuda`, `cuda:1`, `mps`. `auto` will choose the device depending on the hardware platform and the installed torch capabilities. |
| `precision` | `auto` | Floating point precision. One of `auto`, `float16` or `float32`. `float16` will consume half the memory of `float32` but produce slightly lower-quality images. The `auto` setting will guess the proper precision based on your video card and operating system |
### Paths

8
invokeai/app/services/config/__init__.py Normal file
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@ -0,0 +1,8 @@
"""
Init file for InvokeAI configure package
"""
from .invokeai_config import ( # noqa F401
InvokeAIAppConfig,
get_invokeai_config,
)

239
invokeai/app/services/config/base.py Normal file
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@ -0,0 +1,239 @@
# Copyright (c) 2023 Lincoln Stein (https://github.com/lstein) and the InvokeAI Development Team
"""
Base class for the InvokeAI configuration system.
It defines a type of pydantic BaseSettings object that
is able to read and write from an omegaconf-based config file,
with overriding of settings from environment variables and/or
the command line.
"""
from __future__ import annotations
import argparse
import os
import pydoc
import sys
from argparse import ArgumentParser
from omegaconf import OmegaConf, DictConfig, ListConfig
from pathlib import Path
from pydantic import BaseSettings
from typing import ClassVar, Dict, List, Literal, Union, get_origin, get_type_hints, get_args
class PagingArgumentParser(argparse.ArgumentParser):
"""
A custom ArgumentParser that uses pydoc to page its output.
It also supports reading defaults from an init file.
"""
def print_help(self, file=None):
text = self.format_help()
pydoc.pager(text)
class InvokeAISettings(BaseSettings):
"""
Runtime configuration settings in which default values are
read from an omegaconf .yaml file.
"""
initconf: ClassVar[DictConfig] = None
argparse_groups: ClassVar[Dict] = {}
def parse_args(self, argv: list = sys.argv[1:]):
parser = self.get_parser()
opt = parser.parse_args(argv)
for name in self.__fields__:
if name not in self._excluded():
value = getattr(opt, name)
if isinstance(value, ListConfig):
value = list(value)
elif isinstance(value, DictConfig):
value = dict(value)
setattr(self, name, value)
def to_yaml(self) -> str:
"""
Return a YAML string representing our settings. This can be used
as the contents of `invokeai.yaml` to restore settings later.
"""
cls = self.__class__
type = get_args(get_type_hints(cls)["type"])[0]
field_dict = dict({type: dict()})
for name, field in self.__fields__.items():
if name in cls._excluded_from_yaml():
continue
category = field.field_info.extra.get("category") or "Uncategorized"
value = getattr(self, name)
if category not in field_dict[type]:
field_dict[type][category] = dict()
# keep paths as strings to make it easier to read
field_dict[type][category][name] = str(value) if isinstance(value, Path) else value
conf = OmegaConf.create(field_dict)
return OmegaConf.to_yaml(conf)
@classmethod
def add_parser_arguments(cls, parser):
if "type" in get_type_hints(cls):
settings_stanza = get_args(get_type_hints(cls)["type"])[0]
else:
settings_stanza = "Uncategorized"
env_prefix = cls.Config.env_prefix if hasattr(cls.Config, "env_prefix") else settings_stanza.upper()
initconf = (
cls.initconf.get(settings_stanza)
if cls.initconf and settings_stanza in cls.initconf
else OmegaConf.create()
)
# create an upcase version of the environment in
# order to achieve case-insensitive environment
# variables (the way Windows does)
upcase_environ = dict()
for key, value in os.environ.items():
upcase_environ[key.upper()] = value
fields = cls.__fields__
cls.argparse_groups = {}
for name, field in fields.items():
if name not in cls._excluded():
current_default = field.default
category = field.field_info.extra.get("category", "Uncategorized")
env_name = env_prefix + "_" + name
if category in initconf and name in initconf.get(category):
field.default = initconf.get(category).get(name)
if env_name.upper() in upcase_environ:
field.default = upcase_environ[env_name.upper()]
cls.add_field_argument(parser, name, field)
field.default = current_default
@classmethod
def cmd_name(self, command_field: str = "type") -> str:
hints = get_type_hints(self)
if command_field in hints:
return get_args(hints[command_field])[0]
else:
return "Uncategorized"
@classmethod
def get_parser(cls) -> ArgumentParser:
parser = PagingArgumentParser(
prog=cls.cmd_name(),
description=cls.__doc__,
)
cls.add_parser_arguments(parser)
return parser
@classmethod
def add_subparser(cls, parser: argparse.ArgumentParser):
parser.add_parser(cls.cmd_name(), help=cls.__doc__)
@classmethod
def _excluded(self) -> List[str]:
# internal fields that shouldn't be exposed as command line options
return ["type", "initconf"]
@classmethod
def _excluded_from_yaml(self) -> List[str]:
# combination of deprecated parameters and internal ones that shouldn't be exposed as invokeai.yaml options
return [
"type",
"initconf",
"version",
"from_file",
"model",
"root",
"max_cache_size",
"max_vram_cache_size",
"always_use_cpu",
"free_gpu_mem",
"xformers_enabled",
"tiled_decode",
]
class Config:
env_file_encoding = "utf-8"
arbitrary_types_allowed = True
case_sensitive = True
@classmethod
def add_field_argument(cls, command_parser, name: str, field, default_override=None):
field_type = get_type_hints(cls).get(name)
default = (
default_override
if default_override is not None
else field.default
if field.default_factory is None
else field.default_factory()
)
if category := field.field_info.extra.get("category"):
if category not in cls.argparse_groups:
cls.argparse_groups[category] = command_parser.add_argument_group(category)
argparse_group = cls.argparse_groups[category]
else:
argparse_group = command_parser
if get_origin(field_type) == Literal:
allowed_values = get_args(field.type_)
allowed_types = set()
for val in allowed_values:
allowed_types.add(type(val))
allowed_types_list = list(allowed_types)
field_type = allowed_types_list[0] if len(allowed_types) == 1 else int_or_float_or_str
argparse_group.add_argument(
f"--{name}",
dest=name,
type=field_type,
default=default,
choices=allowed_values,
help=field.field_info.description,
)
elif get_origin(field_type) == Union:
argparse_group.add_argument(
f"--{name}",
dest=name,
type=int_or_float_or_str,
default=default,
help=field.field_info.description,
)
elif get_origin(field_type) == list:
argparse_group.add_argument(
f"--{name}",
dest=name,
nargs="*",
type=field.type_,
default=default,
action=argparse.BooleanOptionalAction if field.type_ == bool else "store",
help=field.field_info.description,
)
else:
argparse_group.add_argument(
f"--{name}",
dest=name,
type=field.type_,
default=default,
action=argparse.BooleanOptionalAction if field.type_ == bool else "store",
help=field.field_info.description,
)
def int_or_float_or_str(value: str) -> Union[int, float, str]:
"""
Workaround for argparse type checking.
"""
try:
return int(value)
except Exception as e: # noqa F841
pass
try:
return float(value)
except Exception as e: # noqa F841
pass
return str(value)

370
invokeai/app/services/config.py → invokeai/app/services/config/invokeai_config.py
View File

@ -10,37 +10,49 @@ categories returned by `invokeai --help`. The file looks like this:
[file: invokeai.yaml]
InvokeAI:
Paths:
root: /home/lstein/invokeai-main
conf_path: configs/models.yaml
legacy_conf_dir: configs/stable-diffusion
outdir: outputs
autoimport_dir: null
Models:
model: stable-diffusion-1.5
embeddings: true
Memory/Performance:
xformers_enabled: false
sequential_guidance: false
precision: float16
max_cache_size: 6
max_vram_cache_size: 0.5
always_use_cpu: false
free_gpu_mem: false
Features:
esrgan: true
patchmatch: true
internet_available: true
log_tokenization: false
Web Server:
host: 127.0.0.1
port: 8081
port: 9090
allow_origins: []
allow_credentials: true
allow_methods:
- '*'
allow_headers:
- '*'
Features:
esrgan: true
internet_available: true
log_tokenization: false
patchmatch: true
ignore_missing_core_models: false
Paths:
autoimport_dir: autoimport
lora_dir: null
embedding_dir: null
controlnet_dir: null
conf_path: configs/models.yaml
models_dir: models
legacy_conf_dir: configs/stable-diffusion
db_dir: databases
outdir: /home/lstein/invokeai-main/outputs
use_memory_db: false
Logging:
log_handlers:
- console
log_format: plain
log_level: info
Model Cache:
ram: 13.5
vram: 0.25
lazy_offload: true
Device:
device: auto
precision: auto
Generation:
sequential_guidance: false
attention_type: xformers
attention_slice_size: auto
force_tiled_decode: false
The default name of the configuration file is `invokeai.yaml`, located
in INVOKEAI_ROOT. You can replace supersede this by providing any
@ -54,24 +66,23 @@ InvokeAIAppConfig.parse_args() will parse the contents of `sys.argv`
at initialization time. You may pass a list of strings in the optional
`argv` argument to use instead of the system argv:
conf.parse_args(argv=['--xformers_enabled'])
conf.parse_args(argv=['--log_tokenization'])
It is also possible to set a value at initialization time. However, if
you call parse_args() it may be overwritten.
conf = InvokeAIAppConfig(xformers_enabled=True)
conf.parse_args(argv=['--no-xformers'])
conf.xformers_enabled
conf = InvokeAIAppConfig(log_tokenization=True)
conf.parse_args(argv=['--no-log_tokenization'])
conf.log_tokenization
# False
To avoid this, use `get_config()` to retrieve the application-wide
configuration object. This will retain any properties set at object
creation time:
conf = InvokeAIAppConfig.get_config(xformers_enabled=True)
conf.parse_args(argv=['--no-xformers'])
conf.xformers_enabled
conf = InvokeAIAppConfig.get_config(log_tokenization=True)
conf.parse_args(argv=['--no-log_tokenization'])
conf.log_tokenization
# True
Any setting can be overwritten by setting an environment variable of
@ -93,7 +104,7 @@ Typical usage at the top level file:
# get global configuration and print its cache size
conf = InvokeAIAppConfig.get_config()
conf.parse_args()
print(conf.max_cache_size)
print(conf.ram_cache_size)
Typical usage in a backend module:
@ -101,8 +112,7 @@ Typical usage in a backend module:
# get global configuration and print its cache size value
conf = InvokeAIAppConfig.get_config()
print(conf.max_cache_size)
print(conf.ram_cache_size)
Computed properties:
@ -160,16 +170,14 @@ two configs are kept in separate sections of the config file:
"""
from __future__ import annotations
import argparse
import os
import pydoc
import sys
from argparse import ArgumentParser
from pathlib import Path
from typing import ClassVar, Dict, List, Literal, Union, get_origin, get_type_hints, get_args
from typing import ClassVar, Dict, List, Literal, Union, get_type_hints, Optional
from omegaconf import OmegaConf, DictConfig, ListConfig
from pydantic import BaseSettings, Field, parse_obj_as
from omegaconf import OmegaConf, DictConfig
from pydantic import Field, parse_obj_as
from .base import InvokeAISettings
INIT_FILE = Path("invokeai.yaml")
DB_FILE = Path("invokeai.db")
@ -177,195 +185,6 @@ LEGACY_INIT_FILE = Path("invokeai.init")
DEFAULT_MAX_VRAM = 0.5
class InvokeAISettings(BaseSettings):
"""
Runtime configuration settings in which default values are
read from an omegaconf .yaml file.
"""
initconf: ClassVar[DictConfig] = None
argparse_groups: ClassVar[Dict] = {}
def parse_args(self, argv: list = sys.argv[1:]):
parser = self.get_parser()
opt = parser.parse_args(argv)
for name in self.__fields__:
if name not in self._excluded():
value = getattr(opt, name)
if isinstance(value, ListConfig):
value = list(value)
elif isinstance(value, DictConfig):
value = dict(value)
setattr(self, name, value)
def to_yaml(self) -> str:
"""
Return a YAML string representing our settings. This can be used
as the contents of `invokeai.yaml` to restore settings later.
"""
cls = self.__class__
type = get_args(get_type_hints(cls)["type"])[0]
field_dict = dict({type: dict()})
for name, field in self.__fields__.items():
if name in cls._excluded_from_yaml():
continue
category = field.field_info.extra.get("category") or "Uncategorized"
value = getattr(self, name)
if category not in field_dict[type]:
field_dict[type][category] = dict()
# keep paths as strings to make it easier to read
field_dict[type][category][name] = str(value) if isinstance(value, Path) else value
conf = OmegaConf.create(field_dict)
return OmegaConf.to_yaml(conf)
@classmethod
def add_parser_arguments(cls, parser):
if "type" in get_type_hints(cls):
settings_stanza = get_args(get_type_hints(cls)["type"])[0]
else:
settings_stanza = "Uncategorized"
env_prefix = cls.Config.env_prefix if hasattr(cls.Config, "env_prefix") else settings_stanza.upper()
initconf = (
cls.initconf.get(settings_stanza)
if cls.initconf and settings_stanza in cls.initconf
else OmegaConf.create()
)
# create an upcase version of the environment in
# order to achieve case-insensitive environment
# variables (the way Windows does)
upcase_environ = dict()
for key, value in os.environ.items():
upcase_environ[key.upper()] = value
fields = cls.__fields__
cls.argparse_groups = {}
for name, field in fields.items():
if name not in cls._excluded():
current_default = field.default
category = field.field_info.extra.get("category", "Uncategorized")
env_name = env_prefix + "_" + name
if category in initconf and name in initconf.get(category):
field.default = initconf.get(category).get(name)
if env_name.upper() in upcase_environ:
field.default = upcase_environ[env_name.upper()]
cls.add_field_argument(parser, name, field)
field.default = current_default
@classmethod
def cmd_name(self, command_field: str = "type") -> str:
hints = get_type_hints(self)
if command_field in hints:
return get_args(hints[command_field])[0]
else:
return "Uncategorized"
@classmethod
def get_parser(cls) -> ArgumentParser:
parser = PagingArgumentParser(
prog=cls.cmd_name(),
description=cls.__doc__,
)
cls.add_parser_arguments(parser)
return parser
@classmethod
def add_subparser(cls, parser: argparse.ArgumentParser):
parser.add_parser(cls.cmd_name(), help=cls.__doc__)
@classmethod
def _excluded(self) -> List[str]:
# internal fields that shouldn't be exposed as command line options
return ["type", "initconf"]
@classmethod
def _excluded_from_yaml(self) -> List[str]:
# combination of deprecated parameters and internal ones that shouldn't be exposed as invokeai.yaml options
return [
"type",
"initconf",
"version",
"from_file",
"model",
"root",
]
class Config:
env_file_encoding = "utf-8"
arbitrary_types_allowed = True
case_sensitive = True
@classmethod
def add_field_argument(cls, command_parser, name: str, field, default_override=None):
field_type = get_type_hints(cls).get(name)
default = (
default_override
if default_override is not None
else field.default
if field.default_factory is None
else field.default_factory()
)
if category := field.field_info.extra.get("category"):
if category not in cls.argparse_groups:
cls.argparse_groups[category] = command_parser.add_argument_group(category)
argparse_group = cls.argparse_groups[category]
else:
argparse_group = command_parser
if get_origin(field_type) == Literal:
allowed_values = get_args(field.type_)
allowed_types = set()
for val in allowed_values:
allowed_types.add(type(val))
allowed_types_list = list(allowed_types)
field_type = allowed_types_list[0] if len(allowed_types) == 1 else Union[allowed_types_list] # type: ignore
argparse_group.add_argument(
f"--{name}",
dest=name,
type=field_type,
default=default,
choices=allowed_values,
help=field.field_info.description,
)
elif get_origin(field_type) == list:
argparse_group.add_argument(
f"--{name}",
dest=name,
nargs="*",
type=field.type_,
default=default,
action=argparse.BooleanOptionalAction if field.type_ == bool else "store",
help=field.field_info.description,
)
else:
argparse_group.add_argument(
f"--{name}",
dest=name,
type=field.type_,
default=default,
action=argparse.BooleanOptionalAction if field.type_ == bool else "store",
help=field.field_info.description,
)
def _find_root() -> Path:
venv = Path(os.environ.get("VIRTUAL_ENV") or ".")
if os.environ.get("INVOKEAI_ROOT"):
root = Path(os.environ["INVOKEAI_ROOT"])
elif any([(venv.parent / x).exists() for x in [INIT_FILE, LEGACY_INIT_FILE]]):
root = (venv.parent).resolve()
else:
root = Path("~/invokeai").expanduser().resolve()
return root
class InvokeAIAppConfig(InvokeAISettings):
"""
Generate images using Stable Diffusion. Use "invokeai" to launch
@ -380,6 +199,8 @@ class InvokeAIAppConfig(InvokeAISettings):
# fmt: off
type: Literal["InvokeAI"] = "InvokeAI"
# WEB
host : str = Field(default="127.0.0.1", description="IP address to bind to", category='Web Server')
port : int = Field(default=9090, description="Port to bind to", category='Web Server')
allow_origins : List[str] = Field(default=[], description="Allowed CORS origins", category='Web Server')
@ -387,20 +208,14 @@ class InvokeAIAppConfig(InvokeAISettings):
allow_methods : List[str] = Field(default=["*"], description="Methods allowed for CORS", category='Web Server')
allow_headers : List[str] = Field(default=["*"], description="Headers allowed for CORS", category='Web Server')
# FEATURES
esrgan : bool = Field(default=True, description="Enable/disable upscaling code", category='Features')
internet_available : bool = Field(default=True, description="If true, attempt to download models on the fly; otherwise only use local models", category='Features')
log_tokenization : bool = Field(default=False, description="Enable logging of parsed prompt tokens.", category='Features')
patchmatch : bool = Field(default=True, description="Enable/disable patchmatch inpaint code", category='Features')
ignore_missing_core_models : bool = Field(default=False, description='Ignore missing models in models/core/convert', category='Features')
always_use_cpu : bool = Field(default=False, description="If true, use the CPU for rendering even if a GPU is available.", category='Memory/Performance')
free_gpu_mem : bool = Field(default=False, description="If true, purge model from GPU after each generation.", category='Memory/Performance')
max_cache_size : float = Field(default=6.0, gt=0, description="Maximum memory amount used by model cache for rapid switching", category='Memory/Performance')
max_vram_cache_size : float = Field(default=2.75, ge=0, description="Amount of VRAM reserved for model storage", category='Memory/Performance')
precision : Literal['auto', 'float16', 'float32', 'autocast'] = Field(default='auto', description='Floating point precision', category='Memory/Performance')
sequential_guidance : bool = Field(default=False, description="Whether to calculate guidance in serial instead of in parallel, lowering memory requirements", category='Memory/Performance')
xformers_enabled : bool = Field(default=True, description="Enable/disable memory-efficient attention", category='Memory/Performance')
tiled_decode : bool = Field(default=False, description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty)", category='Memory/Performance')
# PATHS
root : Path = Field(default=None, description='InvokeAI runtime root directory', category='Paths')
autoimport_dir : Path = Field(default='autoimport', description='Path to a directory of models files to be imported on startup.', category='Paths')
lora_dir : Path = Field(default=None, description='Path to a directory of LoRA/LyCORIS models to be imported on startup.', category='Paths')
@ -411,10 +226,10 @@ class InvokeAIAppConfig(InvokeAISettings):
legacy_conf_dir : Path = Field(default='configs/stable-diffusion', description='Path to directory of legacy checkpoint config files', category='Paths')
db_dir : Path = Field(default='databases', description='Path to InvokeAI databases directory', category='Paths')
outdir : Path = Field(default='outputs', description='Default folder for output images', category='Paths')
from_file : Path = Field(default=None, description='Take command input from the indicated file (command-line client only)', category='Paths')
use_memory_db : bool = Field(default=False, description='Use in-memory database for storing image metadata', category='Paths')
ignore_missing_core_models : bool = Field(default=False, description='Ignore missing models in models/core/convert', category='Features')
from_file : Path = Field(default=None, description='Take command input from the indicated file (command-line client only)', category='Paths')
# LOGGING
log_handlers : List[str] = Field(default=["console"], description='Log handler. Valid options are "console", "file=<path>", "syslog=path|address:host:port", "http=<url>"', category="Logging")
# note - would be better to read the log_format values from logging.py, but this creates circular dependencies issues
log_format : Literal['plain', 'color', 'syslog', 'legacy'] = Field(default="color", description='Log format. Use "plain" for text-only, "color" for colorized output, "legacy" for 2.3-style logging and "syslog" for syslog-style', category="Logging")
@ -423,6 +238,31 @@ class InvokeAIAppConfig(InvokeAISettings):
dev_reload : bool = Field(default=False, description="Automatically reload when Python sources are changed.", category="Development")
version : bool = Field(default=False, description="Show InvokeAI version and exit", category="Other")
# CACHE
ram : Union[float, Literal["auto"]] = Field(default=6.0, gt=0, description="Maximum memory amount used by model cache for rapid switching (floating point number or 'auto')", category="Model Cache", )
vram : Union[float, Literal["auto"]] = Field(default=0.25, ge=0, description="Amount of VRAM reserved for model storage (floating point number or 'auto')", category="Model Cache", )
lazy_offload : bool = Field(default=True, description="Keep models in VRAM until their space is needed", category="Model Cache", )
# DEVICE
device : Literal[tuple(["auto", "cpu", "cuda", "cuda:1", "mps"])] = Field(default="auto", description="Generation device", category="Device", )
precision: Literal[tuple(["auto", "float16", "float32", "autocast"])] = Field(default="auto", description="Floating point precision", category="Device", )
# GENERATION
sequential_guidance : bool = Field(default=False, description="Whether to calculate guidance in serial instead of in parallel, lowering memory requirements", category="Generation", )
attention_type : Literal[tuple(["auto", "normal", "xformers", "sliced", "torch-sdp"])] = Field(default="auto", description="Attention type", category="Generation", )
attention_slice_size: Literal[tuple(["auto", "balanced", "max", 1, 2, 3, 4, 5, 6, 7, 8])] = Field(default="auto", description='Slice size, valid when attention_type=="sliced"', category="Generation", )
force_tiled_decode: bool = Field(default=False, description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty)", category="Generation",)
# DEPRECATED FIELDS - STILL HERE IN ORDER TO OBTAN VALUES FROM PRE-3.1 CONFIG FILES
always_use_cpu : bool = Field(default=False, description="If true, use the CPU for rendering even if a GPU is available.", category='Memory/Performance')
free_gpu_mem : Optional[bool] = Field(default=None, description="If true, purge model from GPU after each generation.", category='Memory/Performance')
max_cache_size : Optional[float] = Field(default=None, gt=0, description="Maximum memory amount used by model cache for rapid switching", category='Memory/Performance')
max_vram_cache_size : Optional[float] = Field(default=None, ge=0, description="Amount of VRAM reserved for model storage", category='Memory/Performance')
xformers_enabled : bool = Field(default=True, description="Enable/disable memory-efficient attention", category='Memory/Performance')
tiled_decode : bool = Field(default=False, description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty)", category='Memory/Performance')
# See InvokeAIAppConfig subclass below for CACHE and DEVICE categories
# fmt: on
class Config:
@ -545,11 +385,6 @@ class InvokeAIAppConfig(InvokeAISettings):
"""Return true if precision set to float32"""
return self.precision == "float32"
@property
def disable_xformers(self) -> bool:
"""Return true if xformers_enabled is false"""
return not self.xformers_enabled
@property
def try_patchmatch(self) -> bool:
"""Return true if patchmatch true"""
@ -565,6 +400,27 @@ class InvokeAIAppConfig(InvokeAISettings):
"""invisible watermark node is always active and disabled from Web UIe"""
return True
@property
def ram_cache_size(self) -> float:
return self.max_cache_size or self.ram
@property
def vram_cache_size(self) -> float:
return self.max_vram_cache_size or self.vram
@property
def use_cpu(self) -> bool:
return self.always_use_cpu or self.device == "cpu"
@property
def disable_xformers(self) -> bool:
"""
Return true if enable_xformers is false (reversed logic)
and attention type is not set to xformers.
"""
disabled_in_config = not self.xformers_enabled
return disabled_in_config and self.attention_type != "xformers"
@staticmethod
def find_root() -> Path:
"""
@ -574,19 +430,19 @@ class InvokeAIAppConfig(InvokeAISettings):
return _find_root()
class PagingArgumentParser(argparse.ArgumentParser):
"""
A custom ArgumentParser that uses pydoc to page its output.
It also supports reading defaults from an init file.
"""
def print_help(self, file=None):
text = self.format_help()
pydoc.pager(text)
def get_invokeai_config(**kwargs) -> InvokeAIAppConfig:
"""
Legacy function which returns InvokeAIAppConfig.get_config()
"""
return InvokeAIAppConfig.get_config(**kwargs)
def _find_root() -> Path:
venv = Path(os.environ.get("VIRTUAL_ENV") or ".")
if os.environ.get("INVOKEAI_ROOT"):
root = Path(os.environ["INVOKEAI_ROOT"])
elif any([(venv.parent / x).exists() for x in [INIT_FILE, LEGACY_INIT_FILE]]):
root = (venv.parent).resolve()
else:
root = Path("~/invokeai").expanduser().resolve()
return root

4
invokeai/app/services/model_manager_service.py
View File

@ -330,8 +330,8 @@ class ModelManagerService(ModelManagerServiceBase):
# configuration value. If present, then the
# cache size is set to 2.5 GB times
# the number of max_loaded_models. Otherwise
# use new `max_cache_size` config setting
max_cache_size = config.max_cache_size if hasattr(config, "max_cache_size") else config.max_loaded_models * 2.5
# use new `ram_cache_size` config setting
max_cache_size = config.ram_cache_size
logger.debug(f"Maximum RAM cache size: {max_cache_size} GiB")

176
invokeai/backend/install/invokeai_configure.py
View File

@ -21,6 +21,7 @@ from argparse import Namespace
from enum import Enum
from pathlib import Path
from shutil import get_terminal_size
from typing import get_type_hints, get_args, Any
from urllib import request
import npyscreen
@ -50,6 +51,7 @@ from invokeai.frontend.install.model_install import addModelsForm, process_and_e
# TO DO - Move all the frontend code into invokeai.frontend.install
from invokeai.frontend.install.widgets import (
SingleSelectColumns,
MultiSelectColumns,
CenteredButtonPress,
FileBox,
set_min_terminal_size,
@ -71,6 +73,10 @@ warnings.filterwarnings("ignore")
transformers.logging.set_verbosity_error()
def get_literal_fields(field) -> list[Any]:
return get_args(get_type_hints(InvokeAIAppConfig).get(field))
# --------------------------globals-----------------------
config = InvokeAIAppConfig.get_config()
@ -80,7 +86,11 @@ Model_dir = "models"
Default_config_file = config.model_conf_path
SD_Configs = config.legacy_conf_path
PRECISION_CHOICES = ["auto", "float16", "float32"]
PRECISION_CHOICES = get_literal_fields("precision")
DEVICE_CHOICES = get_literal_fields("device")
ATTENTION_CHOICES = get_literal_fields("attention_type")
ATTENTION_SLICE_CHOICES = get_literal_fields("attention_slice_size")
GENERATION_OPT_CHOICES = ["sequential_guidance", "force_tiled_decode", "lazy_offload"]
GB = 1073741824 # GB in bytes
HAS_CUDA = torch.cuda.is_available()
_, MAX_VRAM = torch.cuda.mem_get_info() if HAS_CUDA else (0, 0)
@ -311,6 +321,7 @@ class editOptsForm(CyclingForm, npyscreen.FormMultiPage):
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.nextrely -= 1
for i in textwrap.wrap(label, width=window_width - 6):
self.add_widget_intelligent(
npyscreen.FixedText,
@ -337,76 +348,129 @@ Use cursor arrows to make a checkbox selection, and space to toggle.
use_two_lines=False,
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,
max_width=45,
relx=5,
scroll_exit=True,
)
self.nextrely -= 1
self.xformers_enabled = self.add_widget_intelligent(
npyscreen.Checkbox,
name="Enable xformers support",
value=old_opts.xformers_enabled,
max_width=30,
relx=50,
scroll_exit=True,
)
self.nextrely -= 1
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=80,
scroll_exit=True,
)
# old settings for defaults
precision = old_opts.precision or ("float32" if program_opts.full_precision else "auto")
device = old_opts.device
attention_type = old_opts.attention_type
attention_slice_size = old_opts.attention_slice_size
self.nextrely += 1
self.add_widget_intelligent(
npyscreen.TitleFixedText,
name="Floating Point Precision",
name="Image Generation Options:",
editable=False,
color="CONTROL",
scroll_exit=True,
)
self.nextrely -= 2
self.generation_options = self.add_widget_intelligent(
MultiSelectColumns,
columns=3,
values=GENERATION_OPT_CHOICES,
value=[GENERATION_OPT_CHOICES.index(x) for x in GENERATION_OPT_CHOICES if getattr(old_opts, x)],
relx=30,
max_height=2,
max_width=80,
scroll_exit=True,
)
self.add_widget_intelligent(
npyscreen.TitleFixedText,
name="Floating Point Precision:",
begin_entry_at=0,
editable=False,
color="CONTROL",
scroll_exit=True,
)
self.nextrely -= 1
self.nextrely -= 2
self.precision = self.add_widget_intelligent(
SingleSelectColumns,
columns=3,
columns=len(PRECISION_CHOICES),
name="Precision",
values=PRECISION_CHOICES,
value=PRECISION_CHOICES.index(precision),
begin_entry_at=3,
max_height=2,
relx=30,
max_width=56,
scroll_exit=True,
)
self.add_widget_intelligent(
npyscreen.TitleFixedText,
name="Generation Device:",
begin_entry_at=0,
editable=False,
color="CONTROL",
scroll_exit=True,
)
self.nextrely -= 2
self.device = self.add_widget_intelligent(
SingleSelectColumns,
columns=len(DEVICE_CHOICES),
values=DEVICE_CHOICES,
value=DEVICE_CHOICES.index(device),
begin_entry_at=3,
relx=30,
max_height=2,
max_width=60,
scroll_exit=True,
)
self.add_widget_intelligent(
npyscreen.TitleFixedText,
name="Attention Type:",
begin_entry_at=0,
editable=False,
color="CONTROL",
scroll_exit=True,
)
self.nextrely -= 2
self.attention_type = self.add_widget_intelligent(
SingleSelectColumns,
columns=len(ATTENTION_CHOICES),
values=ATTENTION_CHOICES,
value=ATTENTION_CHOICES.index(attention_type),
begin_entry_at=3,
max_height=2,
relx=30,
max_width=80,
scroll_exit=True,
)
self.nextrely += 1
self.attention_type.on_changed = self.show_hide_slice_sizes
self.attention_slice_label = self.add_widget_intelligent(
npyscreen.TitleFixedText,
name="Attention Slice Size:",
relx=5,
editable=False,
hidden=attention_type != "sliced",
color="CONTROL",
scroll_exit=True,
)
self.nextrely -= 2
self.attention_slice_size = self.add_widget_intelligent(
SingleSelectColumns,
columns=len(ATTENTION_SLICE_CHOICES),
values=ATTENTION_SLICE_CHOICES,
value=ATTENTION_SLICE_CHOICES.index(attention_slice_size),
relx=30,
hidden=attention_type != "sliced",
max_height=2,
max_width=110,
scroll_exit=True,
)
self.add_widget_intelligent(
npyscreen.TitleFixedText,
name="RAM cache size (GB). Make this at least large enough to hold a single full model.",
name="Model RAM cache size (GB). Make this at least large enough to hold a single full model.",
begin_entry_at=0,
editable=False,
color="CONTROL",
scroll_exit=True,
)
self.nextrely -= 1
self.max_cache_size = self.add_widget_intelligent(
self.ram = self.add_widget_intelligent(
npyscreen.Slider,
value=clip(old_opts.max_cache_size, range=(3.0, MAX_RAM), step=0.5),
value=clip(old_opts.ram_cache_size, range=(3.0, MAX_RAM), step=0.5),
out_of=round(MAX_RAM),
lowest=0.0,
step=0.5,
@ -417,16 +481,16 @@ Use cursor arrows to make a checkbox selection, and space to toggle.
self.nextrely += 1
self.add_widget_intelligent(
npyscreen.TitleFixedText,
name="VRAM cache size (GB). Reserving a small amount of VRAM will modestly speed up the start of image generation.",
name="Model VRAM cache size (GB). Reserving a small amount of VRAM will modestly speed up the start of image generation.",
begin_entry_at=0,
editable=False,
color="CONTROL",
scroll_exit=True,
)
self.nextrely -= 1
self.max_vram_cache_size = self.add_widget_intelligent(
self.vram = self.add_widget_intelligent(
npyscreen.Slider,
value=clip(old_opts.max_vram_cache_size, range=(0, MAX_VRAM), step=0.25),
value=clip(old_opts.vram_cache_size, range=(0, MAX_VRAM), step=0.25),
out_of=round(MAX_VRAM * 2) / 2,
lowest=0.0,
relx=8,
@ -434,7 +498,7 @@ Use cursor arrows to make a checkbox selection, and space to toggle.
scroll_exit=True,
)
else:
self.max_vram_cache_size = DummyWidgetValue.zero
self.vram_cache_size = DummyWidgetValue.zero
self.nextrely += 1
self.outdir = self.add_widget_intelligent(
FileBox,
@ -490,6 +554,11 @@ https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/LICENS
when_pressed_function=self.on_ok,
)
def show_hide_slice_sizes(self, value):
show = ATTENTION_CHOICES[value[0]] == "sliced"
self.attention_slice_label.hidden = not show
self.attention_slice_size.hidden = not show
def on_ok(self):
options = self.marshall_arguments()
if self.validate_field_values(options):
@ -523,12 +592,9 @@ https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/LICENS
new_opts = Namespace()
for attr in [
"ram",
"vram",
"outdir",
"free_gpu_mem",
"max_cache_size",
"max_vram_cache_size",
"xformers_enabled",
"always_use_cpu",
]:
setattr(new_opts, attr, getattr(self, attr).value)
@ -541,6 +607,12 @@ https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/LICENS
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]]
new_opts.device = DEVICE_CHOICES[self.device.value[0]]
new_opts.attention_type = ATTENTION_CHOICES[self.attention_type.value[0]]
new_opts.attention_slice_size = ATTENTION_SLICE_CHOICES[self.attention_slice_size.value[0]]
generation_options = [GENERATION_OPT_CHOICES[x] for x in self.generation_options.value]
for v in GENERATION_OPT_CHOICES:
setattr(new_opts, v, v in generation_options)
return new_opts

3
invokeai/backend/model_management/model_manager.py
View File

@ -341,7 +341,8 @@ class ModelManager(object):
self.logger = logger
self.cache = ModelCache(
max_cache_size=max_cache_size,
max_vram_cache_size=self.app_config.max_vram_cache_size,
max_vram_cache_size=self.app_config.vram_cache_size,
lazy_offloading=self.app_config.lazy_offload,
execution_device=device_type,
precision=precision,
sequential_offload=sequential_offload,

20
invokeai/backend/stable_diffusion/diffusers_pipeline.py
View File

@ -33,7 +33,7 @@ from .diffusion import (
PostprocessingSettings,
BasicConditioningInfo,
)
from ..util import normalize_device
from ..util import normalize_device, auto_detect_slice_size
@dataclass
@ -291,6 +291,24 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
if xformers is available, use it, otherwise use sliced attention.
"""
config = InvokeAIAppConfig.get_config()
if config.attention_type == "xformers":
self.enable_xformers_memory_efficient_attention()
return
elif config.attention_type == "sliced":
slice_size = config.attention_slice_size
if slice_size == "auto":
slice_size = auto_detect_slice_size(latents)
elif slice_size == "balanced":
slice_size = "auto"
self.enable_attention_slicing(slice_size=slice_size)
return
elif config.attention_type == "normal":
self.disable_attention_slicing()
return
elif config.attention_type == "torch-sdp":
raise Exception("torch-sdp attention slicing not yet implemented")
# the remainder if this code is called when attention_type=='auto'
if self.unet.device.type == "cuda":
if is_xformers_available() and not config.disable_xformers:
self.enable_xformers_memory_efficient_attention()

9
invokeai/backend/util/__init__.py
View File

@ -11,4 +11,11 @@ from .devices import ( # noqa: F401
torch_dtype,
)
from .log import write_log # noqa: F401
from .util import ask_user, download_with_resume, instantiate_from_config, url_attachment_name, Chdir # noqa: F401
from .util import ( # noqa: F401
ask_user,
download_with_resume,
instantiate_from_config,
url_attachment_name,
Chdir,
)
from .attention import auto_detect_slice_size # noqa: F401

32
invokeai/backend/util/attention.py Normal file
View File

@ -0,0 +1,32 @@
# Copyright (c) 2023 Lincoln Stein and the InvokeAI Team
"""
Utility routine used for autodetection of optimal slice size
for attention mechanism.
"""
import torch
import psutil
def auto_detect_slice_size(latents: torch.Tensor) -> str:
bytes_per_element_needed_for_baddbmm_duplication = latents.element_size() + 4
max_size_required_for_baddbmm = (
16
* latents.size(dim=2)
* latents.size(dim=3)
* latents.size(dim=2)
* latents.size(dim=3)
* bytes_per_element_needed_for_baddbmm_duplication
)
if latents.device.type in {"cpu", "mps"}:
mem_free = psutil.virtual_memory().free
elif latents.device.type == "cuda":
mem_free, _ = torch.cuda.mem_get_info(latents.device)
else:
raise ValueError(f"unrecognized device {latents.device}")
if max_size_required_for_baddbmm > (mem_free * 3.0 / 4.0):
return "max"
elif torch.backends.mps.is_available():
return "max"
else:
return "balanced"

6
invokeai/backend/util/devices.py
View File

@ -17,13 +17,17 @@ config = InvokeAIAppConfig.get_config()
def choose_torch_device() -> torch.device:
"""Convenience routine for guessing which GPU device to run model on"""
if config.always_use_cpu:
if config.use_cpu: # legacy setting - force CPU
return CPU_DEVICE
elif config.device == "auto":
if torch.cuda.is_available():
return torch.device("cuda")
if hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
return torch.device("mps")
else:
return CPU_DEVICE
else:
return torch.device(config.device)
def choose_precision(device: torch.device) -> str:

56
invokeai/frontend/install/widgets.py
View File

@ -17,8 +17,8 @@ from shutil import get_terminal_size
from curses import BUTTON2_CLICKED, BUTTON3_CLICKED
# minimum size for UIs
MIN_COLS = 130
MIN_LINES = 38
MIN_COLS = 150
MIN_LINES = 40
class WindowTooSmallException(Exception):
@ -277,6 +277,9 @@ class SingleSelectColumns(SelectColumnBase, SingleSelectWithChanged):
def h_cursor_line_right(self, ch):
self.h_exit_down("bye bye")
def h_cursor_line_left(self, ch):
self.h_exit_up("bye bye")
class TextBoxInner(npyscreen.MultiLineEdit):
def __init__(self, *args, **kwargs):
@ -324,55 +327,6 @@ class TextBoxInner(npyscreen.MultiLineEdit):
if bstate & (BUTTON2_CLICKED | BUTTON3_CLICKED):
self.h_paste()
# def update(self, clear=True):
# if clear:
# self.clear()
# HEIGHT = self.height
# WIDTH = self.width
# # draw box.
# self.parent.curses_pad.hline(self.rely, self.relx, curses.ACS_HLINE, WIDTH)
# self.parent.curses_pad.hline(
# self.rely + HEIGHT, self.relx, curses.ACS_HLINE, WIDTH
# )
# self.parent.curses_pad.vline(
# self.rely, self.relx, curses.ACS_VLINE, self.height
# )
# self.parent.curses_pad.vline(
# self.rely, self.relx + WIDTH, curses.ACS_VLINE, HEIGHT
# )
# # draw corners
# self.parent.curses_pad.addch(
# self.rely,
# self.relx,
# curses.ACS_ULCORNER,
# )
# self.parent.curses_pad.addch(
# self.rely,
# self.relx + WIDTH,
# curses.ACS_URCORNER,
# )
# self.parent.curses_pad.addch(
# self.rely + HEIGHT,
# self.relx,
# curses.ACS_LLCORNER,
# )
# self.parent.curses_pad.addch(
# self.rely + HEIGHT,
# self.relx + WIDTH,
# curses.ACS_LRCORNER,
# )
# # fool our superclass into thinking drawing area is smaller - this is really hacky but it seems to work
# (relx, rely, height, width) = (self.relx, self.rely, self.height, self.width)
# self.relx += 1
# self.rely += 1
# self.height -= 1
# self.width -= 1
# super().update(clear=False)
# (self.relx, self.rely, self.height, self.width) = (relx, rely, height, width)
class TextBox(npyscreen.BoxTitle):
_contained_widget = TextBoxInner

30
tests/test_config.py
View File

@ -5,6 +5,8 @@ import pytest
from omegaconf import OmegaConf
from pathlib import Path
from invokeai.app.services.config import InvokeAIAppConfig
@pytest.fixture
def patch_rootdir(tmp_path: Path, monkeypatch: Any) -> None:
@ -34,6 +36,21 @@ InvokeAI:
"""
)
init3 = OmegaConf.create(
"""
InvokeAI:
Generation:
sequential_guidance: true
attention_type: xformers
attention_slice_size: 7
forced_tiled_decode: True
Device:
device: cpu
Model Cache:
ram: 1.25
"""
)
def test_use_init(patch_rootdir):
# note that we explicitly set omegaconf dict and argv here
@ -56,9 +73,18 @@ def test_use_init(patch_rootdir):
assert not hasattr(conf2, "invalid_attribute")
def test_argv_override(patch_rootdir):
from invokeai.app.services.config import InvokeAIAppConfig
def test_legacy():
conf = InvokeAIAppConfig.get_config()
assert conf
conf.parse_args(conf=init3, argv=[])
assert conf.xformers_enabled
assert conf.device == "cpu"
assert conf.use_cpu
assert conf.ram == 1.25
assert conf.ram_cache_size == 1.25
def test_argv_override():
conf = InvokeAIAppConfig.get_config()
conf.parse_args(conf=init1, argv=["--always_use_cpu", "--max_cache=10"])
assert conf.always_use_cpu