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Merge branch 'main' into feat/unify-logging

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This commit is contained in:
Lincoln Stein 2023-07-27 15:20:07 -04:00
commit 64bd11541a
155 changed files with 5536 additions and 6302 deletions
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6
.dev_scripts/diff_images.py
View File

@ -20,13 +20,13 @@ def calc_images_mean_L1(image1_path, image2_path):
def parse_args():
parser = argparse.ArgumentParser()
parser.add_argument('image1_path')
parser.add_argument('image2_path')
parser.add_argument("image1_path")
parser.add_argument("image2_path")
args = parser.parse_args()
return args
if __name__ == '__main__':
if __name__ == "__main__":
args = parse_args()
mean_L1 = calc_images_mean_L1(args.image1_path, args.image2_path)
print(mean_L1)

1
.git-blame-ignore-revs
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@ -1 +1,2 @@
b3dccfaeb636599c02effc377cdd8a87d658256c
218b6d0546b990fc449c876fb99f44b50c4daa35

27
.github/workflows/style-checks.yml vendored Normal file
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@ -0,0 +1,27 @@
name: Black # TODO: add isort and flake8 later
on:
pull_request: {}
push:
branches: master
tags: "*"
jobs:
test:
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v3
- name: Setup Python
uses: actions/setup-python@v4
with:
python-version: '3.10'
- name: Install dependencies with pip
run: |
pip install --upgrade pip wheel
pip install .[test]
# - run: isort --check-only .
- run: black --check .
# - run: flake8

1
.gitignore vendored
View File

@ -38,7 +38,6 @@ develop-eggs/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/

10
.pre-commit-config.yaml Normal file
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@ -0,0 +1,10 @@
# See https://pre-commit.com/ for usage and config
repos:
- repo: local
hooks:
- id: black
name: black
stages: [commit]
language: system
entry: black
types: [python]

55
installer/lib/installer.py
View File

@ -141,15 +141,16 @@ class Installer:
# upgrade pip in Python 3.9 environments
if int(platform.python_version_tuple()[1]) == 9:
from plumbum import FG, local
pip = local[get_pip_from_venv(venv_dir)]
pip[ "install", "--upgrade", "pip"] & FG
pip["install", "--upgrade", "pip"] & FG
return venv_dir
def install(self, root: str = "~/invokeai-3", version: str = "latest", yes_to_all=False, find_links: Path = None) -> None:
def install(
self, root: str = "~/invokeai-3", version: str = "latest", yes_to_all=False, find_links: Path = None
) -> None:
"""
Install the InvokeAI application into the given runtime path
@ -175,7 +176,7 @@ class Installer:
self.instance = InvokeAiInstance(runtime=self.dest, venv=self.venv, version=version)
# install dependencies and the InvokeAI application
(extra_index_url,optional_modules) = get_torch_source() if not yes_to_all else (None,None)
(extra_index_url, optional_modules) = get_torch_source() if not yes_to_all else (None, None)
self.instance.install(
extra_index_url,
optional_modules,
@ -188,6 +189,7 @@ class Installer:
# run through the configuration flow
self.instance.configure()
class InvokeAiInstance:
"""
Manages an installed instance of InvokeAI, comprising a virtual environment and a runtime directory.
@ -196,7 +198,6 @@ class InvokeAiInstance:
"""
def __init__(self, runtime: Path, venv: Path, version: str) -> None:
self.runtime = runtime
self.venv = venv
self.pip = get_pip_from_venv(venv)
@ -312,7 +313,7 @@ class InvokeAiInstance:
"install",
"--require-virtualenv",
"--use-pep517",
str(src)+(optional_modules if optional_modules else ''),
str(src) + (optional_modules if optional_modules else ""),
"--find-links" if find_links is not None else None,
find_links,
"--extra-index-url" if extra_index_url is not None else None,
@ -329,15 +330,15 @@ class InvokeAiInstance:
# set sys.argv to a consistent state
new_argv = [sys.argv[0]]
for i in range(1,len(sys.argv)):
for i in range(1, len(sys.argv)):
el = sys.argv[i]
if el in ['-r','--root']:
if el in ["-r", "--root"]:
new_argv.append(el)
new_argv.append(sys.argv[i+1])
elif el in ['-y','--yes','--yes-to-all']:
new_argv.append(sys.argv[i + 1])
elif el in ["-y", "--yes", "--yes-to-all"]:
new_argv.append(el)
sys.argv = new_argv
import requests # to catch download exceptions
from messages import introduction
@ -353,16 +354,16 @@ class InvokeAiInstance:
invokeai_configure()
succeeded = True
except requests.exceptions.ConnectionError as e:
print(f'\nA network error was encountered during configuration and download: {str(e)}')
print(f"\nA network error was encountered during configuration and download: {str(e)}")
except OSError as e:
print(f'\nAn OS error was encountered during configuration and download: {str(e)}')
print(f"\nAn OS error was encountered during configuration and download: {str(e)}")
except Exception as e:
print(f'\nA problem was encountered during the configuration and download steps: {str(e)}')
print(f"\nA problem was encountered during the configuration and download steps: {str(e)}")
finally:
if not succeeded:
print('To try again, find the "invokeai" directory, run the script "invoke.sh" or "invoke.bat"')
print('and choose option 7 to fix a broken install, optionally followed by option 5 to install models.')
print('Alternatively you can relaunch the installer.')
print("and choose option 7 to fix a broken install, optionally followed by option 5 to install models.")
print("Alternatively you can relaunch the installer.")
def install_user_scripts(self):
"""
@ -371,11 +372,11 @@ class InvokeAiInstance:
ext = "bat" if OS == "Windows" else "sh"
#scripts = ['invoke', 'update']
scripts = ['invoke']
# scripts = ['invoke', 'update']
scripts = ["invoke"]
for script in scripts:
src = Path(__file__).parent / '..' / "templates" / f"{script}.{ext}.in"
src = Path(__file__).parent / ".." / "templates" / f"{script}.{ext}.in"
dest = self.runtime / f"{script}.{ext}"
shutil.copy(src, dest)
os.chmod(dest, 0o0755)
@ -420,11 +421,7 @@ def set_sys_path(venv_path: Path) -> None:
# filter out any paths in sys.path that may be system- or user-wide
# but leave the temporary bootstrap virtualenv as it contains packages we
# temporarily need at install time
sys.path = list(filter(
lambda p: not p.endswith("-packages")
or p.find(BOOTSTRAP_VENV_PREFIX) != -1,
sys.path
))
sys.path = list(filter(lambda p: not p.endswith("-packages") or p.find(BOOTSTRAP_VENV_PREFIX) != -1, sys.path))
# determine site-packages/lib directory location for the venv
lib = "Lib" if OS == "Windows" else f"lib/python{sys.version_info.major}.{sys.version_info.minor}"
@ -433,7 +430,7 @@ def set_sys_path(venv_path: Path) -> None:
sys.path.append(str(Path(venv_path, lib, "site-packages").expanduser().resolve()))
def get_torch_source() -> (Union[str, None],str):
def get_torch_source() -> (Union[str, None], str):
"""
Determine the extra index URL for pip to use for torch installation.
This depends on the OS and the graphics accelerator in use.
@ -461,9 +458,9 @@ def get_torch_source() -> (Union[str, None],str):
elif device == "cpu":
url = "https://download.pytorch.org/whl/cpu"
if device == 'cuda':
url = 'https://download.pytorch.org/whl/cu117'
optional_modules = '[xformers]'
if device == "cuda":
url = "https://download.pytorch.org/whl/cu117"
optional_modules = "[xformers]"
# in all other cases, Torch wheels should be coming from PyPi as of Torch 1.13

2
installer/lib/main.py
View File

@ -41,7 +41,7 @@ if __name__ == "__main__":
type=Path,
default=None,
)
args = parser.parse_args()
inst = Installer()

21
installer/lib/messages.py
View File

@ -36,13 +36,15 @@ else:
def welcome():
@group()
def text():
if (platform_specific := _platform_specific_help()) != "":
yield platform_specific
yield ""
yield Text.from_markup("Some of the installation steps take a long time to run. Please be patient. If the script appears to hang for more than 10 minutes, please interrupt with [i]Control-C[/] and retry.", justify="center")
yield Text.from_markup(
"Some of the installation steps take a long time to run. Please be patient. If the script appears to hang for more than 10 minutes, please interrupt with [i]Control-C[/] and retry.",
justify="center",
)
console.rule()
print(
@ -58,6 +60,7 @@ def welcome():
)
console.line()
def confirm_install(dest: Path) -> bool:
if dest.exists():
print(f":exclamation: Directory {dest} already exists :exclamation:")
@ -92,7 +95,6 @@ def dest_path(dest=None) -> Path:
dest_confirmed = confirm_install(dest)
while not dest_confirmed:
# if the given destination already exists, the starting point for browsing is its parent directory.
# the user may have made a typo, or otherwise wants to place the root dir next to an existing one.
# if the destination dir does NOT exist, then the user must have changed their mind about the selection.
@ -300,15 +302,20 @@ def introduction() -> None:
)
console.line(2)
def _platform_specific_help()->str:
def _platform_specific_help() -> str:
if OS == "Darwin":
text = Text.from_markup("""[b wheat1]macOS Users![/]\n\nPlease be sure you have the [b wheat1]Xcode command-line tools[/] installed before continuing.\nIf not, cancel with [i]Control-C[/] and follow the Xcode install instructions at [deep_sky_blue1]https://www.freecodecamp.org/news/install-xcode-command-line-tools/[/].""")
text = Text.from_markup(
"""[b wheat1]macOS Users![/]\n\nPlease be sure you have the [b wheat1]Xcode command-line tools[/] installed before continuing.\nIf not, cancel with [i]Control-C[/] and follow the Xcode install instructions at [deep_sky_blue1]https://www.freecodecamp.org/news/install-xcode-command-line-tools/[/]."""
)
elif OS == "Windows":
text = Text.from_markup("""[b wheat1]Windows Users![/]\n\nBefore you start, please do the following:
text = Text.from_markup(
"""[b wheat1]Windows Users![/]\n\nBefore you start, please do the following:
1. Double-click on the file [b wheat1]WinLongPathsEnabled.reg[/] in order to
enable long path support on your system.
2. Make sure you have the [b wheat1]Visual C++ core libraries[/] installed. If not, install from
[deep_sky_blue1]https://learn.microsoft.com/en-US/cpp/windows/latest-supported-vc-redist?view=msvc-170[/]""")
[deep_sky_blue1]https://learn.microsoft.com/en-US/cpp/windows/latest-supported-vc-redist?view=msvc-170[/]"""
)
else:
text = ""
return text

8
invokeai/app/api/dependencies.py
View File

@ -78,9 +78,7 @@ class ApiDependencies:
image_record_storage = SqliteImageRecordStorage(db_location)
image_file_storage = DiskImageFileStorage(f"{output_folder}/images")
names = SimpleNameService()
latents = ForwardCacheLatentsStorage(
DiskLatentsStorage(f"{output_folder}/latents")
)
latents = ForwardCacheLatentsStorage(DiskLatentsStorage(f"{output_folder}/latents"))
board_record_storage = SqliteBoardRecordStorage(db_location)
board_image_record_storage = SqliteBoardImageRecordStorage(db_location)
@ -125,9 +123,7 @@ class ApiDependencies:
boards=boards,
board_images=board_images,
queue=MemoryInvocationQueue(),
graph_library=SqliteItemStorage[LibraryGraph](
filename=db_location, table_name="graphs"
),
graph_library=SqliteItemStorage[LibraryGraph](filename=db_location, table_name="graphs"),
graph_execution_manager=graph_execution_manager,
processor=DefaultInvocationProcessor(),
configuration=config,

46
invokeai/app/api/routers/app_info.py
View File

@ -15,6 +15,7 @@ from invokeai.version import __version__
from ..dependencies import ApiDependencies
from invokeai.backend.util.logging import logging
class LogLevel(int, Enum):
NotSet = logging.NOTSET
Debug = logging.DEBUG
@ -23,10 +24,12 @@ class LogLevel(int, Enum):
Error = logging.ERROR
Critical = logging.CRITICAL
class Upscaler(BaseModel):
upscaling_method: str = Field(description="Name of upscaling method")
upscaling_models: list[str] = Field(description="List of upscaling models for this method")
app_router = APIRouter(prefix="/v1/app", tags=["app"])
@ -45,38 +48,30 @@ class AppConfig(BaseModel):
watermarking_methods: list[str] = Field(description="List of invisible watermark methods")
@app_router.get(
"/version", operation_id="app_version", status_code=200, response_model=AppVersion
)
@app_router.get("/version", operation_id="app_version", status_code=200, response_model=AppVersion)
async def get_version() -> AppVersion:
return AppVersion(version=__version__)
@app_router.get(
"/config", operation_id="get_config", status_code=200, response_model=AppConfig
)
@app_router.get("/config", operation_id="get_config", status_code=200, response_model=AppConfig)
async def get_config() -> AppConfig:
infill_methods = ['tile']
infill_methods = ["tile"]
if PatchMatch.patchmatch_available():
infill_methods.append('patchmatch')
infill_methods.append("patchmatch")
upscaling_models = []
for model in typing.get_args(ESRGAN_MODELS):
upscaling_models.append(str(Path(model).stem))
upscaler = Upscaler(
upscaling_method = 'esrgan',
upscaling_models = upscaling_models
)
upscaler = Upscaler(upscaling_method="esrgan", upscaling_models=upscaling_models)
nsfw_methods = []
if SafetyChecker.safety_checker_available():
nsfw_methods.append('nsfw_checker')
nsfw_methods.append("nsfw_checker")
watermarking_methods = []
if InvisibleWatermark.invisible_watermark_available():
watermarking_methods.append('invisible_watermark')
watermarking_methods.append("invisible_watermark")
return AppConfig(
infill_methods=infill_methods,
upscaling_methods=[upscaler],
@ -84,25 +79,26 @@ async def get_config() -> AppConfig:
watermarking_methods=watermarking_methods,
)
@app_router.get(
"/logging",
operation_id="get_log_level",
responses={200: {"description" : "The operation was successful"}},
response_model = LogLevel,
responses={200: {"description": "The operation was successful"}},
response_model=LogLevel,
)
async def get_log_level(
) -> LogLevel:
async def get_log_level() -> LogLevel:
"""Returns the log level"""
return LogLevel(ApiDependencies.invoker.services.logger.level)
@app_router.post(
"/logging",
operation_id="set_log_level",
responses={200: {"description" : "The operation was successful"}},
response_model = LogLevel,
responses={200: {"description": "The operation was successful"}},
response_model=LogLevel,
)
async def set_log_level(
level: LogLevel = Body(description="New log verbosity level"),
level: LogLevel = Body(description="New log verbosity level"),
) -> LogLevel:
"""Sets the log verbosity level"""
ApiDependencies.invoker.services.logger.setLevel(level)

1
invokeai/app/api/routers/board_images.py
View File

@ -52,4 +52,3 @@ async def remove_board_image(
return result
except Exception as e:
raise HTTPException(status_code=500, detail="Failed to update board")

24
invokeai/app/api/routers/boards.py
View File

@ -18,9 +18,7 @@ class DeleteBoardResult(BaseModel):
deleted_board_images: list[str] = Field(
description="The image names of the board-images relationships that were deleted."
)
deleted_images: list[str] = Field(
description="The names of the images that were deleted."
)
deleted_images: list[str] = Field(description="The names of the images that were deleted.")
@boards_router.post(
@ -73,22 +71,16 @@ async def update_board(
) -> BoardDTO:
"""Updates a board"""
try:
result = ApiDependencies.invoker.services.boards.update(
board_id=board_id, changes=changes
)
result = ApiDependencies.invoker.services.boards.update(board_id=board_id, changes=changes)
return result
except Exception as e:
raise HTTPException(status_code=500, detail="Failed to update board")
@boards_router.delete(
"/{board_id}", operation_id="delete_board", response_model=DeleteBoardResult
)
@boards_router.delete("/{board_id}", operation_id="delete_board", response_model=DeleteBoardResult)
async def delete_board(
board_id: str = Path(description="The id of board to delete"),
include_images: Optional[bool] = Query(
description="Permanently delete all images on the board", default=False
),
include_images: Optional[bool] = Query(description="Permanently delete all images on the board", default=False),
) -> DeleteBoardResult:
"""Deletes a board"""
try:
@ -96,9 +88,7 @@ async def delete_board(
deleted_images = ApiDependencies.invoker.services.board_images.get_all_board_image_names_for_board(
board_id=board_id
)
ApiDependencies.invoker.services.images.delete_images_on_board(
board_id=board_id
)
ApiDependencies.invoker.services.images.delete_images_on_board(board_id=board_id)
ApiDependencies.invoker.services.boards.delete(board_id=board_id)
return DeleteBoardResult(
board_id=board_id,
@ -127,9 +117,7 @@ async def delete_board(
async def list_boards(
all: Optional[bool] = Query(default=None, description="Whether to list all boards"),
offset: Optional[int] = Query(default=None, description="The page offset"),
limit: Optional[int] = Query(
default=None, description="The number of boards per page"
),
limit: Optional[int] = Query(default=None, description="The number of boards per page"),
) -> Union[OffsetPaginatedResults[BoardDTO], list[BoardDTO]]:
"""Gets a list of boards"""
if all:

40
invokeai/app/api/routers/images.py
View File

@ -40,15 +40,9 @@ async def upload_image(
response: Response,
image_category: ImageCategory = Query(description="The category of the image"),
is_intermediate: bool = Query(description="Whether this is an intermediate image"),
board_id: Optional[str] = Query(
default=None, description="The board to add this image to, if any"
),
session_id: Optional[str] = Query(
default=None, description="The session ID associated with this upload, if any"
),
crop_visible: Optional[bool] = Query(
default=False, description="Whether to crop the image"
),
board_id: Optional[str] = Query(default=None, description="The board to add this image to, if any"),
session_id: Optional[str] = Query(default=None, description="The session ID associated with this upload, if any"),
crop_visible: Optional[bool] = Query(default=False, description="Whether to crop the image"),
) -> ImageDTO:
"""Uploads an image"""
if not file.content_type.startswith("image"):
@ -115,9 +109,7 @@ async def clear_intermediates() -> int:
)
async def update_image(
image_name: str = Path(description="The name of the image to update"),
image_changes: ImageRecordChanges = Body(
description="The changes to apply to the image"
),
image_changes: ImageRecordChanges = Body(description="The changes to apply to the image"),
) -> ImageDTO:
"""Updates an image"""
@ -212,15 +204,11 @@ async def get_image_thumbnail(
"""Gets a thumbnail image file"""
try:
path = ApiDependencies.invoker.services.images.get_path(
image_name, thumbnail=True
)
path = ApiDependencies.invoker.services.images.get_path(image_name, thumbnail=True)
if not ApiDependencies.invoker.services.images.validate_path(path):
raise HTTPException(status_code=404)
response = FileResponse(
path, media_type="image/webp", content_disposition_type="inline"
)
response = FileResponse(path, media_type="image/webp", content_disposition_type="inline")
response.headers["Cache-Control"] = f"max-age={IMAGE_MAX_AGE}"
return response
except Exception as e:
@ -239,9 +227,7 @@ async def get_image_urls(
try:
image_url = ApiDependencies.invoker.services.images.get_url(image_name)
thumbnail_url = ApiDependencies.invoker.services.images.get_url(
image_name, thumbnail=True
)
thumbnail_url = ApiDependencies.invoker.services.images.get_url(image_name, thumbnail=True)
return ImageUrlsDTO(
image_name=image_name,
image_url=image_url,
@ -257,15 +243,9 @@ async def get_image_urls(
response_model=OffsetPaginatedResults[ImageDTO],
)
async def list_image_dtos(
image_origin: Optional[ResourceOrigin] = Query(
default=None, description="The origin of images to list."
),
categories: Optional[list[ImageCategory]] = Query(
default=None, description="The categories of image to include."
),
is_intermediate: Optional[bool] = Query(
default=None, description="Whether to list intermediate images."
),
image_origin: Optional[ResourceOrigin] = Query(default=None, description="The origin of images to list."),
categories: Optional[list[ImageCategory]] = Query(default=None, description="The categories of image to include."),
is_intermediate: Optional[bool] = Query(default=None, description="Whether to list intermediate images."),
board_id: Optional[str] = Query(
default=None,
description="The board id to filter by. Use 'none' to find images without a board.",

297
invokeai/app/api/routers/models.py
View File

@ -28,49 +28,52 @@ ConvertModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
MergeModelResponse = Union[tuple(OPENAPI_MODEL_CONFIGS)]
ImportModelAttributes = Union[tuple(OPENAPI_MODEL_CONFIGS)]
class ModelsList(BaseModel):
models: list[Union[tuple(OPENAPI_MODEL_CONFIGS)]]
@models_router.get(
"/",
operation_id="list_models",
responses={200: {"model": ModelsList }},
responses={200: {"model": ModelsList}},
)
async def list_models(
base_models: Optional[List[BaseModelType]] = Query(default=None, description="Base models to include"),
model_type: Optional[ModelType] = Query(default=None, description="The type of model to get"),
) -> ModelsList:
"""Gets a list of models"""
if base_models and len(base_models)>0:
if base_models and len(base_models) > 0:
models_raw = list()
for base_model in base_models:
models_raw.extend(ApiDependencies.invoker.services.model_manager.list_models(base_model, model_type))
else:
models_raw = ApiDependencies.invoker.services.model_manager.list_models(None, model_type)
models = parse_obj_as(ModelsList, { "models": models_raw })
models = parse_obj_as(ModelsList, {"models": models_raw})
return models
@models_router.patch(
"/{base_model}/{model_type}/{model_name}",
operation_id="update_model",
responses={200: {"description" : "The model was updated successfully"},
400: {"description" : "Bad request"},
404: {"description" : "The model could not be found"},
409: {"description" : "There is already a model corresponding to the new name"},
},
status_code = 200,
response_model = UpdateModelResponse,
responses={
200: {"description": "The model was updated successfully"},
400: {"description": "Bad request"},
404: {"description": "The model could not be found"},
409: {"description": "There is already a model corresponding to the new name"},
},
status_code=200,
response_model=UpdateModelResponse,
)
async def update_model(
base_model: BaseModelType = Path(description="Base model"),
model_type: ModelType = Path(description="The type of model"),
model_name: str = Path(description="model name"),
info: Union[tuple(OPENAPI_MODEL_CONFIGS)] = Body(description="Model configuration"),
base_model: BaseModelType = Path(description="Base model"),
model_type: ModelType = Path(description="The type of model"),
model_name: str = Path(description="model name"),
info: Union[tuple(OPENAPI_MODEL_CONFIGS)] = Body(description="Model configuration"),
) -> UpdateModelResponse:
""" Update model contents with a new config. If the model name or base fields are changed, then the model is renamed. """
"""Update model contents with a new config. If the model name or base fields are changed, then the model is renamed."""
logger = ApiDependencies.invoker.services.logger
try:
previous_info = ApiDependencies.invoker.services.model_manager.list_model(
model_name=model_name,
@ -81,13 +84,13 @@ async def update_model(
# rename operation requested
if info.model_name != model_name or info.base_model != base_model:
ApiDependencies.invoker.services.model_manager.rename_model(
base_model = base_model,
model_type = model_type,
model_name = model_name,
new_name = info.model_name,
new_base = info.base_model,
base_model=base_model,
model_type=model_type,
model_name=model_name,
new_name=info.model_name,
new_base=info.base_model,
)
logger.info(f'Successfully renamed {base_model}/{model_name}=>{info.base_model}/{info.model_name}')
logger.info(f"Successfully renamed {base_model}/{model_name}=>{info.base_model}/{info.model_name}")
# update information to support an update of attributes
model_name = info.model_name
base_model = info.base_model
@ -96,16 +99,15 @@ async def update_model(
base_model=base_model,
model_type=model_type,
)
if new_info.get('path') != previous_info.get('path'): # model manager moved model path during rename - don't overwrite it
info.path = new_info.get('path')
if new_info.get("path") != previous_info.get(
"path"
): # model manager moved model path during rename - don't overwrite it
info.path = new_info.get("path")
ApiDependencies.invoker.services.model_manager.update_model(
model_name=model_name,
base_model=base_model,
model_type=model_type,
model_attributes=info.dict()
model_name=model_name, base_model=base_model, model_type=model_type, model_attributes=info.dict()
)
model_raw = ApiDependencies.invoker.services.model_manager.list_model(
model_name=model_name,
base_model=base_model,
@ -123,49 +125,48 @@ async def update_model(
return model_response
@models_router.post(
"/import",
operation_id="import_model",
responses= {
201: {"description" : "The model imported successfully"},
404: {"description" : "The model could not be found"},
415: {"description" : "Unrecognized file/folder format"},
424: {"description" : "The model appeared to import successfully, but could not be found in the model manager"},
409: {"description" : "There is already a model corresponding to this path or repo_id"},
responses={
201: {"description": "The model imported successfully"},
404: {"description": "The model could not be found"},
415: {"description": "Unrecognized file/folder format"},
424: {"description": "The model appeared to import successfully, but could not be found in the model manager"},
409: {"description": "There is already a model corresponding to this path or repo_id"},
},
status_code=201,
response_model=ImportModelResponse
response_model=ImportModelResponse,
)
async def import_model(
location: str = Body(description="A model path, repo_id or URL to import"),
prediction_type: Optional[Literal['v_prediction','epsilon','sample']] = \
Body(description='Prediction type for SDv2 checkpoint files', default="v_prediction"),
location: str = Body(description="A model path, repo_id or URL to import"),
prediction_type: Optional[Literal["v_prediction", "epsilon", "sample"]] = Body(
description="Prediction type for SDv2 checkpoint files", default="v_prediction"
),
) -> ImportModelResponse:
""" Add a model using its local path, repo_id, or remote URL. Model characteristics will be probed and configured automatically """
"""Add a model using its local path, repo_id, or remote URL. Model characteristics will be probed and configured automatically"""
items_to_import = {location}
prediction_types = { x.value: x for x in SchedulerPredictionType }
prediction_types = {x.value: x for x in SchedulerPredictionType}
logger = ApiDependencies.invoker.services.logger
try:
installed_models = ApiDependencies.invoker.services.model_manager.heuristic_import(
items_to_import = items_to_import,
prediction_type_helper = lambda x: prediction_types.get(prediction_type)
items_to_import=items_to_import, prediction_type_helper=lambda x: prediction_types.get(prediction_type)
)
info = installed_models.get(location)
if not info:
logger.error("Import failed")
raise HTTPException(status_code=415)
logger.info(f'Successfully imported {location}, got {info}')
logger.info(f"Successfully imported {location}, got {info}")
model_raw = ApiDependencies.invoker.services.model_manager.list_model(
model_name=info.name,
base_model=info.base_model,
model_type=info.model_type
model_name=info.name, base_model=info.base_model, model_type=info.model_type
)
return parse_obj_as(ImportModelResponse, model_raw)
except ModelNotFoundException as e:
logger.error(str(e))
raise HTTPException(status_code=404, detail=str(e))
@ -175,38 +176,34 @@ async def import_model(
except ValueError as e:
logger.error(str(e))
raise HTTPException(status_code=409, detail=str(e))
@models_router.post(
"/add",
operation_id="add_model",
responses= {
201: {"description" : "The model added successfully"},
404: {"description" : "The model could not be found"},
424: {"description" : "The model appeared to add successfully, but could not be found in the model manager"},
409: {"description" : "There is already a model corresponding to this path or repo_id"},
responses={
201: {"description": "The model added successfully"},
404: {"description": "The model could not be found"},
424: {"description": "The model appeared to add successfully, but could not be found in the model manager"},
409: {"description": "There is already a model corresponding to this path or repo_id"},
},
status_code=201,
response_model=ImportModelResponse
response_model=ImportModelResponse,
)
async def add_model(
info: Union[tuple(OPENAPI_MODEL_CONFIGS)] = Body(description="Model configuration"),
info: Union[tuple(OPENAPI_MODEL_CONFIGS)] = Body(description="Model configuration"),
) -> ImportModelResponse:
""" Add a model using the configuration information appropriate for its type. Only local models can be added by path"""
"""Add a model using the configuration information appropriate for its type. Only local models can be added by path"""
logger = ApiDependencies.invoker.services.logger
try:
ApiDependencies.invoker.services.model_manager.add_model(
info.model_name,
info.base_model,
info.model_type,
model_attributes = info.dict()
info.model_name, info.base_model, info.model_type, model_attributes=info.dict()
)
logger.info(f'Successfully added {info.model_name}')
logger.info(f"Successfully added {info.model_name}")
model_raw = ApiDependencies.invoker.services.model_manager.list_model(
model_name=info.model_name,
base_model=info.base_model,
model_type=info.model_type
model_name=info.model_name, base_model=info.base_model, model_type=info.model_type
)
return parse_obj_as(ImportModelResponse, model_raw)
except ModelNotFoundException as e:
@ -216,66 +213,66 @@ async def add_model(
logger.error(str(e))
raise HTTPException(status_code=409, detail=str(e))
@models_router.delete(
"/{base_model}/{model_type}/{model_name}",
operation_id="del_model",
responses={
204: { "description": "Model deleted successfully" },
404: { "description": "Model not found" }
},
status_code = 204,
response_model = None,
responses={204: {"description": "Model deleted successfully"}, 404: {"description": "Model not found"}},
status_code=204,
response_model=None,
)
async def delete_model(
base_model: BaseModelType = Path(description="Base model"),
model_type: ModelType = Path(description="The type of model"),
model_name: str = Path(description="model name"),
base_model: BaseModelType = Path(description="Base model"),
model_type: ModelType = Path(description="The type of model"),
model_name: str = Path(description="model name"),
) -> Response:
"""Delete Model"""
logger = ApiDependencies.invoker.services.logger
try:
ApiDependencies.invoker.services.model_manager.del_model(model_name,
base_model = base_model,
model_type = model_type
)
ApiDependencies.invoker.services.model_manager.del_model(
model_name, base_model=base_model, model_type=model_type
)
logger.info(f"Deleted model: {model_name}")
return Response(status_code=204)
except ModelNotFoundException as e:
logger.error(str(e))
raise HTTPException(status_code=404, detail=str(e))
@models_router.put(
"/convert/{base_model}/{model_type}/{model_name}",
operation_id="convert_model",
responses={
200: { "description": "Model converted successfully" },
400: {"description" : "Bad request" },
404: { "description": "Model not found" },
200: {"description": "Model converted successfully"},
400: {"description": "Bad request"},
404: {"description": "Model not found"},
},
status_code = 200,
response_model = ConvertModelResponse,
status_code=200,
response_model=ConvertModelResponse,
)
async def convert_model(
base_model: BaseModelType = Path(description="Base model"),
model_type: ModelType = Path(description="The type of model"),
model_name: str = Path(description="model name"),
convert_dest_directory: Optional[str] = Query(default=None, description="Save the converted model to the designated directory"),
base_model: BaseModelType = Path(description="Base model"),
model_type: ModelType = Path(description="The type of model"),
model_name: str = Path(description="model name"),
convert_dest_directory: Optional[str] = Query(
default=None, description="Save the converted model to the designated directory"
),
) -> ConvertModelResponse:
"""Convert a checkpoint model into a diffusers model, optionally saving to the indicated destination directory, or `models` if none."""
logger = ApiDependencies.invoker.services.logger
try:
logger.info(f"Converting model: {model_name}")
dest = pathlib.Path(convert_dest_directory) if convert_dest_directory else None
ApiDependencies.invoker.services.model_manager.convert_model(model_name,
base_model = base_model,
model_type = model_type,
convert_dest_directory = dest,
)
model_raw = ApiDependencies.invoker.services.model_manager.list_model(model_name,
base_model = base_model,
model_type = model_type)
ApiDependencies.invoker.services.model_manager.convert_model(
model_name,
base_model=base_model,
model_type=model_type,
convert_dest_directory=dest,
)
model_raw = ApiDependencies.invoker.services.model_manager.list_model(
model_name, base_model=base_model, model_type=model_type
)
response = parse_obj_as(ConvertModelResponse, model_raw)
except ModelNotFoundException as e:
raise HTTPException(status_code=404, detail=f"Model '{model_name}' not found: {str(e)}")
@ -283,91 +280,101 @@ async def convert_model(
raise HTTPException(status_code=400, detail=str(e))
return response
@models_router.get(
"/search",
operation_id="search_for_models",
responses={
200: { "description": "Directory searched successfully" },
404: { "description": "Invalid directory path" },
200: {"description": "Directory searched successfully"},
404: {"description": "Invalid directory path"},
},
status_code = 200,
response_model = List[pathlib.Path]
status_code=200,
response_model=List[pathlib.Path],
)
async def search_for_models(
search_path: pathlib.Path = Query(description="Directory path to search for models")
)->List[pathlib.Path]:
search_path: pathlib.Path = Query(description="Directory path to search for models"),
) -> List[pathlib.Path]:
if not search_path.is_dir():
raise HTTPException(status_code=404, detail=f"The search path '{search_path}' does not exist or is not directory")
raise HTTPException(
status_code=404, detail=f"The search path '{search_path}' does not exist or is not directory"
)
return ApiDependencies.invoker.services.model_manager.search_for_models(search_path)
@models_router.get(
"/ckpt_confs",
operation_id="list_ckpt_configs",
responses={
200: { "description" : "paths retrieved successfully" },
200: {"description": "paths retrieved successfully"},
},
status_code = 200,
response_model = List[pathlib.Path]
status_code=200,
response_model=List[pathlib.Path],
)
async def list_ckpt_configs(
)->List[pathlib.Path]:
async def list_ckpt_configs() -> List[pathlib.Path]:
"""Return a list of the legacy checkpoint configuration files stored in `ROOT/configs/stable-diffusion`, relative to ROOT."""
return ApiDependencies.invoker.services.model_manager.list_checkpoint_configs()
@models_router.post(
"/sync",
operation_id="sync_to_config",
responses={
201: { "description": "synchronization successful" },
201: {"description": "synchronization successful"},
},
status_code = 201,
response_model = bool
status_code=201,
response_model=bool,
)
async def sync_to_config(
)->bool:
async def sync_to_config() -> bool:
"""Call after making changes to models.yaml, autoimport directories or models directory to synchronize
in-memory data structures with disk data structures."""
ApiDependencies.invoker.services.model_manager.sync_to_config()
return True
@models_router.put(
"/merge/{base_model}",
operation_id="merge_models",
responses={
200: { "description": "Model converted successfully" },
400: { "description": "Incompatible models" },
404: { "description": "One or more models not found" },
200: {"description": "Model converted successfully"},
400: {"description": "Incompatible models"},
404: {"description": "One or more models not found"},
},
status_code = 200,
response_model = MergeModelResponse,
status_code=200,
response_model=MergeModelResponse,
)
async def merge_models(
base_model: BaseModelType = Path(description="Base model"),
model_names: List[str] = Body(description="model name", min_items=2, max_items=3),
merged_model_name: Optional[str] = Body(description="Name of destination model"),
alpha: Optional[float] = Body(description="Alpha weighting strength to apply to 2d and 3d models", default=0.5),
interp: Optional[MergeInterpolationMethod] = Body(description="Interpolation method"),
force: Optional[bool] = Body(description="Force merging of models created with different versions of diffusers", default=False),
merge_dest_directory: Optional[str] = Body(description="Save the merged model to the designated directory (with 'merged_model_name' appended)", default=None)
base_model: BaseModelType = Path(description="Base model"),
model_names: List[str] = Body(description="model name", min_items=2, max_items=3),
merged_model_name: Optional[str] = Body(description="Name of destination model"),
alpha: Optional[float] = Body(description="Alpha weighting strength to apply to 2d and 3d models", default=0.5),
interp: Optional[MergeInterpolationMethod] = Body(description="Interpolation method"),
force: Optional[bool] = Body(
description="Force merging of models created with different versions of diffusers", default=False
),
merge_dest_directory: Optional[str] = Body(
description="Save the merged model to the designated directory (with 'merged_model_name' appended)",
default=None,
),
) -> MergeModelResponse:
"""Convert a checkpoint model into a diffusers model"""
logger = ApiDependencies.invoker.services.logger
try:
logger.info(f"Merging models: {model_names} into {merge_dest_directory or '<MODELS>'}/{merged_model_name}")
dest = pathlib.Path(merge_dest_directory) if merge_dest_directory else None
result = ApiDependencies.invoker.services.model_manager.merge_models(model_names,
base_model,
merged_model_name=merged_model_name or "+".join(model_names),
alpha=alpha,
interp=interp,
force=force,
merge_dest_directory = dest
)
model_raw = ApiDependencies.invoker.services.model_manager.list_model(result.name,
base_model = base_model,
model_type = ModelType.Main,
)
result = ApiDependencies.invoker.services.model_manager.merge_models(
model_names,
base_model,
merged_model_name=merged_model_name or "+".join(model_names),
alpha=alpha,
interp=interp,
force=force,
merge_dest_directory=dest,
)
model_raw = ApiDependencies.invoker.services.model_manager.list_model(
result.name,
base_model=base_model,
model_type=ModelType.Main,
)
response = parse_obj_as(ConvertModelResponse, model_raw)
except ModelNotFoundException:
raise HTTPException(status_code=404, detail=f"One or more of the models '{model_names}' not found")

34
invokeai/app/api/routers/sessions.py
View File

@ -30,9 +30,7 @@ session_router = APIRouter(prefix="/v1/sessions", tags=["sessions"])
},
)
async def create_session(
graph: Optional[Graph] = Body(
default=None, description="The graph to initialize the session with"
)
graph: Optional[Graph] = Body(default=None, description="The graph to initialize the session with")
) -> GraphExecutionState:
"""Creates a new session, optionally initializing it with an invocation graph"""
session = ApiDependencies.invoker.create_execution_state(graph)
@ -51,13 +49,9 @@ async def list_sessions(
) -> PaginatedResults[GraphExecutionState]:
"""Gets a list of sessions, optionally searching"""
if query == "":
result = ApiDependencies.invoker.services.graph_execution_manager.list(
page, per_page
)
result = ApiDependencies.invoker.services.graph_execution_manager.list(page, per_page)
else:
result = ApiDependencies.invoker.services.graph_execution_manager.search(
query, page, per_page
)
result = ApiDependencies.invoker.services.graph_execution_manager.search(query, page, per_page)
return result
@ -91,9 +85,9 @@ async def get_session(
)
async def add_node(
session_id: str = Path(description="The id of the session"),
node: Annotated[
Union[BaseInvocation.get_invocations()], Field(discriminator="type") # type: ignore
] = Body(description="The node to add"),
node: Annotated[Union[BaseInvocation.get_invocations()], Field(discriminator="type")] = Body( # type: ignore
description="The node to add"
),
) -> str:
"""Adds a node to the graph"""
session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
@ -124,9 +118,9 @@ async def add_node(
async def update_node(
session_id: str = Path(description="The id of the session"),
node_path: str = Path(description="The path to the node in the graph"),
node: Annotated[
Union[BaseInvocation.get_invocations()], Field(discriminator="type") # type: ignore
] = Body(description="The new node"),
node: Annotated[Union[BaseInvocation.get_invocations()], Field(discriminator="type")] = Body( # type: ignore
description="The new node"
),
) -> GraphExecutionState:
"""Updates a node in the graph and removes all linked edges"""
session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
@ -230,7 +224,7 @@ async def delete_edge(
try:
edge = Edge(
source=EdgeConnection(node_id=from_node_id, field=from_field),
destination=EdgeConnection(node_id=to_node_id, field=to_field)
destination=EdgeConnection(node_id=to_node_id, field=to_field),
)
session.delete_edge(edge)
ApiDependencies.invoker.services.graph_execution_manager.set(
@ -255,9 +249,7 @@ async def delete_edge(
)
async def invoke_session(
session_id: str = Path(description="The id of the session to invoke"),
all: bool = Query(
default=False, description="Whether or not to invoke all remaining invocations"
),
all: bool = Query(default=False, description="Whether or not to invoke all remaining invocations"),
) -> Response:
"""Invokes a session"""
session = ApiDependencies.invoker.services.graph_execution_manager.get(session_id)
@ -274,9 +266,7 @@ async def invoke_session(
@session_router.delete(
"/{session_id}/invoke",
operation_id="cancel_session_invoke",
responses={
202: {"description": "The invocation is canceled"}
},
responses={202: {"description": "The invocation is canceled"}},
)
async def cancel_session_invoke(
session_id: str = Path(description="The id of the session to cancel"),

4
invokeai/app/api/sockets.py
View File

@ -16,9 +16,7 @@ class SocketIO:
self.__sio.on("subscribe", handler=self._handle_sub)
self.__sio.on("unsubscribe", handler=self._handle_unsub)
local_handler.register(
event_name=EventServiceBase.session_event, _func=self._handle_session_event
)
local_handler.register(event_name=EventServiceBase.session_event, _func=self._handle_session_event)
async def _handle_session_event(self, event: Event):
await self.__sio.emit(

41
invokeai/app/api_app.py
View File

@ -17,9 +17,10 @@ from fastapi_events.middleware import EventHandlerASGIMiddleware
from pathlib import Path
from pydantic.schema import schema
#This should come early so that modules can log their initialization properly
# This should come early so that modules can log their initialization properly
from .services.config import InvokeAIAppConfig
from ..backend.util.logging import InvokeAILogger
app_config = InvokeAIAppConfig.get_config()
app_config.parse_args()
logger = InvokeAILogger.getLogger(config=app_config)
@ -28,7 +29,7 @@ from invokeai.version.invokeai_version import __version__
# we call this early so that the message appears before
# other invokeai initialization messages
if app_config.version:
print(f'InvokeAI version {__version__}')
print(f"InvokeAI version {__version__}")
sys.exit(0)
import invokeai.frontend.web as web_dir
@ -38,17 +39,18 @@ from .api.dependencies import ApiDependencies
from .api.routers import sessions, models, images, boards, board_images, app_info
from .api.sockets import SocketIO
from .invocations.baseinvocation import BaseInvocation
import torch
import invokeai.backend.util.hotfixes
if torch.backends.mps.is_available():
import invokeai.backend.util.mps_fixes
# fix for windows mimetypes registry entries being borked
# see https://github.com/invoke-ai/InvokeAI/discussions/3684#discussioncomment-6391352
mimetypes.add_type('application/javascript', '.js')
mimetypes.add_type('text/css', '.css')
mimetypes.add_type("application/javascript", ".js")
mimetypes.add_type("text/css", ".css")
# Create the app
# TODO: create this all in a method so configuration/etc. can be passed in?
@ -58,14 +60,13 @@ app = FastAPI(title="Invoke AI", docs_url=None, redoc_url=None)
event_handler_id: int = id(app)
app.add_middleware(
EventHandlerASGIMiddleware,
handlers=[
local_handler
], # TODO: consider doing this in services to support different configurations
handlers=[local_handler], # TODO: consider doing this in services to support different configurations
middleware_id=event_handler_id,
)
socket_io = SocketIO(app)
# Add startup event to load dependencies
@app.on_event("startup")
async def startup_event():
@ -77,9 +78,7 @@ async def startup_event():
allow_headers=app_config.allow_headers,
)
ApiDependencies.initialize(
config=app_config, event_handler_id=event_handler_id, logger=logger
)
ApiDependencies.initialize(config=app_config, event_handler_id=event_handler_id, logger=logger)
# Shut down threads
@ -104,7 +103,8 @@ app.include_router(boards.boards_router, prefix="/api")
app.include_router(board_images.board_images_router, prefix="/api")
app.include_router(app_info.app_router, prefix='/api')
app.include_router(app_info.app_router, prefix="/api")
# Build a custom OpenAPI to include all outputs
# TODO: can outputs be included on metadata of invocation schemas somehow?
@ -145,6 +145,7 @@ def custom_openapi():
invoker_schema["output"] = outputs_ref
from invokeai.backend.model_management.models import get_model_config_enums
for model_config_format_enum in set(get_model_config_enums()):
name = model_config_format_enum.__qualname__
@ -167,7 +168,8 @@ def custom_openapi():
app.openapi = custom_openapi
# Override API doc favicons
app.mount("/static", StaticFiles(directory=Path(web_dir.__path__[0], 'static/dream_web')), name="static")
app.mount("/static", StaticFiles(directory=Path(web_dir.__path__[0], "static/dream_web")), name="static")
@app.get("/docs", include_in_schema=False)
def overridden_swagger():
@ -188,11 +190,8 @@ def overridden_redoc():
# Must mount *after* the other routes else it borks em
app.mount("/",
StaticFiles(directory=Path(web_dir.__path__[0],"dist"),
html=True
), name="ui"
)
app.mount("/", StaticFiles(directory=Path(web_dir.__path__[0], "dist"), html=True), name="ui")
def invoke_api():
def find_port(port: int):
@ -204,10 +203,11 @@ def invoke_api():
return find_port(port=port + 1)
else:
return port
from invokeai.backend.install.check_root import check_invokeai_root
check_invokeai_root(app_config) # note, may exit with an exception if root not set up
port = find_port(app_config.port)
if port != app_config.port:
logger.warn(f"Port {app_config.port} in use, using port {port}")
@ -232,5 +232,6 @@ def invoke_api():
loop.run_until_complete(server.serve())
if __name__ == "__main__":
invoke_api()

50
invokeai/app/cli/commands.py
View File

@ -14,8 +14,14 @@ from ..services.graph import GraphExecutionState, LibraryGraph, Edge
from ..services.invoker import Invoker
def add_field_argument(command_parser, name: str, field, default_override = None):
default = default_override if default_override is not None else field.default if field.default_factory is None else field.default_factory()
def add_field_argument(command_parser, name: str, field, default_override=None):
default = (
default_override
if default_override is not None
else field.default
if field.default_factory is None
else field.default_factory()
)
if get_origin(field.type_) == Literal:
allowed_values = get_args(field.type_)
allowed_types = set()
@ -47,8 +53,8 @@ def add_parsers(
commands: list[type],
command_field: str = "type",
exclude_fields: list[str] = ["id", "type"],
add_arguments: Union[Callable[[argparse.ArgumentParser], None],None] = None
):
add_arguments: Union[Callable[[argparse.ArgumentParser], None], None] = None,
):
"""Adds parsers for each command to the subparsers"""
# Create subparsers for each command
@ -61,7 +67,7 @@ def add_parsers(
add_arguments(command_parser)
# Convert all fields to arguments
fields = command.__fields__ # type: ignore
fields = command.__fields__ # type: ignore
for name, field in fields.items():
if name in exclude_fields:
continue
@ -70,13 +76,11 @@ def add_parsers(
def add_graph_parsers(
subparsers,
graphs: list[LibraryGraph],
add_arguments: Union[Callable[[argparse.ArgumentParser], None], None] = None
subparsers, graphs: list[LibraryGraph], add_arguments: Union[Callable[[argparse.ArgumentParser], None], None] = None
):
for graph in graphs:
command_parser = subparsers.add_parser(graph.name, help=graph.description)
if add_arguments is not None:
add_arguments(command_parser)
@ -128,6 +132,7 @@ class CliContext:
class ExitCli(Exception):
"""Exception to exit the CLI"""
pass
@ -155,7 +160,7 @@ class BaseCommand(ABC, BaseModel):
@classmethod
def get_commands_map(cls):
# Get the type strings out of the literals and into a dictionary
return dict(map(lambda t: (get_args(get_type_hints(t)['type'])[0], t),BaseCommand.get_all_subclasses()))
return dict(map(lambda t: (get_args(get_type_hints(t)["type"])[0], t), BaseCommand.get_all_subclasses()))
@abstractmethod
def run(self, context: CliContext) -> None:
@ -165,7 +170,8 @@ class BaseCommand(ABC, BaseModel):
class ExitCommand(BaseCommand):
"""Exits the CLI"""
type: Literal['exit'] = 'exit'
type: Literal["exit"] = "exit"
def run(self, context: CliContext) -> None:
raise ExitCli()
@ -173,7 +179,8 @@ class ExitCommand(BaseCommand):
class HelpCommand(BaseCommand):
"""Shows help"""
type: Literal['help'] = 'help'
type: Literal["help"] = "help"
def run(self, context: CliContext) -> None:
context.parser.print_help()
@ -183,11 +190,7 @@ def get_graph_execution_history(
graph_execution_state: GraphExecutionState,
) -> Iterable[str]:
"""Gets the history of fully-executed invocations for a graph execution"""
return (
n
for n in reversed(graph_execution_state.executed_history)
if n in graph_execution_state.graph.nodes
)
return (n for n in reversed(graph_execution_state.executed_history) if n in graph_execution_state.graph.nodes)
def get_invocation_command(invocation) -> str:
@ -218,7 +221,8 @@ def get_invocation_command(invocation) -> str:
class HistoryCommand(BaseCommand):
"""Shows the invocation history"""
type: Literal['history'] = 'history'
type: Literal["history"] = "history"
# Inputs
# fmt: off
@ -235,7 +239,8 @@ class HistoryCommand(BaseCommand):
class SetDefaultCommand(BaseCommand):
"""Sets a default value for a field"""
type: Literal['default'] = 'default'
type: Literal["default"] = "default"
# Inputs
# fmt: off
@ -253,7 +258,8 @@ class SetDefaultCommand(BaseCommand):
class DrawGraphCommand(BaseCommand):
"""Debugs a graph"""
type: Literal['draw_graph'] = 'draw_graph'
type: Literal["draw_graph"] = "draw_graph"
def run(self, context: CliContext) -> None:
session: GraphExecutionState = context.invoker.services.graph_execution_manager.get(context.session.id)
@ -271,7 +277,8 @@ class DrawGraphCommand(BaseCommand):
class DrawExecutionGraphCommand(BaseCommand):
"""Debugs an execution graph"""
type: Literal['draw_xgraph'] = 'draw_xgraph'
type: Literal["draw_xgraph"] = "draw_xgraph"
def run(self, context: CliContext) -> None:
session: GraphExecutionState = context.invoker.services.graph_execution_manager.get(context.session.id)
@ -286,6 +293,7 @@ class DrawExecutionGraphCommand(BaseCommand):
plt.axis("off")
plt.show()
class SortedHelpFormatter(argparse.HelpFormatter):
def _iter_indented_subactions(self, action):
try:

56
invokeai/app/cli/completer.py
View File

@ -19,8 +19,8 @@ from ..services.invocation_services import InvocationServices
# singleton object, class variable
completer = None
class Completer(object):
def __init__(self, model_manager: ModelManager):
self.commands = self.get_commands()
self.matches = None
@ -43,7 +43,7 @@ class Completer(object):
except IndexError:
pass
options = options or list(self.parse_commands().keys())
if not text: # first time
self.matches = options
else:
@ -56,17 +56,17 @@ class Completer(object):
return match
@classmethod
def get_commands(self)->List[object]:
def get_commands(self) -> List[object]:
"""
Return a list of all the client commands and invocations.
"""
return BaseCommand.get_commands() + BaseInvocation.get_invocations()
def get_current_command(self, buffer: str)->tuple[str, str]:
def get_current_command(self, buffer: str) -> tuple[str, str]:
"""
Parse the readline buffer to find the most recent command and its switch.
"""
if len(buffer)==0:
if len(buffer) == 0:
return None, None
tokens = shlex.split(buffer)
command = None
@ -78,11 +78,11 @@ class Completer(object):
else:
switch = t
# don't try to autocomplete switches that are already complete
if switch and buffer.endswith(' '):
switch=None
return command or '', switch or ''
if switch and buffer.endswith(" "):
switch = None
return command or "", switch or ""
def parse_commands(self)->Dict[str, List[str]]:
def parse_commands(self) -> Dict[str, List[str]]:
"""
Return a dict in which the keys are the command name
and the values are the parameters the command takes.
@ -90,11 +90,11 @@ class Completer(object):
result = dict()
for command in self.commands:
hints = get_type_hints(command)
name = get_args(hints['type'])[0]
result.update({name:hints})
name = get_args(hints["type"])[0]
result.update({name: hints})
return result
def get_command_options(self, command: str, switch: str)->List[str]:
def get_command_options(self, command: str, switch: str) -> List[str]:
"""
Return all the parameters that can be passed to the command as
command-line switches. Returns None if the command is unrecognized.
@ -102,42 +102,46 @@ class Completer(object):
parsed_commands = self.parse_commands()
if command not in parsed_commands:
return None
# handle switches in the format "-foo=bar"
argument = None
if switch and '=' in switch:
switch, argument = switch.split('=')
parameter = switch.strip('-')
if switch and "=" in switch:
switch, argument = switch.split("=")
parameter = switch.strip("-")
if parameter in parsed_commands[command]:
if argument is None:
return self.get_parameter_options(parameter, parsed_commands[command][parameter])
else:
return [f"--{parameter}={x}" for x in self.get_parameter_options(parameter, parsed_commands[command][parameter])]
return [
f"--{parameter}={x}"
for x in self.get_parameter_options(parameter, parsed_commands[command][parameter])
]
else:
return [f"--{x}" for x in parsed_commands[command].keys()]
def get_parameter_options(self, parameter: str, typehint)->List[str]:
def get_parameter_options(self, parameter: str, typehint) -> List[str]:
"""
Given a parameter type (such as Literal), offers autocompletions.
"""
if get_origin(typehint) == Literal:
return get_args(typehint)
if parameter == 'model':
if parameter == "model":
return self.manager.model_names()
def _pre_input_hook(self):
if self.linebuffer:
readline.insert_text(self.linebuffer)
readline.redisplay()
self.linebuffer = None
def set_autocompleter(services: InvocationServices) -> Completer:
global completer
if completer:
return completer
completer = Completer(services.model_manager)
readline.set_completer(completer.complete)
@ -162,8 +166,6 @@ def set_autocompleter(services: InvocationServices) -> Completer:
pass
except OSError: # file likely corrupted
newname = f"{histfile}.old"
logger.error(
f"Your history file {histfile} couldn't be loaded and may be corrupted. Renaming it to {newname}"
)
logger.error(f"Your history file {histfile} couldn't be loaded and may be corrupted. Renaming it to {newname}")
histfile.replace(Path(newname))
atexit.register(readline.write_history_file, histfile)

115
invokeai/app/cli_app.py
View File

@ -13,6 +13,7 @@ from pydantic.fields import Field
# This should come early so that the logger can pick up its configuration options
from .services.config import InvokeAIAppConfig
from invokeai.backend.util.logging import InvokeAILogger
config = InvokeAIAppConfig.get_config()
config.parse_args()
logger = InvokeAILogger().getLogger(config=config)
@ -20,7 +21,7 @@ from invokeai.version.invokeai_version import __version__
# we call this early so that the message appears before other invokeai initialization messages
if config.version:
print(f'InvokeAI version {__version__}')
print(f"InvokeAI version {__version__}")
sys.exit(0)
from invokeai.app.services.board_image_record_storage import (
@ -36,18 +37,21 @@ from invokeai.app.services.image_record_storage import SqliteImageRecordStorage
from invokeai.app.services.images import ImageService, ImageServiceDependencies
from invokeai.app.services.resource_name import SimpleNameService
from invokeai.app.services.urls import LocalUrlService
from .services.default_graphs import (default_text_to_image_graph_id,
create_system_graphs)
from .services.default_graphs import default_text_to_image_graph_id, create_system_graphs
from .services.latent_storage import DiskLatentsStorage, ForwardCacheLatentsStorage
from .cli.commands import (BaseCommand, CliContext, ExitCli,
SortedHelpFormatter, add_graph_parsers, add_parsers)
from .cli.commands import BaseCommand, CliContext, ExitCli, SortedHelpFormatter, add_graph_parsers, add_parsers
from .cli.completer import set_autocompleter
from .invocations.baseinvocation import BaseInvocation
from .services.events import EventServiceBase
from .services.graph import (Edge, EdgeConnection, GraphExecutionState,
GraphInvocation, LibraryGraph,
are_connection_types_compatible)
from .services.graph import (
Edge,
EdgeConnection,
GraphExecutionState,
GraphInvocation,
LibraryGraph,
are_connection_types_compatible,
)
from .services.image_file_storage import DiskImageFileStorage
from .services.invocation_queue import MemoryInvocationQueue
from .services.invocation_services import InvocationServices
@ -58,6 +62,7 @@ from .services.sqlite import SqliteItemStorage
import torch
import invokeai.backend.util.hotfixes
if torch.backends.mps.is_available():
import invokeai.backend.util.mps_fixes
@ -69,6 +74,7 @@ class CliCommand(BaseModel):
class InvalidArgs(Exception):
pass
def add_invocation_args(command_parser):
# Add linking capability
command_parser.add_argument(
@ -113,7 +119,7 @@ def get_command_parser(services: InvocationServices) -> argparse.ArgumentParser:
return parser
class NodeField():
class NodeField:
alias: str
node_path: str
field: str
@ -126,15 +132,20 @@ class NodeField():
self.field_type = field_type
def fields_from_type_hints(hints: dict[str, type], node_path: str) -> dict[str,NodeField]:
return {k:NodeField(alias=k, node_path=node_path, field=k, field_type=v) for k, v in hints.items()}
def fields_from_type_hints(hints: dict[str, type], node_path: str) -> dict[str, NodeField]:
return {k: NodeField(alias=k, node_path=node_path, field=k, field_type=v) for k, v in hints.items()}
def get_node_input_field(graph: LibraryGraph, field_alias: str, node_id: str) -> NodeField:
"""Gets the node field for the specified field alias"""
exposed_input = next(e for e in graph.exposed_inputs if e.alias == field_alias)
node_type = type(graph.graph.get_node(exposed_input.node_path))
return NodeField(alias=exposed_input.alias, node_path=f'{node_id}.{exposed_input.node_path}', field=exposed_input.field, field_type=get_type_hints(node_type)[exposed_input.field])
return NodeField(
alias=exposed_input.alias,
node_path=f"{node_id}.{exposed_input.node_path}",
field=exposed_input.field,
field_type=get_type_hints(node_type)[exposed_input.field],
)
def get_node_output_field(graph: LibraryGraph, field_alias: str, node_id: str) -> NodeField:
@ -142,7 +153,12 @@ def get_node_output_field(graph: LibraryGraph, field_alias: str, node_id: str) -
exposed_output = next(e for e in graph.exposed_outputs if e.alias == field_alias)
node_type = type(graph.graph.get_node(exposed_output.node_path))
node_output_type = node_type.get_output_type()
return NodeField(alias=exposed_output.alias, node_path=f'{node_id}.{exposed_output.node_path}', field=exposed_output.field, field_type=get_type_hints(node_output_type)[exposed_output.field])
return NodeField(
alias=exposed_output.alias,
node_path=f"{node_id}.{exposed_output.node_path}",
field=exposed_output.field,
field_type=get_type_hints(node_output_type)[exposed_output.field],
)
def get_node_inputs(invocation: BaseInvocation, context: CliContext) -> dict[str, NodeField]:
@ -165,9 +181,7 @@ def get_node_outputs(invocation: BaseInvocation, context: CliContext) -> dict[st
return {e.alias: get_node_output_field(graph, e.alias, invocation.id) for e in graph.exposed_outputs}
def generate_matching_edges(
a: BaseInvocation, b: BaseInvocation, context: CliContext
) -> list[Edge]:
def generate_matching_edges(a: BaseInvocation, b: BaseInvocation, context: CliContext) -> list[Edge]:
"""Generates all possible edges between two invocations"""
afields = get_node_outputs(a, context)
bfields = get_node_inputs(b, context)
@ -179,12 +193,14 @@ def generate_matching_edges(
matching_fields = matching_fields.difference(invalid_fields)
# Validate types
matching_fields = [f for f in matching_fields if are_connection_types_compatible(afields[f].field_type, bfields[f].field_type)]
matching_fields = [
f for f in matching_fields if are_connection_types_compatible(afields[f].field_type, bfields[f].field_type)
]
edges = [
Edge(
source=EdgeConnection(node_id=afields[alias].node_path, field=afields[alias].field),
destination=EdgeConnection(node_id=bfields[alias].node_path, field=bfields[alias].field)
destination=EdgeConnection(node_id=bfields[alias].node_path, field=bfields[alias].field),
)
for alias in matching_fields
]
@ -193,6 +209,7 @@ def generate_matching_edges(
class SessionError(Exception):
"""Raised when a session error has occurred"""
pass
@ -209,22 +226,23 @@ def invoke_all(context: CliContext):
context.invoker.services.logger.error(
f"Error in node {n} (source node {context.session.prepared_source_mapping[n]}): {context.session.errors[n]}"
)
raise SessionError()
def invoke_cli():
logger.info(f'InvokeAI version {__version__}')
logger.info(f"InvokeAI version {__version__}")
# get the optional list of invocations to execute on the command line
parser = config.get_parser()
parser.add_argument('commands',nargs='*')
parser.add_argument("commands", nargs="*")
invocation_commands = parser.parse_args().commands
# get the optional file to read commands from.
# Simplest is to use it for STDIN
if infile := config.from_file:
sys.stdin = open(infile,"r")
model_manager = ModelManagerService(config,logger)
sys.stdin = open(infile, "r")
model_manager = ModelManagerService(config, logger)
events = EventServiceBase()
output_folder = config.output_path
@ -234,13 +252,13 @@ def invoke_cli():
db_location = ":memory:"
else:
db_location = config.db_path
db_location.parent.mkdir(parents=True,exist_ok=True)
db_location.parent.mkdir(parents=True, exist_ok=True)
logger.info(f'InvokeAI database location is "{db_location}"')
graph_execution_manager = SqliteItemStorage[GraphExecutionState](
filename=db_location, table_name="graph_executions"
)
filename=db_location, table_name="graph_executions"
)
urls = LocalUrlService()
image_record_storage = SqliteImageRecordStorage(db_location)
@ -281,24 +299,21 @@ def invoke_cli():
graph_execution_manager=graph_execution_manager,
)
)
services = InvocationServices(
model_manager=model_manager,
events=events,
latents = ForwardCacheLatentsStorage(DiskLatentsStorage(f'{output_folder}/latents')),
latents=ForwardCacheLatentsStorage(DiskLatentsStorage(f"{output_folder}/latents")),
images=images,
boards=boards,
board_images=board_images,
queue=MemoryInvocationQueue(),
graph_library=SqliteItemStorage[LibraryGraph](
filename=db_location, table_name="graphs"
),
graph_library=SqliteItemStorage[LibraryGraph](filename=db_location, table_name="graphs"),
graph_execution_manager=graph_execution_manager,
processor=DefaultInvocationProcessor(),
logger=logger,
configuration=config,
)
system_graphs = create_system_graphs(services.graph_library)
system_graph_names = set([g.name for g in system_graphs])
@ -308,7 +323,7 @@ def invoke_cli():
session: GraphExecutionState = invoker.create_execution_state()
parser = get_command_parser(services)
re_negid = re.compile('^-[0-9]+$')
re_negid = re.compile("^-[0-9]+$")
# Uncomment to print out previous sessions at startup
# print(services.session_manager.list())
@ -318,7 +333,7 @@ def invoke_cli():
command_line_args_exist = len(invocation_commands) > 0
done = False
while not done:
try:
if command_line_args_exist:
@ -332,7 +347,7 @@ def invoke_cli():
try:
# Refresh the state of the session
#history = list(get_graph_execution_history(context.session))
# history = list(get_graph_execution_history(context.session))
history = list(reversed(context.nodes_added))
# Split the command for piping
@ -353,17 +368,17 @@ def invoke_cli():
args[field_name] = field_default
# Parse invocation
command: CliCommand = None # type:ignore
command: CliCommand = None # type:ignore
system_graph: Optional[LibraryGraph] = None
if args['type'] in system_graph_names:
system_graph = next(filter(lambda g: g.name == args['type'], system_graphs))
if args["type"] in system_graph_names:
system_graph = next(filter(lambda g: g.name == args["type"], system_graphs))
invocation = GraphInvocation(graph=system_graph.graph, id=str(current_id))
for exposed_input in system_graph.exposed_inputs:
if exposed_input.alias in args:
node = invocation.graph.get_node(exposed_input.node_path)
field = exposed_input.field
setattr(node, field, args[exposed_input.alias])
command = CliCommand(command = invocation)
command = CliCommand(command=invocation)
context.graph_nodes[invocation.id] = system_graph.id
else:
args["id"] = current_id
@ -385,17 +400,13 @@ def invoke_cli():
# Pipe previous command output (if there was a previous command)
edges: list[Edge] = list()
if len(history) > 0 or current_id != start_id:
from_id = (
history[0] if current_id == start_id else str(current_id - 1)
)
from_id = history[0] if current_id == start_id else str(current_id - 1)
from_node = (
next(filter(lambda n: n[0].id == from_id, new_invocations))[0]
if current_id != start_id
else context.session.graph.get_node(from_id)
)
matching_edges = generate_matching_edges(
from_node, command.command, context
)
matching_edges = generate_matching_edges(from_node, command.command, context)
edges.extend(matching_edges)
# Parse provided links
@ -406,16 +417,18 @@ def invoke_cli():
node_id = str(current_id + int(node_id))
link_node = context.session.graph.get_node(node_id)
matching_edges = generate_matching_edges(
link_node, command.command, context
)
matching_edges = generate_matching_edges(link_node, command.command, context)
matching_destinations = [e.destination for e in matching_edges]
edges = [e for e in edges if e.destination not in matching_destinations]
edges.extend(matching_edges)
if "link" in args and args["link"]:
for link in args["link"]:
edges = [e for e in edges if e.destination.node_id != command.command.id or e.destination.field != link[2]]
edges = [
e
for e in edges
if e.destination.node_id != command.command.id or e.destination.field != link[2]
]
node_id = link[0]
if re_negid.match(node_id):
@ -428,7 +441,7 @@ def invoke_cli():
edges.append(
Edge(
source=EdgeConnection(node_id=node_output.node_path, field=node_output.field),
destination=EdgeConnection(node_id=node_input.node_path, field=node_input.field)
destination=EdgeConnection(node_id=node_input.node_path, field=node_input.field),
)
)

6
invokeai/app/invocations/__init__.py
View File

@ -4,9 +4,5 @@ __all__ = []
dirname = os.path.dirname(os.path.abspath(__file__))
for f in os.listdir(dirname):
if (
f != "__init__.py"
and os.path.isfile("%s/%s" % (dirname, f))
and f[-3:] == ".py"
):
if f != "__init__.py" and os.path.isfile("%s/%s" % (dirname, f)) and f[-3:] == ".py":
__all__.append(f[:-3])

3
invokeai/app/invocations/baseinvocation.py
View File

@ -4,8 +4,7 @@ from __future__ import annotations
from abc import ABC, abstractmethod
from inspect import signature
from typing import (TYPE_CHECKING, Dict, List, Literal, TypedDict, get_args,
get_type_hints)
from typing import TYPE_CHECKING, Dict, List, Literal, TypedDict, get_args, get_type_hints
from pydantic import BaseConfig, BaseModel, Field

44
invokeai/app/invocations/collections.py
View File

@ -8,8 +8,7 @@ from pydantic import Field, validator
from invokeai.app.models.image import ImageField
from invokeai.app.util.misc import SEED_MAX, get_random_seed
from .baseinvocation import (BaseInvocation, BaseInvocationOutput,
InvocationConfig, InvocationContext, UIConfig)
from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationConfig, InvocationContext, UIConfig
class IntCollectionOutput(BaseInvocationOutput):
@ -27,8 +26,7 @@ class FloatCollectionOutput(BaseInvocationOutput):
type: Literal["float_collection"] = "float_collection"
# Outputs
collection: list[float] = Field(
default=[], description="The float collection")
collection: list[float] = Field(default=[], description="The float collection")
class ImageCollectionOutput(BaseInvocationOutput):
@ -37,8 +35,7 @@ class ImageCollectionOutput(BaseInvocationOutput):
type: Literal["image_collection"] = "image_collection"
# Outputs
collection: list[ImageField] = Field(
default=[], description="The output images")
collection: list[ImageField] = Field(default=[], description="The output images")
class Config:
schema_extra = {"required": ["type", "collection"]}
@ -56,10 +53,7 @@ class RangeInvocation(BaseInvocation):
class Config(InvocationConfig):
schema_extra = {
"ui": {
"title": "Range",
"tags": ["range", "integer", "collection"]
},
"ui": {"title": "Range", "tags": ["range", "integer", "collection"]},
}
@validator("stop")
@ -69,9 +63,7 @@ class RangeInvocation(BaseInvocation):
return v
def invoke(self, context: InvocationContext) -> IntCollectionOutput:
return IntCollectionOutput(
collection=list(range(self.start, self.stop, self.step))
)
return IntCollectionOutput(collection=list(range(self.start, self.stop, self.step)))
class RangeOfSizeInvocation(BaseInvocation):
@ -86,18 +78,11 @@ class RangeOfSizeInvocation(BaseInvocation):
class Config(InvocationConfig):
schema_extra = {
"ui": {
"title": "Sized Range",
"tags": ["range", "integer", "size", "collection"]
},
"ui": {"title": "Sized Range", "tags": ["range", "integer", "size", "collection"]},
}
def invoke(self, context: InvocationContext) -> IntCollectionOutput:
return IntCollectionOutput(
collection=list(
range(
self.start, self.start + self.size,
self.step)))
return IntCollectionOutput(collection=list(range(self.start, self.start + self.size, self.step)))
class RandomRangeInvocation(BaseInvocation):
@ -107,9 +92,7 @@ class RandomRangeInvocation(BaseInvocation):
# Inputs
low: int = Field(default=0, description="The inclusive low value")
high: int = Field(
default=np.iinfo(np.int32).max, description="The exclusive high value"
)
high: int = Field(default=np.iinfo(np.int32).max, description="The exclusive high value")
size: int = Field(default=1, description="The number of values to generate")
seed: int = Field(
ge=0,
@ -120,19 +103,12 @@ class RandomRangeInvocation(BaseInvocation):
class Config(InvocationConfig):
schema_extra = {
"ui": {
"title": "Random Range",
"tags": ["range", "integer", "random", "collection"]
},
"ui": {"title": "Random Range", "tags": ["range", "integer", "random", "collection"]},
}
def invoke(self, context: InvocationContext) -> IntCollectionOutput:
rng = np.random.default_rng(self.seed)
return IntCollectionOutput(
collection=list(
rng.integers(
low=self.low, high=self.high,
size=self.size)))
return IntCollectionOutput(collection=list(rng.integers(low=self.low, high=self.high, size=self.size)))
class ImageCollectionInvocation(BaseInvocation):

261
invokeai/app/invocations/compel.py
View File

@ -3,64 +3,63 @@ from pydantic import BaseModel, Field
import re
import torch
from compel import Compel, ReturnedEmbeddingsType
from compel.prompt_parser import (Blend, Conjunction,
CrossAttentionControlSubstitute,
FlattenedPrompt, Fragment)
from compel.prompt_parser import Blend, Conjunction, CrossAttentionControlSubstitute, FlattenedPrompt, Fragment
from ...backend.util.devices import torch_dtype
from ...backend.model_management import ModelType
from ...backend.model_management.models import ModelNotFoundException
from ...backend.model_management.lora import ModelPatcher
from ...backend.stable_diffusion.diffusion import InvokeAIDiffuserComponent
from .baseinvocation import (BaseInvocation, BaseInvocationOutput,
InvocationConfig, InvocationContext)
from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationConfig, InvocationContext
from .model import ClipField
from dataclasses import dataclass
class ConditioningField(BaseModel):
conditioning_name: Optional[str] = Field(
default=None, description="The name of conditioning data")
conditioning_name: Optional[str] = Field(default=None, description="The name of conditioning data")
class Config:
schema_extra = {"required": ["conditioning_name"]}
@dataclass
class BasicConditioningInfo:
#type: Literal["basic_conditioning"] = "basic_conditioning"
# type: Literal["basic_conditioning"] = "basic_conditioning"
embeds: torch.Tensor
extra_conditioning: Optional[InvokeAIDiffuserComponent.ExtraConditioningInfo]
# weight: float
# mode: ConditioningAlgo
@dataclass
class SDXLConditioningInfo(BasicConditioningInfo):
#type: Literal["sdxl_conditioning"] = "sdxl_conditioning"
# type: Literal["sdxl_conditioning"] = "sdxl_conditioning"
pooled_embeds: torch.Tensor
add_time_ids: torch.Tensor
ConditioningInfoType = Annotated[
Union[BasicConditioningInfo, SDXLConditioningInfo],
Field(discriminator="type")
]
ConditioningInfoType = Annotated[Union[BasicConditioningInfo, SDXLConditioningInfo], Field(discriminator="type")]
@dataclass
class ConditioningFieldData:
conditionings: List[Union[BasicConditioningInfo, SDXLConditioningInfo]]
#unconditioned: Optional[torch.Tensor]
# unconditioned: Optional[torch.Tensor]
#class ConditioningAlgo(str, Enum):
# class ConditioningAlgo(str, Enum):
# Compose = "compose"
# ComposeEx = "compose_ex"
# PerpNeg = "perp_neg"
class CompelOutput(BaseInvocationOutput):
"""Compel parser output"""
#fmt: off
# fmt: off
type: Literal["compel_output"] = "compel_output"
conditioning: ConditioningField = Field(default=None, description="Conditioning")
#fmt: on
# fmt: on
class CompelInvocation(BaseInvocation):
@ -74,33 +73,28 @@ class CompelInvocation(BaseInvocation):
# Schema customisation
class Config(InvocationConfig):
schema_extra = {
"ui": {
"title": "Prompt (Compel)",
"tags": ["prompt", "compel"],
"type_hints": {
"model": "model"
}
},
"ui": {"title": "Prompt (Compel)", "tags": ["prompt", "compel"], "type_hints": {"model": "model"}},
}
@torch.no_grad()
def invoke(self, context: InvocationContext) -> CompelOutput:
tokenizer_info = context.services.model_manager.get_model(
**self.clip.tokenizer.dict(), context=context,
**self.clip.tokenizer.dict(),
context=context,
)
text_encoder_info = context.services.model_manager.get_model(
**self.clip.text_encoder.dict(), context=context,
**self.clip.text_encoder.dict(),
context=context,
)
def _lora_loader():
for lora in self.clip.loras:
lora_info = context.services.model_manager.get_model(
**lora.dict(exclude={"weight"}), context=context)
lora_info = context.services.model_manager.get_model(**lora.dict(exclude={"weight"}), context=context)
yield (lora_info.context.model, lora.weight)
del lora_info
return
#loras = [(context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
# loras = [(context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
ti_list = []
for trigger in re.findall(r"<[a-zA-Z0-9., _-]+>", self.prompt):
@ -116,15 +110,18 @@ class CompelInvocation(BaseInvocation):
)
except ModelNotFoundException:
# print(e)
#import traceback
#print(traceback.format_exc())
print(f"Warn: trigger: \"{trigger}\" not found")
with ModelPatcher.apply_lora_text_encoder(text_encoder_info.context.model, _lora_loader()),\
ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (tokenizer, ti_manager),\
ModelPatcher.apply_clip_skip(text_encoder_info.context.model, self.clip.skipped_layers),\
text_encoder_info as text_encoder:
# import traceback
# print(traceback.format_exc())
print(f'Warn: trigger: "{trigger}" not found')
with ModelPatcher.apply_lora_text_encoder(
text_encoder_info.context.model, _lora_loader()
), ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (
tokenizer,
ti_manager,
), ModelPatcher.apply_clip_skip(
text_encoder_info.context.model, self.clip.skipped_layers
), text_encoder_info as text_encoder:
compel = Compel(
tokenizer=tokenizer,
text_encoder=text_encoder,
@ -139,14 +136,12 @@ class CompelInvocation(BaseInvocation):
if context.services.configuration.log_tokenization:
log_tokenization_for_prompt_object(prompt, tokenizer)
c, options = compel.build_conditioning_tensor_for_prompt_object(
prompt)
c, options = compel.build_conditioning_tensor_for_prompt_object(prompt)
ec = InvokeAIDiffuserComponent.ExtraConditioningInfo(
tokens_count_including_eos_bos=get_max_token_count(
tokenizer, conjunction),
cross_attention_control_args=options.get(
"cross_attention_control", None),)
tokens_count_including_eos_bos=get_max_token_count(tokenizer, conjunction),
cross_attention_control_args=options.get("cross_attention_control", None),
)
c = c.detach().to("cpu")
@ -168,24 +163,26 @@ class CompelInvocation(BaseInvocation):
),
)
class SDXLPromptInvocationBase:
def run_clip_raw(self, context, clip_field, prompt, get_pooled):
tokenizer_info = context.services.model_manager.get_model(
**clip_field.tokenizer.dict(), context=context,
**clip_field.tokenizer.dict(),
context=context,
)
text_encoder_info = context.services.model_manager.get_model(
**clip_field.text_encoder.dict(), context=context,
**clip_field.text_encoder.dict(),
context=context,
)
def _lora_loader():
for lora in clip_field.loras:
lora_info = context.services.model_manager.get_model(
**lora.dict(exclude={"weight"}), context=context)
lora_info = context.services.model_manager.get_model(**lora.dict(exclude={"weight"}), context=context)
yield (lora_info.context.model, lora.weight)
del lora_info
return
#loras = [(context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
# loras = [(context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
ti_list = []
for trigger in re.findall(r"<[a-zA-Z0-9., _-]+>", prompt):
@ -201,15 +198,18 @@ class SDXLPromptInvocationBase:
)
except ModelNotFoundException:
# print(e)
#import traceback
#print(traceback.format_exc())
print(f"Warn: trigger: \"{trigger}\" not found")
with ModelPatcher.apply_lora_text_encoder(text_encoder_info.context.model, _lora_loader()),\
ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (tokenizer, ti_manager),\
ModelPatcher.apply_clip_skip(text_encoder_info.context.model, clip_field.skipped_layers),\
text_encoder_info as text_encoder:
# import traceback
# print(traceback.format_exc())
print(f'Warn: trigger: "{trigger}" not found')
with ModelPatcher.apply_lora_text_encoder(
text_encoder_info.context.model, _lora_loader()
), ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (
tokenizer,
ti_manager,
), ModelPatcher.apply_clip_skip(
text_encoder_info.context.model, clip_field.skipped_layers
), text_encoder_info as text_encoder:
text_inputs = tokenizer(
prompt,
padding="max_length",
@ -241,21 +241,22 @@ class SDXLPromptInvocationBase:
def run_clip_compel(self, context, clip_field, prompt, get_pooled):
tokenizer_info = context.services.model_manager.get_model(
**clip_field.tokenizer.dict(), context=context,
**clip_field.tokenizer.dict(),
context=context,
)
text_encoder_info = context.services.model_manager.get_model(
**clip_field.text_encoder.dict(), context=context,
**clip_field.text_encoder.dict(),
context=context,
)
def _lora_loader():
for lora in clip_field.loras:
lora_info = context.services.model_manager.get_model(
**lora.dict(exclude={"weight"}), context=context)
lora_info = context.services.model_manager.get_model(**lora.dict(exclude={"weight"}), context=context)
yield (lora_info.context.model, lora.weight)
del lora_info
return
#loras = [(context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
# loras = [(context.services.model_manager.get_model(**lora.dict(exclude={"weight"})).context.model, lora.weight) for lora in self.clip.loras]
ti_list = []
for trigger in re.findall(r"<[a-zA-Z0-9., _-]+>", prompt):
@ -271,22 +272,25 @@ class SDXLPromptInvocationBase:
)
except ModelNotFoundException:
# print(e)
#import traceback
#print(traceback.format_exc())
print(f"Warn: trigger: \"{trigger}\" not found")
with ModelPatcher.apply_lora_text_encoder(text_encoder_info.context.model, _lora_loader()),\
ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (tokenizer, ti_manager),\
ModelPatcher.apply_clip_skip(text_encoder_info.context.model, clip_field.skipped_layers),\
text_encoder_info as text_encoder:
# import traceback
# print(traceback.format_exc())
print(f'Warn: trigger: "{trigger}" not found')
with ModelPatcher.apply_lora_text_encoder(
text_encoder_info.context.model, _lora_loader()
), ModelPatcher.apply_ti(tokenizer_info.context.model, text_encoder_info.context.model, ti_list) as (
tokenizer,
ti_manager,
), ModelPatcher.apply_clip_skip(
text_encoder_info.context.model, clip_field.skipped_layers
), text_encoder_info as text_encoder:
compel = Compel(
tokenizer=tokenizer,
text_encoder=text_encoder,
textual_inversion_manager=ti_manager,
dtype_for_device_getter=torch_dtype,
truncate_long_prompts=True, # TODO:
returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED, # TODO: clip skip
returned_embeddings_type=ReturnedEmbeddingsType.PENULTIMATE_HIDDEN_STATES_NON_NORMALIZED, # TODO: clip skip
requires_pooled=True,
)
@ -320,6 +324,7 @@ class SDXLPromptInvocationBase:
return c, c_pooled, ec
class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
"""Parse prompt using compel package to conditioning."""
@ -339,13 +344,7 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
# Schema customisation
class Config(InvocationConfig):
schema_extra = {
"ui": {
"title": "SDXL Prompt (Compel)",
"tags": ["prompt", "compel"],
"type_hints": {
"model": "model"
}
},
"ui": {"title": "SDXL Prompt (Compel)", "tags": ["prompt", "compel"], "type_hints": {"model": "model"}},
}
@torch.no_grad()
@ -360,9 +359,7 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
crop_coords = (self.crop_top, self.crop_left)
target_size = (self.target_height, self.target_width)
add_time_ids = torch.tensor([
original_size + crop_coords + target_size
])
add_time_ids = torch.tensor([original_size + crop_coords + target_size])
conditioning_data = ConditioningFieldData(
conditionings=[
@ -384,12 +381,13 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
),
)
class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
"""Parse prompt using compel package to conditioning."""
type: Literal["sdxl_refiner_compel_prompt"] = "sdxl_refiner_compel_prompt"
style: str = Field(default="", description="Style prompt") # TODO: ?
style: str = Field(default="", description="Style prompt") # TODO: ?
original_width: int = Field(1024, description="")
original_height: int = Field(1024, description="")
crop_top: int = Field(0, description="")
@ -403,9 +401,7 @@ class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase
"ui": {
"title": "SDXL Refiner Prompt (Compel)",
"tags": ["prompt", "compel"],
"type_hints": {
"model": "model"
}
"type_hints": {"model": "model"},
},
}
@ -416,9 +412,7 @@ class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase
original_size = (self.original_height, self.original_width)
crop_coords = (self.crop_top, self.crop_left)
add_time_ids = torch.tensor([
original_size + crop_coords + (self.aesthetic_score,)
])
add_time_ids = torch.tensor([original_size + crop_coords + (self.aesthetic_score,)])
conditioning_data = ConditioningFieldData(
conditionings=[
@ -426,7 +420,7 @@ class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase
embeds=c2,
pooled_embeds=c2_pooled,
add_time_ids=add_time_ids,
extra_conditioning=ec2, # or None
extra_conditioning=ec2, # or None
)
]
)
@ -440,6 +434,7 @@ class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase
),
)
class SDXLRawPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
"""Pass unmodified prompt to conditioning without compel processing."""
@ -459,13 +454,7 @@ class SDXLRawPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
# Schema customisation
class Config(InvocationConfig):
schema_extra = {
"ui": {
"title": "SDXL Prompt (Raw)",
"tags": ["prompt", "compel"],
"type_hints": {
"model": "model"
}
},
"ui": {"title": "SDXL Prompt (Raw)", "tags": ["prompt", "compel"], "type_hints": {"model": "model"}},
}
@torch.no_grad()
@ -480,9 +469,7 @@ class SDXLRawPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
crop_coords = (self.crop_top, self.crop_left)
target_size = (self.target_height, self.target_width)
add_time_ids = torch.tensor([
original_size + crop_coords + target_size
])
add_time_ids = torch.tensor([original_size + crop_coords + target_size])
conditioning_data = ConditioningFieldData(
conditionings=[
@ -504,12 +491,13 @@ class SDXLRawPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
),
)
class SDXLRefinerRawPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
"""Parse prompt using compel package to conditioning."""
type: Literal["sdxl_refiner_raw_prompt"] = "sdxl_refiner_raw_prompt"
style: str = Field(default="", description="Style prompt") # TODO: ?
style: str = Field(default="", description="Style prompt") # TODO: ?
original_width: int = Field(1024, description="")
original_height: int = Field(1024, description="")
crop_top: int = Field(0, description="")
@ -523,9 +511,7 @@ class SDXLRefinerRawPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
"ui": {
"title": "SDXL Refiner Prompt (Raw)",
"tags": ["prompt", "compel"],
"type_hints": {
"model": "model"
}
"type_hints": {"model": "model"},
},
}
@ -536,9 +522,7 @@ class SDXLRefinerRawPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
original_size = (self.original_height, self.original_width)
crop_coords = (self.crop_top, self.crop_left)
add_time_ids = torch.tensor([
original_size + crop_coords + (self.aesthetic_score,)
])
add_time_ids = torch.tensor([original_size + crop_coords + (self.aesthetic_score,)])
conditioning_data = ConditioningFieldData(
conditionings=[
@ -546,7 +530,7 @@ class SDXLRefinerRawPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
embeds=c2,
pooled_embeds=c2_pooled,
add_time_ids=add_time_ids,
extra_conditioning=ec2, # or None
extra_conditioning=ec2, # or None
)
]
)
@ -563,11 +547,14 @@ class SDXLRefinerRawPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
class ClipSkipInvocationOutput(BaseInvocationOutput):
"""Clip skip node output"""
type: Literal["clip_skip_output"] = "clip_skip_output"
clip: ClipField = Field(None, description="Clip with skipped layers")
class ClipSkipInvocation(BaseInvocation):
"""Skip layers in clip text_encoder model."""
type: Literal["clip_skip"] = "clip_skip"
clip: ClipField = Field(None, description="Clip to use")
@ -575,10 +562,7 @@ class ClipSkipInvocation(BaseInvocation):
class Config(InvocationConfig):
schema_extra = {
"ui": {
"title": "CLIP Skip",
"tags": ["clip", "skip"]
},
"ui": {"title": "CLIP Skip", "tags": ["clip", "skip"]},
}
def invoke(self, context: InvocationContext) -> ClipSkipInvocationOutput:
@ -589,46 +573,26 @@ class ClipSkipInvocation(BaseInvocation):
def get_max_token_count(
tokenizer, prompt: Union[FlattenedPrompt, Blend, Conjunction],
truncate_if_too_long=False) -> int:
tokenizer, prompt: Union[FlattenedPrompt, Blend, Conjunction], truncate_if_too_long=False
) -> int:
if type(prompt) is Blend:
blend: Blend = prompt
return max(
[
get_max_token_count(tokenizer, p, truncate_if_too_long)
for p in blend.prompts
]
)
return max([get_max_token_count(tokenizer, p, truncate_if_too_long) for p in blend.prompts])
elif type(prompt) is Conjunction:
conjunction: Conjunction = prompt
return sum(
[
get_max_token_count(tokenizer, p, truncate_if_too_long)
for p in conjunction.prompts
]
)
return sum([get_max_token_count(tokenizer, p, truncate_if_too_long) for p in conjunction.prompts])
else:
return len(
get_tokens_for_prompt_object(
tokenizer, prompt, truncate_if_too_long))
return len(get_tokens_for_prompt_object(tokenizer, prompt, truncate_if_too_long))
def get_tokens_for_prompt_object(
tokenizer, parsed_prompt: FlattenedPrompt, truncate_if_too_long=True
) -> List[str]:
def get_tokens_for_prompt_object(tokenizer, parsed_prompt: FlattenedPrompt, truncate_if_too_long=True) -> List[str]:
if type(parsed_prompt) is Blend:
raise ValueError(
"Blend is not supported here - you need to get tokens for each of its .children"
)
raise ValueError("Blend is not supported here - you need to get tokens for each of its .children")
text_fragments = [
x.text
if type(x) is Fragment
else (
" ".join([f.text for f in x.original])
if type(x) is CrossAttentionControlSubstitute
else str(x)
)
else (" ".join([f.text for f in x.original]) if type(x) is CrossAttentionControlSubstitute else str(x))
for x in parsed_prompt.children
]
text = " ".join(text_fragments)
@ -639,25 +603,17 @@ def get_tokens_for_prompt_object(
return tokens
def log_tokenization_for_conjunction(
c: Conjunction, tokenizer, display_label_prefix=None
):
def log_tokenization_for_conjunction(c: Conjunction, tokenizer, display_label_prefix=None):
display_label_prefix = display_label_prefix or ""
for i, p in enumerate(c.prompts):
if len(c.prompts) > 1:
this_display_label_prefix = f"{display_label_prefix}(conjunction part {i + 1}, weight={c.weights[i]})"
else:
this_display_label_prefix = display_label_prefix
log_tokenization_for_prompt_object(
p,
tokenizer,
display_label_prefix=this_display_label_prefix
)
log_tokenization_for_prompt_object(p, tokenizer, display_label_prefix=this_display_label_prefix)
def log_tokenization_for_prompt_object(
p: Union[Blend, FlattenedPrompt], tokenizer, display_label_prefix=None
):
def log_tokenization_for_prompt_object(p: Union[Blend, FlattenedPrompt], tokenizer, display_label_prefix=None):
display_label_prefix = display_label_prefix or ""
if type(p) is Blend:
blend: Blend = p
@ -694,13 +650,10 @@ def log_tokenization_for_prompt_object(
)
else:
text = " ".join([x.text for x in flattened_prompt.children])
log_tokenization_for_text(
text, tokenizer, display_label=display_label_prefix
)
log_tokenization_for_text(text, tokenizer, display_label=display_label_prefix)
def log_tokenization_for_text(
text, tokenizer, display_label=None, truncate_if_too_long=False):
def log_tokenization_for_text(text, tokenizer, display_label=None, truncate_if_too_long=False):
"""shows how the prompt is tokenized
# usually tokens have '</w>' to indicate end-of-word,
# but for readability it has been replaced with ' '

368
invokeai/app/invocations/controlnet_image_processors.py
View File

@ -6,20 +6,29 @@ from typing import Dict, List, Literal, Optional, Union
import cv2
import numpy as np
from controlnet_aux import (CannyDetector, ContentShuffleDetector, HEDdetector,
LeresDetector, LineartAnimeDetector,
LineartDetector, MediapipeFaceDetector,
MidasDetector, MLSDdetector, NormalBaeDetector,
OpenposeDetector, PidiNetDetector, SamDetector,
ZoeDetector)
from controlnet_aux import (
CannyDetector,
ContentShuffleDetector,
HEDdetector,
LeresDetector,
LineartAnimeDetector,
LineartDetector,
MediapipeFaceDetector,
MidasDetector,
MLSDdetector,
NormalBaeDetector,
OpenposeDetector,
PidiNetDetector,
SamDetector,
ZoeDetector,
)
from controlnet_aux.util import HWC3, ade_palette
from PIL import Image
from pydantic import BaseModel, Field, validator
from ...backend.model_management import BaseModelType, ModelType
from ..models.image import ImageCategory, ImageField, ResourceOrigin
from .baseinvocation import (BaseInvocation, BaseInvocationOutput,
InvocationConfig, InvocationContext)
from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationConfig, InvocationContext
from ..models.image import ImageOutput, PILInvocationConfig
CONTROLNET_DEFAULT_MODELS = [
@ -34,7 +43,6 @@ CONTROLNET_DEFAULT_MODELS = [
"lllyasviel/sd-controlnet-scribble",
"lllyasviel/sd-controlnet-normal",
"lllyasviel/sd-controlnet-mlsd",
#############################################
# lllyasviel sd v1.5, ControlNet v1.1 models
#############################################
@ -56,7 +64,6 @@ CONTROLNET_DEFAULT_MODELS = [
"lllyasviel/control_v11e_sd15_shuffle",
"lllyasviel/control_v11e_sd15_ip2p",
"lllyasviel/control_v11f1e_sd15_tile",
#################################################
# thibaud sd v2.1 models (ControlNet v1.0? or v1.1?
##################################################
@ -71,7 +78,6 @@ CONTROLNET_DEFAULT_MODELS = [
"thibaud/controlnet-sd21-lineart-diffusers",
"thibaud/controlnet-sd21-normalbae-diffusers",
"thibaud/controlnet-sd21-ade20k-diffusers",
##############################################
# ControlNetMediaPipeface, ControlNet v1.1
##############################################
@ -83,10 +89,17 @@ CONTROLNET_DEFAULT_MODELS = [
]
CONTROLNET_NAME_VALUES = Literal[tuple(CONTROLNET_DEFAULT_MODELS)]
CONTROLNET_MODE_VALUES = Literal[tuple(
["balanced", "more_prompt", "more_control", "unbalanced"])]
CONTROLNET_RESIZE_VALUES = Literal[tuple(
["just_resize", "crop_resize", "fill_resize", "just_resize_simple",])]
CONTROLNET_MODE_VALUES = Literal[tuple(["balanced", "more_prompt", "more_control", "unbalanced"])]
CONTROLNET_RESIZE_VALUES = Literal[
tuple(
[
"just_resize",
"crop_resize",
"fill_resize",
"just_resize_simple",
]
)
]
class ControlNetModelField(BaseModel):
@ -98,21 +111,17 @@ class ControlNetModelField(BaseModel):
class ControlField(BaseModel):
image: ImageField = Field(default=None, description="The control image")
control_model: Optional[ControlNetModelField] = Field(
default=None, description="The ControlNet model to use")
control_model: Optional[ControlNetModelField] = Field(default=None, description="The ControlNet model to use")
# control_weight: Optional[float] = Field(default=1, description="weight given to controlnet")
control_weight: Union[float, List[float]] = Field(
default=1, description="The weight given to the ControlNet")
control_weight: Union[float, List[float]] = Field(default=1, description="The weight given to the ControlNet")
begin_step_percent: float = Field(
default=0, ge=0, le=1,
description="When the ControlNet is first applied (% of total steps)")
default=0, ge=0, le=1, description="When the ControlNet is first applied (% of total steps)"
)
end_step_percent: float = Field(
default=1, ge=0, le=1,
description="When the ControlNet is last applied (% of total steps)")
control_mode: CONTROLNET_MODE_VALUES = Field(
default="balanced", description="The control mode to use")
resize_mode: CONTROLNET_RESIZE_VALUES = Field(
default="just_resize", description="The resize mode to use")
default=1, ge=0, le=1, description="When the ControlNet is last applied (% of total steps)"
)
control_mode: CONTROLNET_MODE_VALUES = Field(default="balanced", description="The control mode to use")
resize_mode: CONTROLNET_RESIZE_VALUES = Field(default="just_resize", description="The resize mode to use")
@validator("control_weight")
def validate_control_weight(cls, v):
@ -120,11 +129,10 @@ class ControlField(BaseModel):
if isinstance(v, list):
for i in v:
if i < -1 or i > 2:
raise ValueError(
'Control weights must be within -1 to 2 range')
raise ValueError("Control weights must be within -1 to 2 range")
else:
if v < -1 or v > 2:
raise ValueError('Control weights must be within -1 to 2 range')
raise ValueError("Control weights must be within -1 to 2 range")
return v
class Config:
@ -136,12 +144,13 @@ class ControlField(BaseModel):
"control_model": "controlnet_model",
# "control_weight": "number",
}
}
},
}
class ControlOutput(BaseInvocationOutput):
"""node output for ControlNet info"""
# fmt: off
type: Literal["control_output"] = "control_output"
control: ControlField = Field(default=None, description="The control info")
@ -150,6 +159,7 @@ class ControlOutput(BaseInvocationOutput):
class ControlNetInvocation(BaseInvocation):
"""Collects ControlNet info to pass to other nodes"""
# fmt: off
type: Literal["controlnet"] = "controlnet"
# Inputs
@ -176,7 +186,7 @@ class ControlNetInvocation(BaseInvocation):
# "cfg_scale": "float",
"cfg_scale": "number",
"control_weight": "float",
}
},
},
}
@ -205,10 +215,7 @@ class ImageProcessorInvocation(BaseInvocation, PILInvocationConfig):
class Config(InvocationConfig):
schema_extra = {
"ui": {
"title": "Image Processor",
"tags": ["image", "processor"]
},
"ui": {"title": "Image Processor", "tags": ["image", "processor"]},
}
def run_processor(self, image):
@ -233,7 +240,7 @@ class ImageProcessorInvocation(BaseInvocation, PILInvocationConfig):
image_category=ImageCategory.CONTROL,
session_id=context.graph_execution_state_id,
node_id=self.id,
is_intermediate=self.is_intermediate
is_intermediate=self.is_intermediate,
)
"""Builds an ImageOutput and its ImageField"""
@ -248,9 +255,9 @@ class ImageProcessorInvocation(BaseInvocation, PILInvocationConfig):
)
class CannyImageProcessorInvocation(
ImageProcessorInvocation, PILInvocationConfig):
class CannyImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
"""Canny edge detection for ControlNet"""
# fmt: off
type: Literal["canny_image_processor"] = "canny_image_processor"
# Input
@ -260,22 +267,18 @@ class CannyImageProcessorInvocation(
class Config(InvocationConfig):
schema_extra = {
"ui": {
"title": "Canny Processor",
"tags": ["controlnet", "canny", "image", "processor"]
},
"ui": {"title": "Canny Processor", "tags": ["controlnet", "canny", "image", "processor"]},
}
def run_processor(self, image):
canny_processor = CannyDetector()
processed_image = canny_processor(
image, self.low_threshold, self.high_threshold)
processed_image = canny_processor(image, self.low_threshold, self.high_threshold)
return processed_image
class HedImageProcessorInvocation(
ImageProcessorInvocation, PILInvocationConfig):
class HedImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
"""Applies HED edge detection to image"""
# fmt: off
type: Literal["hed_image_processor"] = "hed_image_processor"
# Inputs
@ -288,27 +291,25 @@ class HedImageProcessorInvocation(
class Config(InvocationConfig):
schema_extra = {
"ui": {
"title": "Softedge(HED) Processor",
"tags": ["controlnet", "softedge", "hed", "image", "processor"]
},
"ui": {"title": "Softedge(HED) Processor", "tags": ["controlnet", "softedge", "hed", "image", "processor"]},
}
def run_processor(self, image):
hed_processor = HEDdetector.from_pretrained("lllyasviel/Annotators")
processed_image = hed_processor(image,
detect_resolution=self.detect_resolution,
image_resolution=self.image_resolution,
# safe not supported in controlnet_aux v0.0.3
# safe=self.safe,
scribble=self.scribble,
)
processed_image = hed_processor(
image,
detect_resolution=self.detect_resolution,
image_resolution=self.image_resolution,
# safe not supported in controlnet_aux v0.0.3
# safe=self.safe,
scribble=self.scribble,
)
return processed_image
class LineartImageProcessorInvocation(
ImageProcessorInvocation, PILInvocationConfig):
class LineartImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
"""Applies line art processing to image"""
# fmt: off
type: Literal["lineart_image_processor"] = "lineart_image_processor"
# Inputs
@ -319,24 +320,20 @@ class LineartImageProcessorInvocation(
class Config(InvocationConfig):
schema_extra = {
"ui": {
"title": "Lineart Processor",
"tags": ["controlnet", "lineart", "image", "processor"]
},
"ui": {"title": "Lineart Processor", "tags": ["controlnet", "lineart", "image", "processor"]},
}
def run_processor(self, image):
lineart_processor = LineartDetector.from_pretrained(
"lllyasviel/Annotators")
lineart_processor = LineartDetector.from_pretrained("lllyasviel/Annotators")
processed_image = lineart_processor(
image, detect_resolution=self.detect_resolution,
image_resolution=self.image_resolution, coarse=self.coarse)
image, detect_resolution=self.detect_resolution, image_resolution=self.image_resolution, coarse=self.coarse
)
return processed_image
class LineartAnimeImageProcessorInvocation(
ImageProcessorInvocation, PILInvocationConfig):
class LineartAnimeImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
"""Applies line art anime processing to image"""
# fmt: off
type: Literal["lineart_anime_image_processor"] = "lineart_anime_image_processor"
# Inputs
@ -348,23 +345,23 @@ class LineartAnimeImageProcessorInvocation(
schema_extra = {
"ui": {
"title": "Lineart Anime Processor",
"tags": ["controlnet", "lineart", "anime", "image", "processor"]
"tags": ["controlnet", "lineart", "anime", "image", "processor"],
},
}
def run_processor(self, image):
processor = LineartAnimeDetector.from_pretrained(
"lllyasviel/Annotators")
processed_image = processor(image,
detect_resolution=self.detect_resolution,
image_resolution=self.image_resolution,
)
processor = LineartAnimeDetector.from_pretrained("lllyasviel/Annotators")
processed_image = processor(
image,
detect_resolution=self.detect_resolution,
image_resolution=self.image_resolution,
)
return processed_image
class OpenposeImageProcessorInvocation(
ImageProcessorInvocation, PILInvocationConfig):
class OpenposeImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
"""Applies Openpose processing to image"""
# fmt: off
type: Literal["openpose_image_processor"] = "openpose_image_processor"
# Inputs
@ -375,25 +372,23 @@ class OpenposeImageProcessorInvocation(
class Config(InvocationConfig):
schema_extra = {
"ui": {
"title": "Openpose Processor",
"tags": ["controlnet", "openpose", "image", "processor"]
},
"ui": {"title": "Openpose Processor", "tags": ["controlnet", "openpose", "image", "processor"]},
}
def run_processor(self, image):
openpose_processor = OpenposeDetector.from_pretrained(
"lllyasviel/Annotators")
openpose_processor = OpenposeDetector.from_pretrained("lllyasviel/Annotators")
processed_image = openpose_processor(
image, detect_resolution=self.detect_resolution,
image,
detect_resolution=self.detect_resolution,
image_resolution=self.image_resolution,
hand_and_face=self.hand_and_face,)
hand_and_face=self.hand_and_face,
)
return processed_image
class MidasDepthImageProcessorInvocation(
ImageProcessorInvocation, PILInvocationConfig):
class MidasDepthImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
"""Applies Midas depth processing to image"""
# fmt: off
type: Literal["midas_depth_image_processor"] = "midas_depth_image_processor"
# Inputs
@ -405,26 +400,24 @@ class MidasDepthImageProcessorInvocation(
class Config(InvocationConfig):
schema_extra = {
"ui": {
"title": "Midas (Depth) Processor",
"tags": ["controlnet", "midas", "depth", "image", "processor"]
},
"ui": {"title": "Midas (Depth) Processor", "tags": ["controlnet", "midas", "depth", "image", "processor"]},
}
def run_processor(self, image):
midas_processor = MidasDetector.from_pretrained("lllyasviel/Annotators")
processed_image = midas_processor(image,
a=np.pi * self.a_mult,
bg_th=self.bg_th,
# dept_and_normal not supported in controlnet_aux v0.0.3
# depth_and_normal=self.depth_and_normal,
)
processed_image = midas_processor(
image,
a=np.pi * self.a_mult,
bg_th=self.bg_th,
# dept_and_normal not supported in controlnet_aux v0.0.3
# depth_and_normal=self.depth_and_normal,
)
return processed_image
class NormalbaeImageProcessorInvocation(
ImageProcessorInvocation, PILInvocationConfig):
class NormalbaeImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
"""Applies NormalBae processing to image"""
# fmt: off
type: Literal["normalbae_image_processor"] = "normalbae_image_processor"
# Inputs
@ -434,24 +427,20 @@ class NormalbaeImageProcessorInvocation(
class Config(InvocationConfig):
schema_extra = {
"ui": {
"title": "Normal BAE Processor",
"tags": ["controlnet", "normal", "bae", "image", "processor"]
},
"ui": {"title": "Normal BAE Processor", "tags": ["controlnet", "normal", "bae", "image", "processor"]},
}
def run_processor(self, image):
normalbae_processor = NormalBaeDetector.from_pretrained(
"lllyasviel/Annotators")
normalbae_processor = NormalBaeDetector.from_pretrained("lllyasviel/Annotators")
processed_image = normalbae_processor(
image, detect_resolution=self.detect_resolution,
image_resolution=self.image_resolution)
image, detect_resolution=self.detect_resolution, image_resolution=self.image_resolution
)
return processed_image
class MlsdImageProcessorInvocation(
ImageProcessorInvocation, PILInvocationConfig):
class MlsdImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
"""Applies MLSD processing to image"""
# fmt: off
type: Literal["mlsd_image_processor"] = "mlsd_image_processor"
# Inputs
@ -463,24 +452,24 @@ class MlsdImageProcessorInvocation(
class Config(InvocationConfig):
schema_extra = {
"ui": {
"title": "MLSD Processor",
"tags": ["controlnet", "mlsd", "image", "processor"]
},
"ui": {"title": "MLSD Processor", "tags": ["controlnet", "mlsd", "image", "processor"]},
}
def run_processor(self, image):
mlsd_processor = MLSDdetector.from_pretrained("lllyasviel/Annotators")
processed_image = mlsd_processor(
image, detect_resolution=self.detect_resolution,
image_resolution=self.image_resolution, thr_v=self.thr_v,
thr_d=self.thr_d)
image,
detect_resolution=self.detect_resolution,
image_resolution=self.image_resolution,
thr_v=self.thr_v,
thr_d=self.thr_d,
)
return processed_image
class PidiImageProcessorInvocation(
ImageProcessorInvocation, PILInvocationConfig):
class PidiImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
"""Applies PIDI processing to image"""
# fmt: off
type: Literal["pidi_image_processor"] = "pidi_image_processor"
# Inputs
@ -492,25 +481,24 @@ class PidiImageProcessorInvocation(
class Config(InvocationConfig):
schema_extra = {
"ui": {
"title": "PIDI Processor",
"tags": ["controlnet", "pidi", "image", "processor"]
},
"ui": {"title": "PIDI Processor", "tags": ["controlnet", "pidi", "image", "processor"]},
}
def run_processor(self, image):
pidi_processor = PidiNetDetector.from_pretrained(
"lllyasviel/Annotators")
pidi_processor = PidiNetDetector.from_pretrained("lllyasviel/Annotators")
processed_image = pidi_processor(
image, detect_resolution=self.detect_resolution,
image_resolution=self.image_resolution, safe=self.safe,
scribble=self.scribble)
image,
detect_resolution=self.detect_resolution,
image_resolution=self.image_resolution,
safe=self.safe,
scribble=self.scribble,
)
return processed_image
class ContentShuffleImageProcessorInvocation(
ImageProcessorInvocation, PILInvocationConfig):
class ContentShuffleImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
"""Applies content shuffle processing to image"""
# fmt: off
type: Literal["content_shuffle_image_processor"] = "content_shuffle_image_processor"
# Inputs
@ -525,48 +513,45 @@ class ContentShuffleImageProcessorInvocation(
schema_extra = {
"ui": {
"title": "Content Shuffle Processor",
"tags": ["controlnet", "contentshuffle", "image", "processor"]
"tags": ["controlnet", "contentshuffle", "image", "processor"],
},
}
def run_processor(self, image):
content_shuffle_processor = ContentShuffleDetector()
processed_image = content_shuffle_processor(image,
detect_resolution=self.detect_resolution,
image_resolution=self.image_resolution,
h=self.h,
w=self.w,
f=self.f
)
processed_image = content_shuffle_processor(
image,
detect_resolution=self.detect_resolution,
image_resolution=self.image_resolution,
h=self.h,
w=self.w,
f=self.f,
)
return processed_image
# should work with controlnet_aux >= 0.0.4 and timm <= 0.6.13
class ZoeDepthImageProcessorInvocation(
ImageProcessorInvocation, PILInvocationConfig):
class ZoeDepthImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
"""Applies Zoe depth processing to image"""
# fmt: off
type: Literal["zoe_depth_image_processor"] = "zoe_depth_image_processor"
# fmt: on
class Config(InvocationConfig):
schema_extra = {
"ui": {
"title": "Zoe (Depth) Processor",
"tags": ["controlnet", "zoe", "depth", "image", "processor"]
},
"ui": {"title": "Zoe (Depth) Processor", "tags": ["controlnet", "zoe", "depth", "image", "processor"]},
}
def run_processor(self, image):
zoe_depth_processor = ZoeDetector.from_pretrained(
"lllyasviel/Annotators")
zoe_depth_processor = ZoeDetector.from_pretrained("lllyasviel/Annotators")
processed_image = zoe_depth_processor(image)
return processed_image
class MediapipeFaceProcessorInvocation(
ImageProcessorInvocation, PILInvocationConfig):
class MediapipeFaceProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
"""Applies mediapipe face processing to image"""
# fmt: off
type: Literal["mediapipe_face_processor"] = "mediapipe_face_processor"
# Inputs
@ -576,26 +561,22 @@ class MediapipeFaceProcessorInvocation(
class Config(InvocationConfig):
schema_extra = {
"ui": {
"title": "Mediapipe Processor",
"tags": ["controlnet", "mediapipe", "image", "processor"]
},
"ui": {"title": "Mediapipe Processor", "tags": ["controlnet", "mediapipe", "image", "processor"]},
}
def run_processor(self, image):
# MediaPipeFaceDetector throws an error if image has alpha channel
# so convert to RGB if needed
if image.mode == 'RGBA':
image = image.convert('RGB')
if image.mode == "RGBA":
image = image.convert("RGB")
mediapipe_face_processor = MediapipeFaceDetector()
processed_image = mediapipe_face_processor(
image, max_faces=self.max_faces, min_confidence=self.min_confidence)
processed_image = mediapipe_face_processor(image, max_faces=self.max_faces, min_confidence=self.min_confidence)
return processed_image
class LeresImageProcessorInvocation(
ImageProcessorInvocation, PILInvocationConfig):
class LeresImageProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
"""Applies leres processing to image"""
# fmt: off
type: Literal["leres_image_processor"] = "leres_image_processor"
# Inputs
@ -608,24 +589,23 @@ class LeresImageProcessorInvocation(
class Config(InvocationConfig):
schema_extra = {
"ui": {
"title": "Leres (Depth) Processor",
"tags": ["controlnet", "leres", "depth", "image", "processor"]
},
"ui": {"title": "Leres (Depth) Processor", "tags": ["controlnet", "leres", "depth", "image", "processor"]},
}
def run_processor(self, image):
leres_processor = LeresDetector.from_pretrained("lllyasviel/Annotators")
processed_image = leres_processor(
image, thr_a=self.thr_a, thr_b=self.thr_b, boost=self.boost,
image,
thr_a=self.thr_a,
thr_b=self.thr_b,
boost=self.boost,
detect_resolution=self.detect_resolution,
image_resolution=self.image_resolution)
image_resolution=self.image_resolution,
)
return processed_image
class TileResamplerProcessorInvocation(
ImageProcessorInvocation, PILInvocationConfig):
class TileResamplerProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
# fmt: off
type: Literal["tile_image_processor"] = "tile_image_processor"
# Inputs
@ -637,16 +617,17 @@ class TileResamplerProcessorInvocation(
schema_extra = {
"ui": {
"title": "Tile Resample Processor",
"tags": ["controlnet", "tile", "resample", "image", "processor"]
"tags": ["controlnet", "tile", "resample", "image", "processor"],
},
}
# tile_resample copied from sd-webui-controlnet/scripts/processor.py
def tile_resample(self,
np_img: np.ndarray,
res=512, # never used?
down_sampling_rate=1.0,
):
def tile_resample(
self,
np_img: np.ndarray,
res=512, # never used?
down_sampling_rate=1.0,
):
np_img = HWC3(np_img)
if down_sampling_rate < 1.1:
return np_img
@ -658,36 +639,41 @@ class TileResamplerProcessorInvocation(
def run_processor(self, img):
np_img = np.array(img, dtype=np.uint8)
processed_np_image = self.tile_resample(np_img,
# res=self.tile_size,
down_sampling_rate=self.down_sampling_rate
)
processed_np_image = self.tile_resample(
np_img,
# res=self.tile_size,
down_sampling_rate=self.down_sampling_rate,
)
processed_image = Image.fromarray(processed_np_image)
return processed_image
class SegmentAnythingProcessorInvocation(
ImageProcessorInvocation, PILInvocationConfig):
class SegmentAnythingProcessorInvocation(ImageProcessorInvocation, PILInvocationConfig):
"""Applies segment anything processing to image"""
# fmt: off
type: Literal["segment_anything_processor"] = "segment_anything_processor"
# fmt: on
class Config(InvocationConfig):
schema_extra = {"ui": {"title": "Segment Anything Processor", "tags": [
"controlnet", "segment", "anything", "sam", "image", "processor"]}, }
schema_extra = {
"ui": {
"title": "Segment Anything Processor",
"tags": ["controlnet", "segment", "anything", "sam", "image", "processor"],
},
}
def run_processor(self, image):
# segment_anything_processor = SamDetector.from_pretrained("ybelkada/segment-anything", subfolder="checkpoints")
segment_anything_processor = SamDetectorReproducibleColors.from_pretrained(
"ybelkada/segment-anything", subfolder="checkpoints")
"ybelkada/segment-anything", subfolder="checkpoints"
)
np_img = np.array(image, dtype=np.uint8)
processed_image = segment_anything_processor(np_img)
return processed_image
class SamDetectorReproducibleColors(SamDetector):
# overriding SamDetector.show_anns() method to use reproducible colors for segmentation image
# base class show_anns() method randomizes colors,
# which seems to also lead to non-reproducible image generation
@ -695,19 +681,15 @@ class SamDetectorReproducibleColors(SamDetector):
def show_anns(self, anns: List[Dict]):
if len(anns) == 0:
return
sorted_anns = sorted(anns, key=(lambda x: x['area']), reverse=True)
h, w = anns[0]['segmentation'].shape
final_img = Image.fromarray(
np.zeros((h, w, 3), dtype=np.uint8), mode="RGB")
sorted_anns = sorted(anns, key=(lambda x: x["area"]), reverse=True)
h, w = anns[0]["segmentation"].shape
final_img = Image.fromarray(np.zeros((h, w, 3), dtype=np.uint8), mode="RGB")
palette = ade_palette()
for i, ann in enumerate(sorted_anns):
m = ann['segmentation']
m = ann["segmentation"]
img = np.empty((m.shape[0], m.shape[1], 3), dtype=np.uint8)
# doing modulo just in case number of annotated regions exceeds number of colors in palette
ann_color = palette[i % len(palette)]
img[:, :] = ann_color
final_img.paste(
Image.fromarray(img, mode="RGB"),
(0, 0),
Image.fromarray(np.uint8(m * 255)))
final_img.paste(Image.fromarray(img, mode="RGB"), (0, 0), Image.fromarray(np.uint8(m * 255)))
return np.array(final_img, dtype=np.uint8)

5
invokeai/app/invocations/cv.py
View File

@ -37,10 +37,7 @@ class CvInpaintInvocation(BaseInvocation, CvInvocationConfig):
class Config(InvocationConfig):
schema_extra = {
"ui": {
"title": "OpenCV Inpaint",
"tags": ["opencv", "inpaint"]
},
"ui": {"title": "OpenCV Inpaint", "tags": ["opencv", "inpaint"]},
}
def invoke(self, context: InvocationContext) -> ImageOutput:

97
invokeai/app/invocations/generate.py
View File

@ -6,8 +6,7 @@ from typing import Literal, Optional, get_args
import torch
from pydantic import Field
from invokeai.app.models.image import (ColorField, ImageCategory, ImageField,
ResourceOrigin)
from invokeai.app.models.image import ColorField, ImageCategory, ImageField, ResourceOrigin
from invokeai.app.util.misc import SEED_MAX, get_random_seed
from invokeai.backend.generator.inpaint import infill_methods
@ -25,13 +24,12 @@ from contextlib import contextmanager, ExitStack, ContextDecorator
SAMPLER_NAME_VALUES = Literal[tuple(InvokeAIGenerator.schedulers())]
INFILL_METHODS = Literal[tuple(infill_methods())]
DEFAULT_INFILL_METHOD = (
"patchmatch" if "patchmatch" in get_args(INFILL_METHODS) else "tile"
)
DEFAULT_INFILL_METHOD = "patchmatch" if "patchmatch" in get_args(INFILL_METHODS) else "tile"
from .latent import get_scheduler
class OldModelContext(ContextDecorator):
model: StableDiffusionGeneratorPipeline
@ -44,6 +42,7 @@ class OldModelContext(ContextDecorator):
def __exit__(self, *exc):
return False
class OldModelInfo:
name: str
hash: str
@ -64,20 +63,34 @@ class InpaintInvocation(BaseInvocation):
positive_conditioning: Optional[ConditioningField] = Field(description="Positive conditioning for generation")
negative_conditioning: Optional[ConditioningField] = Field(description="Negative conditioning for generation")
seed: int = Field(ge=0, le=SEED_MAX, description="The seed to use (omit for random)", default_factory=get_random_seed)
steps: int = Field(default=30, gt=0, description="The number of steps to use to generate the image")
width: int = Field(default=512, multiple_of=8, gt=0, description="The width of the resulting image", )
height: int = Field(default=512, multiple_of=8, gt=0, description="The height of the resulting image", )
cfg_scale: float = Field(default=7.5, ge=1, description="The Classifier-Free Guidance, higher values may result in a result closer to the prompt", )
scheduler: SAMPLER_NAME_VALUES = Field(default="euler", description="The scheduler to use" )
seed: int = Field(
ge=0, le=SEED_MAX, description="The seed to use (omit for random)", default_factory=get_random_seed
)
steps: int = Field(default=30, gt=0, description="The number of steps to use to generate the image")
width: int = Field(
default=512,
multiple_of=8,
gt=0,
description="The width of the resulting image",
)
height: int = Field(
default=512,
multiple_of=8,
gt=0,
description="The height of the resulting image",
)
cfg_scale: float = Field(
default=7.5,
ge=1,
description="The Classifier-Free Guidance, higher values may result in a result closer to the prompt",
)
scheduler: SAMPLER_NAME_VALUES = Field(default="euler", description="The scheduler to use")
unet: UNetField = Field(default=None, description="UNet model")
vae: VaeField = Field(default=None, description="Vae model")
# Inputs
image: Optional[ImageField] = Field(description="The input image")
strength: float = Field(
default=0.75, gt=0, le=1, description="The strength of the original image"
)
strength: float = Field(default=0.75, gt=0, le=1, description="The strength of the original image")
fit: bool = Field(
default=True,
description="Whether or not the result should be fit to the aspect ratio of the input image",
@ -86,18 +99,10 @@ class InpaintInvocation(BaseInvocation):
# Inputs
mask: Optional[ImageField] = Field(description="The mask")
seam_size: int = Field(default=96, ge=1, description="The seam inpaint size (px)")
seam_blur: int = Field(
default=16, ge=0, description="The seam inpaint blur radius (px)"
)
seam_strength: float = Field(
default=0.75, gt=0, le=1, description="The seam inpaint strength"
)
seam_steps: int = Field(
default=30, ge=1, description="The number of steps to use for seam inpaint"
)
tile_size: int = Field(
default=32, ge=1, description="The tile infill method size (px)"
)
seam_blur: int = Field(default=16, ge=0, description="The seam inpaint blur radius (px)")
seam_strength: float = Field(default=0.75, gt=0, le=1, description="The seam inpaint strength")
seam_steps: int = Field(default=30, ge=1, description="The number of steps to use for seam inpaint")
tile_size: int = Field(default=32, ge=1, description="The tile infill method size (px)")
infill_method: INFILL_METHODS = Field(
default=DEFAULT_INFILL_METHOD,
description="The method used to infill empty regions (px)",
@ -128,10 +133,7 @@ class InpaintInvocation(BaseInvocation):
# Schema customisation
class Config(InvocationConfig):
schema_extra = {
"ui": {
"tags": ["stable-diffusion", "image"],
"title": "Inpaint"
},
"ui": {"tags": ["stable-diffusion", "image"], "title": "Inpaint"},
}
def dispatch_progress(
@ -162,18 +164,23 @@ class InpaintInvocation(BaseInvocation):
def _lora_loader():
for lora in self.unet.loras:
lora_info = context.services.model_manager.get_model(
**lora.dict(exclude={"weight"}), context=context,)
**lora.dict(exclude={"weight"}),
context=context,
)
yield (lora_info.context.model, lora.weight)
del lora_info
return
unet_info = context.services.model_manager.get_model(**self.unet.unet.dict(), context=context,)
vae_info = context.services.model_manager.get_model(**self.vae.vae.dict(), context=context,)
with vae_info as vae,\
ModelPatcher.apply_lora_unet(unet_info.context.model, _lora_loader()),\
unet_info as unet:
unet_info = context.services.model_manager.get_model(
**self.unet.unet.dict(),
context=context,
)
vae_info = context.services.model_manager.get_model(
**self.vae.vae.dict(),
context=context,
)
with vae_info as vae, ModelPatcher.apply_lora_unet(unet_info.context.model, _lora_loader()), unet_info as unet:
device = context.services.model_manager.mgr.cache.execution_device
dtype = context.services.model_manager.mgr.cache.precision
@ -197,21 +204,11 @@ class InpaintInvocation(BaseInvocation):
)
def invoke(self, context: InvocationContext) -> ImageOutput:
image = (
None
if self.image is None
else context.services.images.get_pil_image(self.image.image_name)
)
mask = (
None
if self.mask is None
else context.services.images.get_pil_image(self.mask.image_name)
)
image = None if self.image is None else context.services.images.get_pil_image(self.image.image_name)
mask = None if self.mask is None else context.services.images.get_pil_image(self.mask.image_name)
# Get the source node id (we are invoking the prepared node)
graph_execution_state = context.services.graph_execution_manager.get(
context.graph_execution_state_id
)
graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
source_node_id = graph_execution_state.prepared_source_mapping[self.id]
scheduler = get_scheduler(

136
invokeai/app/invocations/image.py
View File

@ -9,9 +9,13 @@ from pathlib import Path
from typing import Union
from invokeai.app.invocations.metadata import CoreMetadata
from ..models.image import (
ImageCategory, ImageField, ResourceOrigin,
PILInvocationConfig, ImageOutput, MaskOutput,
)
ImageCategory,
ImageField,
ResourceOrigin,
PILInvocationConfig,
ImageOutput,
MaskOutput,
)
from .baseinvocation import (
BaseInvocation,
InvocationContext,
@ -20,6 +24,7 @@ from .baseinvocation import (
from invokeai.backend.image_util.safety_checker import SafetyChecker
from invokeai.backend.image_util.invisible_watermark import InvisibleWatermark
class LoadImageInvocation(BaseInvocation):
"""Load an image and provide it as output."""
@ -34,10 +39,7 @@ class LoadImageInvocation(BaseInvocation):
class Config(InvocationConfig):
schema_extra = {
"ui": {
"title": "Load Image",
"tags": ["image", "load"]
},
"ui": {"title": "Load Image", "tags": ["image", "load"]},
}
def invoke(self, context: InvocationContext) -> ImageOutput:
@ -56,16 +58,11 @@ class ShowImageInvocation(BaseInvocation):
type: Literal["show_image"] = "show_image"
# Inputs
image: Optional[ImageField] = Field(
default=None, description="The image to show"
)
image: Optional[ImageField] = Field(default=None, description="The image to show")
class Config(InvocationConfig):
schema_extra = {
"ui": {
"title": "Show Image",
"tags": ["image", "show"]
},
"ui": {"title": "Show Image", "tags": ["image", "show"]},
}
def invoke(self, context: InvocationContext) -> ImageOutput:
@ -98,18 +95,13 @@ class ImageCropInvocation(BaseInvocation, PILInvocationConfig):
class Config(InvocationConfig):
schema_extra = {
"ui": {
"title": "Crop Image",
"tags": ["image", "crop"]
},
"ui": {"title": "Crop Image", "tags": ["image", "crop"]},
}
def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.services.images.get_pil_image(self.image.image_name)
image_crop = Image.new(
mode="RGBA", size=(self.width, self.height), color=(0, 0, 0, 0)
)
image_crop = Image.new(mode="RGBA", size=(self.width, self.height), color=(0, 0, 0, 0))
image_crop.paste(image, (-self.x, -self.y))
image_dto = context.services.images.create(
@ -144,21 +136,14 @@ class ImagePasteInvocation(BaseInvocation, PILInvocationConfig):
class Config(InvocationConfig):
schema_extra = {
"ui": {
"title": "Paste Image",
"tags": ["image", "paste"]
},
"ui": {"title": "Paste Image", "tags": ["image", "paste"]},
}
def invoke(self, context: InvocationContext) -> ImageOutput:
base_image = context.services.images.get_pil_image(self.base_image.image_name)
image = context.services.images.get_pil_image(self.image.image_name)
mask = (
None
if self.mask is None
else ImageOps.invert(
context.services.images.get_pil_image(self.mask.image_name)
)
None if self.mask is None else ImageOps.invert(context.services.images.get_pil_image(self.mask.image_name))
)
# TODO: probably shouldn't invert mask here... should user be required to do it?
@ -167,9 +152,7 @@ class ImagePasteInvocation(BaseInvocation, PILInvocationConfig):
max_x = max(base_image.width, image.width + self.x)
max_y = max(base_image.height, image.height + self.y)
new_image = Image.new(
mode="RGBA", size=(max_x - min_x, max_y - min_y), color=(0, 0, 0, 0)
)
new_image = Image.new(mode="RGBA", size=(max_x - min_x, max_y - min_y), color=(0, 0, 0, 0))
new_image.paste(base_image, (abs(min_x), abs(min_y)))
new_image.paste(image, (max(0, self.x), max(0, self.y)), mask=mask)
@ -202,10 +185,7 @@ class MaskFromAlphaInvocation(BaseInvocation, PILInvocationConfig):
class Config(InvocationConfig):
schema_extra = {
"ui": {
"title": "Mask From Alpha",
"tags": ["image", "mask", "alpha"]
},
"ui": {"title": "Mask From Alpha", "tags": ["image", "mask", "alpha"]},
}
def invoke(self, context: InvocationContext) -> MaskOutput:
@ -244,10 +224,7 @@ class ImageMultiplyInvocation(BaseInvocation, PILInvocationConfig):
class Config(InvocationConfig):
schema_extra = {
"ui": {
"title": "Multiply Images",
"tags": ["image", "multiply"]
},
"ui": {"title": "Multiply Images", "tags": ["image", "multiply"]},
}
def invoke(self, context: InvocationContext) -> ImageOutput:
@ -288,10 +265,7 @@ class ImageChannelInvocation(BaseInvocation, PILInvocationConfig):
class Config(InvocationConfig):
schema_extra = {
"ui": {
"title": "Image Channel",
"tags": ["image", "channel"]
},
"ui": {"title": "Image Channel", "tags": ["image", "channel"]},
}
def invoke(self, context: InvocationContext) -> ImageOutput:
@ -331,10 +305,7 @@ class ImageConvertInvocation(BaseInvocation, PILInvocationConfig):
class Config(InvocationConfig):
schema_extra = {
"ui": {
"title": "Convert Image",
"tags": ["image", "convert"]
},
"ui": {"title": "Convert Image", "tags": ["image", "convert"]},
}
def invoke(self, context: InvocationContext) -> ImageOutput:
@ -357,6 +328,7 @@ class ImageConvertInvocation(BaseInvocation, PILInvocationConfig):
height=image_dto.height,
)
class ImageBlurInvocation(BaseInvocation, PILInvocationConfig):
"""Blurs an image"""
@ -371,19 +343,14 @@ class ImageBlurInvocation(BaseInvocation, PILInvocationConfig):
class Config(InvocationConfig):
schema_extra = {
"ui": {
"title": "Blur Image",
"tags": ["image", "blur"]
},
"ui": {"title": "Blur Image", "tags": ["image", "blur"]},
}
def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.services.images.get_pil_image(self.image.image_name)
blur = (
ImageFilter.GaussianBlur(self.radius)
if self.blur_type == "gaussian"
else ImageFilter.BoxBlur(self.radius)
ImageFilter.GaussianBlur(self.radius) if self.blur_type == "gaussian" else ImageFilter.BoxBlur(self.radius)
)
blur_image = image.filter(blur)
@ -438,10 +405,7 @@ class ImageResizeInvocation(BaseInvocation, PILInvocationConfig):
class Config(InvocationConfig):
schema_extra = {
"ui": {
"title": "Resize Image",
"tags": ["image", "resize"]
},
"ui": {"title": "Resize Image", "tags": ["image", "resize"]},
}
def invoke(self, context: InvocationContext) -> ImageOutput:
@ -484,10 +448,7 @@ class ImageScaleInvocation(BaseInvocation, PILInvocationConfig):
class Config(InvocationConfig):
schema_extra = {
"ui": {
"title": "Scale Image",
"tags": ["image", "scale"]
},
"ui": {"title": "Scale Image", "tags": ["image", "scale"]},
}
def invoke(self, context: InvocationContext) -> ImageOutput:
@ -532,10 +493,7 @@ class ImageLerpInvocation(BaseInvocation, PILInvocationConfig):
class Config(InvocationConfig):
schema_extra = {
"ui": {
"title": "Image Linear Interpolation",
"tags": ["image", "linear", "interpolation", "lerp"]
},
"ui": {"title": "Image Linear Interpolation", "tags": ["image", "linear", "interpolation", "lerp"]},
}
def invoke(self, context: InvocationContext) -> ImageOutput:
@ -561,6 +519,7 @@ class ImageLerpInvocation(BaseInvocation, PILInvocationConfig):
height=image_dto.height,
)
class ImageInverseLerpInvocation(BaseInvocation, PILInvocationConfig):
"""Inverse linear interpolation of all pixels of an image"""
@ -577,7 +536,7 @@ class ImageInverseLerpInvocation(BaseInvocation, PILInvocationConfig):
schema_extra = {
"ui": {
"title": "Image Inverse Linear Interpolation",
"tags": ["image", "linear", "interpolation", "inverse"]
"tags": ["image", "linear", "interpolation", "inverse"],
},
}
@ -585,12 +544,7 @@ class ImageInverseLerpInvocation(BaseInvocation, PILInvocationConfig):
image = context.services.images.get_pil_image(self.image.image_name)
image_arr = numpy.asarray(image, dtype=numpy.float32)
image_arr = (
numpy.minimum(
numpy.maximum(image_arr - self.min, 0) / float(self.max - self.min), 1
)
* 255
)
image_arr = numpy.minimum(numpy.maximum(image_arr - self.min, 0) / float(self.max - self.min), 1) * 255
ilerp_image = Image.fromarray(numpy.uint8(image_arr))
@ -609,6 +563,7 @@ class ImageInverseLerpInvocation(BaseInvocation, PILInvocationConfig):
height=image_dto.height,
)
class ImageNSFWBlurInvocation(BaseInvocation, PILInvocationConfig):
"""Add blur to NSFW-flagged images"""
@ -622,22 +577,19 @@ class ImageNSFWBlurInvocation(BaseInvocation, PILInvocationConfig):
class Config(InvocationConfig):
schema_extra = {
"ui": {
"title": "Blur NSFW Images",
"tags": ["image", "nsfw", "checker"]
},
"ui": {"title": "Blur NSFW Images", "tags": ["image", "nsfw", "checker"]},
}
def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.services.images.get_pil_image(self.image.image_name)
logger = context.services.logger
logger.debug("Running NSFW checker")
if SafetyChecker.has_nsfw_concept(image):
logger.info("A potentially NSFW image has been detected. Image will be blurred.")
blurry_image = image.filter(filter=ImageFilter.GaussianBlur(radius=32))
caution = self._get_caution_img()
blurry_image.paste(caution,(0,0),caution)
blurry_image.paste(caution, (0, 0), caution)
image = blurry_image
image_dto = context.services.images.create(
@ -649,20 +601,22 @@ class ImageNSFWBlurInvocation(BaseInvocation, PILInvocationConfig):
is_intermediate=self.is_intermediate,
metadata=self.metadata.dict() if self.metadata else None,
)
return ImageOutput(
image=ImageField(image_name=image_dto.image_name),
width=image_dto.width,
height=image_dto.height,
)
def _get_caution_img(self)->Image:
def _get_caution_img(self) -> Image:
import invokeai.app.assets.images as image_assets
caution = Image.open(Path(image_assets.__path__[0]) / 'caution.png')
return caution.resize((caution.width // 2, caution.height //2))
caution = Image.open(Path(image_assets.__path__[0]) / "caution.png")
return caution.resize((caution.width // 2, caution.height // 2))
class ImageWatermarkInvocation(BaseInvocation, PILInvocationConfig):
""" Add an invisible watermark to an image """
"""Add an invisible watermark to an image"""
# fmt: off
type: Literal["img_watermark"] = "img_watermark"
@ -675,10 +629,7 @@ class ImageWatermarkInvocation(BaseInvocation, PILInvocationConfig):
class Config(InvocationConfig):
schema_extra = {
"ui": {
"title": "Add Invisible Watermark",
"tags": ["image", "watermark", "invisible"]
},
"ui": {"title": "Add Invisible Watermark", "tags": ["image", "watermark", "invisible"]},
}
def invoke(self, context: InvocationContext) -> ImageOutput:
@ -699,6 +650,3 @@ class ImageWatermarkInvocation(BaseInvocation, PILInvocationConfig):
width=image_dto.width,
height=image_dto.height,
)

47
invokeai/app/invocations/infill.py
View File

@ -30,9 +30,7 @@ def infill_methods() -> list[str]:
INFILL_METHODS = Literal[tuple(infill_methods())]
DEFAULT_INFILL_METHOD = (
"patchmatch" if "patchmatch" in get_args(INFILL_METHODS) else "tile"
)
DEFAULT_INFILL_METHOD = "patchmatch" if "patchmatch" in get_args(INFILL_METHODS) else "tile"
def infill_patchmatch(im: Image.Image) -> Image.Image:
@ -44,9 +42,7 @@ def infill_patchmatch(im: Image.Image) -> Image.Image:
return im
# Patchmatch (note, we may want to expose patch_size? Increasing it significantly impacts performance though)
im_patched_np = PatchMatch.inpaint(
im.convert("RGB"), ImageOps.invert(im.split()[-1]), patch_size=3
)
im_patched_np = PatchMatch.inpaint(im.convert("RGB"), ImageOps.invert(im.split()[-1]), patch_size=3)
im_patched = Image.fromarray(im_patched_np, mode="RGB")
return im_patched
@ -68,9 +64,7 @@ def get_tile_images(image: np.ndarray, width=8, height=8):
)
def tile_fill_missing(
im: Image.Image, tile_size: int = 16, seed: Optional[int] = None
) -> Image.Image:
def tile_fill_missing(im: Image.Image, tile_size: int = 16, seed: Optional[int] = None) -> Image.Image:
# Only fill if there's an alpha layer
if im.mode != "RGBA":
return im
@ -103,9 +97,7 @@ def tile_fill_missing(
# Find all invalid tiles and replace with a random valid tile
replace_count = (tiles_mask == False).sum()
rng = np.random.default_rng(seed=seed)
tiles_all[np.logical_not(tiles_mask)] = filtered_tiles[
rng.choice(filtered_tiles.shape[0], replace_count), :, :, :
]
tiles_all[np.logical_not(tiles_mask)] = filtered_tiles[rng.choice(filtered_tiles.shape[0], replace_count), :, :, :]
# Convert back to an image
tiles_all = tiles_all.reshape(tshape)
@ -126,9 +118,7 @@ class InfillColorInvocation(BaseInvocation):
"""Infills transparent areas of an image with a solid color"""
type: Literal["infill_rgba"] = "infill_rgba"
image: Optional[ImageField] = Field(
default=None, description="The image to infill"
)
image: Optional[ImageField] = Field(default=None, description="The image to infill")
color: ColorField = Field(
default=ColorField(r=127, g=127, b=127, a=255),
description="The color to use to infill",
@ -136,10 +126,7 @@ class InfillColorInvocation(BaseInvocation):
class Config(InvocationConfig):
schema_extra = {
"ui": {
"title": "Color Infill",
"tags": ["image", "inpaint", "color", "infill"]
},
"ui": {"title": "Color Infill", "tags": ["image", "inpaint", "color", "infill"]},
}
def invoke(self, context: InvocationContext) -> ImageOutput:
@ -171,9 +158,7 @@ class InfillTileInvocation(BaseInvocation):
type: Literal["infill_tile"] = "infill_tile"
image: Optional[ImageField] = Field(
default=None, description="The image to infill"
)
image: Optional[ImageField] = Field(default=None, description="The image to infill")
tile_size: int = Field(default=32, ge=1, description="The tile size (px)")
seed: int = Field(
ge=0,
@ -184,18 +169,13 @@ class InfillTileInvocation(BaseInvocation):
class Config(InvocationConfig):
schema_extra = {
"ui": {
"title": "Tile Infill",
"tags": ["image", "inpaint", "tile", "infill"]
},
"ui": {"title": "Tile Infill", "tags": ["image", "inpaint", "tile", "infill"]},
}
def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.services.images.get_pil_image(self.image.image_name)
infilled = tile_fill_missing(
image.copy(), seed=self.seed, tile_size=self.tile_size
)
infilled = tile_fill_missing(image.copy(), seed=self.seed, tile_size=self.tile_size)
infilled.paste(image, (0, 0), image.split()[-1])
image_dto = context.services.images.create(
@ -219,16 +199,11 @@ class InfillPatchMatchInvocation(BaseInvocation):
type: Literal["infill_patchmatch"] = "infill_patchmatch"
image: Optional[ImageField] = Field(
default=None, description="The image to infill"
)
image: Optional[ImageField] = Field(default=None, description="The image to infill")
class Config(InvocationConfig):
schema_extra = {
"ui": {
"title": "Patch Match Infill",
"tags": ["image", "inpaint", "patchmatch", "infill"]
},
"ui": {"title": "Patch Match Infill", "tags": ["image", "inpaint", "patchmatch", "infill"]},
}
def invoke(self, context: InvocationContext) -> ImageOutput:

219
invokeai/app/invocations/latent.py
View File

@ -17,15 +17,16 @@ from invokeai.backend.model_management.models.base import ModelType
from ...backend.model_management.lora import ModelPatcher
from ...backend.stable_diffusion import PipelineIntermediateState
from ...backend.stable_diffusion.diffusers_pipeline import (
ConditioningData, ControlNetData, StableDiffusionGeneratorPipeline,
image_resized_to_grid_as_tensor)
from ...backend.stable_diffusion.diffusion.shared_invokeai_diffusion import \
PostprocessingSettings
ConditioningData,
ControlNetData,
StableDiffusionGeneratorPipeline,
image_resized_to_grid_as_tensor,
)
from ...backend.stable_diffusion.diffusion.shared_invokeai_diffusion import PostprocessingSettings
from ...backend.stable_diffusion.schedulers import SCHEDULER_MAP
from ...backend.util.devices import choose_torch_device, torch_dtype, choose_precision
from ..models.image import ImageCategory, ImageField, ResourceOrigin
from .baseinvocation import (BaseInvocation, BaseInvocationOutput,
InvocationConfig, InvocationContext)
from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationConfig, InvocationContext
from .compel import ConditioningField
from .controlnet_image_processors import ControlField
from .image import ImageOutput
@ -46,8 +47,7 @@ DEFAULT_PRECISION = choose_precision(choose_torch_device())
class LatentsField(BaseModel):
"""A latents field used for passing latents between invocations"""
latents_name: Optional[str] = Field(
default=None, description="The name of the latents")
latents_name: Optional[str] = Field(default=None, description="The name of the latents")
class Config:
schema_extra = {"required": ["latents_name"]}
@ -55,14 +55,15 @@ class LatentsField(BaseModel):
class LatentsOutput(BaseInvocationOutput):
"""Base class for invocations that output latents"""
#fmt: off
# fmt: off
type: Literal["latents_output"] = "latents_output"
# Inputs
latents: LatentsField = Field(default=None, description="The output latents")
width: int = Field(description="The width of the latents in pixels")
height: int = Field(description="The height of the latents in pixels")
#fmt: on
# fmt: on
def build_latents_output(latents_name: str, latents: torch.Tensor):
@ -73,9 +74,7 @@ def build_latents_output(latents_name: str, latents: torch.Tensor):
)
SAMPLER_NAME_VALUES = Literal[
tuple(list(SCHEDULER_MAP.keys()))
]
SAMPLER_NAME_VALUES = Literal[tuple(list(SCHEDULER_MAP.keys()))]
def get_scheduler(
@ -83,11 +82,10 @@ def get_scheduler(
scheduler_info: ModelInfo,
scheduler_name: str,
) -> Scheduler:
scheduler_class, scheduler_extra_config = SCHEDULER_MAP.get(
scheduler_name, SCHEDULER_MAP['ddim']
)
scheduler_class, scheduler_extra_config = SCHEDULER_MAP.get(scheduler_name, SCHEDULER_MAP["ddim"])
orig_scheduler_info = context.services.model_manager.get_model(
**scheduler_info.dict(), context=context,
**scheduler_info.dict(),
context=context,
)
with orig_scheduler_info as orig_scheduler:
scheduler_config = orig_scheduler.config
@ -102,7 +100,7 @@ def get_scheduler(
scheduler = scheduler_class.from_config(scheduler_config)
# hack copied over from generate.py
if not hasattr(scheduler, 'uses_inpainting_model'):
if not hasattr(scheduler, "uses_inpainting_model"):
scheduler.uses_inpainting_model = lambda: False
return scheduler
@ -123,8 +121,8 @@ class TextToLatentsInvocation(BaseInvocation):
scheduler: SAMPLER_NAME_VALUES = Field(default="euler", description="The scheduler to use" )
unet: UNetField = Field(default=None, description="UNet submodel")
control: Union[ControlField, list[ControlField]] = Field(default=None, description="The control to use")
#seamless: bool = Field(default=False, description="Whether or not to generate an image that can tile without seams", )
#seamless_axes: str = Field(default="", description="The axes to tile the image on, 'x' and/or 'y'")
# seamless: bool = Field(default=False, description="Whether or not to generate an image that can tile without seams", )
# seamless_axes: str = Field(default="", description="The axes to tile the image on, 'x' and/or 'y'")
# fmt: on
@validator("cfg_scale")
@ -133,10 +131,10 @@ class TextToLatentsInvocation(BaseInvocation):
if isinstance(v, list):
for i in v:
if i < 1:
raise ValueError('cfg_scale must be greater than 1')
raise ValueError("cfg_scale must be greater than 1")
else:
if v < 1:
raise ValueError('cfg_scale must be greater than 1')
raise ValueError("cfg_scale must be greater than 1")
return v
# Schema customisation
@ -149,8 +147,8 @@ class TextToLatentsInvocation(BaseInvocation):
"model": "model",
"control": "control",
# "cfg_scale": "float",
"cfg_scale": "number"
}
"cfg_scale": "number",
},
},
}
@ -190,16 +188,14 @@ class TextToLatentsInvocation(BaseInvocation):
threshold=0.0, # threshold,
warmup=0.2, # warmup,
h_symmetry_time_pct=None, # h_symmetry_time_pct,
v_symmetry_time_pct=None # v_symmetry_time_pct,
v_symmetry_time_pct=None, # v_symmetry_time_pct,
),
)
conditioning_data = conditioning_data.add_scheduler_args_if_applicable(
scheduler,
# for ddim scheduler
eta=0.0, # ddim_eta
# for ancestral and sde schedulers
generator=torch.Generator(device=unet.device).manual_seed(0),
)
@ -247,7 +243,6 @@ class TextToLatentsInvocation(BaseInvocation):
exit_stack: ExitStack,
do_classifier_free_guidance: bool = True,
) -> List[ControlNetData]:
# assuming fixed dimensional scaling of 8:1 for image:latents
control_height_resize = latents_shape[2] * 8
control_width_resize = latents_shape[3] * 8
@ -261,7 +256,7 @@ class TextToLatentsInvocation(BaseInvocation):
control_list = control_input
else:
control_list = None
if (control_list is None):
if control_list is None:
control_data = None
# from above handling, any control that is not None should now be of type list[ControlField]
else:
@ -281,9 +276,7 @@ class TextToLatentsInvocation(BaseInvocation):
control_models.append(control_model)
control_image_field = control_info.image
input_image = context.services.images.get_pil_image(
control_image_field.image_name
)
input_image = context.services.images.get_pil_image(control_image_field.image_name)
# self.image.image_type, self.image.image_name
# FIXME: still need to test with different widths, heights, devices, dtypes
# and add in batch_size, num_images_per_prompt?
@ -321,9 +314,7 @@ class TextToLatentsInvocation(BaseInvocation):
noise = context.services.latents.get(self.noise.latents_name)
# Get the source node id (we are invoking the prepared node)
graph_execution_state = context.services.graph_execution_manager.get(
context.graph_execution_state_id
)
graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
source_node_id = graph_execution_state.prepared_source_mapping[self.id]
def step_callback(state: PipelineIntermediateState):
@ -332,19 +323,20 @@ class TextToLatentsInvocation(BaseInvocation):
def _lora_loader():
for lora in self.unet.loras:
lora_info = context.services.model_manager.get_model(
**lora.dict(exclude={"weight"}), context=context,
**lora.dict(exclude={"weight"}),
context=context,
)
yield (lora_info.context.model, lora.weight)
del lora_info
return
unet_info = context.services.model_manager.get_model(
**self.unet.unet.dict(), context=context,
**self.unet.unet.dict(),
context=context,
)
with ExitStack() as exit_stack,\
ModelPatcher.apply_lora_unet(unet_info.context.model, _lora_loader()),\
unet_info as unet:
with ExitStack() as exit_stack, ModelPatcher.apply_lora_unet(
unet_info.context.model, _lora_loader()
), unet_info as unet:
noise = noise.to(device=unet.device, dtype=unet.dtype)
scheduler = get_scheduler(
@ -357,7 +349,9 @@ class TextToLatentsInvocation(BaseInvocation):
conditioning_data = self.get_conditioning_data(context, scheduler, unet)
control_data = self.prep_control_data(
model=pipeline, context=context, control_input=self.control,
model=pipeline,
context=context,
control_input=self.control,
latents_shape=noise.shape,
# do_classifier_free_guidance=(self.cfg_scale >= 1.0))
do_classifier_free_guidance=True,
@ -378,7 +372,7 @@ class TextToLatentsInvocation(BaseInvocation):
result_latents = result_latents.to("cpu")
torch.cuda.empty_cache()
name = f'{context.graph_execution_state_id}__{self.id}'
name = f"{context.graph_execution_state_id}__{self.id}"
context.services.latents.save(name, result_latents)
return build_latents_output(latents_name=name, latents=result_latents)
@ -389,11 +383,8 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
type: Literal["l2l"] = "l2l"
# Inputs
latents: Optional[LatentsField] = Field(
description="The latents to use as a base image")
strength: float = Field(
default=0.7, ge=0, le=1,
description="The strength of the latents to use")
latents: Optional[LatentsField] = Field(description="The latents to use as a base image")
strength: float = Field(default=0.7, ge=0, le=1, description="The strength of the latents to use")
# Schema customisation
class Config(InvocationConfig):
@ -405,7 +396,7 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
"model": "model",
"control": "control",
"cfg_scale": "number",
}
},
},
}
@ -415,9 +406,7 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
latent = context.services.latents.get(self.latents.latents_name)
# Get the source node id (we are invoking the prepared node)
graph_execution_state = context.services.graph_execution_manager.get(
context.graph_execution_state_id
)
graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
source_node_id = graph_execution_state.prepared_source_mapping[self.id]
def step_callback(state: PipelineIntermediateState):
@ -426,19 +415,20 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
def _lora_loader():
for lora in self.unet.loras:
lora_info = context.services.model_manager.get_model(
**lora.dict(exclude={"weight"}), context=context,
**lora.dict(exclude={"weight"}),
context=context,
)
yield (lora_info.context.model, lora.weight)
del lora_info
return
unet_info = context.services.model_manager.get_model(
**self.unet.unet.dict(), context=context,
**self.unet.unet.dict(),
context=context,
)
with ExitStack() as exit_stack,\
ModelPatcher.apply_lora_unet(unet_info.context.model, _lora_loader()),\
unet_info as unet:
with ExitStack() as exit_stack, ModelPatcher.apply_lora_unet(
unet_info.context.model, _lora_loader()
), unet_info as unet:
noise = noise.to(device=unet.device, dtype=unet.dtype)
latent = latent.to(device=unet.device, dtype=unet.dtype)
@ -452,7 +442,9 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
conditioning_data = self.get_conditioning_data(context, scheduler, unet)
control_data = self.prep_control_data(
model=pipeline, context=context, control_input=self.control,
model=pipeline,
context=context,
control_input=self.control,
latents_shape=noise.shape,
# do_classifier_free_guidance=(self.cfg_scale >= 1.0))
do_classifier_free_guidance=True,
@ -460,8 +452,8 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
)
# TODO: Verify the noise is the right size
initial_latents = latent if self.strength < 1.0 else torch.zeros_like(
latent, device=unet.device, dtype=latent.dtype
initial_latents = (
latent if self.strength < 1.0 else torch.zeros_like(latent, device=unet.device, dtype=latent.dtype)
)
timesteps, _ = pipeline.get_img2img_timesteps(
@ -477,14 +469,14 @@ class LatentsToLatentsInvocation(TextToLatentsInvocation):
num_inference_steps=self.steps,
conditioning_data=conditioning_data,
control_data=control_data, # list[ControlNetData]
callback=step_callback
callback=step_callback,
)
# https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
result_latents = result_latents.to("cpu")
torch.cuda.empty_cache()
name = f'{context.graph_execution_state_id}__{self.id}'
name = f"{context.graph_execution_state_id}__{self.id}"
context.services.latents.save(name, result_latents)
return build_latents_output(latents_name=name, latents=result_latents)
@ -496,14 +488,13 @@ class LatentsToImageInvocation(BaseInvocation):
type: Literal["l2i"] = "l2i"
# Inputs
latents: Optional[LatentsField] = Field(
description="The latents to generate an image from")
latents: Optional[LatentsField] = Field(description="The latents to generate an image from")
vae: VaeField = Field(default=None, description="Vae submodel")
tiled: bool = Field(
default=False,
description="Decode latents by overlapping tiles(less memory consumption)")
fp32: bool = Field(DEFAULT_PRECISION=='float32', description="Decode in full precision")
metadata: Optional[CoreMetadata] = Field(default=None, description="Optional core metadata to be written to the image")
tiled: bool = Field(default=False, description="Decode latents by overlapping tiles(less memory consumption)")
fp32: bool = Field(DEFAULT_PRECISION == "float32", description="Decode in full precision")
metadata: Optional[CoreMetadata] = Field(
default=None, description="Optional core metadata to be written to the image"
)
# Schema customisation
class Config(InvocationConfig):
@ -519,7 +510,8 @@ class LatentsToImageInvocation(BaseInvocation):
latents = context.services.latents.get(self.latents.latents_name)
vae_info = context.services.model_manager.get_model(
**self.vae.vae.dict(), context=context,
**self.vae.vae.dict(),
context=context,
)
with vae_info as vae:
@ -586,8 +578,7 @@ class LatentsToImageInvocation(BaseInvocation):
)
LATENTS_INTERPOLATION_MODE = Literal["nearest", "linear",
"bilinear", "bicubic", "trilinear", "area", "nearest-exact"]
LATENTS_INTERPOLATION_MODE = Literal["nearest", "linear", "bilinear", "bicubic", "trilinear", "area", "nearest-exact"]
class ResizeLatentsInvocation(BaseInvocation):
@ -596,36 +587,30 @@ class ResizeLatentsInvocation(BaseInvocation):
type: Literal["lresize"] = "lresize"
# Inputs
latents: Optional[LatentsField] = Field(
description="The latents to resize")
width: Union[int, None] = Field(default=512,
ge=64, multiple_of=8, description="The width to resize to (px)")
height: Union[int, None] = Field(default=512,
ge=64, multiple_of=8, description="The height to resize to (px)")
mode: LATENTS_INTERPOLATION_MODE = Field(
default="bilinear", description="The interpolation mode")
latents: Optional[LatentsField] = Field(description="The latents to resize")
width: Union[int, None] = Field(default=512, ge=64, multiple_of=8, description="The width to resize to (px)")
height: Union[int, None] = Field(default=512, ge=64, multiple_of=8, description="The height to resize to (px)")
mode: LATENTS_INTERPOLATION_MODE = Field(default="bilinear", description="The interpolation mode")
antialias: bool = Field(
default=False,
description="Whether or not to antialias (applied in bilinear and bicubic modes only)")
default=False, description="Whether or not to antialias (applied in bilinear and bicubic modes only)"
)
class Config(InvocationConfig):
schema_extra = {
"ui": {
"title": "Resize Latents",
"tags": ["latents", "resize"]
},
"ui": {"title": "Resize Latents", "tags": ["latents", "resize"]},
}
def invoke(self, context: InvocationContext) -> LatentsOutput:
latents = context.services.latents.get(self.latents.latents_name)
# TODO:
device=choose_torch_device()
device = choose_torch_device()
resized_latents = torch.nn.functional.interpolate(
latents.to(device), size=(self.height // 8, self.width // 8),
mode=self.mode, antialias=self.antialias
if self.mode in ["bilinear", "bicubic"] else False,
latents.to(device),
size=(self.height // 8, self.width // 8),
mode=self.mode,
antialias=self.antialias if self.mode in ["bilinear", "bicubic"] else False,
)
# https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
@ -644,35 +629,30 @@ class ScaleLatentsInvocation(BaseInvocation):
type: Literal["lscale"] = "lscale"
# Inputs
latents: Optional[LatentsField] = Field(
description="The latents to scale")
scale_factor: float = Field(
gt=0, description="The factor by which to scale the latents")
mode: LATENTS_INTERPOLATION_MODE = Field(
default="bilinear", description="The interpolation mode")
latents: Optional[LatentsField] = Field(description="The latents to scale")
scale_factor: float = Field(gt=0, description="The factor by which to scale the latents")
mode: LATENTS_INTERPOLATION_MODE = Field(default="bilinear", description="The interpolation mode")
antialias: bool = Field(
default=False,
description="Whether or not to antialias (applied in bilinear and bicubic modes only)")
default=False, description="Whether or not to antialias (applied in bilinear and bicubic modes only)"
)
class Config(InvocationConfig):
schema_extra = {
"ui": {
"title": "Scale Latents",
"tags": ["latents", "scale"]
},
"ui": {"title": "Scale Latents", "tags": ["latents", "scale"]},
}
def invoke(self, context: InvocationContext) -> LatentsOutput:
latents = context.services.latents.get(self.latents.latents_name)
# TODO:
device=choose_torch_device()
device = choose_torch_device()
# resizing
resized_latents = torch.nn.functional.interpolate(
latents.to(device), scale_factor=self.scale_factor, mode=self.mode,
antialias=self.antialias
if self.mode in ["bilinear", "bicubic"] else False,
latents.to(device),
scale_factor=self.scale_factor,
mode=self.mode,
antialias=self.antialias if self.mode in ["bilinear", "bicubic"] else False,
)
# https://discuss.huggingface.co/t/memory-usage-by-later-pipeline-stages/23699
@ -693,19 +673,13 @@ class ImageToLatentsInvocation(BaseInvocation):
# Inputs
image: Optional[ImageField] = Field(description="The image to encode")
vae: VaeField = Field(default=None, description="Vae submodel")
tiled: bool = Field(
default=False,
description="Encode latents by overlaping tiles(less memory consumption)")
fp32: bool = Field(DEFAULT_PRECISION=='float32', description="Decode in full precision")
tiled: bool = Field(default=False, description="Encode latents by overlaping tiles(less memory consumption)")
fp32: bool = Field(DEFAULT_PRECISION == "float32", description="Decode in full precision")
# Schema customisation
class Config(InvocationConfig):
schema_extra = {
"ui": {
"title": "Image To Latents",
"tags": ["latents", "image"]
},
"ui": {"title": "Image To Latents", "tags": ["latents", "image"]},
}
@torch.no_grad()
@ -715,9 +689,10 @@ class ImageToLatentsInvocation(BaseInvocation):
# )
image = context.services.images.get_pil_image(self.image.image_name)
#vae_info = context.services.model_manager.get_model(**self.vae.vae.dict())
# vae_info = context.services.model_manager.get_model(**self.vae.vae.dict())
vae_info = context.services.model_manager.get_model(
**self.vae.vae.dict(), context=context,
**self.vae.vae.dict(),
context=context,
)
image_tensor = image_resized_to_grid_as_tensor(image.convert("RGB"))
@ -744,12 +719,12 @@ class ImageToLatentsInvocation(BaseInvocation):
vae.post_quant_conv.to(orig_dtype)
vae.decoder.conv_in.to(orig_dtype)
vae.decoder.mid_block.to(orig_dtype)
#else:
# else:
# latents = latents.float()
else:
vae.to(dtype=torch.float16)
#latents = latents.half()
# latents = latents.half()
if self.tiled:
vae.enable_tiling()
@ -760,9 +735,7 @@ class ImageToLatentsInvocation(BaseInvocation):
image_tensor = image_tensor.to(device=vae.device, dtype=vae.dtype)
with torch.inference_mode():
image_tensor_dist = vae.encode(image_tensor).latent_dist
latents = image_tensor_dist.sample().to(
dtype=vae.dtype
) # FIXME: uses torch.randn. make reproducible!
latents = image_tensor_dist.sample().to(dtype=vae.dtype) # FIXME: uses torch.randn. make reproducible!
latents = vae.config.scaling_factor * latents
latents = latents.to(dtype=orig_dtype)

25
invokeai/app/invocations/math.py
View File

@ -54,10 +54,7 @@ class AddInvocation(BaseInvocation, MathInvocationConfig):
class Config(InvocationConfig):
schema_extra = {
"ui": {
"title": "Add",
"tags": ["math", "add"]
},
"ui": {"title": "Add", "tags": ["math", "add"]},
}
def invoke(self, context: InvocationContext) -> IntOutput:
@ -75,10 +72,7 @@ class SubtractInvocation(BaseInvocation, MathInvocationConfig):
class Config(InvocationConfig):
schema_extra = {
"ui": {
"title": "Subtract",
"tags": ["math", "subtract"]
},
"ui": {"title": "Subtract", "tags": ["math", "subtract"]},
}
def invoke(self, context: InvocationContext) -> IntOutput:
@ -96,10 +90,7 @@ class MultiplyInvocation(BaseInvocation, MathInvocationConfig):
class Config(InvocationConfig):
schema_extra = {
"ui": {
"title": "Multiply",
"tags": ["math", "multiply"]
},
"ui": {"title": "Multiply", "tags": ["math", "multiply"]},
}
def invoke(self, context: InvocationContext) -> IntOutput:
@ -117,10 +108,7 @@ class DivideInvocation(BaseInvocation, MathInvocationConfig):
class Config(InvocationConfig):
schema_extra = {
"ui": {
"title": "Divide",
"tags": ["math", "divide"]
},
"ui": {"title": "Divide", "tags": ["math", "divide"]},
}
def invoke(self, context: InvocationContext) -> IntOutput:
@ -140,10 +128,7 @@ class RandomIntInvocation(BaseInvocation):
class Config(InvocationConfig):
schema_extra = {
"ui": {
"title": "Random Integer",
"tags": ["math", "random", "integer"]
},
"ui": {"title": "Random Integer", "tags": ["math", "random", "integer"]},
}
def invoke(self, context: InvocationContext) -> IntOutput:

69
invokeai/app/invocations/metadata.py
View File

@ -11,6 +11,7 @@ from invokeai.app.invocations.baseinvocation import (
from invokeai.app.invocations.controlnet_image_processors import ControlField
from invokeai.app.invocations.model import LoRAModelField, MainModelField, VAEModelField
class LoRAMetadataField(BaseModel):
"""LoRA metadata for an image generated in InvokeAI."""
@ -37,9 +38,7 @@ class CoreMetadata(BaseModel):
description="The number of skipped CLIP layers",
)
model: MainModelField = Field(description="The main model used for inference")
controlnets: list[ControlField] = Field(
description="The ControlNets used for inference"
)
controlnets: list[ControlField] = Field(description="The ControlNets used for inference")
loras: list[LoRAMetadataField] = Field(description="The LoRAs used for inference")
vae: Union[VAEModelField, None] = Field(
default=None,
@ -51,38 +50,24 @@ class CoreMetadata(BaseModel):
default=None,
description="The strength used for latents-to-latents",
)
init_image: Union[str, None] = Field(
default=None, description="The name of the initial image"
)
init_image: Union[str, None] = Field(default=None, description="The name of the initial image")
# SDXL
positive_style_prompt: Union[str, None] = Field(
default=None, description="The positive style prompt parameter"
)
negative_style_prompt: Union[str, None] = Field(
default=None, description="The negative style prompt parameter"
)
positive_style_prompt: Union[str, None] = Field(default=None, description="The positive style prompt parameter")
negative_style_prompt: Union[str, None] = Field(default=None, description="The negative style prompt parameter")
# SDXL Refiner
refiner_model: Union[MainModelField, None] = Field(
default=None, description="The SDXL Refiner model used"
)
refiner_model: Union[MainModelField, None] = Field(default=None, description="The SDXL Refiner model used")
refiner_cfg_scale: Union[float, None] = Field(
default=None,
description="The classifier-free guidance scale parameter used for the refiner",
)
refiner_steps: Union[int, None] = Field(
default=None, description="The number of steps used for the refiner"
)
refiner_scheduler: Union[str, None] = Field(
default=None, description="The scheduler used for the refiner"
)
refiner_steps: Union[int, None] = Field(default=None, description="The number of steps used for the refiner")
refiner_scheduler: Union[str, None] = Field(default=None, description="The scheduler used for the refiner")
refiner_aesthetic_store: Union[float, None] = Field(
default=None, description="The aesthetic score used for the refiner"
)
refiner_start: Union[float, None] = Field(
default=None, description="The start value used for refiner denoising"
)
refiner_start: Union[float, None] = Field(default=None, description="The start value used for refiner denoising")
class ImageMetadata(BaseModel):
@ -92,9 +77,7 @@ class ImageMetadata(BaseModel):
default=None,
description="The image's core metadata, if it was created in the Linear or Canvas UI",
)
graph: Optional[dict] = Field(
default=None, description="The graph that created the image"
)
graph: Optional[dict] = Field(default=None, description="The graph that created the image")
class MetadataAccumulatorOutput(BaseInvocationOutput):
@ -126,50 +109,34 @@ class MetadataAccumulatorInvocation(BaseInvocation):
description="The number of skipped CLIP layers",
)
model: MainModelField = Field(description="The main model used for inference")
controlnets: list[ControlField] = Field(
description="The ControlNets used for inference"
)
controlnets: list[ControlField] = Field(description="The ControlNets used for inference")
loras: list[LoRAMetadataField] = Field(description="The LoRAs used for inference")
strength: Union[float, None] = Field(
default=None,
description="The strength used for latents-to-latents",
)
init_image: Union[str, None] = Field(
default=None, description="The name of the initial image"
)
init_image: Union[str, None] = Field(default=None, description="The name of the initial image")
vae: Union[VAEModelField, None] = Field(
default=None,
description="The VAE used for decoding, if the main model's default was not used",
)
# SDXL
positive_style_prompt: Union[str, None] = Field(
default=None, description="The positive style prompt parameter"
)
negative_style_prompt: Union[str, None] = Field(
default=None, description="The negative style prompt parameter"
)
positive_style_prompt: Union[str, None] = Field(default=None, description="The positive style prompt parameter")
negative_style_prompt: Union[str, None] = Field(default=None, description="The negative style prompt parameter")
# SDXL Refiner
refiner_model: Union[MainModelField, None] = Field(
default=None, description="The SDXL Refiner model used"
)
refiner_model: Union[MainModelField, None] = Field(default=None, description="The SDXL Refiner model used")
refiner_cfg_scale: Union[float, None] = Field(
default=None,
description="The classifier-free guidance scale parameter used for the refiner",
)
refiner_steps: Union[int, None] = Field(
default=None, description="The number of steps used for the refiner"
)
refiner_scheduler: Union[str, None] = Field(
default=None, description="The scheduler used for the refiner"
)
refiner_steps: Union[int, None] = Field(default=None, description="The number of steps used for the refiner")
refiner_scheduler: Union[str, None] = Field(default=None, description="The scheduler used for the refiner")
refiner_aesthetic_store: Union[float, None] = Field(
default=None, description="The aesthetic score used for the refiner"
)
refiner_start: Union[float, None] = Field(
default=None, description="The start value used for refiner denoising"
)
refiner_start: Union[float, None] = Field(default=None, description="The start value used for refiner denoising")
class Config(InvocationConfig):
schema_extra = {

24
invokeai/app/invocations/model.py
View File

@ -4,17 +4,14 @@ from typing import List, Literal, Optional, Union
from pydantic import BaseModel, Field
from ...backend.model_management import BaseModelType, ModelType, SubModelType
from .baseinvocation import (BaseInvocation, BaseInvocationOutput,
InvocationConfig, InvocationContext)
from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationConfig, InvocationContext
class ModelInfo(BaseModel):
model_name: str = Field(description="Info to load submodel")
base_model: BaseModelType = Field(description="Base model")
model_type: ModelType = Field(description="Info to load submodel")
submodel: Optional[SubModelType] = Field(
default=None, description="Info to load submodel"
)
submodel: Optional[SubModelType] = Field(default=None, description="Info to load submodel")
class LoraInfo(ModelInfo):
@ -33,6 +30,7 @@ class ClipField(BaseModel):
skipped_layers: int = Field(description="Number of skipped layers in text_encoder")
loras: List[LoraInfo] = Field(description="Loras to apply on model loading")
class VaeField(BaseModel):
# TODO: better naming?
vae: ModelInfo = Field(description="Info to load vae submodel")
@ -49,6 +47,7 @@ class ModelLoaderOutput(BaseInvocationOutput):
vae: VaeField = Field(default=None, description="Vae submodel")
# fmt: on
class MainModelField(BaseModel):
"""Main model field"""
@ -62,6 +61,7 @@ class LoRAModelField(BaseModel):
model_name: str = Field(description="Name of the LoRA model")
base_model: BaseModelType = Field(description="Base model")
class MainModelLoaderInvocation(BaseInvocation):
"""Loads a main model, outputting its submodels."""
@ -180,7 +180,7 @@ class MainModelLoaderInvocation(BaseInvocation):
),
)
class LoraLoaderOutput(BaseInvocationOutput):
"""Model loader output"""
@ -197,9 +197,7 @@ class LoraLoaderInvocation(BaseInvocation):
type: Literal["lora_loader"] = "lora_loader"
lora: Union[LoRAModelField, None] = Field(
default=None, description="Lora model name"
)
lora: Union[LoRAModelField, None] = Field(default=None, description="Lora model name")
weight: float = Field(default=0.75, description="With what weight to apply lora")
unet: Optional[UNetField] = Field(description="UNet model for applying lora")
@ -228,14 +226,10 @@ class LoraLoaderInvocation(BaseInvocation):
):
raise Exception(f"Unkown lora name: {lora_name}!")
if self.unet is not None and any(
lora.model_name == lora_name for lora in self.unet.loras
):
if self.unet is not None and any(lora.model_name == lora_name for lora in self.unet.loras):
raise Exception(f'Lora "{lora_name}" already applied to unet')
if self.clip is not None and any(
lora.model_name == lora_name for lora in self.clip.loras
):
if self.clip is not None and any(lora.model_name == lora_name for lora in self.clip.loras):
raise Exception(f'Lora "{lora_name}" already applied to clip')
output = LoraLoaderOutput()

83
invokeai/app/invocations/param_easing.py
View File

@ -12,16 +12,37 @@ import matplotlib.pyplot as plt
from easing_functions import (
LinearInOut,
QuadEaseInOut, QuadEaseIn, QuadEaseOut,
CubicEaseInOut, CubicEaseIn, CubicEaseOut,
QuarticEaseInOut, QuarticEaseIn, QuarticEaseOut,
QuinticEaseInOut, QuinticEaseIn, QuinticEaseOut,
SineEaseInOut, SineEaseIn, SineEaseOut,
CircularEaseIn, CircularEaseInOut, CircularEaseOut,
ExponentialEaseInOut, ExponentialEaseIn, ExponentialEaseOut,
ElasticEaseIn, ElasticEaseInOut, ElasticEaseOut,
BackEaseIn, BackEaseInOut, BackEaseOut,
BounceEaseIn, BounceEaseInOut, BounceEaseOut)
QuadEaseInOut,
QuadEaseIn,
QuadEaseOut,
CubicEaseInOut,
CubicEaseIn,
CubicEaseOut,
QuarticEaseInOut,
QuarticEaseIn,
QuarticEaseOut,
QuinticEaseInOut,
QuinticEaseIn,
QuinticEaseOut,
SineEaseInOut,
SineEaseIn,
SineEaseOut,
CircularEaseIn,
CircularEaseInOut,
CircularEaseOut,
ExponentialEaseInOut,
ExponentialEaseIn,
ExponentialEaseOut,
ElasticEaseIn,
ElasticEaseInOut,
ElasticEaseOut,
BackEaseIn,
BackEaseInOut,
BackEaseOut,
BounceEaseIn,
BounceEaseInOut,
BounceEaseOut,
)
from .baseinvocation import (
BaseInvocation,
@ -45,17 +66,12 @@ class FloatLinearRangeInvocation(BaseInvocation):
class Config(InvocationConfig):
schema_extra = {
"ui": {
"title": "Linear Range (Float)",
"tags": ["math", "float", "linear", "range"]
},
"ui": {"title": "Linear Range (Float)", "tags": ["math", "float", "linear", "range"]},
}
def invoke(self, context: InvocationContext) -> FloatCollectionOutput:
param_list = list(np.linspace(self.start, self.stop, self.steps))
return FloatCollectionOutput(
collection=param_list
)
return FloatCollectionOutput(collection=param_list)
EASING_FUNCTIONS_MAP = {
@ -92,9 +108,7 @@ EASING_FUNCTIONS_MAP = {
"BounceInOut": BounceEaseInOut,
}
EASING_FUNCTION_KEYS: Any = Literal[
tuple(list(EASING_FUNCTIONS_MAP.keys()))
]
EASING_FUNCTION_KEYS: Any = Literal[tuple(list(EASING_FUNCTIONS_MAP.keys()))]
# actually I think for now could just use CollectionOutput (which is list[Any]
@ -123,13 +137,9 @@ class StepParamEasingInvocation(BaseInvocation):
class Config(InvocationConfig):
schema_extra = {
"ui": {
"title": "Param Easing By Step",
"tags": ["param", "step", "easing"]
},
"ui": {"title": "Param Easing By Step", "tags": ["param", "step", "easing"]},
}
def invoke(self, context: InvocationContext) -> FloatCollectionOutput:
log_diagnostics = False
# convert from start_step_percent to nearest step <= (steps * start_step_percent)
@ -170,12 +180,13 @@ class StepParamEasingInvocation(BaseInvocation):
# and create reverse copy of list[1:end-1]
# but if even then number_of_steps/2 === ceil(number_of_steps/2), so can just use ceil always
base_easing_duration = int(np.ceil(num_easing_steps/2.0))
if log_diagnostics: context.services.logger.debug("base easing duration: " + str(base_easing_duration))
even_num_steps = (num_easing_steps % 2 == 0) # even number of steps
easing_function = easing_class(start=self.start_value,
end=self.end_value,
duration=base_easing_duration - 1)
base_easing_duration = int(np.ceil(num_easing_steps / 2.0))
if log_diagnostics:
context.services.logger.debug("base easing duration: " + str(base_easing_duration))
even_num_steps = num_easing_steps % 2 == 0 # even number of steps
easing_function = easing_class(
start=self.start_value, end=self.end_value, duration=base_easing_duration - 1
)
base_easing_vals = list()
for step_index in range(base_easing_duration):
easing_val = easing_function.ease(step_index)
@ -214,9 +225,7 @@ class StepParamEasingInvocation(BaseInvocation):
#
else: # no mirroring (default)
easing_function = easing_class(start=self.start_value,
end=self.end_value,
duration=num_easing_steps - 1)
easing_function = easing_class(start=self.start_value, end=self.end_value, duration=num_easing_steps - 1)
for step_index in range(num_easing_steps):
step_val = easing_function.ease(step_index)
easing_list.append(step_val)
@ -240,13 +249,11 @@ class StepParamEasingInvocation(BaseInvocation):
ax = plt.gca()
ax.xaxis.set_major_locator(MaxNLocator(integer=True))
buf = io.BytesIO()
plt.savefig(buf, format='png')
plt.savefig(buf, format="png")
buf.seek(0)
im = PIL.Image.open(buf)
im.show()
buf.close()
# output array of size steps, each entry list[i] is param value for step i
return FloatCollectionOutput(
collection=param_list
)
return FloatCollectionOutput(collection=param_list)

50
invokeai/app/invocations/params.py
View File

@ -4,67 +4,63 @@ from typing import Literal
from pydantic import Field
from .baseinvocation import (BaseInvocation, BaseInvocationOutput,
InvocationConfig, InvocationContext)
from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationConfig, InvocationContext
from .math import FloatOutput, IntOutput
# Pass-through parameter nodes - used by subgraphs
class ParamIntInvocation(BaseInvocation):
"""An integer parameter"""
#fmt: off
# fmt: off
type: Literal["param_int"] = "param_int"
a: int = Field(default=0, description="The integer value")
#fmt: on
# fmt: on
class Config(InvocationConfig):
schema_extra = {
"ui": {
"tags": ["param", "integer"],
"title": "Integer Parameter"
},
}
schema_extra = {
"ui": {"tags": ["param", "integer"], "title": "Integer Parameter"},
}
def invoke(self, context: InvocationContext) -> IntOutput:
return IntOutput(a=self.a)
class ParamFloatInvocation(BaseInvocation):
"""A float parameter"""
#fmt: off
# fmt: off
type: Literal["param_float"] = "param_float"
param: float = Field(default=0.0, description="The float value")
#fmt: on
# fmt: on
class Config(InvocationConfig):
schema_extra = {
"ui": {
"tags": ["param", "float"],
"title": "Float Parameter"
},
}
schema_extra = {
"ui": {"tags": ["param", "float"], "title": "Float Parameter"},
}
def invoke(self, context: InvocationContext) -> FloatOutput:
return FloatOutput(param=self.param)
class StringOutput(BaseInvocationOutput):
"""A string output"""
type: Literal["string_output"] = "string_output"
text: str = Field(default=None, description="The output string")
class ParamStringInvocation(BaseInvocation):
"""A string parameter"""
type: Literal['param_string'] = 'param_string'
text: str = Field(default='', description='The string value')
type: Literal["param_string"] = "param_string"
text: str = Field(default="", description="The string value")
class Config(InvocationConfig):
schema_extra = {
"ui": {
"tags": ["param", "string"],
"title": "String Parameter"
},
}
schema_extra = {
"ui": {"tags": ["param", "string"], "title": "String Parameter"},
}
def invoke(self, context: InvocationContext) -> StringOutput:
return StringOutput(text=self.text)

39
invokeai/app/invocations/prompt.py
View File

@ -7,19 +7,21 @@ from pydantic import Field, validator
from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationConfig, InvocationContext
from dynamicprompts.generators import RandomPromptGenerator, CombinatorialPromptGenerator
class PromptOutput(BaseInvocationOutput):
"""Base class for invocations that output a prompt"""
#fmt: off
# fmt: off
type: Literal["prompt"] = "prompt"
prompt: str = Field(default=None, description="The output prompt")
#fmt: on
# fmt: on
class Config:
schema_extra = {
'required': [
'type',
'prompt',
"required": [
"type",
"prompt",
]
}
@ -44,16 +46,11 @@ class DynamicPromptInvocation(BaseInvocation):
type: Literal["dynamic_prompt"] = "dynamic_prompt"
prompt: str = Field(description="The prompt to parse with dynamicprompts")
max_prompts: int = Field(default=1, description="The number of prompts to generate")
combinatorial: bool = Field(
default=False, description="Whether to use the combinatorial generator"
)
combinatorial: bool = Field(default=False, description="Whether to use the combinatorial generator")
class Config(InvocationConfig):
schema_extra = {
"ui": {
"title": "Dynamic Prompt",
"tags": ["prompt", "dynamic"]
},
"ui": {"title": "Dynamic Prompt", "tags": ["prompt", "dynamic"]},
}
def invoke(self, context: InvocationContext) -> PromptCollectionOutput:
@ -65,10 +62,11 @@ class DynamicPromptInvocation(BaseInvocation):
prompts = generator.generate(self.prompt, num_images=self.max_prompts)
return PromptCollectionOutput(prompt_collection=prompts, count=len(prompts))
class PromptsFromFileInvocation(BaseInvocation):
'''Loads prompts from a text file'''
"""Loads prompts from a text file"""
# fmt: off
type: Literal['prompt_from_file'] = 'prompt_from_file'
@ -78,14 +76,11 @@ class PromptsFromFileInvocation(BaseInvocation):
post_prompt: Optional[str] = Field(description="String to append to each prompt")
start_line: int = Field(default=1, ge=1, description="Line in the file to start start from")
max_prompts: int = Field(default=1, ge=0, description="Max lines to read from file (0=all)")
#fmt: on
# fmt: on
class Config(InvocationConfig):
schema_extra = {
"ui": {
"title": "Prompts From File",
"tags": ["prompt", "file"]
},
"ui": {"title": "Prompts From File", "tags": ["prompt", "file"]},
}
@validator("file_path")
@ -103,11 +98,13 @@ class PromptsFromFileInvocation(BaseInvocation):
with open(file_path) as f:
for i, line in enumerate(f):
if i >= start_line and i < end_line:
prompts.append((pre_prompt or '') + line.strip() + (post_prompt or ''))
prompts.append((pre_prompt or "") + line.strip() + (post_prompt or ""))
if i >= end_line:
break
return prompts
def invoke(self, context: InvocationContext) -> PromptCollectionOutput:
prompts = self.promptsFromFile(self.file_path, self.pre_prompt, self.post_prompt, self.start_line, self.max_prompts)
prompts = self.promptsFromFile(
self.file_path, self.pre_prompt, self.post_prompt, self.start_line, self.max_prompts
)
return PromptCollectionOutput(prompt_collection=prompts, count=len(prompts))

157
invokeai/app/invocations/sdxl.py
View File

@ -7,13 +7,13 @@ from pydantic import Field, validator
from ...backend.model_management import ModelType, SubModelType
from invokeai.app.util.step_callback import stable_diffusion_xl_step_callback
from .baseinvocation import (BaseInvocation, BaseInvocationOutput,
InvocationConfig, InvocationContext)
from .baseinvocation import BaseInvocation, BaseInvocationOutput, InvocationConfig, InvocationContext
from .model import UNetField, ClipField, VaeField, MainModelField, ModelInfo
from .compel import ConditioningField
from .latent import LatentsField, SAMPLER_NAME_VALUES, LatentsOutput, get_scheduler, build_latents_output
class SDXLModelLoaderOutput(BaseInvocationOutput):
"""SDXL base model loader output"""
@ -26,16 +26,19 @@ class SDXLModelLoaderOutput(BaseInvocationOutput):
vae: VaeField = Field(default=None, description="Vae submodel")
# fmt: on
class SDXLRefinerModelLoaderOutput(BaseInvocationOutput):
"""SDXL refiner model loader output"""
# fmt: off
type: Literal["sdxl_refiner_model_loader_output"] = "sdxl_refiner_model_loader_output"
unet: UNetField = Field(default=None, description="UNet submodel")
clip2: ClipField = Field(default=None, description="Tokenizer and text_encoder submodels")
vae: VaeField = Field(default=None, description="Vae submodel")
# fmt: on
#fmt: on
# fmt: on
class SDXLModelLoaderInvocation(BaseInvocation):
"""Loads an sdxl base model, outputting its submodels."""
@ -125,8 +128,10 @@ class SDXLModelLoaderInvocation(BaseInvocation):
),
)
class SDXLRefinerModelLoaderInvocation(BaseInvocation):
"""Loads an sdxl refiner model, outputting its submodels."""
type: Literal["sdxl_refiner_model_loader"] = "sdxl_refiner_model_loader"
model: MainModelField = Field(description="The model to load")
@ -196,7 +201,8 @@ class SDXLRefinerModelLoaderInvocation(BaseInvocation):
),
),
)
# Text to image
class SDXLTextToLatentsInvocation(BaseInvocation):
"""Generates latents from conditionings."""
@ -213,9 +219,9 @@ class SDXLTextToLatentsInvocation(BaseInvocation):
scheduler: SAMPLER_NAME_VALUES = Field(default="euler", description="The scheduler to use" )
unet: UNetField = Field(default=None, description="UNet submodel")
denoising_end: float = Field(default=1.0, gt=0, le=1, description="")
#control: Union[ControlField, list[ControlField]] = Field(default=None, description="The control to use")
#seamless: bool = Field(default=False, description="Whether or not to generate an image that can tile without seams", )
#seamless_axes: str = Field(default="", description="The axes to tile the image on, 'x' and/or 'y'")
# control: Union[ControlField, list[ControlField]] = Field(default=None, description="The control to use")
# seamless: bool = Field(default=False, description="Whether or not to generate an image that can tile without seams", )
# seamless_axes: str = Field(default="", description="The axes to tile the image on, 'x' and/or 'y'")
# fmt: on
@validator("cfg_scale")
@ -224,10 +230,10 @@ class SDXLTextToLatentsInvocation(BaseInvocation):
if isinstance(v, list):
for i in v:
if i < 1:
raise ValueError('cfg_scale must be greater than 1')
raise ValueError("cfg_scale must be greater than 1")
else:
if v < 1:
raise ValueError('cfg_scale must be greater than 1')
raise ValueError("cfg_scale must be greater than 1")
return v
# Schema customisation
@ -237,10 +243,10 @@ class SDXLTextToLatentsInvocation(BaseInvocation):
"title": "SDXL Text To Latents",
"tags": ["latents"],
"type_hints": {
"model": "model",
# "cfg_scale": "float",
"cfg_scale": "number"
}
"model": "model",
# "cfg_scale": "float",
"cfg_scale": "number",
},
},
}
@ -265,9 +271,7 @@ class SDXLTextToLatentsInvocation(BaseInvocation):
# https://github.com/huggingface/diffusers/blob/3ebbaf7c96801271f9e6c21400033b6aa5ffcf29/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion.py#L375
@torch.no_grad()
def invoke(self, context: InvocationContext) -> LatentsOutput:
graph_execution_state = context.services.graph_execution_manager.get(
context.graph_execution_state_id
)
graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
source_node_id = graph_execution_state.prepared_source_mapping[self.id]
latents = context.services.latents.get(self.noise.latents_name)
@ -293,14 +297,10 @@ class SDXLTextToLatentsInvocation(BaseInvocation):
latents = latents * scheduler.init_noise_sigma
unet_info = context.services.model_manager.get_model(
**self.unet.unet.dict(), context=context
)
unet_info = context.services.model_manager.get_model(**self.unet.unet.dict(), context=context)
do_classifier_free_guidance = True
cross_attention_kwargs = None
with unet_info as unet:
extra_step_kwargs = dict()
if "eta" in set(inspect.signature(scheduler.step).parameters.keys()):
extra_step_kwargs.update(
@ -350,10 +350,10 @@ class SDXLTextToLatentsInvocation(BaseInvocation):
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + self.cfg_scale * (noise_pred_text - noise_pred_uncond)
#del noise_pred_uncond
#del noise_pred_text
# del noise_pred_uncond
# del noise_pred_text
#if do_classifier_free_guidance and guidance_rescale > 0.0:
# if do_classifier_free_guidance and guidance_rescale > 0.0:
# # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
# noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
@ -364,7 +364,7 @@ class SDXLTextToLatentsInvocation(BaseInvocation):
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % scheduler.order == 0):
progress_bar.update()
self.dispatch_progress(context, source_node_id, latents, i, num_inference_steps)
#if callback is not None and i % callback_steps == 0:
# if callback is not None and i % callback_steps == 0:
# callback(i, t, latents)
else:
negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.to(device=unet.device, dtype=unet.dtype)
@ -378,13 +378,13 @@ class SDXLTextToLatentsInvocation(BaseInvocation):
with tqdm(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# expand the latents if we are doing classifier free guidance
#latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
# latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = scheduler.scale_model_input(latents, t)
#import gc
#gc.collect()
#torch.cuda.empty_cache()
# import gc
# gc.collect()
# torch.cuda.empty_cache()
# predict the noise residual
@ -411,42 +411,41 @@ class SDXLTextToLatentsInvocation(BaseInvocation):
# perform guidance
noise_pred = noise_pred_uncond + self.cfg_scale * (noise_pred_text - noise_pred_uncond)
#del noise_pred_text
#del noise_pred_uncond
#import gc
#gc.collect()
#torch.cuda.empty_cache()
# del noise_pred_text
# del noise_pred_uncond
# import gc
# gc.collect()
# torch.cuda.empty_cache()
#if do_classifier_free_guidance and guidance_rescale > 0.0:
# if do_classifier_free_guidance and guidance_rescale > 0.0:
# # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
# noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
# compute the previous noisy sample x_t -> x_t-1
latents = scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
#del noise_pred
#import gc
#gc.collect()
#torch.cuda.empty_cache()
# del noise_pred
# import gc
# gc.collect()
# torch.cuda.empty_cache()
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % scheduler.order == 0):
progress_bar.update()
self.dispatch_progress(context, source_node_id, latents, i, num_inference_steps)
#if callback is not None and i % callback_steps == 0:
# if callback is not None and i % callback_steps == 0:
# callback(i, t, latents)
#################
latents = latents.to("cpu")
torch.cuda.empty_cache()
name = f'{context.graph_execution_state_id}__{self.id}'
name = f"{context.graph_execution_state_id}__{self.id}"
context.services.latents.save(name, latents)
return build_latents_output(latents_name=name, latents=latents)
class SDXLLatentsToLatentsInvocation(BaseInvocation):
"""Generates latents from conditionings."""
@ -466,9 +465,9 @@ class SDXLLatentsToLatentsInvocation(BaseInvocation):
denoising_start: float = Field(default=0.0, ge=0, le=1, description="")
denoising_end: float = Field(default=1.0, ge=0, le=1, description="")
#control: Union[ControlField, list[ControlField]] = Field(default=None, description="The control to use")
#seamless: bool = Field(default=False, description="Whether or not to generate an image that can tile without seams", )
#seamless_axes: str = Field(default="", description="The axes to tile the image on, 'x' and/or 'y'")
# control: Union[ControlField, list[ControlField]] = Field(default=None, description="The control to use")
# seamless: bool = Field(default=False, description="Whether or not to generate an image that can tile without seams", )
# seamless_axes: str = Field(default="", description="The axes to tile the image on, 'x' and/or 'y'")
# fmt: on
@validator("cfg_scale")
@ -477,10 +476,10 @@ class SDXLLatentsToLatentsInvocation(BaseInvocation):
if isinstance(v, list):
for i in v:
if i < 1:
raise ValueError('cfg_scale must be greater than 1')
raise ValueError("cfg_scale must be greater than 1")
else:
if v < 1:
raise ValueError('cfg_scale must be greater than 1')
raise ValueError("cfg_scale must be greater than 1")
return v
# Schema customisation
@ -490,10 +489,10 @@ class SDXLLatentsToLatentsInvocation(BaseInvocation):
"title": "SDXL Latents to Latents",
"tags": ["latents"],
"type_hints": {
"model": "model",
# "cfg_scale": "float",
"cfg_scale": "number"
}
"model": "model",
# "cfg_scale": "float",
"cfg_scale": "number",
},
},
}
@ -518,9 +517,7 @@ class SDXLLatentsToLatentsInvocation(BaseInvocation):
# https://github.com/huggingface/diffusers/blob/3ebbaf7c96801271f9e6c21400033b6aa5ffcf29/src/diffusers/pipelines/stable_diffusion/pipeline_onnx_stable_diffusion.py#L375
@torch.no_grad()
def invoke(self, context: InvocationContext) -> LatentsOutput:
graph_execution_state = context.services.graph_execution_manager.get(
context.graph_execution_state_id
)
graph_execution_state = context.services.graph_execution_manager.get(context.graph_execution_state_id)
source_node_id = graph_execution_state.prepared_source_mapping[self.id]
latents = context.services.latents.get(self.latents.latents_name)
@ -545,7 +542,7 @@ class SDXLLatentsToLatentsInvocation(BaseInvocation):
scheduler.set_timesteps(num_inference_steps)
t_start = int(round(self.denoising_start * num_inference_steps))
timesteps = scheduler.timesteps[t_start * scheduler.order:]
timesteps = scheduler.timesteps[t_start * scheduler.order :]
num_inference_steps = num_inference_steps - t_start
# apply noise(if provided)
@ -555,12 +552,12 @@ class SDXLLatentsToLatentsInvocation(BaseInvocation):
del noise
unet_info = context.services.model_manager.get_model(
**self.unet.unet.dict(), context=context,
**self.unet.unet.dict(),
context=context,
)
do_classifier_free_guidance = True
cross_attention_kwargs = None
with unet_info as unet:
# apply scheduler extra args
extra_step_kwargs = dict()
if "eta" in set(inspect.signature(scheduler.step).parameters.keys()):
@ -611,10 +608,10 @@ class SDXLLatentsToLatentsInvocation(BaseInvocation):
if do_classifier_free_guidance:
noise_pred_uncond, noise_pred_text = noise_pred.chunk(2)
noise_pred = noise_pred_uncond + self.cfg_scale * (noise_pred_text - noise_pred_uncond)
#del noise_pred_uncond
#del noise_pred_text
# del noise_pred_uncond
# del noise_pred_text
#if do_classifier_free_guidance and guidance_rescale > 0.0:
# if do_classifier_free_guidance and guidance_rescale > 0.0:
# # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
# noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
@ -625,7 +622,7 @@ class SDXLLatentsToLatentsInvocation(BaseInvocation):
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % scheduler.order == 0):
progress_bar.update()
self.dispatch_progress(context, source_node_id, latents, i, num_inference_steps)
#if callback is not None and i % callback_steps == 0:
# if callback is not None and i % callback_steps == 0:
# callback(i, t, latents)
else:
negative_pooled_prompt_embeds = negative_pooled_prompt_embeds.to(device=unet.device, dtype=unet.dtype)
@ -639,13 +636,13 @@ class SDXLLatentsToLatentsInvocation(BaseInvocation):
with tqdm(total=num_inference_steps) as progress_bar:
for i, t in enumerate(timesteps):
# expand the latents if we are doing classifier free guidance
#latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
# latent_model_input = torch.cat([latents] * 2) if do_classifier_free_guidance else latents
latent_model_input = scheduler.scale_model_input(latents, t)
#import gc
#gc.collect()
#torch.cuda.empty_cache()
# import gc
# gc.collect()
# torch.cuda.empty_cache()
# predict the noise residual
@ -672,38 +669,36 @@ class SDXLLatentsToLatentsInvocation(BaseInvocation):
# perform guidance
noise_pred = noise_pred_uncond + self.cfg_scale * (noise_pred_text - noise_pred_uncond)
#del noise_pred_text
#del noise_pred_uncond
#import gc
#gc.collect()
#torch.cuda.empty_cache()
# del noise_pred_text
# del noise_pred_uncond
# import gc
# gc.collect()
# torch.cuda.empty_cache()
#if do_classifier_free_guidance and guidance_rescale > 0.0:
# if do_classifier_free_guidance and guidance_rescale > 0.0:
# # Based on 3.4. in https://arxiv.org/pdf/2305.08891.pdf
# noise_pred = rescale_noise_cfg(noise_pred, noise_pred_text, guidance_rescale=guidance_rescale)
# compute the previous noisy sample x_t -> x_t-1
latents = scheduler.step(noise_pred, t, latents, **extra_step_kwargs, return_dict=False)[0]
#del noise_pred
#import gc
#gc.collect()
#torch.cuda.empty_cache()
# del noise_pred
# import gc
# gc.collect()
# torch.cuda.empty_cache()
# call the callback, if provided
if i == len(timesteps) - 1 or ((i + 1) > num_warmup_steps and (i + 1) % scheduler.order == 0):
progress_bar.update()
self.dispatch_progress(context, source_node_id, latents, i, num_inference_steps)
#if callback is not None and i % callback_steps == 0:
# if callback is not None and i % callback_steps == 0:
# callback(i, t, latents)
#################
latents = latents.to("cpu")
torch.cuda.empty_cache()
name = f'{context.graph_execution_state_id}__{self.id}'
name = f"{context.graph_execution_state_id}__{self.id}"
context.services.latents.save(name, latents)
return build_latents_output(latents_name=name, latents=latents)

13
invokeai/app/invocations/upscale.py
View File

@ -29,16 +29,11 @@ class ESRGANInvocation(BaseInvocation):
type: Literal["esrgan"] = "esrgan"
image: Union[ImageField, None] = Field(default=None, description="The input image")
model_name: ESRGAN_MODELS = Field(
default="RealESRGAN_x4plus.pth", description="The Real-ESRGAN model to use"
)
model_name: ESRGAN_MODELS = Field(default="RealESRGAN_x4plus.pth", description="The Real-ESRGAN model to use")
class Config(InvocationConfig):
schema_extra = {
"ui": {
"title": "Upscale (RealESRGAN)",
"tags": ["image", "upscale", "realesrgan"]
},
"ui": {"title": "Upscale (RealESRGAN)", "tags": ["image", "upscale", "realesrgan"]},
}
def invoke(self, context: InvocationContext) -> ImageOutput:
@ -108,9 +103,7 @@ class ESRGANInvocation(BaseInvocation):
upscaled_image, img_mode = upsampler.enhance(cv_image)
# back to PIL
pil_image = Image.fromarray(
cv.cvtColor(upscaled_image, cv.COLOR_BGR2RGB)
).convert("RGBA")
pil_image = Image.fromarray(cv.cvtColor(upscaled_image, cv.COLOR_BGR2RGB)).convert("RGBA")
image_dto = context.services.images.create(
image=pil_image,

1
invokeai/app/models/exceptions.py
View File

@ -1,3 +1,4 @@
class CanceledException(Exception):
"""Execution canceled by user."""
pass

6
invokeai/app/models/image.py
View File

@ -8,6 +8,7 @@ from ..invocations.baseinvocation import (
InvocationConfig,
)
class ImageField(BaseModel):
"""An image field used for passing image objects between invocations"""
@ -34,6 +35,7 @@ class ProgressImage(BaseModel):
height: int = Field(description="The effective height of the image in pixels")
dataURL: str = Field(description="The image data as a b64 data URL")
class PILInvocationConfig(BaseModel):
"""Helper class to provide all PIL invocations with additional config"""
@ -44,6 +46,7 @@ class PILInvocationConfig(BaseModel):
},
}
class ImageOutput(BaseInvocationOutput):
"""Base class for invocations that output an image"""
@ -76,6 +79,7 @@ class MaskOutput(BaseInvocationOutput):
]
}
class ResourceOrigin(str, Enum, metaclass=MetaEnum):
"""The origin of a resource (eg image).
@ -132,5 +136,3 @@ class InvalidImageCategoryException(ValueError):
def __init__(self, message="Invalid image category."):
super().__init__(message)

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

@ -207,9 +207,7 @@ class SqliteBoardImageRecordStorage(BoardImageRecordStorageBase):
raise e
finally:
self._lock.release()
return OffsetPaginatedResults(
items=images, offset=offset, limit=limit, total=count
)
return OffsetPaginatedResults(items=images, offset=offset, limit=limit, total=count)
def get_all_board_image_names_for_board(self, board_id: str) -> list[str]:
try:

8
invokeai/app/services/board_images.py
View File

@ -102,9 +102,7 @@ class BoardImagesService(BoardImagesServiceABC):
self,
board_id: str,
) -> list[str]:
return self._services.board_image_records.get_all_board_image_names_for_board(
board_id
)
return self._services.board_image_records.get_all_board_image_names_for_board(board_id)
def get_board_for_image(
self,
@ -114,9 +112,7 @@ class BoardImagesService(BoardImagesServiceABC):
return board_id
def board_record_to_dto(
board_record: BoardRecord, cover_image_name: Optional[str], image_count: int
) -> BoardDTO:
def board_record_to_dto(board_record: BoardRecord, cover_image_name: Optional[str], image_count: int) -> BoardDTO:
"""Converts a board record to a board DTO."""
return BoardDTO(
**board_record.dict(exclude={"cover_image_name"}),

8
invokeai/app/services/board_record_storage.py
View File

@ -15,9 +15,7 @@ from pydantic import BaseModel, Field, Extra
class BoardChanges(BaseModel, extra=Extra.forbid):
board_name: Optional[str] = Field(description="The board's new name.")
cover_image_name: Optional[str] = Field(
description="The name of the board's new cover image."
)
cover_image_name: Optional[str] = Field(description="The name of the board's new cover image.")
class BoardRecordNotFoundException(Exception):
@ -292,9 +290,7 @@ class SqliteBoardRecordStorage(BoardRecordStorageBase):
count = cast(int, self._cursor.fetchone()[0])
return OffsetPaginatedResults[BoardRecord](
items=boards, offset=offset, limit=limit, total=count
)
return OffsetPaginatedResults[BoardRecord](items=boards, offset=offset, limit=limit, total=count)
except sqlite3.Error as e:
self._conn.rollback()

42
invokeai/app/services/boards.py
View File

@ -108,16 +108,12 @@ class BoardService(BoardServiceABC):
def get_dto(self, board_id: str) -> BoardDTO:
board_record = self._services.board_records.get(board_id)
cover_image = self._services.image_records.get_most_recent_image_for_board(
board_record.board_id
)
cover_image = self._services.image_records.get_most_recent_image_for_board(board_record.board_id)
if cover_image:
cover_image_name = cover_image.image_name
else:
cover_image_name = None
image_count = self._services.board_image_records.get_image_count_for_board(
board_id
)
image_count = self._services.board_image_records.get_image_count_for_board(board_id)
return board_record_to_dto(board_record, cover_image_name, image_count)
def update(
@ -126,60 +122,44 @@ class BoardService(BoardServiceABC):
changes: BoardChanges,
) -> BoardDTO:
board_record = self._services.board_records.update(board_id, changes)
cover_image = self._services.image_records.get_most_recent_image_for_board(
board_record.board_id
)
cover_image = self._services.image_records.get_most_recent_image_for_board(board_record.board_id)
if cover_image:
cover_image_name = cover_image.image_name
else:
cover_image_name = None
image_count = self._services.board_image_records.get_image_count_for_board(
board_id
)
image_count = self._services.board_image_records.get_image_count_for_board(board_id)
return board_record_to_dto(board_record, cover_image_name, image_count)
def delete(self, board_id: str) -> None:
self._services.board_records.delete(board_id)
def get_many(
self, offset: int = 0, limit: int = 10
) -> OffsetPaginatedResults[BoardDTO]:
def get_many(self, offset: int = 0, limit: int = 10) -> OffsetPaginatedResults[BoardDTO]:
board_records = self._services.board_records.get_many(offset, limit)
board_dtos = []
for r in board_records.items:
cover_image = self._services.image_records.get_most_recent_image_for_board(
r.board_id
)
cover_image = self._services.image_records.get_most_recent_image_for_board(r.board_id)
if cover_image:
cover_image_name = cover_image.image_name
else:
cover_image_name = None
image_count = self._services.board_image_records.get_image_count_for_board(
r.board_id
)
image_count = self._services.board_image_records.get_image_count_for_board(r.board_id)
board_dtos.append(board_record_to_dto(r, cover_image_name, image_count))
return OffsetPaginatedResults[BoardDTO](
items=board_dtos, offset=offset, limit=limit, total=len(board_dtos)
)
return OffsetPaginatedResults[BoardDTO](items=board_dtos, offset=offset, limit=limit, total=len(board_dtos))
def get_all(self) -> list[BoardDTO]:
board_records = self._services.board_records.get_all()
board_dtos = []
for r in board_records:
cover_image = self._services.image_records.get_most_recent_image_for_board(
r.board_id
)
cover_image = self._services.image_records.get_most_recent_image_for_board(r.board_id)
if cover_image:
cover_image_name = cover_image.image_name
else:
cover_image_name = None
image_count = self._services.board_image_records.get_image_count_for_board(
r.board_id
)
image_count = self._services.board_image_records.get_image_count_for_board(r.board_id)
board_dtos.append(board_record_to_dto(r, cover_image_name, image_count))
return board_dtos
return board_dtos

243
invokeai/app/services/config.py
View File

@ -1,6 +1,6 @@
# Copyright (c) 2023 Lincoln Stein (https://github.com/lstein) and the InvokeAI Development Team
'''Invokeai configuration system.
"""Invokeai configuration system.
Arguments and fields are taken from the pydantic definition of the
model. Defaults can be set by creating a yaml configuration file that
@ -158,7 +158,7 @@ two configs are kept in separate sections of the config file:
outdir: outputs
...
'''
"""
from __future__ import annotations
import argparse
import pydoc
@ -170,64 +170,68 @@ from pathlib import Path
from pydantic import BaseSettings, Field, parse_obj_as
from typing import ClassVar, Dict, List, Set, Literal, Union, get_origin, get_type_hints, get_args
INIT_FILE = Path('invokeai.yaml')
MODEL_CORE = Path('models/core')
DB_FILE = Path('invokeai.db')
LEGACY_INIT_FILE = Path('invokeai.init')
INIT_FILE = Path("invokeai.yaml")
MODEL_CORE = Path("models/core")
DB_FILE = Path("invokeai.db")
LEGACY_INIT_FILE = Path("invokeai.init")
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:]):
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():
setattr(self, name, getattr(opt,name))
setattr(self, name, getattr(opt, name))
def to_yaml(self)->str:
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():
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)
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
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]
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()
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()
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():
for key, value in os.environ.items():
upcase_environ[key.upper()] = value
fields = cls.__fields__
@ -237,8 +241,8 @@ class InvokeAISettings(BaseSettings):
if name not in cls._excluded():
current_default = field.default
category = field.field_info.extra.get("category","Uncategorized")
env_name = env_prefix + '_' + name
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:
@ -248,15 +252,15 @@ class InvokeAISettings(BaseSettings):
field.default = current_default
@classmethod
def cmd_name(self, command_field: str='type')->str:
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'
return "Uncategorized"
@classmethod
def get_parser(cls)->ArgumentParser:
def get_parser(cls) -> ArgumentParser:
parser = PagingArgumentParser(
prog=cls.cmd_name(),
description=cls.__doc__,
@ -269,24 +273,41 @@ class InvokeAISettings(BaseSettings):
parser.add_parser(cls.cmd_name(), help=cls.__doc__)
@classmethod
def _excluded(self)->List[str]:
def _excluded(self) -> List[str]:
# internal fields that shouldn't be exposed as command line options
return ['type','initconf']
return ["type", "initconf"]
@classmethod
def _excluded_from_yaml(self)->List[str]:
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', 'gpu_mem_reserved', 'max_loaded_models', 'version', 'from_file', 'model', 'restore', 'root', 'nsfw_checker']
return [
"type",
"initconf",
"gpu_mem_reserved",
"max_loaded_models",
"version",
"from_file",
"model",
"restore",
"root",
"nsfw_checker",
]
class Config:
env_file_encoding = 'utf-8'
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):
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()
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)
@ -315,10 +336,10 @@ class InvokeAISettings(BaseSettings):
argparse_group.add_argument(
f"--{name}",
dest=name,
nargs='*',
nargs="*",
type=field.type_,
default=default,
action=argparse.BooleanOptionalAction if field.type_==bool else 'store',
action=argparse.BooleanOptionalAction if field.type_ == bool else "store",
help=field.field_info.description,
)
else:
@ -327,31 +348,35 @@ class InvokeAISettings(BaseSettings):
dest=name,
type=field.type_,
default=default,
action=argparse.BooleanOptionalAction if field.type_==bool else 'store',
action=argparse.BooleanOptionalAction if field.type_ == bool else "store",
help=field.field_info.description,
)
def _find_root()->Path:
def _find_root() -> Path:
venv = Path(os.environ.get("VIRTUAL_ENV") or ".")
if os.environ.get("INVOKEAI_ROOT"):
root = Path(os.environ.get("INVOKEAI_ROOT")).resolve()
elif any([(venv.parent/x).exists() for x in [INIT_FILE, LEGACY_INIT_FILE, MODEL_CORE]]):
elif any([(venv.parent / x).exists() for x in [INIT_FILE, LEGACY_INIT_FILE, MODEL_CORE]]):
root = (venv.parent).resolve()
else:
root = Path("~/invokeai").expanduser().resolve()
return root
class InvokeAIAppConfig(InvokeAISettings):
'''
Generate images using Stable Diffusion. Use "invokeai" to launch
the command-line client (recommended for experts only), or
"invokeai-web" to launch the web server. Global options
can be changed by editing the file "INVOKEAI_ROOT/invokeai.yaml" or by
setting environment variables INVOKEAI_<setting>.
'''
"""
Generate images using Stable Diffusion. Use "invokeai" to launch
the command-line client (recommended for experts only), or
"invokeai-web" to launch the web server. Global options
can be changed by editing the file "INVOKEAI_ROOT/invokeai.yaml" or by
setting environment variables INVOKEAI_<setting>.
"""
singleton_config: ClassVar[InvokeAIAppConfig] = None
singleton_init: ClassVar[Dict] = None
#fmt: off
# fmt: off
type: Literal["InvokeAI"] = "InvokeAI"
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')
@ -399,16 +424,16 @@ setting environment variables INVOKEAI_<setting>.
log_level : Literal[tuple(["debug","info","warning","error","critical"])] = Field(default="info", description="Emit logging messages at this level or higher", category="Logging")
version : bool = Field(default=False, description="Show InvokeAI version and exit", category="Other")
#fmt: on
# fmt: on
def parse_args(self, argv: List[str]=None, conf: DictConfig = None, clobber=False):
'''
def parse_args(self, argv: List[str] = None, conf: DictConfig = None, clobber=False):
"""
Update settings with contents of init file, environment, and
command-line settings.
:param conf: alternate Omegaconf dictionary object
:param argv: aternate sys.argv list
:param clobber: ovewrite any initialization parameters passed during initialization
'''
"""
# Set the runtime root directory. We parse command-line switches here
# in order to pick up the --root_dir option.
super().parse_args(argv)
@ -425,135 +450,139 @@ setting environment variables INVOKEAI_<setting>.
if self.singleton_init and not clobber:
hints = get_type_hints(self.__class__)
for k in self.singleton_init:
setattr(self,k,parse_obj_as(hints[k],self.singleton_init[k]))
setattr(self, k, parse_obj_as(hints[k], self.singleton_init[k]))
@classmethod
def get_config(cls,**kwargs)->InvokeAIAppConfig:
'''
def get_config(cls, **kwargs) -> InvokeAIAppConfig:
"""
This returns a singleton InvokeAIAppConfig configuration object.
'''
if cls.singleton_config is None \
or type(cls.singleton_config)!=cls \
or (kwargs and cls.singleton_init != kwargs):
"""
if (
cls.singleton_config is None
or type(cls.singleton_config) != cls
or (kwargs and cls.singleton_init != kwargs)
):
cls.singleton_config = cls(**kwargs)
cls.singleton_init = kwargs
return cls.singleton_config
@property
def root_path(self)->Path:
'''
def root_path(self) -> Path:
"""
Path to the runtime root directory
'''
"""
if self.root:
return Path(self.root).expanduser().absolute()
else:
return self.find_root()
@property
def root_dir(self)->Path:
'''
def root_dir(self) -> Path:
"""
Alias for above.
'''
"""
return self.root_path
def _resolve(self,partial_path:Path)->Path:
def _resolve(self, partial_path: Path) -> Path:
return (self.root_path / partial_path).resolve()
@property
def init_file_path(self)->Path:
'''
def init_file_path(self) -> Path:
"""
Path to invokeai.yaml
'''
"""
return self._resolve(INIT_FILE)
@property
def output_path(self)->Path:
'''
def output_path(self) -> Path:
"""
Path to defaults outputs directory.
'''
"""
return self._resolve(self.outdir)
@property
def db_path(self)->Path:
'''
def db_path(self) -> Path:
"""
Path to the invokeai.db file.
'''
"""
return self._resolve(self.db_dir) / DB_FILE
@property
def model_conf_path(self)->Path:
'''
def model_conf_path(self) -> Path:
"""
Path to models configuration file.
'''
"""
return self._resolve(self.conf_path)
@property
def legacy_conf_path(self)->Path:
'''
def legacy_conf_path(self) -> Path:
"""
Path to directory of legacy configuration files (e.g. v1-inference.yaml)
'''
"""
return self._resolve(self.legacy_conf_dir)
@property
def models_path(self)->Path:
'''
def models_path(self) -> Path:
"""
Path to the models directory
'''
"""
return self._resolve(self.models_dir)
@property
def autoconvert_path(self)->Path:
'''
def autoconvert_path(self) -> Path:
"""
Path to the directory containing models to be imported automatically at startup.
'''
"""
return self._resolve(self.autoconvert_dir) if self.autoconvert_dir else None
# the following methods support legacy calls leftover from the Globals era
@property
def full_precision(self)->bool:
def full_precision(self) -> bool:
"""Return true if precision set to float32"""
return self.precision=='float32'
return self.precision == "float32"
@property
def disable_xformers(self)->bool:
def disable_xformers(self) -> bool:
"""Return true if xformers_enabled is false"""
return not self.xformers_enabled
@property
def try_patchmatch(self)->bool:
def try_patchmatch(self) -> bool:
"""Return true if patchmatch true"""
return self.patchmatch
@property
def nsfw_checker(self)->bool:
""" NSFW node is always active and disabled from Web UIe"""
def nsfw_checker(self) -> bool:
"""NSFW node is always active and disabled from Web UIe"""
return True
@property
def invisible_watermark(self)->bool:
""" invisible watermark node is always active and disabled from Web UIe"""
def invisible_watermark(self) -> bool:
"""invisible watermark node is always active and disabled from Web UIe"""
return True
@staticmethod
def find_root()->Path:
'''
def find_root() -> Path:
"""
Choose the runtime root directory when not specified on command line or
init file.
'''
"""
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:
'''
def get_invokeai_config(**kwargs) -> InvokeAIAppConfig:
"""
Legacy function which returns InvokeAIAppConfig.get_config()
'''
"""
return InvokeAIAppConfig.get_config(**kwargs)

85
invokeai/app/services/default_graphs.py
View File

@ -7,57 +7,80 @@ from .graph import Edge, EdgeConnection, ExposedNodeInput, ExposedNodeOutput, Gr
from .item_storage import ItemStorageABC
default_text_to_image_graph_id = '539b2af5-2b4d-4d8c-8071-e54a3255fc74'
default_text_to_image_graph_id = "539b2af5-2b4d-4d8c-8071-e54a3255fc74"
def create_text_to_image() -> LibraryGraph:
return LibraryGraph(
id=default_text_to_image_graph_id,
name='t2i',
description='Converts text to an image',
name="t2i",
description="Converts text to an image",
graph=Graph(
nodes={
'width': ParamIntInvocation(id='width', a=512),
'height': ParamIntInvocation(id='height', a=512),
'seed': ParamIntInvocation(id='seed', a=-1),
'3': NoiseInvocation(id='3'),
'4': CompelInvocation(id='4'),
'5': CompelInvocation(id='5'),
'6': TextToLatentsInvocation(id='6'),
'7': LatentsToImageInvocation(id='7'),
'8': ImageNSFWBlurInvocation(id='8'),
"width": ParamIntInvocation(id="width", a=512),
"height": ParamIntInvocation(id="height", a=512),
"seed": ParamIntInvocation(id="seed", a=-1),
"3": NoiseInvocation(id="3"),
"4": CompelInvocation(id="4"),
"5": CompelInvocation(id="5"),
"6": TextToLatentsInvocation(id="6"),
"7": LatentsToImageInvocation(id="7"),
"8": ImageNSFWBlurInvocation(id="8"),
},
edges=[
Edge(source=EdgeConnection(node_id='width', field='a'), destination=EdgeConnection(node_id='3', field='width')),
Edge(source=EdgeConnection(node_id='height', field='a'), destination=EdgeConnection(node_id='3', field='height')),
Edge(source=EdgeConnection(node_id='seed', field='a'), destination=EdgeConnection(node_id='3', field='seed')),
Edge(source=EdgeConnection(node_id='3', field='noise'), destination=EdgeConnection(node_id='6', field='noise')),
Edge(source=EdgeConnection(node_id='6', field='latents'), destination=EdgeConnection(node_id='7', field='latents')),
Edge(source=EdgeConnection(node_id='4', field='conditioning'), destination=EdgeConnection(node_id='6', field='positive_conditioning')),
Edge(source=EdgeConnection(node_id='5', field='conditioning'), destination=EdgeConnection(node_id='6', field='negative_conditioning')),
Edge(source=EdgeConnection(node_id='7', field='image'), destination=EdgeConnection(node_id='8', field='image')),
]
Edge(
source=EdgeConnection(node_id="width", field="a"),
destination=EdgeConnection(node_id="3", field="width"),
),
Edge(
source=EdgeConnection(node_id="height", field="a"),
destination=EdgeConnection(node_id="3", field="height"),
),
Edge(
source=EdgeConnection(node_id="seed", field="a"),
destination=EdgeConnection(node_id="3", field="seed"),
),
Edge(
source=EdgeConnection(node_id="3", field="noise"),
destination=EdgeConnection(node_id="6", field="noise"),
),
Edge(
source=EdgeConnection(node_id="6", field="latents"),
destination=EdgeConnection(node_id="7", field="latents"),
),
Edge(
source=EdgeConnection(node_id="4", field="conditioning"),
destination=EdgeConnection(node_id="6", field="positive_conditioning"),
),
Edge(
source=EdgeConnection(node_id="5", field="conditioning"),
destination=EdgeConnection(node_id="6", field="negative_conditioning"),
),
Edge(
source=EdgeConnection(node_id="7", field="image"),
destination=EdgeConnection(node_id="8", field="image"),
),
],
),
exposed_inputs=[
ExposedNodeInput(node_path='4', field='prompt', alias='positive_prompt'),
ExposedNodeInput(node_path='5', field='prompt', alias='negative_prompt'),
ExposedNodeInput(node_path='width', field='a', alias='width'),
ExposedNodeInput(node_path='height', field='a', alias='height'),
ExposedNodeInput(node_path='seed', field='a', alias='seed'),
ExposedNodeInput(node_path="4", field="prompt", alias="positive_prompt"),
ExposedNodeInput(node_path="5", field="prompt", alias="negative_prompt"),
ExposedNodeInput(node_path="width", field="a", alias="width"),
ExposedNodeInput(node_path="height", field="a", alias="height"),
ExposedNodeInput(node_path="seed", field="a", alias="seed"),
],
exposed_outputs=[
ExposedNodeOutput(node_path='8', field='image', alias='image')
])
exposed_outputs=[ExposedNodeOutput(node_path="8", field="image", alias="image")],
)
def create_system_graphs(graph_library: ItemStorageABC[LibraryGraph]) -> list[LibraryGraph]:
"""Creates the default system graphs, or adds new versions if the old ones don't match"""
# TODO: Uncomment this when we are ready to fix this up to prevent breaking changes
graphs: list[LibraryGraph] = list()
# text_to_image = graph_library.get(default_text_to_image_graph_id)
# # TODO: Check if the graph is the same as the default one, and if not, update it
# #if text_to_image is None:
text_to_image = create_text_to_image()

8
invokeai/app/services/events.py
View File

@ -44,9 +44,7 @@ class EventServiceBase:
graph_execution_state_id=graph_execution_state_id,
node=node,
source_node_id=source_node_id,
progress_image=progress_image.dict()
if progress_image is not None
else None,
progress_image=progress_image.dict() if progress_image is not None else None,
step=step,
total_steps=total_steps,
),
@ -90,9 +88,7 @@ class EventServiceBase:
),
)
def emit_invocation_started(
self, graph_execution_state_id: str, node: dict, source_node_id: str
) -> None:
def emit_invocation_started(self, graph_execution_state_id: str, node: dict, source_node_id: str) -> None:
"""Emitted when an invocation has started"""
self.__emit_session_event(
event_name="invocation_started",

367
invokeai/app/services/graph.py
View File

@ -28,6 +28,7 @@ from ..invocations.baseinvocation import (
# in 3.10 this would be "from types import NoneType"
NoneType = type(None)
class EdgeConnection(BaseModel):
node_id: str = Field(description="The id of the node for this edge connection")
field: str = Field(description="The field for this connection")
@ -61,6 +62,7 @@ def get_input_field(node: BaseInvocation, field: str) -> Any:
node_input_field = node_inputs.get(field) or None
return node_input_field
def is_union_subtype(t1, t2):
t1_args = get_args(t1)
t2_args = get_args(t2)
@ -71,6 +73,7 @@ def is_union_subtype(t1, t2):
# t1 is a Union, check that all of its types are in t2_args
return all(arg in t2_args for arg in t1_args)
def is_list_or_contains_list(t):
t_args = get_args(t)
@ -154,15 +157,17 @@ class GraphInvocationOutput(BaseInvocationOutput):
class Config:
schema_extra = {
'required': [
'type',
'image',
"required": [
"type",
"image",
]
}
# TODO: Fill this out and move to invocations
class GraphInvocation(BaseInvocation):
"""Execute a graph"""
type: Literal["graph"] = "graph"
# TODO: figure out how to create a default here
@ -182,23 +187,21 @@ class IterateInvocationOutput(BaseInvocationOutput):
class Config:
schema_extra = {
'required': [
'type',
'item',
"required": [
"type",
"item",
]
}
# TODO: Fill this out and move to invocations
class IterateInvocation(BaseInvocation):
"""Iterates over a list of items"""
type: Literal["iterate"] = "iterate"
collection: list[Any] = Field(
description="The list of items to iterate over", default_factory=list
)
index: int = Field(
description="The index, will be provided on executed iterators", default=0
)
collection: list[Any] = Field(description="The list of items to iterate over", default_factory=list)
index: int = Field(description="The index, will be provided on executed iterators", default=0)
def invoke(self, context: InvocationContext) -> IterateInvocationOutput:
"""Produces the outputs as values"""
@ -212,12 +215,13 @@ class CollectInvocationOutput(BaseInvocationOutput):
class Config:
schema_extra = {
'required': [
'type',
'collection',
"required": [
"type",
"collection",
]
}
class CollectInvocation(BaseInvocation):
"""Collects values into a collection"""
@ -269,9 +273,7 @@ class Graph(BaseModel):
if node_path in self.nodes:
return (self, node_path)
node_id = (
node_path if "." not in node_path else node_path[: node_path.index(".")]
)
node_id = node_path if "." not in node_path else node_path[: node_path.index(".")]
if node_id not in self.nodes:
raise NodeNotFoundError(f"Node {node_path} not found in graph")
@ -333,9 +335,7 @@ class Graph(BaseModel):
return False
# Validate all edges reference nodes in the graph
node_ids = set(
[e.source.node_id for e in self.edges] + [e.destination.node_id for e in self.edges]
)
node_ids = set([e.source.node_id for e in self.edges] + [e.destination.node_id for e in self.edges])
if not all((self.has_node(node_id) for node_id in node_ids)):
return False
@ -361,22 +361,14 @@ class Graph(BaseModel):
# Validate all iterators
# TODO: may need to validate all iterators in subgraphs so edge connections in parent graphs will be available
if not all(
(
self._is_iterator_connection_valid(n.id)
for n in self.nodes.values()
if isinstance(n, IterateInvocation)
)
(self._is_iterator_connection_valid(n.id) for n in self.nodes.values() if isinstance(n, IterateInvocation))
):
return False
# Validate all collectors
# TODO: may need to validate all collectors in subgraphs so edge connections in parent graphs will be available
if not all(
(
self._is_collector_connection_valid(n.id)
for n in self.nodes.values()
if isinstance(n, CollectInvocation)
)
(self._is_collector_connection_valid(n.id) for n in self.nodes.values() if isinstance(n, CollectInvocation))
):
return False
@ -395,48 +387,51 @@ class Graph(BaseModel):
# Validate that an edge to this node+field doesn't already exist
input_edges = self._get_input_edges(edge.destination.node_id, edge.destination.field)
if len(input_edges) > 0 and not isinstance(to_node, CollectInvocation):
raise InvalidEdgeError(f'Edge to node {edge.destination.node_id} field {edge.destination.field} already exists')
raise InvalidEdgeError(
f"Edge to node {edge.destination.node_id} field {edge.destination.field} already exists"
)
# Validate that no cycles would be created
g = self.nx_graph_flat()
g.add_edge(edge.source.node_id, edge.destination.node_id)
if not nx.is_directed_acyclic_graph(g):
raise InvalidEdgeError(f'Edge creates a cycle in the graph: {edge.source.node_id} -> {edge.destination.node_id}')
raise InvalidEdgeError(
f"Edge creates a cycle in the graph: {edge.source.node_id} -> {edge.destination.node_id}"
)
# Validate that the field types are compatible
if not are_connections_compatible(
from_node, edge.source.field, to_node, edge.destination.field
):
raise InvalidEdgeError(f'Fields are incompatible: cannot connect {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}')
if not are_connections_compatible(from_node, edge.source.field, to_node, edge.destination.field):
raise InvalidEdgeError(
f"Fields are incompatible: cannot connect {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}"
)
# Validate if iterator output type matches iterator input type (if this edge results in both being set)
if isinstance(to_node, IterateInvocation) and edge.destination.field == "collection":
if not self._is_iterator_connection_valid(
edge.destination.node_id, new_input=edge.source
):
raise InvalidEdgeError(f'Iterator input type does not match iterator output type: {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}')
if not self._is_iterator_connection_valid(edge.destination.node_id, new_input=edge.source):
raise InvalidEdgeError(
f"Iterator input type does not match iterator output type: {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}"
)
# Validate if iterator input type matches output type (if this edge results in both being set)
if isinstance(from_node, IterateInvocation) and edge.source.field == "item":
if not self._is_iterator_connection_valid(
edge.source.node_id, new_output=edge.destination
):
raise InvalidEdgeError(f'Iterator output type does not match iterator input type:, {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}')
if not self._is_iterator_connection_valid(edge.source.node_id, new_output=edge.destination):
raise InvalidEdgeError(
f"Iterator output type does not match iterator input type:, {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}"
)
# Validate if collector input type matches output type (if this edge results in both being set)
if isinstance(to_node, CollectInvocation) and edge.destination.field == "item":
if not self._is_collector_connection_valid(
edge.destination.node_id, new_input=edge.source
):
raise InvalidEdgeError(f'Collector output type does not match collector input type: {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}')
if not self._is_collector_connection_valid(edge.destination.node_id, new_input=edge.source):
raise InvalidEdgeError(
f"Collector output type does not match collector input type: {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}"
)
# Validate if collector output type matches input type (if this edge results in both being set)
if isinstance(from_node, CollectInvocation) and edge.source.field == "collection":
if not self._is_collector_connection_valid(
edge.source.node_id, new_output=edge.destination
):
raise InvalidEdgeError(f'Collector input type does not match collector output type: {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}')
if not self._is_collector_connection_valid(edge.source.node_id, new_output=edge.destination):
raise InvalidEdgeError(
f"Collector input type does not match collector output type: {edge.source.node_id}.{edge.source.field} to {edge.destination.node_id}.{edge.destination.field}"
)
def has_node(self, node_path: str) -> bool:
"""Determines whether or not a node exists in the graph."""
@ -465,17 +460,13 @@ class Graph(BaseModel):
# Ensure the node type matches the new node
if type(node) != type(new_node):
raise TypeError(
f"Node {node_path} is type {type(node)} but new node is type {type(new_node)}"
)
raise TypeError(f"Node {node_path} is type {type(node)} but new node is type {type(new_node)}")
# Ensure the new id is either the same or is not in the graph
prefix = None if "." not in node_path else node_path[: node_path.rindex(".")]
new_path = self._get_node_path(new_node.id, prefix=prefix)
if new_node.id != node.id and self.has_node(new_path):
raise NodeAlreadyInGraphError(
"Node with id {new_node.id} already exists in graph"
)
raise NodeAlreadyInGraphError("Node with id {new_node.id} already exists in graph")
# Set the new node in the graph
graph.nodes[new_node.id] = new_node
@ -497,9 +488,7 @@ class Graph(BaseModel):
graph.add_edge(
Edge(
source=edge.source,
destination=EdgeConnection(
node_id=new_graph_node_path, field=edge.destination.field
)
destination=EdgeConnection(node_id=new_graph_node_path, field=edge.destination.field),
)
)
@ -512,16 +501,12 @@ class Graph(BaseModel):
)
graph.add_edge(
Edge(
source=EdgeConnection(
node_id=new_graph_node_path, field=edge.source.field
),
destination=edge.destination
source=EdgeConnection(node_id=new_graph_node_path, field=edge.source.field),
destination=edge.destination,
)
)
def _get_input_edges(
self, node_path: str, field: Optional[str] = None
) -> list[Edge]:
def _get_input_edges(self, node_path: str, field: Optional[str] = None) -> list[Edge]:
"""Gets all input edges for a node"""
edges = self._get_input_edges_and_graphs(node_path)
@ -538,7 +523,7 @@ class Graph(BaseModel):
destination=EdgeConnection(
node_id=self._get_node_path(e.destination.node_id, prefix=prefix),
field=e.destination.field,
)
),
)
for _, prefix, e in filtered_edges
]
@ -550,32 +535,20 @@ class Graph(BaseModel):
edges = list()
# Return any input edges that appear in this graph
edges.extend(
[(self, prefix, e) for e in self.edges if e.destination.node_id == node_path]
)
edges.extend([(self, prefix, e) for e in self.edges if e.destination.node_id == node_path])
node_id = (
node_path if "." not in node_path else node_path[: node_path.index(".")]
)
node_id = node_path if "." not in node_path else node_path[: node_path.index(".")]
node = self.nodes[node_id]
if isinstance(node, GraphInvocation):
graph = node.graph
graph_path = (
node.id
if prefix is None or prefix == ""
else self._get_node_path(node.id, prefix=prefix)
)
graph_edges = graph._get_input_edges_and_graphs(
node_path[(len(node_id) + 1) :], prefix=graph_path
)
graph_path = node.id if prefix is None or prefix == "" else self._get_node_path(node.id, prefix=prefix)
graph_edges = graph._get_input_edges_and_graphs(node_path[(len(node_id) + 1) :], prefix=graph_path)
edges.extend(graph_edges)
return edges
def _get_output_edges(
self, node_path: str, field: str
) -> list[Edge]:
def _get_output_edges(self, node_path: str, field: str) -> list[Edge]:
"""Gets all output edges for a node"""
edges = self._get_output_edges_and_graphs(node_path)
@ -592,7 +565,7 @@ class Graph(BaseModel):
destination=EdgeConnection(
node_id=self._get_node_path(e.destination.node_id, prefix=prefix),
field=e.destination.field,
)
),
)
for _, prefix, e in filtered_edges
]
@ -604,25 +577,15 @@ class Graph(BaseModel):
edges = list()
# Return any input edges that appear in this graph
edges.extend(
[(self, prefix, e) for e in self.edges if e.source.node_id == node_path]
)
edges.extend([(self, prefix, e) for e in self.edges if e.source.node_id == node_path])
node_id = (
node_path if "." not in node_path else node_path[: node_path.index(".")]
)
node_id = node_path if "." not in node_path else node_path[: node_path.index(".")]
node = self.nodes[node_id]
if isinstance(node, GraphInvocation):
graph = node.graph
graph_path = (
node.id
if prefix is None or prefix == ""
else self._get_node_path(node.id, prefix=prefix)
)
graph_edges = graph._get_output_edges_and_graphs(
node_path[(len(node_id) + 1) :], prefix=graph_path
)
graph_path = node.id if prefix is None or prefix == "" else self._get_node_path(node.id, prefix=prefix)
graph_edges = graph._get_output_edges_and_graphs(node_path[(len(node_id) + 1) :], prefix=graph_path)
edges.extend(graph_edges)
return edges
@ -646,12 +609,8 @@ class Graph(BaseModel):
return False
# Get input and output fields (the fields linked to the iterator's input/output)
input_field = get_output_field(
self.get_node(inputs[0].node_id), inputs[0].field
)
output_fields = list(
[get_input_field(self.get_node(e.node_id), e.field) for e in outputs]
)
input_field = get_output_field(self.get_node(inputs[0].node_id), inputs[0].field)
output_fields = list([get_input_field(self.get_node(e.node_id), e.field) for e in outputs])
# Input type must be a list
if get_origin(input_field) != list:
@ -659,12 +618,7 @@ class Graph(BaseModel):
# Validate that all outputs match the input type
input_field_item_type = get_args(input_field)[0]
if not all(
(
are_connection_types_compatible(input_field_item_type, f)
for f in output_fields
)
):
if not all((are_connection_types_compatible(input_field_item_type, f) for f in output_fields)):
return False
return True
@ -684,35 +638,21 @@ class Graph(BaseModel):
outputs.append(new_output)
# Get input and output fields (the fields linked to the iterator's input/output)
input_fields = list(
[get_output_field(self.get_node(e.node_id), e.field) for e in inputs]
)
output_fields = list(
[get_input_field(self.get_node(e.node_id), e.field) for e in outputs]
)
input_fields = list([get_output_field(self.get_node(e.node_id), e.field) for e in inputs])
output_fields = list([get_input_field(self.get_node(e.node_id), e.field) for e in outputs])
# Validate that all inputs are derived from or match a single type
input_field_types = set(
[
t
for input_field in input_fields
for t in (
[input_field]
if get_origin(input_field) == None
else get_args(input_field)
)
for t in ([input_field] if get_origin(input_field) == None else get_args(input_field))
if t != NoneType
]
) # Get unique types
type_tree = nx.DiGraph()
type_tree.add_nodes_from(input_field_types)
type_tree.add_edges_from(
[
e
for e in itertools.permutations(input_field_types, 2)
if issubclass(e[1], e[0])
]
)
type_tree.add_edges_from([e for e in itertools.permutations(input_field_types, 2) if issubclass(e[1], e[0])])
type_degrees = type_tree.in_degree(type_tree.nodes)
if sum((t[1] == 0 for t in type_degrees)) != 1: # type: ignore
return False # There is more than one root type
@ -729,9 +669,7 @@ class Graph(BaseModel):
return False
# Verify that all outputs match the input type (are a base class or the same class)
if not all(
(issubclass(input_root_type, get_args(f)[0]) for f in output_fields)
):
if not all((issubclass(input_root_type, get_args(f)[0]) for f in output_fields)):
return False
return True
@ -751,9 +689,7 @@ class Graph(BaseModel):
g.add_edges_from(set([(e.source.node_id, e.destination.node_id) for e in self.edges]))
return g
def nx_graph_flat(
self, nx_graph: Optional[nx.DiGraph] = None, prefix: Optional[str] = None
) -> nx.DiGraph:
def nx_graph_flat(self, nx_graph: Optional[nx.DiGraph] = None, prefix: Optional[str] = None) -> nx.DiGraph:
"""Returns a flattened NetworkX DiGraph, including all subgraphs (but not with iterations expanded)"""
g = nx_graph or nx.DiGraph()
@ -762,26 +698,18 @@ class Graph(BaseModel):
[
self._get_node_path(n.id, prefix)
for n in self.nodes.values()
if not isinstance(n, GraphInvocation)
and not isinstance(n, IterateInvocation)
if not isinstance(n, GraphInvocation) and not isinstance(n, IterateInvocation)
]
)
# Expand graph nodes
for sgn in (
gn for gn in self.nodes.values() if isinstance(gn, GraphInvocation)
):
for sgn in (gn for gn in self.nodes.values() if isinstance(gn, GraphInvocation)):
g = sgn.graph.nx_graph_flat(g, self._get_node_path(sgn.id, prefix))
# TODO: figure out if iteration nodes need to be expanded
unique_edges = set([(e.source.node_id, e.destination.node_id) for e in self.edges])
g.add_edges_from(
[
(self._get_node_path(e[0], prefix), self._get_node_path(e[1], prefix))
for e in unique_edges
]
)
g.add_edges_from([(self._get_node_path(e[0], prefix), self._get_node_path(e[1], prefix)) for e in unique_edges])
return g
@ -800,23 +728,19 @@ class GraphExecutionState(BaseModel):
)
# Nodes that have been executed
executed: set[str] = Field(
description="The set of node ids that have been executed", default_factory=set
)
executed: set[str] = Field(description="The set of node ids that have been executed", default_factory=set)
executed_history: list[str] = Field(
description="The list of node ids that have been executed, in order of execution",
default_factory=list,
)
# The results of executed nodes
results: dict[
str, Annotated[InvocationOutputsUnion, Field(discriminator="type")]
] = Field(description="The results of node executions", default_factory=dict)
results: dict[str, Annotated[InvocationOutputsUnion, Field(discriminator="type")]] = Field(
description="The results of node executions", default_factory=dict
)
# Errors raised when executing nodes
errors: dict[str, str] = Field(
description="Errors raised when executing nodes", default_factory=dict
)
errors: dict[str, str] = Field(description="Errors raised when executing nodes", default_factory=dict)
# Map of prepared/executed nodes to their original nodes
prepared_source_mapping: dict[str, str] = Field(
@ -832,16 +756,16 @@ class GraphExecutionState(BaseModel):
class Config:
schema_extra = {
'required': [
'id',
'graph',
'execution_graph',
'executed',
'executed_history',
'results',
'errors',
'prepared_source_mapping',
'source_prepared_mapping',
"required": [
"id",
"graph",
"execution_graph",
"executed",
"executed_history",
"results",
"errors",
"prepared_source_mapping",
"source_prepared_mapping",
]
}
@ -899,9 +823,7 @@ class GraphExecutionState(BaseModel):
"""Returns true if the graph has any errors"""
return len(self.errors) > 0
def _create_execution_node(
self, node_path: str, iteration_node_map: list[tuple[str, str]]
) -> list[str]:
def _create_execution_node(self, node_path: str, iteration_node_map: list[tuple[str, str]]) -> list[str]:
"""Prepares an iteration node and connects all edges, returning the new node id"""
node = self.graph.get_node(node_path)
@ -911,20 +833,12 @@ class GraphExecutionState(BaseModel):
# If this is an iterator node, we must create a copy for each iteration
if isinstance(node, IterateInvocation):
# Get input collection edge (should error if there are no inputs)
input_collection_edge = next(
iter(self.graph._get_input_edges(node_path, "collection"))
)
input_collection_edge = next(iter(self.graph._get_input_edges(node_path, "collection")))
input_collection_prepared_node_id = next(
n[1]
for n in iteration_node_map
if n[0] == input_collection_edge.source.node_id
)
input_collection_prepared_node_output = self.results[
input_collection_prepared_node_id
]
input_collection = getattr(
input_collection_prepared_node_output, input_collection_edge.source.field
n[1] for n in iteration_node_map if n[0] == input_collection_edge.source.node_id
)
input_collection_prepared_node_output = self.results[input_collection_prepared_node_id]
input_collection = getattr(input_collection_prepared_node_output, input_collection_edge.source.field)
self_iteration_count = len(input_collection)
new_nodes = list()
@ -939,9 +853,7 @@ class GraphExecutionState(BaseModel):
# For collect nodes, this may contain multiple inputs to the same field
new_edges = list()
for edge in input_edges:
for input_node_id in (
n[1] for n in iteration_node_map if n[0] == edge.source.node_id
):
for input_node_id in (n[1] for n in iteration_node_map if n[0] == edge.source.node_id):
new_edge = Edge(
source=EdgeConnection(node_id=input_node_id, field=edge.source.field),
destination=EdgeConnection(node_id="", field=edge.destination.field),
@ -982,11 +894,7 @@ class GraphExecutionState(BaseModel):
def _iterator_graph(self) -> nx.DiGraph:
"""Gets a DiGraph with edges to collectors removed so an ancestor search produces all active iterators for any node"""
g = self.graph.nx_graph_flat()
collectors = (
n
for n in self.graph.nodes
if isinstance(self.graph.get_node(n), CollectInvocation)
)
collectors = (n for n in self.graph.nodes if isinstance(self.graph.get_node(n), CollectInvocation))
for c in collectors:
g.remove_edges_from(list(g.in_edges(c)))
return g
@ -994,11 +902,7 @@ class GraphExecutionState(BaseModel):
def _get_node_iterators(self, node_id: str) -> list[str]:
"""Gets iterators for a node"""
g = self._iterator_graph()
iterators = [
n
for n in nx.ancestors(g, node_id)
if isinstance(self.graph.get_node(n), IterateInvocation)
]
iterators = [n for n in nx.ancestors(g, node_id) if isinstance(self.graph.get_node(n), IterateInvocation)]
return iterators
def _prepare(self) -> Optional[str]:
@ -1045,29 +949,18 @@ class GraphExecutionState(BaseModel):
if isinstance(next_node, CollectInvocation):
# Collapse all iterator input mappings and create a single execution node for the collect invocation
all_iteration_mappings = list(
itertools.chain(
*(
((s, p) for p in self.source_prepared_mapping[s])
for s in next_node_parents
)
)
itertools.chain(*(((s, p) for p in self.source_prepared_mapping[s]) for s in next_node_parents))
)
# all_iteration_mappings = list(set(itertools.chain(*prepared_parent_mappings)))
create_results = self._create_execution_node(
next_node_id, all_iteration_mappings
)
create_results = self._create_execution_node(next_node_id, all_iteration_mappings)
if create_results is not None:
new_node_ids.extend(create_results)
else: # Iterators or normal nodes
# Get all iterator combinations for this node
# Will produce a list of lists of prepared iterator nodes, from which results can be iterated
iterator_nodes = self._get_node_iterators(next_node_id)
iterator_nodes_prepared = [
list(self.source_prepared_mapping[n]) for n in iterator_nodes
]
iterator_node_prepared_combinations = list(
itertools.product(*iterator_nodes_prepared)
)
iterator_nodes_prepared = [list(self.source_prepared_mapping[n]) for n in iterator_nodes]
iterator_node_prepared_combinations = list(itertools.product(*iterator_nodes_prepared))
# Select the correct prepared parents for each iteration
# For every iterator, the parent must either not be a child of that iterator, or must match the prepared iteration for that iterator
@ -1096,31 +989,16 @@ class GraphExecutionState(BaseModel):
return next(iter(prepared_nodes))
# Check if the requested node is an iterator
prepared_iterator = next(
(n for n in prepared_nodes if n in prepared_iterator_nodes), None
)
prepared_iterator = next((n for n in prepared_nodes if n in prepared_iterator_nodes), None)
if prepared_iterator is not None:
return prepared_iterator
# Filter to only iterator nodes that are a parent of the specified node, in tuple format (prepared, source)
iterator_source_node_mapping = [
(n, self.prepared_source_mapping[n]) for n in prepared_iterator_nodes
]
parent_iterators = [
itn
for itn in iterator_source_node_mapping
if nx.has_path(graph, itn[1], source_node_path)
]
iterator_source_node_mapping = [(n, self.prepared_source_mapping[n]) for n in prepared_iterator_nodes]
parent_iterators = [itn for itn in iterator_source_node_mapping if nx.has_path(graph, itn[1], source_node_path)]
return next(
(
n
for n in prepared_nodes
if all(
nx.has_path(execution_graph, pit[0], n)
for pit in parent_iterators
)
),
(n for n in prepared_nodes if all(nx.has_path(execution_graph, pit[0], n) for pit in parent_iterators)),
None,
)
@ -1130,13 +1008,13 @@ class GraphExecutionState(BaseModel):
# Depth-first search with pre-order traversal is a depth-first topological sort
sorted_nodes = nx.dfs_preorder_nodes(g)
next_node = next(
(
n
for n in sorted_nodes
if n not in self.executed # the node must not already be executed...
and all((e[0] in self.executed for e in g.in_edges(n))) # ...and all its inputs must be executed
if n not in self.executed # the node must not already be executed...
and all((e[0] in self.executed for e in g.in_edges(n))) # ...and all its inputs must be executed
),
None,
)
@ -1221,15 +1099,18 @@ class ExposedNodeOutput(BaseModel):
field: str = Field(description="The field name of the output")
alias: str = Field(description="The alias of the output")
class LibraryGraph(BaseModel):
id: str = Field(description="The unique identifier for this library graph", default_factory=uuid.uuid4)
graph: Graph = Field(description="The graph")
name: str = Field(description="The name of the graph")
description: str = Field(description="The description of the graph")
exposed_inputs: list[ExposedNodeInput] = Field(description="The inputs exposed by this graph", default_factory=list)
exposed_outputs: list[ExposedNodeOutput] = Field(description="The outputs exposed by this graph", default_factory=list)
exposed_outputs: list[ExposedNodeOutput] = Field(
description="The outputs exposed by this graph", default_factory=list
)
@validator('exposed_inputs', 'exposed_outputs')
@validator("exposed_inputs", "exposed_outputs")
def validate_exposed_aliases(cls, v):
if len(v) != len(set(i.alias for i in v)):
raise ValueError("Duplicate exposed alias")
@ -1237,23 +1118,27 @@ class LibraryGraph(BaseModel):
@root_validator
def validate_exposed_nodes(cls, values):
graph = values['graph']
graph = values["graph"]
# Validate exposed inputs
for exposed_input in values['exposed_inputs']:
for exposed_input in values["exposed_inputs"]:
if not graph.has_node(exposed_input.node_path):
raise ValueError(f"Exposed input node {exposed_input.node_path} does not exist")
node = graph.get_node(exposed_input.node_path)
if get_input_field(node, exposed_input.field) is None:
raise ValueError(f"Exposed input field {exposed_input.field} does not exist on node {exposed_input.node_path}")
raise ValueError(
f"Exposed input field {exposed_input.field} does not exist on node {exposed_input.node_path}"
)
# Validate exposed outputs
for exposed_output in values['exposed_outputs']:
for exposed_output in values["exposed_outputs"]:
if not graph.has_node(exposed_output.node_path):
raise ValueError(f"Exposed output node {exposed_output.node_path} does not exist")
node = graph.get_node(exposed_output.node_path)
if get_output_field(node, exposed_output.field) is None:
raise ValueError(f"Exposed output field {exposed_output.field} does not exist on node {exposed_output.node_path}")
raise ValueError(
f"Exposed output field {exposed_output.field} does not exist on node {exposed_output.node_path}"
)
return values

10
invokeai/app/services/image_file_storage.py
View File

@ -85,9 +85,7 @@ class DiskImageFileStorage(ImageFileStorageBase):
self.__cache_ids = Queue()
self.__max_cache_size = 10 # TODO: get this from config
self.__output_folder: Path = (
output_folder if isinstance(output_folder, Path) else Path(output_folder)
)
self.__output_folder: Path = output_folder if isinstance(output_folder, Path) else Path(output_folder)
self.__thumbnails_folder = self.__output_folder / "thumbnails"
# Validate required output folders at launch
@ -120,7 +118,7 @@ class DiskImageFileStorage(ImageFileStorageBase):
image_path = self.get_path(image_name)
pnginfo = PngImagePlugin.PngInfo()
if metadata is not None:
pnginfo.add_text("invokeai_metadata", json.dumps(metadata))
if graph is not None:
@ -183,9 +181,7 @@ class DiskImageFileStorage(ImageFileStorageBase):
def __set_cache(self, image_name: Path, image: PILImageType):
if not image_name in self.__cache:
self.__cache[image_name] = image
self.__cache_ids.put(
image_name
) # TODO: this should refresh position for LRU cache
self.__cache_ids.put(image_name) # TODO: this should refresh position for LRU cache
if len(self.__cache) > self.__max_cache_size:
cache_id = self.__cache_ids.get()
if cache_id in self.__cache:

9
invokeai/app/services/image_record_storage.py
View File

@ -426,9 +426,7 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
finally:
self._lock.release()
return OffsetPaginatedResults(
items=images, offset=offset, limit=limit, total=count
)
return OffsetPaginatedResults(items=images, offset=offset, limit=limit, total=count)
def delete(self, image_name: str) -> None:
try:
@ -466,7 +464,6 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
finally:
self._lock.release()
def delete_intermediates(self) -> list[str]:
try:
self._lock.acquire()
@ -505,9 +502,7 @@ class SqliteImageRecordStorage(ImageRecordStorageBase):
is_intermediate: bool = False,
) -> datetime:
try:
metadata_json = (
None if metadata is None else json.dumps(metadata)
)
metadata_json = None if metadata is None else json.dumps(metadata)
self._lock.acquire()
self._cursor.execute(
"""--sql

22
invokeai/app/services/images.py
View File

@ -217,12 +217,8 @@ class ImageService(ImageServiceABC):
session_id=session_id,
)
if board_id is not None:
self._services.board_image_records.add_image_to_board(
board_id=board_id, image_name=image_name
)
self._services.image_files.save(
image_name=image_name, image=image, metadata=metadata, graph=graph
)
self._services.board_image_records.add_image_to_board(board_id=board_id, image_name=image_name)
self._services.image_files.save(image_name=image_name, image=image, metadata=metadata, graph=graph)
image_dto = self.get_dto(image_name)
return image_dto
@ -297,9 +293,7 @@ class ImageService(ImageServiceABC):
if not image_record.session_id:
return ImageMetadata()
session_raw = self._services.graph_execution_manager.get_raw(
image_record.session_id
)
session_raw = self._services.graph_execution_manager.get_raw(image_record.session_id)
graph = None
if session_raw:
@ -364,9 +358,7 @@ class ImageService(ImageServiceABC):
r,
self._services.urls.get_image_url(r.image_name),
self._services.urls.get_image_url(r.image_name, True),
self._services.board_image_records.get_board_for_image(
r.image_name
),
self._services.board_image_records.get_board_for_image(r.image_name),
),
results.items,
)
@ -398,11 +390,7 @@ class ImageService(ImageServiceABC):
def delete_images_on_board(self, board_id: str):
try:
image_names = (
self._services.board_image_records.get_all_board_image_names_for_board(
board_id
)
)
image_names = self._services.board_image_records.get_all_board_image_names_for_board(board_id)
for image_name in image_names:
self._services.image_files.delete(image_name)
self._services.image_records.delete_many(image_names)

9
invokeai/app/services/invocation_queue.py
View File

@ -7,6 +7,7 @@ from queue import Queue
from pydantic import BaseModel, Field
from typing import Optional
class InvocationQueueItem(BaseModel):
graph_execution_state_id: str = Field(description="The ID of the graph execution state")
invocation_id: str = Field(description="The ID of the node being invoked")
@ -45,9 +46,11 @@ class MemoryInvocationQueue(InvocationQueueABC):
def get(self) -> InvocationQueueItem:
item = self.__queue.get()
while isinstance(item, InvocationQueueItem) \
and item.graph_execution_state_id in self.__cancellations \
and self.__cancellations[item.graph_execution_state_id] > item.timestamp:
while (
isinstance(item, InvocationQueueItem)
and item.graph_execution_state_id in self.__cancellations
and self.__cancellations[item.graph_execution_state_id] > item.timestamp
):
item = self.__queue.get()
# Clear old items

5
invokeai/app/services/invoker.py
View File

@ -7,6 +7,7 @@ from .graph import Graph, GraphExecutionState
from .invocation_queue import InvocationQueueItem
from .invocation_services import InvocationServices
class Invoker:
"""The invoker, used to execute invocations"""
@ -16,9 +17,7 @@ class Invoker:
self.services = services
self._start()
def invoke(
self, graph_execution_state: GraphExecutionState, invoke_all: bool = False
) -> Optional[str]:
def invoke(self, graph_execution_state: GraphExecutionState, invoke_all: bool = False) -> Optional[str]:
"""Determines the next node to invoke and enqueues it, preparing if needed.
Returns the id of the queued node, or `None` if there are no nodes left to enqueue."""

10
invokeai/app/services/item_storage.py
View File

@ -9,13 +9,15 @@ T = TypeVar("T", bound=BaseModel)
class PaginatedResults(GenericModel, Generic[T]):
"""Paginated results"""
#fmt: off
# fmt: off
items: list[T] = Field(description="Items")
page: int = Field(description="Current Page")
pages: int = Field(description="Total number of pages")
per_page: int = Field(description="Number of items per page")
total: int = Field(description="Total number of items in result")
#fmt: on
# fmt: on
class ItemStorageABC(ABC, Generic[T]):
_on_changed_callbacks: list[Callable[[T], None]]
@ -48,9 +50,7 @@ class ItemStorageABC(ABC, Generic[T]):
pass
@abstractmethod
def search(
self, query: str, page: int = 0, per_page: int = 10
) -> PaginatedResults[T]:
def search(self, query: str, page: int = 0, per_page: int = 10) -> PaginatedResults[T]:
pass
def on_changed(self, on_changed: Callable[[T], None]) -> None:

5
invokeai/app/services/latent_storage.py
View File

@ -7,6 +7,7 @@ from typing import Dict, Union, Optional
import torch
class LatentsStorageBase(ABC):
"""Responsible for storing and retrieving latents."""
@ -25,7 +26,7 @@ class LatentsStorageBase(ABC):
class ForwardCacheLatentsStorage(LatentsStorageBase):
"""Caches the latest N latents in memory, writing-thorugh to and reading from underlying storage"""
__cache: Dict[str, torch.Tensor]
__cache_ids: Queue
__max_cache_size: int
@ -87,8 +88,6 @@ class DiskLatentsStorage(LatentsStorageBase):
def delete(self, name: str) -> None:
latent_path = self.get_path(name)
latent_path.unlink()
def get_path(self, name: str) -> Path:
return self.__output_folder / name

215
invokeai/app/services/model_manager_service.py
View File

@ -103,7 +103,7 @@ class ModelManagerServiceBase(ABC):
}
"""
pass
@abstractmethod
def list_model(self, model_name: str, base_model: BaseModelType, model_type: ModelType) -> dict:
"""
@ -125,7 +125,7 @@ class ModelManagerServiceBase(ABC):
base_model: BaseModelType,
model_type: ModelType,
model_attributes: dict,
clobber: bool = False
clobber: bool = False,
) -> AddModelResult:
"""
Update the named model with a dictionary of attributes. Will fail with an
@ -148,12 +148,12 @@ class ModelManagerServiceBase(ABC):
Update the named model with a dictionary of attributes. Will fail with a
ModelNotFoundException if the name does not already exist.
On a successful update, the config will be changed in memory. Will fail
with an assertion error if provided attributes are incorrect or
On a successful update, the config will be changed in memory. Will fail
with an assertion error if provided attributes are incorrect or
the model name is missing. Call commit() to write changes to disk.
"""
pass
@abstractmethod
def del_model(
self,
@ -169,21 +169,20 @@ class ModelManagerServiceBase(ABC):
pass
@abstractmethod
def rename_model(self,
model_name: str,
base_model: BaseModelType,
model_type: ModelType,
new_name: str,
):
def rename_model(
self,
model_name: str,
base_model: BaseModelType,
model_type: ModelType,
new_name: str,
):
"""
Rename the indicated model.
"""
pass
@abstractmethod
def list_checkpoint_configs(
self
)->List[Path]:
def list_checkpoint_configs(self) -> List[Path]:
"""
List the checkpoint config paths from ROOT/configs/stable-diffusion.
"""
@ -194,7 +193,7 @@ class ModelManagerServiceBase(ABC):
self,
model_name: str,
base_model: BaseModelType,
model_type: Union[ModelType.Main,ModelType.Vae],
model_type: Union[ModelType.Main, ModelType.Vae],
) -> AddModelResult:
"""
Convert a checkpoint file into a diffusers folder, deleting the cached
@ -211,11 +210,12 @@ class ModelManagerServiceBase(ABC):
pass
@abstractmethod
def heuristic_import(self,
items_to_import: set[str],
prediction_type_helper: Optional[Callable[[Path],SchedulerPredictionType]]=None,
)->dict[str, AddModelResult]:
'''Import a list of paths, repo_ids or URLs. Returns the set of
def heuristic_import(
self,
items_to_import: set[str],
prediction_type_helper: Optional[Callable[[Path], SchedulerPredictionType]] = None,
) -> dict[str, AddModelResult]:
"""Import a list of paths, repo_ids or URLs. Returns the set of
successfully imported items.
:param items_to_import: Set of strings corresponding to models to be imported.
:param prediction_type_helper: A callback that receives the Path of a Stable Diffusion 2 checkpoint model and returns a SchedulerPredictionType.
@ -230,19 +230,23 @@ class ModelManagerServiceBase(ABC):
The result is a set of successfully installed models. Each element
of the set is a dict corresponding to the newly-created OmegaConf stanza for
that model.
'''
"""
pass
@abstractmethod
def merge_models(
self,
model_names: List[str] = Field(default=None, min_items=2, max_items=3, description="List of model names to merge"),
base_model: Union[BaseModelType,str] = Field(default=None, description="Base model shared by all models to be merged"),
merged_model_name: str = Field(default=None, description="Name of destination model after merging"),
alpha: Optional[float] = 0.5,
interp: Optional[MergeInterpolationMethod] = None,
force: Optional[bool] = False,
merge_dest_directory: Optional[Path] = None
self,
model_names: List[str] = Field(
default=None, min_items=2, max_items=3, description="List of model names to merge"
),
base_model: Union[BaseModelType, str] = Field(
default=None, description="Base model shared by all models to be merged"
),
merged_model_name: str = Field(default=None, description="Name of destination model after merging"),
alpha: Optional[float] = 0.5,
interp: Optional[MergeInterpolationMethod] = None,
force: Optional[bool] = False,
merge_dest_directory: Optional[Path] = None,
) -> AddModelResult:
"""
Merge two to three diffusrs pipeline models and save as a new model.
@ -250,27 +254,27 @@ class ModelManagerServiceBase(ABC):
:param base_model: Base model to use for all models
:param merged_model_name: Name of destination merged model
:param alpha: Alpha strength to apply to 2d and 3d model
:param interp: Interpolation method. None (default)
:param interp: Interpolation method. None (default)
:param merge_dest_directory: Save the merged model to the designated directory (with 'merged_model_name' appended)
"""
pass
@abstractmethod
def search_for_models(self, directory: Path)->List[Path]:
def search_for_models(self, directory: Path) -> List[Path]:
"""
Return list of all models found in the designated directory.
"""
pass
@abstractmethod
def sync_to_config(self):
"""
Re-read models.yaml, rescan the models directory, and reimport models
Re-read models.yaml, rescan the models directory, and reimport models
in the autoimport directories. Call after making changes outside the
model manager API.
"""
pass
@abstractmethod
def commit(self, conf_file: Optional[Path] = None) -> None:
"""
@ -280,9 +284,11 @@ class ModelManagerServiceBase(ABC):
"""
pass
# simple implementation
class ModelManagerService(ModelManagerServiceBase):
"""Responsible for managing models on disk and in memory"""
def __init__(
self,
config: InvokeAIAppConfig,
@ -298,17 +304,17 @@ class ModelManagerService(ModelManagerServiceBase):
config_file = config.model_conf_path
else:
config_file = config.root_dir / "configs/models.yaml"
logger.debug(f'Config file={config_file}')
logger.debug(f"Config file={config_file}")
device = torch.device(choose_torch_device())
device_name = torch.cuda.get_device_name() if device==torch.device('cuda') else ''
logger.info(f'GPU device = {device} {device_name}')
device_name = torch.cuda.get_device_name() if device == torch.device("cuda") else ""
logger.info(f"GPU device = {device} {device_name}")
precision = config.precision
if precision == "auto":
precision = choose_precision(device)
dtype = torch.float32 if precision == 'float32' else torch.float16
dtype = torch.float32 if precision == "float32" else torch.float16
# this is transitional backward compatibility
# support for the deprecated `max_loaded_models`
@ -316,9 +322,7 @@ class ModelManagerService(ModelManagerServiceBase):
# 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
max_cache_size = config.max_cache_size if hasattr(config, "max_cache_size") else config.max_loaded_models * 2.5
logger.debug(f"Maximum RAM cache size: {max_cache_size} GiB")
@ -332,7 +336,7 @@ class ModelManagerService(ModelManagerServiceBase):
sequential_offload=sequential_offload,
logger=logger,
)
logger.info('Model manager service initialized')
logger.info("Model manager service initialized")
def get_model(
self,
@ -371,7 +375,7 @@ class ModelManagerService(ModelManagerServiceBase):
base_model=base_model,
model_type=model_type,
submodel=submodel,
model_info=model_info
model_info=model_info,
)
return model_info
@ -405,9 +409,7 @@ class ModelManagerService(ModelManagerServiceBase):
return self.mgr.model_names()
def list_models(
self,
base_model: Optional[BaseModelType] = None,
model_type: Optional[ModelType] = None
self, base_model: Optional[BaseModelType] = None, model_type: Optional[ModelType] = None
) -> list[dict]:
"""
Return a list of models.
@ -418,9 +420,7 @@ class ModelManagerService(ModelManagerServiceBase):
"""
Return information about the model using the same format as list_models()
"""
return self.mgr.list_model(model_name=model_name,
base_model=base_model,
model_type=model_type)
return self.mgr.list_model(model_name=model_name, base_model=base_model, model_type=model_type)
def add_model(
self,
@ -429,7 +429,7 @@ class ModelManagerService(ModelManagerServiceBase):
model_type: ModelType,
model_attributes: dict,
clobber: bool = False,
)->None:
) -> None:
"""
Update the named model with a dictionary of attributes. Will fail with an
assertion error if the name already exists. Pass clobber=True to overwrite.
@ -437,7 +437,7 @@ class ModelManagerService(ModelManagerServiceBase):
with an assertion error if provided attributes are incorrect or
the model name is missing. Call commit() to write changes to disk.
"""
self.logger.debug(f'add/update model {model_name}')
self.logger.debug(f"add/update model {model_name}")
return self.mgr.add_model(model_name, base_model, model_type, model_attributes, clobber)
def update_model(
@ -450,15 +450,15 @@ class ModelManagerService(ModelManagerServiceBase):
"""
Update the named model with a dictionary of attributes. Will fail with a
ModelNotFoundException exception if the name does not already exist.
On a successful update, the config will be changed in memory. Will fail
with an assertion error if provided attributes are incorrect or
On a successful update, the config will be changed in memory. Will fail
with an assertion error if provided attributes are incorrect or
the model name is missing. Call commit() to write changes to disk.
"""
self.logger.debug(f'update model {model_name}')
self.logger.debug(f"update model {model_name}")
if not self.model_exists(model_name, base_model, model_type):
raise ModelNotFoundException(f"Unknown model {model_name}")
return self.add_model(model_name, base_model, model_type, model_attributes, clobber=True)
def del_model(
self,
model_name: str,
@ -470,7 +470,7 @@ class ModelManagerService(ModelManagerServiceBase):
then the underlying weight file or diffusers directory will be deleted
as well.
"""
self.logger.debug(f'delete model {model_name}')
self.logger.debug(f"delete model {model_name}")
self.mgr.del_model(model_name, base_model, model_type)
self.mgr.commit()
@ -478,8 +478,10 @@ class ModelManagerService(ModelManagerServiceBase):
self,
model_name: str,
base_model: BaseModelType,
model_type: Union[ModelType.Main,ModelType.Vae],
convert_dest_directory: Optional[Path] = Field(default=None, description="Optional directory location for merged model"),
model_type: Union[ModelType.Main, ModelType.Vae],
convert_dest_directory: Optional[Path] = Field(
default=None, description="Optional directory location for merged model"
),
) -> AddModelResult:
"""
Convert a checkpoint file into a diffusers folder, deleting the cached
@ -494,10 +496,10 @@ class ModelManagerService(ModelManagerServiceBase):
also raise a ValueError in the event that there is a similarly-named diffusers
directory already in place.
"""
self.logger.debug(f'convert model {model_name}')
self.logger.debug(f"convert model {model_name}")
return self.mgr.convert_model(model_name, base_model, model_type, convert_dest_directory)
def commit(self, conf_file: Optional[Path]=None):
def commit(self, conf_file: Optional[Path] = None):
"""
Write current configuration out to the indicated file.
If no conf_file is provided, then replaces the
@ -524,7 +526,7 @@ class ModelManagerService(ModelManagerServiceBase):
base_model=base_model,
model_type=model_type,
submodel=submodel,
model_info=model_info
model_info=model_info,
)
else:
context.services.events.emit_model_load_started(
@ -535,16 +537,16 @@ class ModelManagerService(ModelManagerServiceBase):
submodel=submodel,
)
@property
def logger(self):
return self.mgr.logger
def heuristic_import(self,
items_to_import: set[str],
prediction_type_helper: Optional[Callable[[Path],SchedulerPredictionType]]=None,
)->dict[str, AddModelResult]:
'''Import a list of paths, repo_ids or URLs. Returns the set of
def heuristic_import(
self,
items_to_import: set[str],
prediction_type_helper: Optional[Callable[[Path], SchedulerPredictionType]] = None,
) -> dict[str, AddModelResult]:
"""Import a list of paths, repo_ids or URLs. Returns the set of
successfully imported items.
:param items_to_import: Set of strings corresponding to models to be imported.
:param prediction_type_helper: A callback that receives the Path of a Stable Diffusion 2 checkpoint model and returns a SchedulerPredictionType.
@ -559,18 +561,24 @@ class ModelManagerService(ModelManagerServiceBase):
The result is a set of successfully installed models. Each element
of the set is a dict corresponding to the newly-created OmegaConf stanza for
that model.
'''
return self.mgr.heuristic_import(items_to_import, prediction_type_helper)
"""
return self.mgr.heuristic_import(items_to_import, prediction_type_helper)
def merge_models(
self,
model_names: List[str] = Field(default=None, min_items=2, max_items=3, description="List of model names to merge"),
base_model: Union[BaseModelType,str] = Field(default=None, description="Base model shared by all models to be merged"),
merged_model_name: str = Field(default=None, description="Name of destination model after merging"),
alpha: Optional[float] = 0.5,
interp: Optional[MergeInterpolationMethod] = None,
force: Optional[bool] = False,
merge_dest_directory: Optional[Path] = Field(default=None, description="Optional directory location for merged model"),
self,
model_names: List[str] = Field(
default=None, min_items=2, max_items=3, description="List of model names to merge"
),
base_model: Union[BaseModelType, str] = Field(
default=None, description="Base model shared by all models to be merged"
),
merged_model_name: str = Field(default=None, description="Name of destination model after merging"),
alpha: Optional[float] = 0.5,
interp: Optional[MergeInterpolationMethod] = None,
force: Optional[bool] = False,
merge_dest_directory: Optional[Path] = Field(
default=None, description="Optional directory location for merged model"
),
) -> AddModelResult:
"""
Merge two to three diffusrs pipeline models and save as a new model.
@ -578,25 +586,25 @@ class ModelManagerService(ModelManagerServiceBase):
:param base_model: Base model to use for all models
:param merged_model_name: Name of destination merged model
:param alpha: Alpha strength to apply to 2d and 3d model
:param interp: Interpolation method. None (default)
:param interp: Interpolation method. None (default)
:param merge_dest_directory: Save the merged model to the designated directory (with 'merged_model_name' appended)
"""
merger = ModelMerger(self.mgr)
try:
result = merger.merge_diffusion_models_and_save(
model_names = model_names,
base_model = base_model,
merged_model_name = merged_model_name,
alpha = alpha,
interp = interp,
force = force,
model_names=model_names,
base_model=base_model,
merged_model_name=merged_model_name,
alpha=alpha,
interp=interp,
force=force,
merge_dest_directory=merge_dest_directory,
)
except AssertionError as e:
raise ValueError(e)
return result
def search_for_models(self, directory: Path)->List[Path]:
def search_for_models(self, directory: Path) -> List[Path]:
"""
Return list of all models found in the designated directory.
"""
@ -605,28 +613,29 @@ class ModelManagerService(ModelManagerServiceBase):
def sync_to_config(self):
"""
Re-read models.yaml, rescan the models directory, and reimport models
Re-read models.yaml, rescan the models directory, and reimport models
in the autoimport directories. Call after making changes outside the
model manager API.
"""
return self.mgr.sync_to_config()
def list_checkpoint_configs(self)->List[Path]:
def list_checkpoint_configs(self) -> List[Path]:
"""
List the checkpoint config paths from ROOT/configs/stable-diffusion.
"""
config = self.mgr.app_config
conf_path = config.legacy_conf_path
root_path = config.root_path
return [(conf_path / x).relative_to(root_path) for x in conf_path.glob('**/*.yaml')]
return [(conf_path / x).relative_to(root_path) for x in conf_path.glob("**/*.yaml")]
def rename_model(self,
model_name: str,
base_model: BaseModelType,
model_type: ModelType,
new_name: str = None,
new_base: BaseModelType = None,
):
def rename_model(
self,
model_name: str,
base_model: BaseModelType,
model_type: ModelType,
new_name: str = None,
new_base: BaseModelType = None,
):
"""
Rename the indicated model. Can provide a new name and/or a new base.
:param model_name: Current name of the model
@ -635,10 +644,10 @@ class ModelManagerService(ModelManagerServiceBase):
:param new_name: New name for the model
:param new_base: New base for the model
"""
self.mgr.rename_model(base_model = base_model,
model_type = model_type,
model_name = model_name,
new_name = new_name,
new_base = new_base,
)
self.mgr.rename_model(
base_model=base_model,
model_type=model_type,
model_name=model_name,
new_name=new_name,
new_base=new_base,
)

20
invokeai/app/services/models/board_record.py
View File

@ -11,30 +11,20 @@ class BoardRecord(BaseModel):
"""The unique ID of the board."""
board_name: str = Field(description="The name of the board.")
"""The name of the board."""
created_at: Union[datetime, str] = Field(
description="The created timestamp of the board."
)
created_at: Union[datetime, str] = Field(description="The created timestamp of the board.")
"""The created timestamp of the image."""
updated_at: Union[datetime, str] = Field(
description="The updated timestamp of the board."
)
updated_at: Union[datetime, str] = Field(description="The updated timestamp of the board.")
"""The updated timestamp of the image."""
deleted_at: Union[datetime, str, None] = Field(
description="The deleted timestamp of the board."
)
deleted_at: Union[datetime, str, None] = Field(description="The deleted timestamp of the board.")
"""The updated timestamp of the image."""
cover_image_name: Optional[str] = Field(
description="The name of the cover image of the board."
)
cover_image_name: Optional[str] = Field(description="The name of the cover image of the board.")
"""The name of the cover image of the board."""
class BoardDTO(BoardRecord):
"""Deserialized board record with cover image URL and image count."""
cover_image_name: Optional[str] = Field(
description="The name of the board's cover image."
)
cover_image_name: Optional[str] = Field(description="The name of the board's cover image.")
"""The URL of the thumbnail of the most recent image in the board."""
image_count: int = Field(description="The number of images in the board.")
"""The number of images in the board."""

32
invokeai/app/services/models/image_record.py
View File

@ -20,17 +20,11 @@ class ImageRecord(BaseModel):
"""The actual width of the image in px. This may be different from the width in metadata."""
height: int = Field(description="The height of the image in px.")
"""The actual height of the image in px. This may be different from the height in metadata."""
created_at: Union[datetime.datetime, str] = Field(
description="The created timestamp of the image."
)
created_at: Union[datetime.datetime, str] = Field(description="The created timestamp of the image.")
"""The created timestamp of the image."""
updated_at: Union[datetime.datetime, str] = Field(
description="The updated timestamp of the image."
)
updated_at: Union[datetime.datetime, str] = Field(description="The updated timestamp of the image.")
"""The updated timestamp of the image."""
deleted_at: Union[datetime.datetime, str, None] = Field(
description="The deleted timestamp of the image."
)
deleted_at: Union[datetime.datetime, str, None] = Field(description="The deleted timestamp of the image.")
"""The deleted timestamp of the image."""
is_intermediate: bool = Field(description="Whether this is an intermediate image.")
"""Whether this is an intermediate image."""
@ -55,18 +49,14 @@ class ImageRecordChanges(BaseModel, extra=Extra.forbid):
- `is_intermediate`: change the image's `is_intermediate` flag
"""
image_category: Optional[ImageCategory] = Field(
description="The image's new category."
)
image_category: Optional[ImageCategory] = Field(description="The image's new category.")
"""The image's new category."""
session_id: Optional[StrictStr] = Field(
default=None,
description="The image's new session ID.",
)
"""The image's new session ID."""
is_intermediate: Optional[StrictBool] = Field(
default=None, description="The image's new `is_intermediate` flag."
)
is_intermediate: Optional[StrictBool] = Field(default=None, description="The image's new `is_intermediate` flag.")
"""The image's new `is_intermediate` flag."""
@ -84,9 +74,7 @@ class ImageUrlsDTO(BaseModel):
class ImageDTO(ImageRecord, ImageUrlsDTO):
"""Deserialized image record, enriched for the frontend."""
board_id: Optional[str] = Field(
description="The id of the board the image belongs to, if one exists."
)
board_id: Optional[str] = Field(description="The id of the board the image belongs to, if one exists.")
"""The id of the board the image belongs to, if one exists."""
pass
@ -110,12 +98,8 @@ def deserialize_image_record(image_dict: dict) -> ImageRecord:
# TODO: do we really need to handle default values here? ideally the data is the correct shape...
image_name = image_dict.get("image_name", "unknown")
image_origin = ResourceOrigin(
image_dict.get("image_origin", ResourceOrigin.INTERNAL.value)
)
image_category = ImageCategory(
image_dict.get("image_category", ImageCategory.GENERAL.value)
)
image_origin = ResourceOrigin(image_dict.get("image_origin", ResourceOrigin.INTERNAL.value))
image_category = ImageCategory(image_dict.get("image_category", ImageCategory.GENERAL.value))
width = image_dict.get("width", 0)
height = image_dict.get("height", 0)
session_id = image_dict.get("session_id", None)

42
invokeai/app/services/processor.py
View File

@ -8,6 +8,8 @@ from .invoker import InvocationProcessorABC, Invoker
from ..models.exceptions import CanceledException
import invokeai.backend.util.logging as logger
class DefaultInvocationProcessor(InvocationProcessorABC):
__invoker_thread: Thread
__stop_event: Event
@ -24,9 +26,7 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
target=self.__process,
kwargs=dict(stop_event=self.__stop_event),
)
self.__invoker_thread.daemon = (
True # TODO: make async and do not use threads
)
self.__invoker_thread.daemon = True # TODO: make async and do not use threads
self.__invoker_thread.start()
def stop(self, *args, **kwargs) -> None:
@ -47,10 +47,8 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
continue
try:
graph_execution_state = (
self.__invoker.services.graph_execution_manager.get(
queue_item.graph_execution_state_id
)
graph_execution_state = self.__invoker.services.graph_execution_manager.get(
queue_item.graph_execution_state_id
)
except Exception as e:
self.__invoker.services.logger.error("Exception while retrieving session:\n%s" % e)
@ -60,11 +58,9 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
error=traceback.format_exc(),
)
continue
try:
invocation = graph_execution_state.execution_graph.get_node(
queue_item.invocation_id
)
invocation = graph_execution_state.execution_graph.get_node(queue_item.invocation_id)
except Exception as e:
self.__invoker.services.logger.error("Exception while retrieving invocation:\n%s" % e)
self.__invoker.services.events.emit_invocation_retrieval_error(
@ -82,7 +78,7 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
self.__invoker.services.events.emit_invocation_started(
graph_execution_state_id=graph_execution_state.id,
node=invocation.dict(),
source_node_id=source_node_id
source_node_id=source_node_id,
)
# Invoke
@ -95,18 +91,14 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
)
# Check queue to see if this is canceled, and skip if so
if self.__invoker.services.queue.is_canceled(
graph_execution_state.id
):
if self.__invoker.services.queue.is_canceled(graph_execution_state.id):
continue
# Save outputs and history
graph_execution_state.complete(invocation.id, outputs)
# Save the state changes
self.__invoker.services.graph_execution_manager.set(
graph_execution_state
)
self.__invoker.services.graph_execution_manager.set(graph_execution_state)
# Send complete event
self.__invoker.services.events.emit_invocation_complete(
@ -130,9 +122,7 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
graph_execution_state.set_node_error(invocation.id, error)
# Save the state changes
self.__invoker.services.graph_execution_manager.set(
graph_execution_state
)
self.__invoker.services.graph_execution_manager.set(graph_execution_state)
self.__invoker.services.logger.error("Error while invoking:\n%s" % e)
# Send error event
@ -147,9 +137,7 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
pass
# Check queue to see if this is canceled, and skip if so
if self.__invoker.services.queue.is_canceled(
graph_execution_state.id
):
if self.__invoker.services.queue.is_canceled(graph_execution_state.id):
continue
# Queue any further commands if invoking all
@ -164,12 +152,10 @@ class DefaultInvocationProcessor(InvocationProcessorABC):
node=invocation.dict(),
source_node_id=source_node_id,
error_type=e.__class__.__name__,
error=traceback.format_exc()
error=traceback.format_exc(),
)
elif is_complete:
self.__invoker.services.events.emit_graph_execution_complete(
graph_execution_state.id
)
self.__invoker.services.events.emit_graph_execution_complete(graph_execution_state.id)
except KeyboardInterrupt:
pass # Log something? KeyboardInterrupt is probably not going to be seen by the processor

24
invokeai/app/services/sqlite.py
View File

@ -66,9 +66,7 @@ class SqliteItemStorage(ItemStorageABC, Generic[T]):
def get(self, id: str) -> Optional[T]:
try:
self._lock.acquire()
self._cursor.execute(
f"""SELECT item FROM {self._table_name} WHERE id = ?;""", (str(id),)
)
self._cursor.execute(f"""SELECT item FROM {self._table_name} WHERE id = ?;""", (str(id),))
result = self._cursor.fetchone()
finally:
self._lock.release()
@ -81,9 +79,7 @@ class SqliteItemStorage(ItemStorageABC, Generic[T]):
def get_raw(self, id: str) -> Optional[str]:
try:
self._lock.acquire()
self._cursor.execute(
f"""SELECT item FROM {self._table_name} WHERE id = ?;""", (str(id),)
)
self._cursor.execute(f"""SELECT item FROM {self._table_name} WHERE id = ?;""", (str(id),))
result = self._cursor.fetchone()
finally:
self._lock.release()
@ -96,9 +92,7 @@ class SqliteItemStorage(ItemStorageABC, Generic[T]):
def delete(self, id: str):
try:
self._lock.acquire()
self._cursor.execute(
f"""DELETE FROM {self._table_name} WHERE id = ?;""", (str(id),)
)
self._cursor.execute(f"""DELETE FROM {self._table_name} WHERE id = ?;""", (str(id),))
self._conn.commit()
finally:
self._lock.release()
@ -122,13 +116,9 @@ class SqliteItemStorage(ItemStorageABC, Generic[T]):
pageCount = int(count / per_page) + 1
return PaginatedResults[T](
items=items, page=page, pages=pageCount, per_page=per_page, total=count
)
return PaginatedResults[T](items=items, page=page, pages=pageCount, per_page=per_page, total=count)
def search(
self, query: str, page: int = 0, per_page: int = 10
) -> PaginatedResults[T]:
def search(self, query: str, page: int = 0, per_page: int = 10) -> PaginatedResults[T]:
try:
self._lock.acquire()
self._cursor.execute(
@ -149,6 +139,4 @@ class SqliteItemStorage(ItemStorageABC, Generic[T]):
pageCount = int(count / per_page) + 1
return PaginatedResults[T](
items=items, page=page, pages=pageCount, per_page=per_page, total=count
)
return PaginatedResults[T](items=items, page=page, pages=pageCount, per_page=per_page, total=count)

74
invokeai/app/util/controlnet_utils.py
View File

@ -17,16 +17,8 @@ from controlnet_aux.util import HWC3, resize_image
# If you use this, please Cite "High Quality Edge Thinning using Pure Python", Lvmin Zhang, In Mikubill/sd-webui-controlnet.
lvmin_kernels_raw = [
np.array([
[-1, -1, -1],
[0, 1, 0],
[1, 1, 1]
], dtype=np.int32),
np.array([
[0, -1, -1],
[1, 1, -1],
[0, 1, 0]
], dtype=np.int32)
np.array([[-1, -1, -1], [0, 1, 0], [1, 1, 1]], dtype=np.int32),
np.array([[0, -1, -1], [1, 1, -1], [0, 1, 0]], dtype=np.int32),
]
lvmin_kernels = []
@ -36,16 +28,8 @@ lvmin_kernels += [np.rot90(x, k=2, axes=(0, 1)) for x in lvmin_kernels_raw]
lvmin_kernels += [np.rot90(x, k=3, axes=(0, 1)) for x in lvmin_kernels_raw]
lvmin_prunings_raw = [
np.array([
[-1, -1, -1],
[-1, 1, -1],
[0, 0, -1]
], dtype=np.int32),
np.array([
[-1, -1, -1],
[-1, 1, -1],
[-1, 0, 0]
], dtype=np.int32)
np.array([[-1, -1, -1], [-1, 1, -1], [0, 0, -1]], dtype=np.int32),
np.array([[-1, -1, -1], [-1, 1, -1], [-1, 0, 0]], dtype=np.int32),
]
lvmin_prunings = []
@ -99,10 +83,10 @@ def nake_nms(x):
################################################################################
# FIXME: not using yet, if used in the future will most likely require modification of preprocessors
def pixel_perfect_resolution(
image: np.ndarray,
target_H: int,
target_W: int,
resize_mode: str,
image: np.ndarray,
target_H: int,
target_W: int,
resize_mode: str,
) -> int:
"""
Calculate the estimated resolution for resizing an image while preserving aspect ratio.
@ -135,7 +119,7 @@ def pixel_perfect_resolution(
if resize_mode == "fill_resize":
estimation = min(k0, k1) * float(min(raw_H, raw_W))
else: # "crop_resize" or "just_resize" (or possibly "just_resize_simple"?)
else: # "crop_resize" or "just_resize" (or possibly "just_resize_simple"?)
estimation = max(k0, k1) * float(min(raw_H, raw_W))
# print(f"Pixel Perfect Computation:")
@ -154,13 +138,7 @@ def pixel_perfect_resolution(
# modified for InvokeAI
###########################################################################
# def detectmap_proc(detected_map, module, resize_mode, h, w):
def np_img_resize(
np_img: np.ndarray,
resize_mode: str,
h: int,
w: int,
device: torch.device = torch.device('cpu')
):
def np_img_resize(np_img: np.ndarray, resize_mode: str, h: int, w: int, device: torch.device = torch.device("cpu")):
# if 'inpaint' in module:
# np_img = np_img.astype(np.float32)
# else:
@ -184,15 +162,14 @@ def np_img_resize(
# below is very boring but do not change these. If you change these Apple or Mac may fail.
y = torch.from_numpy(y)
y = y.float() / 255.0
y = rearrange(y, 'h w c -> 1 c h w')
y = rearrange(y, "h w c -> 1 c h w")
y = y.clone()
# y = y.to(devices.get_device_for("controlnet"))
y = y.to(device)
y = y.clone()
return y
def high_quality_resize(x: np.ndarray,
size):
def high_quality_resize(x: np.ndarray, size):
# Written by lvmin
# Super high-quality control map up-scaling, considering binary, seg, and one-pixel edges
inpaint_mask = None
@ -244,7 +221,7 @@ def np_img_resize(
return y
# if resize_mode == external_code.ResizeMode.RESIZE:
if resize_mode == "just_resize": # RESIZE
if resize_mode == "just_resize": # RESIZE
np_img = high_quality_resize(np_img, (w, h))
np_img = safe_numpy(np_img)
return get_pytorch_control(np_img), np_img
@ -270,20 +247,21 @@ def np_img_resize(
new_h, new_w, _ = np_img.shape
pad_h = max(0, (h - new_h) // 2)
pad_w = max(0, (w - new_w) // 2)
high_quality_background[pad_h:pad_h + new_h, pad_w:pad_w + new_w] = np_img
high_quality_background[pad_h : pad_h + new_h, pad_w : pad_w + new_w] = np_img
np_img = high_quality_background
np_img = safe_numpy(np_img)
return get_pytorch_control(np_img), np_img
else: # resize_mode == "crop_resize" (INNER_FIT)
else: # resize_mode == "crop_resize" (INNER_FIT)
k = max(k0, k1)
np_img = high_quality_resize(np_img, (safeint(old_w * k), safeint(old_h * k)))
new_h, new_w, _ = np_img.shape
pad_h = max(0, (new_h - h) // 2)
pad_w = max(0, (new_w - w) // 2)
np_img = np_img[pad_h:pad_h + h, pad_w:pad_w + w]
np_img = np_img[pad_h : pad_h + h, pad_w : pad_w + w]
np_img = safe_numpy(np_img)
return get_pytorch_control(np_img), np_img
def prepare_control_image(
# image used to be Union[PIL.Image.Image, List[PIL.Image.Image], torch.Tensor, List[torch.Tensor]]
# but now should be able to assume that image is a single PIL.Image, which simplifies things
@ -301,15 +279,17 @@ def prepare_control_image(
resize_mode="just_resize_simple",
):
# FIXME: implement "crop_resize_simple" and "fill_resize_simple", or pull them out
if (resize_mode == "just_resize_simple" or
resize_mode == "crop_resize_simple" or
resize_mode == "fill_resize_simple"):
if (
resize_mode == "just_resize_simple"
or resize_mode == "crop_resize_simple"
or resize_mode == "fill_resize_simple"
):
image = image.convert("RGB")
if (resize_mode == "just_resize_simple"):
if resize_mode == "just_resize_simple":
image = image.resize((width, height), resample=PIL_INTERPOLATION["lanczos"])
elif (resize_mode == "crop_resize_simple"): # not yet implemented
elif resize_mode == "crop_resize_simple": # not yet implemented
pass
elif (resize_mode == "fill_resize_simple"): # not yet implemented
elif resize_mode == "fill_resize_simple": # not yet implemented
pass
nimage = np.array(image)
nimage = nimage[None, :]
@ -320,7 +300,7 @@ def prepare_control_image(
timage = torch.from_numpy(nimage)
# use fancy lvmin controlnet resizing
elif (resize_mode == "just_resize" or resize_mode == "crop_resize" or resize_mode == "fill_resize"):
elif resize_mode == "just_resize" or resize_mode == "crop_resize" or resize_mode == "fill_resize":
nimage = np.array(image)
timage, nimage = np_img_resize(
np_img=nimage,
@ -336,7 +316,7 @@ def prepare_control_image(
exit(1)
timage = timage.to(device=device, dtype=dtype)
cfg_injection = (control_mode == "more_control" or control_mode == "unbalanced")
cfg_injection = control_mode == "more_control" or control_mode == "unbalanced"
if do_classifier_free_guidance and not cfg_injection:
timage = torch.cat([timage] * 2)
return timage

24
invokeai/app/util/step_callback.py
View File

@ -9,19 +9,16 @@ from ...backend.stable_diffusion import PipelineIntermediateState
from invokeai.app.services.config import InvokeAIAppConfig
def sample_to_lowres_estimated_image(samples, latent_rgb_factors, smooth_matrix = None):
def sample_to_lowres_estimated_image(samples, latent_rgb_factors, smooth_matrix=None):
latent_image = samples[0].permute(1, 2, 0) @ latent_rgb_factors
if smooth_matrix is not None:
latent_image = latent_image.unsqueeze(0).permute(3, 0, 1, 2)
latent_image = torch.nn.functional.conv2d(latent_image, smooth_matrix.reshape((1,1,3,3)), padding=1)
latent_image = torch.nn.functional.conv2d(latent_image, smooth_matrix.reshape((1, 1, 3, 3)), padding=1)
latent_image = latent_image.permute(1, 2, 3, 0).squeeze(0)
latents_ubyte = (
((latent_image + 1) / 2)
.clamp(0, 1) # change scale from -1..1 to 0..1
.mul(0xFF) # to 0..255
.byte()
((latent_image + 1) / 2).clamp(0, 1).mul(0xFF).byte() # change scale from -1..1 to 0..1 # to 0..255
).cpu()
return Image.fromarray(latents_ubyte.numpy())
@ -92,6 +89,7 @@ def stable_diffusion_step_callback(
total_steps=node["steps"],
)
def stable_diffusion_xl_step_callback(
context: InvocationContext,
node: dict,
@ -106,9 +104,9 @@ def stable_diffusion_xl_step_callback(
sdxl_latent_rgb_factors = torch.tensor(
[
# R G B
[ 0.3816, 0.4930, 0.5320],
[-0.3753, 0.1631, 0.1739],
[ 0.1770, 0.3588, -0.2048],
[0.3816, 0.4930, 0.5320],
[-0.3753, 0.1631, 0.1739],
[0.1770, 0.3588, -0.2048],
[-0.4350, -0.2644, -0.4289],
],
dtype=sample.dtype,
@ -117,9 +115,9 @@ def stable_diffusion_xl_step_callback(
sdxl_smooth_matrix = torch.tensor(
[
#[ 0.0478, 0.1285, 0.0478],
#[ 0.1285, 0.2948, 0.1285],
#[ 0.0478, 0.1285, 0.0478],
# [ 0.0478, 0.1285, 0.0478],
# [ 0.1285, 0.2948, 0.1285],
# [ 0.0478, 0.1285, 0.0478],
[0.0358, 0.0964, 0.0358],
[0.0964, 0.4711, 0.0964],
[0.0358, 0.0964, 0.0358],
@ -143,4 +141,4 @@ def stable_diffusion_xl_step_callback(
progress_image=ProgressImage(width=width, height=height, dataURL=dataURL),
step=step,
total_steps=total_steps,
)
)

13
invokeai/backend/__init__.py
View File

@ -1,15 +1,6 @@
"""
Initialization file for invokeai.backend
"""
from .generator import (
InvokeAIGeneratorBasicParams,
InvokeAIGenerator,
InvokeAIGeneratorOutput,
Img2Img,
Inpaint
)
from .model_management import (
ModelManager, ModelCache, BaseModelType,
ModelType, SubModelType, ModelInfo
)
from .generator import InvokeAIGeneratorBasicParams, InvokeAIGenerator, InvokeAIGeneratorOutput, Img2Img, Inpaint
from .model_management import ModelManager, ModelCache, BaseModelType, ModelType, SubModelType, ModelInfo
from .model_management.models import SilenceWarnings

207
invokeai/backend/generator/base.py
View File

@ -33,61 +33,66 @@ from ..stable_diffusion.schedulers import SCHEDULER_MAP
downsampling = 8
@dataclass
class InvokeAIGeneratorBasicParams:
seed: Optional[int]=None
width: int=512
height: int=512
cfg_scale: float=7.5
steps: int=20
ddim_eta: float=0.0
scheduler: str='ddim'
precision: str='float16'
perlin: float=0.0
threshold: float=0.0
seamless: bool=False
seamless_axes: List[str]=field(default_factory=lambda: ['x', 'y'])
h_symmetry_time_pct: Optional[float]=None
v_symmetry_time_pct: Optional[float]=None
seed: Optional[int] = None
width: int = 512
height: int = 512
cfg_scale: float = 7.5
steps: int = 20
ddim_eta: float = 0.0
scheduler: str = "ddim"
precision: str = "float16"
perlin: float = 0.0
threshold: float = 0.0
seamless: bool = False
seamless_axes: List[str] = field(default_factory=lambda: ["x", "y"])
h_symmetry_time_pct: Optional[float] = None
v_symmetry_time_pct: Optional[float] = None
variation_amount: float = 0.0
with_variations: list=field(default_factory=list)
with_variations: list = field(default_factory=list)
@dataclass
class InvokeAIGeneratorOutput:
'''
"""
InvokeAIGeneratorOutput is a dataclass that contains the outputs of a generation
operation, including the image, its seed, the model name used to generate the image
and the model hash, as well as all the generate() parameters that went into
generating the image (in .params, also available as attributes)
'''
"""
image: Image.Image
seed: int
model_hash: str
attention_maps_images: List[Image.Image]
params: Namespace
# we are interposing a wrapper around the original Generator classes so that
# old code that calls Generate will continue to work.
class InvokeAIGenerator(metaclass=ABCMeta):
def __init__(self,
model_info: dict,
params: InvokeAIGeneratorBasicParams=InvokeAIGeneratorBasicParams(),
**kwargs,
):
self.model_info=model_info
self.params=params
def __init__(
self,
model_info: dict,
params: InvokeAIGeneratorBasicParams = InvokeAIGeneratorBasicParams(),
**kwargs,
):
self.model_info = model_info
self.params = params
self.kwargs = kwargs
def generate(
self,
conditioning: tuple,
scheduler,
callback: Optional[Callable]=None,
step_callback: Optional[Callable]=None,
iterations: int=1,
callback: Optional[Callable] = None,
step_callback: Optional[Callable] = None,
iterations: int = 1,
**keyword_args,
)->Iterator[InvokeAIGeneratorOutput]:
'''
) -> Iterator[InvokeAIGeneratorOutput]:
"""
Return an iterator across the indicated number of generations.
Each time the iterator is called it will return an InvokeAIGeneratorOutput
object. Use like this:
@ -107,7 +112,7 @@ class InvokeAIGenerator(metaclass=ABCMeta):
for o in outputs:
print(o.image, o.seed)
'''
"""
generator_args = dataclasses.asdict(self.params)
generator_args.update(keyword_args)
@ -118,22 +123,21 @@ class InvokeAIGenerator(metaclass=ABCMeta):
gen_class = self._generator_class()
generator = gen_class(model, self.params.precision, **self.kwargs)
if self.params.variation_amount > 0:
generator.set_variation(generator_args.get('seed'),
generator_args.get('variation_amount'),
generator_args.get('with_variations')
)
generator.set_variation(
generator_args.get("seed"),
generator_args.get("variation_amount"),
generator_args.get("with_variations"),
)
if isinstance(model, DiffusionPipeline):
for component in [model.unet, model.vae]:
configure_model_padding(component,
generator_args.get('seamless',False),
generator_args.get('seamless_axes')
)
configure_model_padding(
component, generator_args.get("seamless", False), generator_args.get("seamless_axes")
)
else:
configure_model_padding(model,
generator_args.get('seamless',False),
generator_args.get('seamless_axes')
)
configure_model_padding(
model, generator_args.get("seamless", False), generator_args.get("seamless_axes")
)
iteration_count = range(iterations) if iterations else itertools.count(start=0, step=1)
for i in iteration_count:
@ -147,66 +151,66 @@ class InvokeAIGenerator(metaclass=ABCMeta):
image=results[0][0],
seed=results[0][1],
attention_maps_images=results[0][2],
model_hash = model_hash,
params=Namespace(model_name=model_name,**generator_args),
model_hash=model_hash,
params=Namespace(model_name=model_name, **generator_args),
)
if callback:
callback(output)
yield output
@classmethod
def schedulers(self)->List[str]:
'''
def schedulers(self) -> List[str]:
"""
Return list of all the schedulers that we currently handle.
'''
"""
return list(SCHEDULER_MAP.keys())
def load_generator(self, model: StableDiffusionGeneratorPipeline, generator_class: Type[Generator]):
return generator_class(model, self.params.precision)
@classmethod
def _generator_class(cls)->Type[Generator]:
'''
def _generator_class(cls) -> Type[Generator]:
"""
In derived classes return the name of the generator to apply.
If you don't override will return the name of the derived
class, which nicely parallels the generator class names.
'''
"""
return Generator
# ------------------------------------
class Img2Img(InvokeAIGenerator):
def generate(self,
init_image: Union[Image.Image, torch.FloatTensor],
strength: float=0.75,
**keyword_args
)->Iterator[InvokeAIGeneratorOutput]:
return super().generate(init_image=init_image,
strength=strength,
**keyword_args
)
def generate(
self, init_image: Union[Image.Image, torch.FloatTensor], strength: float = 0.75, **keyword_args
) -> Iterator[InvokeAIGeneratorOutput]:
return super().generate(init_image=init_image, strength=strength, **keyword_args)
@classmethod
def _generator_class(cls):
from .img2img import Img2Img
return Img2Img
# ------------------------------------
# Takes all the arguments of Img2Img and adds the mask image and the seam/infill stuff
class Inpaint(Img2Img):
def generate(self,
mask_image: Union[Image.Image, torch.FloatTensor],
# Seam settings - when 0, doesn't fill seam
seam_size: int = 96,
seam_blur: int = 16,
seam_strength: float = 0.7,
seam_steps: int = 30,
tile_size: int = 32,
inpaint_replace=False,
infill_method=None,
inpaint_width=None,
inpaint_height=None,
inpaint_fill: tuple(int) = (0x7F, 0x7F, 0x7F, 0xFF),
**keyword_args
)->Iterator[InvokeAIGeneratorOutput]:
def generate(
self,
mask_image: Union[Image.Image, torch.FloatTensor],
# Seam settings - when 0, doesn't fill seam
seam_size: int = 96,
seam_blur: int = 16,
seam_strength: float = 0.7,
seam_steps: int = 30,
tile_size: int = 32,
inpaint_replace=False,
infill_method=None,
inpaint_width=None,
inpaint_height=None,
inpaint_fill: tuple(int) = (0x7F, 0x7F, 0x7F, 0xFF),
**keyword_args,
) -> Iterator[InvokeAIGeneratorOutput]:
return super().generate(
mask_image=mask_image,
seam_size=seam_size,
@ -219,13 +223,16 @@ class Inpaint(Img2Img):
inpaint_width=inpaint_width,
inpaint_height=inpaint_height,
inpaint_fill=inpaint_fill,
**keyword_args
**keyword_args,
)
@classmethod
def _generator_class(cls):
from .inpaint import Inpaint
return Inpaint
class Generator:
downsampling_factor: int
latent_channels: int
@ -251,9 +258,7 @@ class Generator:
Returns a function returning an image derived from the prompt and the initial image
Return value depends on the seed at the time you call it
"""
raise NotImplementedError(
"image_iterator() must be implemented in a descendent class"
)
raise NotImplementedError("image_iterator() must be implemented in a descendent class")
def set_variation(self, seed, variation_amount, with_variations):
self.seed = seed
@ -280,9 +285,7 @@ class Generator:
scope = nullcontext
self.free_gpu_mem = free_gpu_mem
attention_maps_images = []
attention_maps_callback = lambda saver: attention_maps_images.append(
saver.get_stacked_maps_image()
)
attention_maps_callback = lambda saver: attention_maps_images.append(saver.get_stacked_maps_image())
make_image = self.get_make_image(
sampler=sampler,
init_image=init_image,
@ -327,11 +330,7 @@ class Generator:
results.append([image, seed, attention_maps_images])
if image_callback is not None:
attention_maps_image = (
None
if len(attention_maps_images) == 0
else attention_maps_images[-1]
)
attention_maps_image = None if len(attention_maps_images) == 0 else attention_maps_images[-1]
image_callback(
image,
seed,
@ -342,9 +341,7 @@ class Generator:
seed = self.new_seed()
# Free up memory from the last generation.
clear_cuda_cache = (
kwargs["clear_cuda_cache"] if "clear_cuda_cache" in kwargs else None
)
clear_cuda_cache = kwargs["clear_cuda_cache"] if "clear_cuda_cache" in kwargs else None
if clear_cuda_cache is not None:
clear_cuda_cache()
@ -371,14 +368,8 @@ class Generator:
# Get the original alpha channel of the mask if there is one.
# Otherwise it is some other black/white image format ('1', 'L' or 'RGB')
pil_init_mask = (
init_mask.getchannel("A")
if init_mask.mode == "RGBA"
else init_mask.convert("L")
)
pil_init_image = init_image.convert(
"RGBA"
) # Add an alpha channel if one doesn't exist
pil_init_mask = init_mask.getchannel("A") if init_mask.mode == "RGBA" else init_mask.convert("L")
pil_init_image = init_image.convert("RGBA") # Add an alpha channel if one doesn't exist
# Build an image with only visible pixels from source to use as reference for color-matching.
init_rgb_pixels = np.asarray(init_image.convert("RGB"), dtype=np.uint8)
@ -404,10 +395,7 @@ class Generator:
np_matched_result[:, :, :] = (
(
(
(
np_matched_result[:, :, :].astype(np.float32)
- gen_means[None, None, :]
)
(np_matched_result[:, :, :].astype(np.float32) - gen_means[None, None, :])
/ gen_std[None, None, :]
)
* init_std[None, None, :]
@ -433,9 +421,7 @@ class Generator:
else:
blurred_init_mask = pil_init_mask
multiplied_blurred_init_mask = ImageChops.multiply(
blurred_init_mask, self.pil_image.split()[-1]
)
multiplied_blurred_init_mask = ImageChops.multiply(blurred_init_mask, self.pil_image.split()[-1])
# Paste original on color-corrected generation (using blurred mask)
matched_result.paste(init_image, (0, 0), mask=multiplied_blurred_init_mask)
@ -461,10 +447,7 @@ class Generator:
latent_image = samples[0].permute(1, 2, 0) @ v1_5_latent_rgb_factors
latents_ubyte = (
((latent_image + 1) / 2)
.clamp(0, 1) # change scale from -1..1 to 0..1
.mul(0xFF) # to 0..255
.byte()
((latent_image + 1) / 2).clamp(0, 1).mul(0xFF).byte() # change scale from -1..1 to 0..1 # to 0..255
).cpu()
return Image.fromarray(latents_ubyte.numpy())
@ -494,9 +477,7 @@ class Generator:
temp_height = int((height + 7) / 8) * 8
noise = torch.stack(
[
rand_perlin_2d(
(temp_height, temp_width), (8, 8), device=self.model.device
).to(fixdevice)
rand_perlin_2d((temp_height, temp_width), (8, 8), device=self.model.device).to(fixdevice)
for _ in range(input_channels)
],
dim=0,
@ -573,8 +554,6 @@ class Generator:
device=device,
)
if self.perlin > 0.0:
perlin_noise = self.get_perlin_noise(
width // self.downsampling_factor, height // self.downsampling_factor
)
perlin_noise = self.get_perlin_noise(width // self.downsampling_factor, height // self.downsampling_factor)
x = (1 - self.perlin) * x + self.perlin * perlin_noise
return x

9
invokeai/backend/generator/img2img.py
View File

@ -77,10 +77,7 @@ class Img2Img(Generator):
callback=step_callback,
seed=seed,
)
if (
pipeline_output.attention_map_saver is not None
and attention_maps_callback is not None
):
if pipeline_output.attention_map_saver is not None and attention_maps_callback is not None:
attention_maps_callback(pipeline_output.attention_map_saver)
return pipeline.numpy_to_pil(pipeline_output.images)[0]
@ -91,7 +88,5 @@ class Img2Img(Generator):
x = torch.randn_like(like, device=device)
if self.perlin > 0.0:
shape = like.shape
x = (1 - self.perlin) * x + self.perlin * self.get_perlin_noise(
shape[3], shape[2]
)
x = (1 - self.perlin) * x + self.perlin * self.get_perlin_noise(shape[3], shape[2])
return x

51
invokeai/backend/generator/inpaint.py
View File

@ -68,15 +68,11 @@ class Inpaint(Img2Img):
return im
# Patchmatch (note, we may want to expose patch_size? Increasing it significantly impacts performance though)
im_patched_np = PatchMatch.inpaint(
im.convert("RGB"), ImageOps.invert(im.split()[-1]), patch_size=3
)
im_patched_np = PatchMatch.inpaint(im.convert("RGB"), ImageOps.invert(im.split()[-1]), patch_size=3)
im_patched = Image.fromarray(im_patched_np, mode="RGB")
return im_patched
def tile_fill_missing(
self, im: Image.Image, tile_size: int = 16, seed: Optional[int] = None
) -> Image.Image:
def tile_fill_missing(self, im: Image.Image, tile_size: int = 16, seed: Optional[int] = None) -> Image.Image:
# Only fill if there's an alpha layer
if im.mode != "RGBA":
return im
@ -127,15 +123,11 @@ class Inpaint(Img2Img):
return si
def mask_edge(
self, mask: Image.Image, edge_size: int, edge_blur: int
) -> Image.Image:
def mask_edge(self, mask: Image.Image, edge_size: int, edge_blur: int) -> Image.Image:
npimg = np.asarray(mask, dtype=np.uint8)
# Detect any partially transparent regions
npgradient = np.uint8(
255 * (1.0 - np.floor(np.abs(0.5 - np.float32(npimg) / 255.0) * 2.0))
)
npgradient = np.uint8(255 * (1.0 - np.floor(np.abs(0.5 - np.float32(npimg) / 255.0) * 2.0)))
# Detect hard edges
npedge = cv2.Canny(npimg, threshold1=100, threshold2=200)
@ -144,9 +136,7 @@ class Inpaint(Img2Img):
npmask = npgradient + npedge
# Expand
npmask = cv2.dilate(
npmask, np.ones((3, 3), np.uint8), iterations=int(edge_size / 2)
)
npmask = cv2.dilate(npmask, np.ones((3, 3), np.uint8), iterations=int(edge_size / 2))
new_mask = Image.fromarray(npmask)
@ -242,25 +232,19 @@ class Inpaint(Img2Img):
if infill_method == "patchmatch" and PatchMatch.patchmatch_available():
init_filled = self.infill_patchmatch(self.pil_image.copy())
elif infill_method == "tile":
init_filled = self.tile_fill_missing(
self.pil_image.copy(), seed=self.seed, tile_size=tile_size
)
init_filled = self.tile_fill_missing(self.pil_image.copy(), seed=self.seed, tile_size=tile_size)
elif infill_method == "solid":
solid_bg = Image.new("RGBA", init_image.size, inpaint_fill)
init_filled = Image.alpha_composite(solid_bg, init_image)
else:
raise ValueError(
f"Non-supported infill type {infill_method}", infill_method
)
raise ValueError(f"Non-supported infill type {infill_method}", infill_method)
init_filled.paste(init_image, (0, 0), init_image.split()[-1])
# Resize if requested for inpainting
if inpaint_width and inpaint_height:
init_filled = init_filled.resize((inpaint_width, inpaint_height))
debug_image(
init_filled, "init_filled", debug_status=self.enable_image_debugging
)
debug_image(init_filled, "init_filled", debug_status=self.enable_image_debugging)
# Create init tensor
init_image = image_resized_to_grid_as_tensor(init_filled.convert("RGB"))
@ -289,9 +273,7 @@ class Inpaint(Img2Img):
"mask_image AFTER multiply with pil_image",
debug_status=self.enable_image_debugging,
)
mask: torch.FloatTensor = image_resized_to_grid_as_tensor(
mask_image, normalize=False
)
mask: torch.FloatTensor = image_resized_to_grid_as_tensor(mask_image, normalize=False)
else:
mask: torch.FloatTensor = mask_image
@ -302,9 +284,9 @@ class Inpaint(Img2Img):
# todo: support cross-attention control
uc, c, _ = conditioning
conditioning_data = ConditioningData(
uc, c, cfg_scale
).add_scheduler_args_if_applicable(pipeline.scheduler, eta=ddim_eta)
conditioning_data = ConditioningData(uc, c, cfg_scale).add_scheduler_args_if_applicable(
pipeline.scheduler, eta=ddim_eta
)
def make_image(x_T: torch.Tensor, seed: int):
pipeline_output = pipeline.inpaint_from_embeddings(
@ -318,15 +300,10 @@ class Inpaint(Img2Img):
seed=seed,
)
if (
pipeline_output.attention_map_saver is not None
and attention_maps_callback is not None
):
if pipeline_output.attention_map_saver is not None and attention_maps_callback is not None:
attention_maps_callback(pipeline_output.attention_map_saver)
result = self.postprocess_size_and_mask(
pipeline.numpy_to_pil(pipeline_output.images)[0]
)
result = self.postprocess_size_and_mask(pipeline.numpy_to_pil(pipeline_output.images)[0])
# Seam paint if this is our first pass (seam_size set to 0 during seam painting)
if seam_size > 0:

4
invokeai/backend/image_util/__init__.py
View File

@ -8,9 +8,7 @@ from .txt2mask import Txt2Mask
from .util import InitImageResizer, make_grid
def debug_image(
debug_image, debug_text, debug_show=True, debug_result=False, debug_status=False
):
def debug_image(debug_image, debug_text, debug_show=True, debug_result=False, debug_status=False):
if not debug_status:
return

14
invokeai/backend/image_util/invisible_watermark.py
View File

@ -9,26 +9,26 @@ from PIL import Image
from imwatermark import WatermarkEncoder
from invokeai.app.services.config import InvokeAIAppConfig
import invokeai.backend.util.logging as logger
config = InvokeAIAppConfig.get_config()
class InvisibleWatermark:
"""
Wrapper around InvisibleWatermark module.
"""
@classmethod
def invisible_watermark_available(self) -> bool:
return config.invisible_watermark
@classmethod
def add_watermark(self, image: Image, watermark_text:str) -> Image:
def add_watermark(self, image: Image, watermark_text: str) -> Image:
if not self.invisible_watermark_available():
return image
logger.debug(f'Applying invisible watermark "{watermark_text}"')
bgr = cv2.cvtColor(np.array(image.convert("RGB")), cv2.COLOR_RGB2BGR)
encoder = WatermarkEncoder()
encoder.set_watermark('bytes', watermark_text.encode('utf-8'))
bgr_encoded = encoder.encode(bgr, 'dwtDct')
return Image.fromarray(
cv2.cvtColor(bgr_encoded, cv2.COLOR_BGR2RGB)
).convert("RGBA")
encoder.set_watermark("bytes", watermark_text.encode("utf-8"))
bgr_encoded = encoder.encode(bgr, "dwtDct")
return Image.fromarray(cv2.cvtColor(bgr_encoded, cv2.COLOR_BGR2RGB)).convert("RGBA")

2
invokeai/backend/image_util/patchmatch.py
View File

@ -7,8 +7,10 @@ be suppressed or deferred
import numpy as np
import invokeai.backend.util.logging as logger
from invokeai.app.services.config import InvokeAIAppConfig
config = InvokeAIAppConfig.get_config()
class PatchMatch:
"""
Thin class wrapper around the patchmatch function.

8
invokeai/backend/image_util/pngwriter.py
View File

@ -34,9 +34,7 @@ class PngWriter:
# saves image named _image_ to outdir/name, writing metadata from prompt
# returns full path of output
def save_image_and_prompt_to_png(
self, image, dream_prompt, name, metadata=None, compress_level=6
):
def save_image_and_prompt_to_png(self, image, dream_prompt, name, metadata=None, compress_level=6):
path = os.path.join(self.outdir, name)
info = PngImagePlugin.PngInfo()
info.add_text("Dream", dream_prompt)
@ -114,8 +112,6 @@ class PromptFormatter:
if opt.variation_amount > 0:
switches.append(f"-v{opt.variation_amount}")
if opt.with_variations:
formatted_variations = ",".join(
f"{seed}:{weight}" for seed, weight in opt.with_variations
)
formatted_variations = ",".join(f"{seed}:{weight}" for seed, weight in opt.with_variations)
switches.append(f"-V{formatted_variations}")
return " ".join(switches)

23
invokeai/backend/image_util/safety_checker.py
View File

@ -9,14 +9,17 @@ from invokeai.backend import SilenceWarnings
from invokeai.app.services.config import InvokeAIAppConfig
from invokeai.backend.util.devices import choose_torch_device
import invokeai.backend.util.logging as logger
config = InvokeAIAppConfig.get_config()
CHECKER_PATH = 'core/convert/stable-diffusion-safety-checker'
CHECKER_PATH = "core/convert/stable-diffusion-safety-checker"
class SafetyChecker:
"""
Wrapper around SafetyChecker model.
"""
safety_checker = None
feature_extractor = None
tried_load: bool = False
@ -25,21 +28,19 @@ class SafetyChecker:
def _load_safety_checker(self):
if self.tried_load:
return
if config.nsfw_checker:
try:
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from transformers import AutoFeatureExtractor
self.safety_checker = StableDiffusionSafetyChecker.from_pretrained(
config.models_path / CHECKER_PATH
)
self.feature_extractor = AutoFeatureExtractor.from_pretrained(
config.models_path / CHECKER_PATH)
logger.info('NSFW checker initialized')
self.safety_checker = StableDiffusionSafetyChecker.from_pretrained(config.models_path / CHECKER_PATH)
self.feature_extractor = AutoFeatureExtractor.from_pretrained(config.models_path / CHECKER_PATH)
logger.info("NSFW checker initialized")
except Exception as e:
logger.warning(f'Could not load NSFW checker: {str(e)}')
logger.warning(f"Could not load NSFW checker: {str(e)}")
else:
logger.info('NSFW checker loading disabled')
logger.info("NSFW checker loading disabled")
self.tried_load = True
@classmethod
@ -51,7 +52,7 @@ class SafetyChecker:
def has_nsfw_concept(self, image: Image) -> bool:
if not self.safety_checker_available():
return False
device = choose_torch_device()
features = self.feature_extractor([image], return_tensors="pt")
features.to(device)

16
invokeai/backend/image_util/seamless.py
View File

@ -5,12 +5,8 @@ def _conv_forward_asymmetric(self, input, weight, bias):
"""
Patch for Conv2d._conv_forward that supports asymmetric padding
"""
working = nn.functional.pad(
input, self.asymmetric_padding["x"], mode=self.asymmetric_padding_mode["x"]
)
working = nn.functional.pad(
working, self.asymmetric_padding["y"], mode=self.asymmetric_padding_mode["y"]
)
working = nn.functional.pad(input, self.asymmetric_padding["x"], mode=self.asymmetric_padding_mode["x"])
working = nn.functional.pad(working, self.asymmetric_padding["y"], mode=self.asymmetric_padding_mode["y"])
return nn.functional.conv2d(
working,
weight,
@ -32,18 +28,14 @@ def configure_model_padding(model, seamless, seamless_axes):
if seamless:
m.asymmetric_padding_mode = {}
m.asymmetric_padding = {}
m.asymmetric_padding_mode["x"] = (
"circular" if ("x" in seamless_axes) else "constant"
)
m.asymmetric_padding_mode["x"] = "circular" if ("x" in seamless_axes) else "constant"
m.asymmetric_padding["x"] = (
m._reversed_padding_repeated_twice[0],
m._reversed_padding_repeated_twice[1],
0,
0,
)
m.asymmetric_padding_mode["y"] = (
"circular" if ("y" in seamless_axes) else "constant"
)
m.asymmetric_padding_mode["y"] = "circular" if ("y" in seamless_axes) else "constant"
m.asymmetric_padding["y"] = (
0,
0,

29
invokeai/backend/image_util/txt2mask.py
View File

@ -39,23 +39,18 @@ CLIPSEG_MODEL = "CIDAS/clipseg-rd64-refined"
CLIPSEG_SIZE = 352
config = InvokeAIAppConfig.get_config()
class SegmentedGrayscale(object):
def __init__(self, image: Image, heatmap: torch.Tensor):
self.heatmap = heatmap
self.image = image
def to_grayscale(self, invert: bool = False) -> Image:
return self._rescale(
Image.fromarray(
np.uint8(255 - self.heatmap * 255 if invert else self.heatmap * 255)
)
)
return self._rescale(Image.fromarray(np.uint8(255 - self.heatmap * 255 if invert else self.heatmap * 255)))
def to_mask(self, threshold: float = 0.5) -> Image:
discrete_heatmap = self.heatmap.lt(threshold).int()
return self._rescale(
Image.fromarray(np.uint8(discrete_heatmap * 255), mode="L")
)
return self._rescale(Image.fromarray(np.uint8(discrete_heatmap * 255), mode="L"))
def to_transparent(self, invert: bool = False) -> Image:
transparent_image = self.image.copy()
@ -67,11 +62,7 @@ class SegmentedGrayscale(object):
# unscales and uncrops the 352x352 heatmap so that it matches the image again
def _rescale(self, heatmap: Image) -> Image:
size = (
self.image.width
if (self.image.width > self.image.height)
else self.image.height
)
size = self.image.width if (self.image.width > self.image.height) else self.image.height
resized_image = heatmap.resize((size, size), resample=Image.Resampling.LANCZOS)
return resized_image.crop((0, 0, self.image.width, self.image.height))
@ -87,12 +78,8 @@ class Txt2Mask(object):
# BUG: we are not doing anything with the device option at this time
self.device = device
self.processor = AutoProcessor.from_pretrained(
CLIPSEG_MODEL, cache_dir=config.cache_dir
)
self.model = CLIPSegForImageSegmentation.from_pretrained(
CLIPSEG_MODEL, cache_dir=config.cache_dir
)
self.processor = AutoProcessor.from_pretrained(CLIPSEG_MODEL, cache_dir=config.cache_dir)
self.model = CLIPSegForImageSegmentation.from_pretrained(CLIPSEG_MODEL, cache_dir=config.cache_dir)
@torch.no_grad()
def segment(self, image, prompt: str) -> SegmentedGrayscale:
@ -107,9 +94,7 @@ class Txt2Mask(object):
image = ImageOps.exif_transpose(image)
img = self._scale_and_crop(image)
inputs = self.processor(
text=[prompt], images=[img], padding=True, return_tensors="pt"
)
inputs = self.processor(text=[prompt], images=[img], padding=True, return_tensors="pt")
outputs = self.model(**inputs)
heatmap = torch.sigmoid(outputs.logits)
return SegmentedGrayscale(image, heatmap)

41
invokeai/backend/install/check_root.py
View File

@ -6,28 +6,31 @@ from invokeai.app.services.config import (
InvokeAIAppConfig,
)
def check_invokeai_root(config: InvokeAIAppConfig):
try:
assert config.model_conf_path.exists(), f'{config.model_conf_path} not found'
assert config.db_path.parent.exists(), f'{config.db_path.parent} not found'
assert config.models_path.exists(), f'{config.models_path} not found'
assert config.model_conf_path.exists(), f"{config.model_conf_path} not found"
assert config.db_path.parent.exists(), f"{config.db_path.parent} not found"
assert config.models_path.exists(), f"{config.models_path} not found"
for model in [
'CLIP-ViT-bigG-14-laion2B-39B-b160k',
'bert-base-uncased',
'clip-vit-large-patch14',
'sd-vae-ft-mse',
'stable-diffusion-2-clip',
'stable-diffusion-safety-checker']:
path = config.models_path / f'core/convert/{model}'
assert path.exists(), f'{path} is missing'
"CLIP-ViT-bigG-14-laion2B-39B-b160k",
"bert-base-uncased",
"clip-vit-large-patch14",
"sd-vae-ft-mse",
"stable-diffusion-2-clip",
"stable-diffusion-safety-checker",
]:
path = config.models_path / f"core/convert/{model}"
assert path.exists(), f"{path} is missing"
except Exception as e:
print()
print(f'An exception has occurred: {str(e)}')
print('== STARTUP ABORTED ==')
print('** One or more necessary files is missing from your InvokeAI root directory **')
print('** Please rerun the configuration script to fix this problem. **')
print('** From the launcher, selection option [7]. **')
print('** From the command line, activate the virtual environment and run "invokeai-configure --yes --skip-sd-weights" **')
input('Press any key to continue...')
print(f"An exception has occurred: {str(e)}")
print("== STARTUP ABORTED ==")
print("** One or more necessary files is missing from your InvokeAI root directory **")
print("** Please rerun the configuration script to fix this problem. **")
print("** From the launcher, selection option [7]. **")
print(
'** From the command line, activate the virtual environment and run "invokeai-configure --yes --skip-sd-weights" **'
)
input("Press any key to continue...")
sys.exit(0)

286
invokeai/backend/install/invokeai_configure.py
View File

@ -60,9 +60,7 @@ from invokeai.backend.install.model_install_backend import (
InstallSelections,
ModelInstall,
)
from invokeai.backend.model_management.model_probe import (
ModelType, BaseModelType
)
from invokeai.backend.model_management.model_probe import ModelType, BaseModelType
warnings.filterwarnings("ignore")
transformers.logging.set_verbosity_error()
@ -77,7 +75,7 @@ Model_dir = "models"
Default_config_file = config.model_conf_path
SD_Configs = config.legacy_conf_path
PRECISION_CHOICES = ['auto','float16','float32']
PRECISION_CHOICES = ["auto", "float16", "float32"]
INIT_FILE_PREAMBLE = """# InvokeAI initialization file
# This is the InvokeAI initialization file, which contains command-line default values.
@ -85,7 +83,8 @@ INIT_FILE_PREAMBLE = """# InvokeAI initialization file
# or renaming it and then running invokeai-configure again.
"""
logger=InvokeAILogger.getLogger()
logger = InvokeAILogger.getLogger()
# --------------------------------------------
def postscript(errors: None):
@ -108,7 +107,9 @@ Add the '--help' argument to see all of the command-line switches available for
"""
else:
message = "\n** There were errors during installation. It is possible some of the models were not fully downloaded.\n"
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."
@ -169,9 +170,7 @@ def download_with_progress_bar(model_url: str, model_dest: str, label: str = "th
logger.info(f"Installing {label} model file {model_url}...")
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))
)
request.urlretrieve(model_url, model_dest, ProgressBar(os.path.basename(model_dest)))
logger.info("...downloaded successfully")
else:
logger.info("...exists")
@ -182,90 +181,93 @@ def download_with_progress_bar(model_url: str, model_dest: str, label: str = "th
def download_conversion_models():
target_dir = config.root_path / 'models/core/convert'
target_dir = config.root_path / "models/core/convert"
kwargs = dict() # for future use
try:
logger.info('Downloading core tokenizers and text encoders')
logger.info("Downloading core tokenizers and text encoders")
# bert
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=DeprecationWarning)
bert = BertTokenizerFast.from_pretrained("bert-base-uncased", **kwargs)
bert.save_pretrained(target_dir / 'bert-base-uncased', safe_serialization=True)
bert.save_pretrained(target_dir / "bert-base-uncased", safe_serialization=True)
# sd-1
repo_id = 'openai/clip-vit-large-patch14'
hf_download_from_pretrained(CLIPTokenizer, repo_id, target_dir / 'clip-vit-large-patch14')
hf_download_from_pretrained(CLIPTextModel, repo_id, target_dir / 'clip-vit-large-patch14')
repo_id = "openai/clip-vit-large-patch14"
hf_download_from_pretrained(CLIPTokenizer, repo_id, target_dir / "clip-vit-large-patch14")
hf_download_from_pretrained(CLIPTextModel, repo_id, target_dir / "clip-vit-large-patch14")
# sd-2
repo_id = "stabilityai/stable-diffusion-2"
pipeline = CLIPTokenizer.from_pretrained(repo_id, subfolder="tokenizer", **kwargs)
pipeline.save_pretrained(target_dir / 'stable-diffusion-2-clip' / 'tokenizer', safe_serialization=True)
pipeline.save_pretrained(target_dir / "stable-diffusion-2-clip" / "tokenizer", safe_serialization=True)
pipeline = CLIPTextModel.from_pretrained(repo_id, subfolder="text_encoder", **kwargs)
pipeline.save_pretrained(target_dir / 'stable-diffusion-2-clip' / 'text_encoder', safe_serialization=True)
pipeline.save_pretrained(target_dir / "stable-diffusion-2-clip" / "text_encoder", safe_serialization=True)
# sd-xl - tokenizer_2
repo_id = "laion/CLIP-ViT-bigG-14-laion2B-39B-b160k"
_, model_name = repo_id.split('/')
_, model_name = repo_id.split("/")
pipeline = CLIPTokenizer.from_pretrained(repo_id, **kwargs)
pipeline.save_pretrained(target_dir / model_name, safe_serialization=True)
pipeline = CLIPTextConfig.from_pretrained(repo_id, **kwargs)
pipeline.save_pretrained(target_dir / model_name, safe_serialization=True)
# VAE
logger.info('Downloading stable diffusion VAE')
vae = AutoencoderKL.from_pretrained('stabilityai/sd-vae-ft-mse', **kwargs)
vae.save_pretrained(target_dir / 'sd-vae-ft-mse', safe_serialization=True)
logger.info("Downloading stable diffusion VAE")
vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse", **kwargs)
vae.save_pretrained(target_dir / "sd-vae-ft-mse", safe_serialization=True)
# safety checking
logger.info('Downloading safety checker')
logger.info("Downloading safety checker")
repo_id = "CompVis/stable-diffusion-safety-checker"
pipeline = AutoFeatureExtractor.from_pretrained(repo_id,**kwargs)
pipeline.save_pretrained(target_dir / 'stable-diffusion-safety-checker', safe_serialization=True)
pipeline = AutoFeatureExtractor.from_pretrained(repo_id, **kwargs)
pipeline.save_pretrained(target_dir / "stable-diffusion-safety-checker", safe_serialization=True)
pipeline = StableDiffusionSafetyChecker.from_pretrained(repo_id,**kwargs)
pipeline.save_pretrained(target_dir / 'stable-diffusion-safety-checker', safe_serialization=True)
pipeline = StableDiffusionSafetyChecker.from_pretrained(repo_id, **kwargs)
pipeline.save_pretrained(target_dir / "stable-diffusion-safety-checker", safe_serialization=True)
except KeyboardInterrupt:
raise
except Exception as e:
logger.error(str(e))
# ---------------------------------------------
def download_realesrgan():
logger.info("Installing ESRGAN Upscaling models...")
URLs = [
dict(
url = "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth",
dest = "core/upscaling/realesrgan/RealESRGAN_x4plus.pth",
description = "RealESRGAN_x4plus.pth",
url="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth",
dest="core/upscaling/realesrgan/RealESRGAN_x4plus.pth",
description="RealESRGAN_x4plus.pth",
),
dict(
url = "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth",
dest = "core/upscaling/realesrgan/RealESRGAN_x4plus_anime_6B.pth",
description = "RealESRGAN_x4plus_anime_6B.pth",
url="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth",
dest="core/upscaling/realesrgan/RealESRGAN_x4plus_anime_6B.pth",
description="RealESRGAN_x4plus_anime_6B.pth",
),
dict(
url= "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth",
dest= "core/upscaling/realesrgan/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth",
description = "ESRGAN_SRx4_DF2KOST_official.pth",
url="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth",
dest="core/upscaling/realesrgan/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth",
description="ESRGAN_SRx4_DF2KOST_official.pth",
),
dict(
url= "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth",
dest= "core/upscaling/realesrgan/RealESRGAN_x2plus.pth",
description = "RealESRGAN_x2plus.pth",
url="https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth",
dest="core/upscaling/realesrgan/RealESRGAN_x2plus.pth",
description="RealESRGAN_x2plus.pth",
),
]
for model in URLs:
download_with_progress_bar(model['url'], config.models_path / model['dest'], model['description'])
download_with_progress_bar(model["url"], config.models_path / model["dest"], model["description"])
# ---------------------------------------------
def download_support_models():
download_realesrgan()
download_conversion_models()
# -------------------------------------
def get_root(root: str = None) -> str:
if root:
@ -275,6 +277,7 @@ def get_root(root: str = None) -> str:
else:
return str(config.root_path)
# -------------------------------------
class editOptsForm(CyclingForm, npyscreen.FormMultiPage):
# for responsive resizing - disabled
@ -283,14 +286,14 @@ class editOptsForm(CyclingForm, npyscreen.FormMultiPage):
def create(self):
program_opts = self.parentApp.program_opts
old_opts = self.parentApp.invokeai_opts
first_time = not (config.root_path / 'invokeai.yaml').exists()
first_time = not (config.root_path / "invokeai.yaml").exists()
access_token = HfFolder.get_token()
window_width, window_height = get_terminal_size()
label = """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.
"""
for i in textwrap.wrap(label,width=window_width-6):
for i in textwrap.wrap(label, width=window_width - 6):
self.add_widget_intelligent(
npyscreen.FixedText,
value=i,
@ -300,7 +303,7 @@ Use cursor arrows to make a checkbox selection, and space to toggle.
self.nextrely += 1
label = """HuggingFace access token (OPTIONAL) for automatic model downloads. See https://huggingface.co/settings/tokens."""
for line in textwrap.wrap(label,width=window_width-6):
for line in textwrap.wrap(label, width=window_width - 6):
self.add_widget_intelligent(
npyscreen.FixedText,
value=line,
@ -343,7 +346,7 @@ Use cursor arrows to make a checkbox selection, and space to toggle.
relx=50,
scroll_exit=True,
)
self.nextrely -=1
self.nextrely -= 1
self.always_use_cpu = self.add_widget_intelligent(
npyscreen.Checkbox,
name="Force CPU to be used on GPU systems",
@ -351,10 +354,8 @@ Use cursor arrows to make a checkbox selection, and space to toggle.
relx=80,
scroll_exit=True,
)
precision = old_opts.precision or (
"float32" if program_opts.full_precision else "auto"
)
self.nextrely +=1
precision = old_opts.precision or ("float32" if program_opts.full_precision else "auto")
self.nextrely += 1
self.add_widget_intelligent(
npyscreen.TitleFixedText,
name="Floating Point Precision",
@ -363,10 +364,10 @@ Use cursor arrows to make a checkbox selection, and space to toggle.
color="CONTROL",
scroll_exit=True,
)
self.nextrely -=1
self.nextrely -= 1
self.precision = self.add_widget_intelligent(
SingleSelectColumns,
columns = 3,
columns=3,
name="Precision",
values=PRECISION_CHOICES,
value=PRECISION_CHOICES.index(precision),
@ -398,25 +399,25 @@ Use cursor arrows to make a checkbox selection, and space to toggle.
scroll_exit=True,
)
self.autoimport_dirs = {}
self.autoimport_dirs['autoimport_dir'] = self.add_widget_intelligent(
FileBox,
name=f'Folder to recursively scan for new checkpoints, ControlNets, LoRAs and TI models',
value=str(config.root_path / config.autoimport_dir),
select_dir=True,
must_exist=False,
use_two_lines=False,
labelColor="GOOD",
begin_entry_at=32,
max_height = 3,
scroll_exit=True
)
self.autoimport_dirs["autoimport_dir"] = self.add_widget_intelligent(
FileBox,
name=f"Folder to recursively scan for new checkpoints, ControlNets, LoRAs and TI models",
value=str(config.root_path / config.autoimport_dir),
select_dir=True,
must_exist=False,
use_two_lines=False,
labelColor="GOOD",
begin_entry_at=32,
max_height=3,
scroll_exit=True,
)
self.nextrely += 1
label = """BY DOWNLOADING THE STABLE DIFFUSION WEIGHT FILES, YOU AGREE TO HAVE READ
AND ACCEPTED THE CREATIVEML RESPONSIBLE AI LICENSES LOCATED AT
https://huggingface.co/spaces/CompVis/stable-diffusion-license and
https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/LICENSE.md
"""
for i in textwrap.wrap(label,width=window_width-6):
for i in textwrap.wrap(label, width=window_width - 6):
self.add_widget_intelligent(
npyscreen.FixedText,
value=i,
@ -431,11 +432,7 @@ https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/LICENS
scroll_exit=True,
)
self.nextrely += 1
label = (
"DONE"
if program_opts.skip_sd_weights or program_opts.default_only
else "NEXT"
)
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,
@ -454,13 +451,11 @@ https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/LICENS
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"
)
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."
@ -478,11 +473,11 @@ https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/LICENS
new_opts = Namespace()
for attr in [
"outdir",
"free_gpu_mem",
"max_cache_size",
"xformers_enabled",
"always_use_cpu",
"outdir",
"free_gpu_mem",
"max_cache_size",
"xformers_enabled",
"always_use_cpu",
]:
setattr(new_opts, attr, getattr(self, attr).value)
@ -495,7 +490,7 @@ 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]]
return new_opts
@ -534,19 +529,20 @@ def edit_opts(program_opts: Namespace, invokeai_opts: Namespace) -> argparse.Nam
editApp.run()
return editApp.new_opts()
def default_startup_options(init_file: Path) -> Namespace:
opts = InvokeAIAppConfig.get_config()
return opts
def default_user_selections(program_opts: Namespace) -> InstallSelections:
try:
installer = ModelInstall(config)
except omegaconf.errors.ConfigKeyError:
logger.warning('Your models.yaml file is corrupt or out of date. Reinitializing')
logger.warning("Your models.yaml file is corrupt or out of date. Reinitializing")
initialize_rootdir(config.root_path, True)
installer = ModelInstall(config)
models = installer.all_models()
return InstallSelections(
install_models=[models[installer.default_model()].path or models[installer.default_model()].repo_id]
@ -556,55 +552,46 @@ def default_user_selections(program_opts: Namespace) -> InstallSelections:
else list(),
)
# -------------------------------------
def initialize_rootdir(root: Path, yes_to_all: bool = False):
logger.info("Initializing InvokeAI runtime directory")
for name in (
"models",
"databases",
"text-inversion-output",
"text-inversion-training-data",
"configs"
):
for name in ("models", "databases", "text-inversion-output", "text-inversion-training-data", "configs"):
os.makedirs(os.path.join(root, name), exist_ok=True)
for model_type in ModelType:
Path(root, 'autoimport', model_type.value).mkdir(parents=True, exist_ok=True)
Path(root, "autoimport", model_type.value).mkdir(parents=True, exist_ok=True)
configs_src = Path(configs.__path__[0])
configs_dest = root / "configs"
if not os.path.samefile(configs_src, configs_dest):
shutil.copytree(configs_src, configs_dest, dirs_exist_ok=True)
dest = root / 'models'
dest = root / "models"
for model_base in BaseModelType:
for model_type in ModelType:
path = dest / model_base.value / model_type.value
path.mkdir(parents=True, exist_ok=True)
path = dest / 'core'
path = dest / "core"
path.mkdir(parents=True, exist_ok=True)
maybe_create_models_yaml(root)
def maybe_create_models_yaml(root: Path):
models_yaml = root / 'configs' / 'models.yaml'
models_yaml = root / "configs" / "models.yaml"
if models_yaml.exists():
if OmegaConf.load(models_yaml).get('__metadata__'): # up to date
if OmegaConf.load(models_yaml).get("__metadata__"): # up to date
return
else:
logger.info('Creating new models.yaml, original saved as models.yaml.orig')
models_yaml.rename(models_yaml.parent / 'models.yaml.orig')
with open(models_yaml,'w') as yaml_file:
yaml_file.write(yaml.dump({'__metadata__':
{'version':'3.0.0'}
}
)
)
logger.info("Creating new models.yaml, original saved as models.yaml.orig")
models_yaml.rename(models_yaml.parent / "models.yaml.orig")
with open(models_yaml, "w") as yaml_file:
yaml_file.write(yaml.dump({"__metadata__": {"version": "3.0.0"}}))
# -------------------------------------
def run_console_ui(
program_opts: Namespace, initfile: Path = None
) -> (Namespace, Namespace):
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)
invokeai_opts.root = program_opts.root
@ -616,8 +603,9 @@ def run_console_ui(
# the install-models application spawns a subprocess to install
# models, and will crash unless this is set before running.
import torch
torch.multiprocessing.set_start_method("spawn")
editApp = EditOptApplication(program_opts, invokeai_opts)
editApp.run()
if editApp.user_cancelled:
@ -634,39 +622,42 @@ def write_opts(opts: Namespace, init_file: Path):
# this will load current settings
new_config = InvokeAIAppConfig.get_config()
new_config.root = config.root
for key,value in opts.__dict__.items():
if hasattr(new_config,key):
setattr(new_config,key,value)
with open(init_file,'w', encoding='utf-8') as file:
for key, value in opts.__dict__.items():
if hasattr(new_config, key):
setattr(new_config, key, value)
with open(init_file, "w", encoding="utf-8") as file:
file.write(new_config.to_yaml())
if hasattr(opts,'hf_token') and opts.hf_token:
if hasattr(opts, "hf_token") and opts.hf_token:
HfLogin(opts.hf_token)
# -------------------------------------
def default_output_dir() -> Path:
return config.root_path / "outputs"
# -------------------------------------
def write_default_options(program_opts: Namespace, initfile: Path):
opt = default_startup_options(initfile)
write_opts(opt, initfile)
# -------------------------------------
# Here we bring in
# the legacy Args object in order to parse
# the old init file and write out the new
# yaml format.
def migrate_init_file(legacy_format:Path):
old = legacy_parser.parse_args([f'@{str(legacy_format)}'])
def migrate_init_file(legacy_format: Path):
old = legacy_parser.parse_args([f"@{str(legacy_format)}"])
new = InvokeAIAppConfig.get_config()
fields = list(get_type_hints(InvokeAIAppConfig).keys())
for attr in fields:
if hasattr(old,attr):
setattr(new,attr,getattr(old,attr))
if hasattr(old, attr):
setattr(new, attr, getattr(old, attr))
# a few places where the field names have changed and we have to
# manually add in the new names/values
@ -674,40 +665,43 @@ def migrate_init_file(legacy_format:Path):
new.conf_path = old.conf
new.root = legacy_format.parent.resolve()
invokeai_yaml = legacy_format.parent / 'invokeai.yaml'
with open(invokeai_yaml,"w", encoding="utf-8") as outfile:
invokeai_yaml = legacy_format.parent / "invokeai.yaml"
with open(invokeai_yaml, "w", encoding="utf-8") as outfile:
outfile.write(new.to_yaml())
legacy_format.replace(legacy_format.parent / 'invokeai.init.orig')
legacy_format.replace(legacy_format.parent / "invokeai.init.orig")
# -------------------------------------
def migrate_models(root: Path):
from invokeai.backend.install.migrate_to_3 import do_migrate
do_migrate(root, root)
def migrate_if_needed(opt: Namespace, root: Path)->bool:
# We check for to see if the runtime directory is correctly initialized.
old_init_file = root / 'invokeai.init'
new_init_file = root / 'invokeai.yaml'
old_hub = root / 'models/hub'
migration_needed = (old_init_file.exists() and not new_init_file.exists()) and old_hub.exists()
if migration_needed:
if opt.yes_to_all or \
yes_or_no(f'{str(config.root_path)} appears to be a 2.3 format root directory. Convert to version 3.0?'):
logger.info('** Migrating invokeai.init to invokeai.yaml')
def migrate_if_needed(opt: Namespace, root: Path) -> bool:
# We check for to see if the runtime directory is correctly initialized.
old_init_file = root / "invokeai.init"
new_init_file = root / "invokeai.yaml"
old_hub = root / "models/hub"
migration_needed = (old_init_file.exists() and not new_init_file.exists()) and old_hub.exists()
if migration_needed:
if opt.yes_to_all or yes_or_no(
f"{str(config.root_path)} appears to be a 2.3 format root directory. Convert to version 3.0?"
):
logger.info("** Migrating invokeai.init to invokeai.yaml")
migrate_init_file(old_init_file)
config.parse_args(argv=[],conf=OmegaConf.load(new_init_file))
config.parse_args(argv=[], conf=OmegaConf.load(new_init_file))
if old_hub.exists():
migrate_models(config.root_path)
else:
print('Cannot continue without conversion. Aborting.')
print("Cannot continue without conversion. Aborting.")
return migration_needed
# -------------------------------------
def main():
parser = argparse.ArgumentParser(description="InvokeAI model downloader")
@ -764,9 +758,9 @@ def main():
invoke_args = []
if opt.root:
invoke_args.extend(['--root',opt.root])
invoke_args.extend(["--root", opt.root])
if opt.full_precision:
invoke_args.extend(['--precision','float32'])
invoke_args.extend(["--precision", "float32"])
config.parse_args(invoke_args)
logger = InvokeAILogger().getLogger(config=config)
@ -782,22 +776,18 @@ def main():
initialize_rootdir(config.root_path, opt.yes_to_all)
models_to_download = default_user_selections(opt)
new_init_file = config.root_path / 'invokeai.yaml'
new_init_file = config.root_path / "invokeai.yaml"
if opt.yes_to_all:
write_default_options(opt, new_init_file)
init_options = Namespace(
precision="float32" if opt.full_precision else "float16"
)
init_options = Namespace(precision="float32" if opt.full_precision else "float16")
else:
init_options, models_to_download = run_console_ui(opt, new_init_file)
if init_options:
write_opts(init_options, new_init_file)
else:
logger.info(
'\n** CANCELLED AT USER\'S REQUEST. USE THE "invoke.sh" LAUNCHER TO RUN LATER **\n'
)
logger.info('\n** CANCELLED AT USER\'S REQUEST. USE THE "invoke.sh" LAUNCHER TO RUN LATER **\n')
sys.exit(0)
if opt.skip_support_models:
logger.info("Skipping support models at user's request")
else:
@ -811,7 +801,7 @@ def main():
postscript(errors=errors)
if not opt.yes_to_all:
input('Press any key to continue...')
input("Press any key to continue...")
except KeyboardInterrupt:
print("\nGoodbye! Come back soon.")

341
invokeai/backend/install/legacy_arg_parsing.py
View File

@ -47,17 +47,18 @@ PRECISION_CHOICES = [
"float16",
]
class FileArgumentParser(ArgumentParser):
"""
Supports reading defaults from an init file.
"""
def convert_arg_line_to_args(self, arg_line):
return shlex.split(arg_line, comments=True)
legacy_parser = FileArgumentParser(
description=
"""
description="""
Generate images using Stable Diffusion.
Use --web to launch the web interface.
Use --from_file to load prompts from a file path or standard input ("-").
@ -65,304 +66,279 @@ Generate images using Stable Diffusion.
Other command-line arguments are defaults that can usually be overridden
prompt the command prompt.
""",
fromfile_prefix_chars='@',
fromfile_prefix_chars="@",
)
general_group = legacy_parser.add_argument_group('General')
model_group = legacy_parser.add_argument_group('Model selection')
file_group = legacy_parser.add_argument_group('Input/output')
web_server_group = legacy_parser.add_argument_group('Web server')
render_group = legacy_parser.add_argument_group('Rendering')
postprocessing_group = legacy_parser.add_argument_group('Postprocessing')
deprecated_group = legacy_parser.add_argument_group('Deprecated options')
general_group = legacy_parser.add_argument_group("General")
model_group = legacy_parser.add_argument_group("Model selection")
file_group = legacy_parser.add_argument_group("Input/output")
web_server_group = legacy_parser.add_argument_group("Web server")
render_group = legacy_parser.add_argument_group("Rendering")
postprocessing_group = legacy_parser.add_argument_group("Postprocessing")
deprecated_group = legacy_parser.add_argument_group("Deprecated options")
deprecated_group.add_argument('--laion400m')
deprecated_group.add_argument('--weights') # deprecated
general_group.add_argument(
'--version','-V',
action='store_true',
help='Print InvokeAI version number'
)
deprecated_group.add_argument("--laion400m")
deprecated_group.add_argument("--weights") # deprecated
general_group.add_argument("--version", "-V", action="store_true", help="Print InvokeAI version number")
model_group.add_argument(
'--root_dir',
"--root_dir",
default=None,
help='Path to directory containing "models", "outputs" and "configs". If not present will read from environment variable INVOKEAI_ROOT. Defaults to ~/invokeai.',
)
model_group.add_argument(
'--config',
'-c',
'-config',
dest='conf',
default='./configs/models.yaml',
help='Path to configuration file for alternate models.',
"--config",
"-c",
"-config",
dest="conf",
default="./configs/models.yaml",
help="Path to configuration file for alternate models.",
)
model_group.add_argument(
'--model',
"--model",
help='Indicates which diffusion model to load (defaults to "default" stanza in configs/models.yaml)',
)
model_group.add_argument(
'--weight_dirs',
nargs='+',
"--weight_dirs",
nargs="+",
type=str,
help='List of one or more directories that will be auto-scanned for new model weights to import',
help="List of one or more directories that will be auto-scanned for new model weights to import",
)
model_group.add_argument(
'--png_compression','-z',
"--png_compression",
"-z",
type=int,
default=6,
choices=range(0,9),
dest='png_compression',
help='level of PNG compression, from 0 (none) to 9 (maximum). Default is 6.'
choices=range(0, 9),
dest="png_compression",
help="level of PNG compression, from 0 (none) to 9 (maximum). Default is 6.",
)
model_group.add_argument(
'-F',
'--full_precision',
dest='full_precision',
action='store_true',
help='Deprecated way to set --precision=float32',
"-F",
"--full_precision",
dest="full_precision",
action="store_true",
help="Deprecated way to set --precision=float32",
)
model_group.add_argument(
'--max_loaded_models',
dest='max_loaded_models',
"--max_loaded_models",
dest="max_loaded_models",
type=int,
default=2,
help='Maximum number of models to keep in memory for fast switching, including the one in GPU',
help="Maximum number of models to keep in memory for fast switching, including the one in GPU",
)
model_group.add_argument(
'--free_gpu_mem',
dest='free_gpu_mem',
action='store_true',
help='Force free gpu memory before final decoding',
"--free_gpu_mem",
dest="free_gpu_mem",
action="store_true",
help="Force free gpu memory before final decoding",
)
model_group.add_argument(
'--sequential_guidance',
dest='sequential_guidance',
action='store_true',
help="Calculate guidance in serial instead of in parallel, lowering memory requirement "
"at the expense of speed",
"--sequential_guidance",
dest="sequential_guidance",
action="store_true",
help="Calculate guidance in serial instead of in parallel, lowering memory requirement " "at the expense of speed",
)
model_group.add_argument(
'--xformers',
"--xformers",
action=argparse.BooleanOptionalAction,
default=True,
help='Enable/disable xformers support (default enabled if installed)',
help="Enable/disable xformers support (default enabled if installed)",
)
model_group.add_argument(
"--always_use_cpu",
dest="always_use_cpu",
action="store_true",
help="Force use of CPU even if GPU is available"
"--always_use_cpu", dest="always_use_cpu", action="store_true", help="Force use of CPU even if GPU is available"
)
model_group.add_argument(
'--precision',
dest='precision',
"--precision",
dest="precision",
type=str,
choices=PRECISION_CHOICES,
metavar='PRECISION',
metavar="PRECISION",
help=f'Set model precision. Defaults to auto selected based on device. Options: {", ".join(PRECISION_CHOICES)}',
default='auto',
default="auto",
)
model_group.add_argument(
'--ckpt_convert',
"--ckpt_convert",
action=argparse.BooleanOptionalAction,
dest='ckpt_convert',
dest="ckpt_convert",
default=True,
help='Deprecated option. Legacy ckpt files are now always converted to diffusers when loaded.'
help="Deprecated option. Legacy ckpt files are now always converted to diffusers when loaded.",
)
model_group.add_argument(
'--internet',
"--internet",
action=argparse.BooleanOptionalAction,
dest='internet_available',
dest="internet_available",
default=True,
help='Indicate whether internet is available for just-in-time model downloading (default: probe automatically).',
help="Indicate whether internet is available for just-in-time model downloading (default: probe automatically).",
)
model_group.add_argument(
'--nsfw_checker',
'--safety_checker',
"--nsfw_checker",
"--safety_checker",
action=argparse.BooleanOptionalAction,
dest='safety_checker',
dest="safety_checker",
default=False,
help='Check for and blur potentially NSFW images. Use --no-nsfw_checker to disable.',
help="Check for and blur potentially NSFW images. Use --no-nsfw_checker to disable.",
)
model_group.add_argument(
'--autoimport',
"--autoimport",
default=None,
type=str,
help='Check the indicated directory for .ckpt/.safetensors weights files at startup and import directly',
help="Check the indicated directory for .ckpt/.safetensors weights files at startup and import directly",
)
model_group.add_argument(
'--autoconvert',
"--autoconvert",
default=None,
type=str,
help='Check the indicated directory for .ckpt/.safetensors weights files at startup and import as optimized diffuser models',
help="Check the indicated directory for .ckpt/.safetensors weights files at startup and import as optimized diffuser models",
)
model_group.add_argument(
'--patchmatch',
"--patchmatch",
action=argparse.BooleanOptionalAction,
default=True,
help='Load the patchmatch extension for outpainting. Use --no-patchmatch to disable.',
help="Load the patchmatch extension for outpainting. Use --no-patchmatch to disable.",
)
file_group.add_argument(
'--from_file',
dest='infile',
"--from_file",
dest="infile",
type=str,
help='If specified, load prompts from this file',
help="If specified, load prompts from this file",
)
file_group.add_argument(
'--outdir',
'-o',
"--outdir",
"-o",
type=str,
help='Directory to save generated images and a log of prompts and seeds. Default: ROOTDIR/outputs',
default='outputs',
help="Directory to save generated images and a log of prompts and seeds. Default: ROOTDIR/outputs",
default="outputs",
)
file_group.add_argument(
'--prompt_as_dir',
'-p',
action='store_true',
help='Place images in subdirectories named after the prompt.',
"--prompt_as_dir",
"-p",
action="store_true",
help="Place images in subdirectories named after the prompt.",
)
render_group.add_argument(
'--fnformat',
default='{prefix}.{seed}.png',
"--fnformat",
default="{prefix}.{seed}.png",
type=str,
help='Overwrite the filename format. You can use any argument as wildcard enclosed in curly braces. Default is {prefix}.{seed}.png',
help="Overwrite the filename format. You can use any argument as wildcard enclosed in curly braces. Default is {prefix}.{seed}.png",
)
render_group.add_argument("-s", "--steps", type=int, default=50, help="Number of steps")
render_group.add_argument(
'-s',
'--steps',
"-W",
"--width",
type=int,
default=50,
help='Number of steps'
help="Image width, multiple of 64",
)
render_group.add_argument(
'-W',
'--width',
"-H",
"--height",
type=int,
help='Image width, multiple of 64',
help="Image height, multiple of 64",
)
render_group.add_argument(
'-H',
'--height',
type=int,
help='Image height, multiple of 64',
)
render_group.add_argument(
'-C',
'--cfg_scale',
"-C",
"--cfg_scale",
default=7.5,
type=float,
help='Classifier free guidance (CFG) scale - higher numbers cause generator to "try" harder.',
)
render_group.add_argument(
'--sampler',
'-A',
'-m',
dest='sampler_name',
"--sampler",
"-A",
"-m",
dest="sampler_name",
type=str,
choices=SAMPLER_CHOICES,
metavar='SAMPLER_NAME',
metavar="SAMPLER_NAME",
help=f'Set the default sampler. Supported samplers: {", ".join(SAMPLER_CHOICES)}',
default='k_lms',
default="k_lms",
)
render_group.add_argument(
'--log_tokenization',
'-t',
action='store_true',
help='shows how the prompt is split into tokens'
"--log_tokenization", "-t", action="store_true", help="shows how the prompt is split into tokens"
)
render_group.add_argument(
'-f',
'--strength',
"-f",
"--strength",
type=float,
help='img2img strength for noising/unnoising. 0.0 preserves image exactly, 1.0 replaces it completely',
help="img2img strength for noising/unnoising. 0.0 preserves image exactly, 1.0 replaces it completely",
)
render_group.add_argument(
'-T',
'-fit',
'--fit',
"-T",
"-fit",
"--fit",
action=argparse.BooleanOptionalAction,
help='If specified, will resize the input image to fit within the dimensions of width x height (512x512 default)',
help="If specified, will resize the input image to fit within the dimensions of width x height (512x512 default)",
)
render_group.add_argument("--grid", "-g", action=argparse.BooleanOptionalAction, help="generate a grid")
render_group.add_argument(
'--grid',
'-g',
action=argparse.BooleanOptionalAction,
help='generate a grid'
)
render_group.add_argument(
'--embedding_directory',
'--embedding_path',
dest='embedding_path',
default='embeddings',
"--embedding_directory",
"--embedding_path",
dest="embedding_path",
default="embeddings",
type=str,
help='Path to a directory containing .bin and/or .pt files, or a single .bin/.pt file. You may use subdirectories. (default is ROOTDIR/embeddings)'
help="Path to a directory containing .bin and/or .pt files, or a single .bin/.pt file. You may use subdirectories. (default is ROOTDIR/embeddings)",
)
render_group.add_argument(
'--lora_directory',
dest='lora_path',
default='loras',
"--lora_directory",
dest="lora_path",
default="loras",
type=str,
help='Path to a directory containing LoRA files; subdirectories are not supported. (default is ROOTDIR/loras)'
help="Path to a directory containing LoRA files; subdirectories are not supported. (default is ROOTDIR/loras)",
)
render_group.add_argument(
'--embeddings',
"--embeddings",
action=argparse.BooleanOptionalAction,
default=True,
help='Enable embedding directory (default). Use --no-embeddings to disable.',
help="Enable embedding directory (default). Use --no-embeddings to disable.",
)
render_group.add_argument("--enable_image_debugging", action="store_true", help="Generates debugging image to display")
render_group.add_argument(
'--enable_image_debugging',
action='store_true',
help='Generates debugging image to display'
)
render_group.add_argument(
'--karras_max',
"--karras_max",
type=int,
default=None,
help="control the point at which the K* samplers will shift from using the Karras noise schedule (good for low step counts) to the LatentDiffusion noise schedule (good for high step counts). Set to 0 to use LatentDiffusion for all step values, and to a high value (e.g. 1000) to use Karras for all step values. [29]."
help="control the point at which the K* samplers will shift from using the Karras noise schedule (good for low step counts) to the LatentDiffusion noise schedule (good for high step counts). Set to 0 to use LatentDiffusion for all step values, and to a high value (e.g. 1000) to use Karras for all step values. [29].",
)
# Restoration related args
postprocessing_group.add_argument(
'--no_restore',
dest='restore',
action='store_false',
help='Disable face restoration with GFPGAN or codeformer',
"--no_restore",
dest="restore",
action="store_false",
help="Disable face restoration with GFPGAN or codeformer",
)
postprocessing_group.add_argument(
'--no_upscale',
dest='esrgan',
action='store_false',
help='Disable upscaling with ESRGAN',
"--no_upscale",
dest="esrgan",
action="store_false",
help="Disable upscaling with ESRGAN",
)
postprocessing_group.add_argument(
'--esrgan_bg_tile',
"--esrgan_bg_tile",
type=int,
default=400,
help='Tile size for background sampler, 0 for no tile during testing. Default: 400.',
help="Tile size for background sampler, 0 for no tile during testing. Default: 400.",
)
postprocessing_group.add_argument(
'--esrgan_denoise_str',
"--esrgan_denoise_str",
type=float,
default=0.75,
help='esrgan denoise str. 0 is no denoise, 1 is max denoise. Default: 0.75',
help="esrgan denoise str. 0 is no denoise, 1 is max denoise. Default: 0.75",
)
postprocessing_group.add_argument(
'--gfpgan_model_path',
"--gfpgan_model_path",
type=str,
default='./models/gfpgan/GFPGANv1.4.pth',
help='Indicates the path to the GFPGAN model',
default="./models/gfpgan/GFPGANv1.4.pth",
help="Indicates the path to the GFPGAN model",
)
web_server_group.add_argument(
'--web',
dest='web',
action='store_true',
help='Start in web server mode.',
"--web",
dest="web",
action="store_true",
help="Start in web server mode.",
)
web_server_group.add_argument(
'--web_develop',
dest='web_develop',
action='store_true',
help='Start in web server development mode.',
"--web_develop",
dest="web_develop",
action="store_true",
help="Start in web server development mode.",
)
web_server_group.add_argument(
"--web_verbose",
@ -376,32 +352,27 @@ web_server_group.add_argument(
help="Additional allowed origins, comma-separated",
)
web_server_group.add_argument(
'--host',
"--host",
type=str,
default='127.0.0.1',
help='Web server: Host or IP to listen on. Set to 0.0.0.0 to accept traffic from other devices on your network.'
default="127.0.0.1",
help="Web server: Host or IP to listen on. Set to 0.0.0.0 to accept traffic from other devices on your network.",
)
web_server_group.add_argument("--port", type=int, default="9090", help="Web server: Port to listen on")
web_server_group.add_argument(
'--port',
type=int,
default='9090',
help='Web server: Port to listen on'
)
web_server_group.add_argument(
'--certfile',
"--certfile",
type=str,
default=None,
help='Web server: Path to certificate file to use for SSL. Use together with --keyfile'
help="Web server: Path to certificate file to use for SSL. Use together with --keyfile",
)
web_server_group.add_argument(
'--keyfile',
"--keyfile",
type=str,
default=None,
help='Web server: Path to private key file to use for SSL. Use together with --certfile'
help="Web server: Path to private key file to use for SSL. Use together with --certfile",
)
web_server_group.add_argument(
'--gui',
dest='gui',
action='store_true',
help='Start InvokeAI GUI',
"--gui",
dest="gui",
action="store_true",
help="Start InvokeAI GUI",
)

550
invokeai/backend/install/migrate_to_3.py
View File

@ -1,7 +1,7 @@
'''
"""
Migrate the models directory and models.yaml file from an existing
InvokeAI 2.3 installation to 3.0.0.
'''
"""
import os
import argparse
@ -29,14 +29,13 @@ from transformers import (
import invokeai.backend.util.logging as logger
from invokeai.app.services.config import InvokeAIAppConfig
from invokeai.backend.model_management import ModelManager
from invokeai.backend.model_management.model_probe import (
ModelProbe, ModelType, BaseModelType, ModelProbeInfo
)
from invokeai.backend.model_management.model_probe import ModelProbe, ModelType, BaseModelType, ModelProbeInfo
warnings.filterwarnings("ignore")
transformers.logging.set_verbosity_error()
diffusers.logging.set_verbosity_error()
# holder for paths that we will migrate
@dataclass
class ModelPaths:
@ -45,81 +44,82 @@ class ModelPaths:
loras: Path
controlnets: Path
class MigrateTo3(object):
def __init__(self,
from_root: Path,
to_models: Path,
model_manager: ModelManager,
src_paths: ModelPaths,
):
def __init__(
self,
from_root: Path,
to_models: Path,
model_manager: ModelManager,
src_paths: ModelPaths,
):
self.root_directory = from_root
self.dest_models = to_models
self.mgr = model_manager
self.src_paths = src_paths
@classmethod
def initialize_yaml(cls, yaml_file: Path):
with open(yaml_file, 'w') as file:
file.write(
yaml.dump(
{
'__metadata__': {'version':'3.0.0'}
}
)
)
with open(yaml_file, "w") as file:
file.write(yaml.dump({"__metadata__": {"version": "3.0.0"}}))
def create_directory_structure(self):
'''
"""
Create the basic directory structure for the models folder.
'''
for model_base in [BaseModelType.StableDiffusion1,BaseModelType.StableDiffusion2]:
for model_type in [ModelType.Main, ModelType.Vae, ModelType.Lora,
ModelType.ControlNet,ModelType.TextualInversion]:
"""
for model_base in [BaseModelType.StableDiffusion1, BaseModelType.StableDiffusion2]:
for model_type in [
ModelType.Main,
ModelType.Vae,
ModelType.Lora,
ModelType.ControlNet,
ModelType.TextualInversion,
]:
path = self.dest_models / model_base.value / model_type.value
path.mkdir(parents=True, exist_ok=True)
path = self.dest_models / 'core'
path = self.dest_models / "core"
path.mkdir(parents=True, exist_ok=True)
@staticmethod
def copy_file(src:Path,dest:Path):
'''
def copy_file(src: Path, dest: Path):
"""
copy a single file with logging
'''
"""
if dest.exists():
logger.info(f'Skipping existing {str(dest)}')
logger.info(f"Skipping existing {str(dest)}")
return
logger.info(f'Copying {str(src)} to {str(dest)}')
logger.info(f"Copying {str(src)} to {str(dest)}")
try:
shutil.copy(src, dest)
except Exception as e:
logger.error(f'COPY FAILED: {str(e)}')
logger.error(f"COPY FAILED: {str(e)}")
@staticmethod
def copy_dir(src:Path,dest:Path):
'''
def copy_dir(src: Path, dest: Path):
"""
Recursively copy a directory with logging
'''
"""
if dest.exists():
logger.info(f'Skipping existing {str(dest)}')
logger.info(f"Skipping existing {str(dest)}")
return
logger.info(f'Copying {str(src)} to {str(dest)}')
logger.info(f"Copying {str(src)} to {str(dest)}")
try:
shutil.copytree(src, dest)
except Exception as e:
logger.error(f'COPY FAILED: {str(e)}')
logger.error(f"COPY FAILED: {str(e)}")
def migrate_models(self, src_dir: Path):
'''
"""
Recursively walk through src directory, probe anything
that looks like a model, and copy the model into the
appropriate location within the destination models directory.
'''
"""
directories_scanned = set()
for root, dirs, files in os.walk(src_dir):
for d in dirs:
try:
model = Path(root,d)
model = Path(root, d)
info = ModelProbe().heuristic_probe(model)
if not info:
continue
@ -136,9 +136,9 @@ class MigrateTo3(object):
# don't copy raw learned_embeds.bin or pytorch_lora_weights.bin
# let them be copied as part of a tree copy operation
try:
if f in {'learned_embeds.bin','pytorch_lora_weights.bin'}:
if f in {"learned_embeds.bin", "pytorch_lora_weights.bin"}:
continue
model = Path(root,f)
model = Path(root, f)
if model.parent in directories_scanned:
continue
info = ModelProbe().heuristic_probe(model)
@ -154,148 +154,146 @@ class MigrateTo3(object):
logger.error(str(e))
def migrate_support_models(self):
'''
"""
Copy the clipseg, upscaler, and restoration models to their new
locations.
'''
"""
dest_directory = self.dest_models
if (self.root_directory / 'models/clipseg').exists():
self.copy_dir(self.root_directory / 'models/clipseg', dest_directory / 'core/misc/clipseg')
if (self.root_directory / 'models/realesrgan').exists():
self.copy_dir(self.root_directory / 'models/realesrgan', dest_directory / 'core/upscaling/realesrgan')
for d in ['codeformer','gfpgan']:
path = self.root_directory / 'models' / d
if (self.root_directory / "models/clipseg").exists():
self.copy_dir(self.root_directory / "models/clipseg", dest_directory / "core/misc/clipseg")
if (self.root_directory / "models/realesrgan").exists():
self.copy_dir(self.root_directory / "models/realesrgan", dest_directory / "core/upscaling/realesrgan")
for d in ["codeformer", "gfpgan"]:
path = self.root_directory / "models" / d
if path.exists():
self.copy_dir(path,dest_directory / f'core/face_restoration/{d}')
self.copy_dir(path, dest_directory / f"core/face_restoration/{d}")
def migrate_tuning_models(self):
'''
"""
Migrate the embeddings, loras and controlnets directories to their new homes.
'''
"""
for src in [self.src_paths.embeddings, self.src_paths.loras, self.src_paths.controlnets]:
if not src:
continue
if src.is_dir():
logger.info(f'Scanning {src}')
logger.info(f"Scanning {src}")
self.migrate_models(src)
else:
logger.info(f'{src} directory not found; skipping')
logger.info(f"{src} directory not found; skipping")
continue
def migrate_conversion_models(self):
'''
"""
Migrate all the models that are needed by the ckpt_to_diffusers conversion
script.
'''
"""
dest_directory = self.dest_models
kwargs = dict(
cache_dir = self.root_directory / 'models/hub',
#local_files_only = True
cache_dir=self.root_directory / "models/hub",
# local_files_only = True
)
try:
logger.info('Migrating core tokenizers and text encoders')
target_dir = dest_directory / 'core' / 'convert'
logger.info("Migrating core tokenizers and text encoders")
target_dir = dest_directory / "core" / "convert"
self._migrate_pretrained(BertTokenizerFast,
repo_id='bert-base-uncased',
dest = target_dir / 'bert-base-uncased',
**kwargs)
self._migrate_pretrained(
BertTokenizerFast, repo_id="bert-base-uncased", dest=target_dir / "bert-base-uncased", **kwargs
)
# sd-1
repo_id = 'openai/clip-vit-large-patch14'
self._migrate_pretrained(CLIPTokenizer,
repo_id= repo_id,
dest= target_dir / 'clip-vit-large-patch14',
**kwargs)
self._migrate_pretrained(CLIPTextModel,
repo_id = repo_id,
dest = target_dir / 'clip-vit-large-patch14',
force = True,
**kwargs)
repo_id = "openai/clip-vit-large-patch14"
self._migrate_pretrained(
CLIPTokenizer, repo_id=repo_id, dest=target_dir / "clip-vit-large-patch14", **kwargs
)
self._migrate_pretrained(
CLIPTextModel, repo_id=repo_id, dest=target_dir / "clip-vit-large-patch14", force=True, **kwargs
)
# sd-2
repo_id = "stabilityai/stable-diffusion-2"
self._migrate_pretrained(CLIPTokenizer,
repo_id = repo_id,
dest = target_dir / 'stable-diffusion-2-clip' / 'tokenizer',
**{'subfolder':'tokenizer',**kwargs}
)
self._migrate_pretrained(CLIPTextModel,
repo_id = repo_id,
dest = target_dir / 'stable-diffusion-2-clip' / 'text_encoder',
**{'subfolder':'text_encoder',**kwargs}
)
self._migrate_pretrained(
CLIPTokenizer,
repo_id=repo_id,
dest=target_dir / "stable-diffusion-2-clip" / "tokenizer",
**{"subfolder": "tokenizer", **kwargs},
)
self._migrate_pretrained(
CLIPTextModel,
repo_id=repo_id,
dest=target_dir / "stable-diffusion-2-clip" / "text_encoder",
**{"subfolder": "text_encoder", **kwargs},
)
# VAE
logger.info('Migrating stable diffusion VAE')
self._migrate_pretrained(AutoencoderKL,
repo_id = 'stabilityai/sd-vae-ft-mse',
dest = target_dir / 'sd-vae-ft-mse',
**kwargs)
logger.info("Migrating stable diffusion VAE")
self._migrate_pretrained(
AutoencoderKL, repo_id="stabilityai/sd-vae-ft-mse", dest=target_dir / "sd-vae-ft-mse", **kwargs
)
# safety checking
logger.info('Migrating safety checker')
logger.info("Migrating safety checker")
repo_id = "CompVis/stable-diffusion-safety-checker"
self._migrate_pretrained(AutoFeatureExtractor,
repo_id = repo_id,
dest = target_dir / 'stable-diffusion-safety-checker',
**kwargs)
self._migrate_pretrained(StableDiffusionSafetyChecker,
repo_id = repo_id,
dest = target_dir / 'stable-diffusion-safety-checker',
**kwargs)
self._migrate_pretrained(
AutoFeatureExtractor, repo_id=repo_id, dest=target_dir / "stable-diffusion-safety-checker", **kwargs
)
self._migrate_pretrained(
StableDiffusionSafetyChecker,
repo_id=repo_id,
dest=target_dir / "stable-diffusion-safety-checker",
**kwargs,
)
except KeyboardInterrupt:
raise
except Exception as e:
logger.error(str(e))
def _model_probe_to_path(self, info: ModelProbeInfo)->Path:
def _model_probe_to_path(self, info: ModelProbeInfo) -> Path:
return Path(self.dest_models, info.base_type.value, info.model_type.value)
def _migrate_pretrained(self, model_class, repo_id: str, dest: Path, force:bool=False, **kwargs):
def _migrate_pretrained(self, model_class, repo_id: str, dest: Path, force: bool = False, **kwargs):
if dest.exists() and not force:
logger.info(f'Skipping existing {dest}')
logger.info(f"Skipping existing {dest}")
return
model = model_class.from_pretrained(repo_id, **kwargs)
self._save_pretrained(model, dest, overwrite=force)
def _save_pretrained(self, model, dest: Path, overwrite: bool=False):
def _save_pretrained(self, model, dest: Path, overwrite: bool = False):
model_name = dest.name
if overwrite:
model.save_pretrained(dest, safe_serialization=True)
else:
download_path = dest.with_name(f'{model_name}.downloading')
download_path = dest.with_name(f"{model_name}.downloading")
model.save_pretrained(download_path, safe_serialization=True)
download_path.replace(dest)
def _download_vae(self, repo_id: str, subfolder:str=None)->Path:
vae = AutoencoderKL.from_pretrained(repo_id, cache_dir=self.root_directory / 'models/hub', subfolder=subfolder)
def _download_vae(self, repo_id: str, subfolder: str = None) -> Path:
vae = AutoencoderKL.from_pretrained(repo_id, cache_dir=self.root_directory / "models/hub", subfolder=subfolder)
info = ModelProbe().heuristic_probe(vae)
_, model_name = repo_id.split('/')
_, model_name = repo_id.split("/")
dest = self._model_probe_to_path(info) / self.unique_name(model_name, info)
vae.save_pretrained(dest, safe_serialization=True)
return dest
def _vae_path(self, vae: Union[str,dict])->Path:
'''
def _vae_path(self, vae: Union[str, dict]) -> Path:
"""
Convert 2.3 VAE stanza to a straight path.
'''
"""
vae_path = None
# First get a path
if isinstance(vae,str):
if isinstance(vae, str):
vae_path = vae
elif isinstance(vae,DictConfig):
if p := vae.get('path'):
elif isinstance(vae, DictConfig):
if p := vae.get("path"):
vae_path = p
elif repo_id := vae.get('repo_id'):
if repo_id=='stabilityai/sd-vae-ft-mse': # this guy is already downloaded
vae_path = 'models/core/convert/sd-vae-ft-mse'
elif repo_id := vae.get("repo_id"):
if repo_id == "stabilityai/sd-vae-ft-mse": # this guy is already downloaded
vae_path = "models/core/convert/sd-vae-ft-mse"
return vae_path
else:
vae_path = self._download_vae(repo_id, vae.get('subfolder'))
vae_path = self._download_vae(repo_id, vae.get("subfolder"))
assert vae_path is not None, "Couldn't find VAE for this model"
@ -307,152 +305,144 @@ class MigrateTo3(object):
dest = self._model_probe_to_path(info) / vae_path.name
if not dest.exists():
if vae_path.is_dir():
self.copy_dir(vae_path,dest)
self.copy_dir(vae_path, dest)
else:
self.copy_file(vae_path,dest)
self.copy_file(vae_path, dest)
vae_path = dest
if vae_path.is_relative_to(self.dest_models):
rel_path = vae_path.relative_to(self.dest_models)
return Path('models',rel_path)
return Path("models", rel_path)
else:
return vae_path
def migrate_repo_id(self, repo_id: str, model_name: str=None, **extra_config):
'''
def migrate_repo_id(self, repo_id: str, model_name: str = None, **extra_config):
"""
Migrate a locally-cached diffusers pipeline identified with a repo_id
'''
"""
dest_dir = self.dest_models
cache = self.root_directory / 'models/hub'
cache = self.root_directory / "models/hub"
kwargs = dict(
cache_dir = cache,
safety_checker = None,
cache_dir=cache,
safety_checker=None,
# local_files_only = True,
)
owner,repo_name = repo_id.split('/')
owner, repo_name = repo_id.split("/")
model_name = model_name or repo_name
model = cache / '--'.join(['models',owner,repo_name])
if len(list(model.glob('snapshots/**/model_index.json')))==0:
model = cache / "--".join(["models", owner, repo_name])
if len(list(model.glob("snapshots/**/model_index.json"))) == 0:
return
revisions = [x.name for x in model.glob('refs/*')]
revisions = [x.name for x in model.glob("refs/*")]
# if an fp16 is available we use that
revision = 'fp16' if len(revisions) > 1 and 'fp16' in revisions else revisions[0]
pipeline = StableDiffusionPipeline.from_pretrained(
repo_id,
revision=revision,
**kwargs)
revision = "fp16" if len(revisions) > 1 and "fp16" in revisions else revisions[0]
pipeline = StableDiffusionPipeline.from_pretrained(repo_id, revision=revision, **kwargs)
info = ModelProbe().heuristic_probe(pipeline)
if not info:
return
if self.mgr.model_exists(model_name, info.base_type, info.model_type):
logger.warning(f'A model named {model_name} already exists at the destination. Skipping migration.')
logger.warning(f"A model named {model_name} already exists at the destination. Skipping migration.")
return
dest = self._model_probe_to_path(info) / model_name
self._save_pretrained(pipeline, dest)
rel_path = Path('models',dest.relative_to(dest_dir))
rel_path = Path("models", dest.relative_to(dest_dir))
self._add_model(model_name, info, rel_path, **extra_config)
def migrate_path(self, location: Path, model_name: str=None, **extra_config):
'''
def migrate_path(self, location: Path, model_name: str = None, **extra_config):
"""
Migrate a model referred to using 'weights' or 'path'
'''
"""
# handle relative paths
dest_dir = self.dest_models
location = self.root_directory / location
model_name = model_name or location.stem
info = ModelProbe().heuristic_probe(location)
if not info:
return
if self.mgr.model_exists(model_name, info.base_type, info.model_type):
logger.warning(f'A model named {model_name} already exists at the destination. Skipping migration.')
logger.warning(f"A model named {model_name} already exists at the destination. Skipping migration.")
return
# uh oh, weights is in the old models directory - move it into the new one
if Path(location).is_relative_to(self.src_paths.models):
dest = Path(dest_dir, info.base_type.value, info.model_type.value, location.name)
if location.is_dir():
self.copy_dir(location,dest)
self.copy_dir(location, dest)
else:
self.copy_file(location,dest)
location = Path('models', info.base_type.value, info.model_type.value, location.name)
self.copy_file(location, dest)
location = Path("models", info.base_type.value, info.model_type.value, location.name)
self._add_model(model_name, info, location, **extra_config)
def _add_model(self,
model_name: str,
info: ModelProbeInfo,
location: Path,
**extra_config):
def _add_model(self, model_name: str, info: ModelProbeInfo, location: Path, **extra_config):
if info.model_type != ModelType.Main:
return
self.mgr.add_model(
model_name = model_name,
base_model = info.base_type,
model_type = info.model_type,
clobber = True,
model_attributes = {
'path': str(location),
'description': f'A {info.base_type.value} {info.model_type.value} model',
'model_format': info.format,
'variant': info.variant_type.value,
**extra_config,
}
)
def migrate_defined_models(self):
'''
Migrate models defined in models.yaml
'''
# find any models referred to in old models.yaml
conf = OmegaConf.load(self.root_directory / 'configs/models.yaml')
for model_name, stanza in conf.items():
self.mgr.add_model(
model_name=model_name,
base_model=info.base_type,
model_type=info.model_type,
clobber=True,
model_attributes={
"path": str(location),
"description": f"A {info.base_type.value} {info.model_type.value} model",
"model_format": info.format,
"variant": info.variant_type.value,
**extra_config,
},
)
def migrate_defined_models(self):
"""
Migrate models defined in models.yaml
"""
# find any models referred to in old models.yaml
conf = OmegaConf.load(self.root_directory / "configs/models.yaml")
for model_name, stanza in conf.items():
try:
passthru_args = {}
if vae := stanza.get('vae'):
if vae := stanza.get("vae"):
try:
passthru_args['vae'] = str(self._vae_path(vae))
passthru_args["vae"] = str(self._vae_path(vae))
except Exception as e:
logger.warning(f'Could not find a VAE matching "{vae}" for model "{model_name}"')
logger.warning(str(e))
if config := stanza.get('config'):
passthru_args['config'] = config
if config := stanza.get("config"):
passthru_args["config"] = config
if description:= stanza.get('description'):
passthru_args['description'] = description
if repo_id := stanza.get('repo_id'):
logger.info(f'Migrating diffusers model {model_name}')
if description := stanza.get("description"):
passthru_args["description"] = description
if repo_id := stanza.get("repo_id"):
logger.info(f"Migrating diffusers model {model_name}")
self.migrate_repo_id(repo_id, model_name, **passthru_args)
elif location := stanza.get('weights'):
logger.info(f'Migrating checkpoint model {model_name}')
elif location := stanza.get("weights"):
logger.info(f"Migrating checkpoint model {model_name}")
self.migrate_path(Path(location), model_name, **passthru_args)
elif location := stanza.get('path'):
logger.info(f'Migrating diffusers model {model_name}')
elif location := stanza.get("path"):
logger.info(f"Migrating diffusers model {model_name}")
self.migrate_path(Path(location), model_name, **passthru_args)
except KeyboardInterrupt:
raise
except Exception as e:
logger.error(str(e))
def migrate(self):
self.create_directory_structure()
# the configure script is doing this
@ -461,67 +451,71 @@ class MigrateTo3(object):
self.migrate_tuning_models()
self.migrate_defined_models()
def _parse_legacy_initfile(root: Path, initfile: Path)->ModelPaths:
'''
def _parse_legacy_initfile(root: Path, initfile: Path) -> ModelPaths:
"""
Returns tuple of (embedding_path, lora_path, controlnet_path)
'''
parser = argparse.ArgumentParser(fromfile_prefix_chars='@')
"""
parser = argparse.ArgumentParser(fromfile_prefix_chars="@")
parser.add_argument(
'--embedding_directory',
'--embedding_path',
"--embedding_directory",
"--embedding_path",
type=Path,
dest='embedding_path',
default=Path('embeddings'),
dest="embedding_path",
default=Path("embeddings"),
)
parser.add_argument(
'--lora_directory',
dest='lora_path',
"--lora_directory",
dest="lora_path",
type=Path,
default=Path('loras'),
default=Path("loras"),
)
opt,_ = parser.parse_known_args([f'@{str(initfile)}'])
opt, _ = parser.parse_known_args([f"@{str(initfile)}"])
return ModelPaths(
models = root / 'models',
embeddings = root / str(opt.embedding_path).strip('"'),
loras = root / str(opt.lora_path).strip('"'),
controlnets = root / 'controlnets',
models=root / "models",
embeddings=root / str(opt.embedding_path).strip('"'),
loras=root / str(opt.lora_path).strip('"'),
controlnets=root / "controlnets",
)
def _parse_legacy_yamlfile(root: Path, initfile: Path)->ModelPaths:
'''
def _parse_legacy_yamlfile(root: Path, initfile: Path) -> ModelPaths:
"""
Returns tuple of (embedding_path, lora_path, controlnet_path)
'''
"""
# Don't use the config object because it is unforgiving of version updates
# Just use omegaconf directly
opt = OmegaConf.load(initfile)
paths = opt.InvokeAI.Paths
models = paths.get('models_dir','models')
embeddings = paths.get('embedding_dir','embeddings')
loras = paths.get('lora_dir','loras')
controlnets = paths.get('controlnet_dir','controlnets')
models = paths.get("models_dir", "models")
embeddings = paths.get("embedding_dir", "embeddings")
loras = paths.get("lora_dir", "loras")
controlnets = paths.get("controlnet_dir", "controlnets")
return ModelPaths(
models = root / models,
embeddings = root / embeddings,
loras = root /loras,
controlnets = root / controlnets,
models=root / models,
embeddings=root / embeddings,
loras=root / loras,
controlnets=root / controlnets,
)
def get_legacy_embeddings(root: Path) -> ModelPaths:
path = root / 'invokeai.init'
path = root / "invokeai.init"
if path.exists():
return _parse_legacy_initfile(root, path)
path = root / 'invokeai.yaml'
path = root / "invokeai.yaml"
if path.exists():
return _parse_legacy_yamlfile(root, path)
def do_migrate(src_directory: Path, dest_directory: Path):
"""
Migrate models from src to dest InvokeAI root directories
"""
config_file = dest_directory / 'configs' / 'models.yaml.3'
dest_models = dest_directory / 'models.3'
version_3 = (dest_directory / 'models' / 'core').exists()
config_file = dest_directory / "configs" / "models.yaml.3"
dest_models = dest_directory / "models.3"
version_3 = (dest_directory / "models" / "core").exists()
# Here we create the destination models.yaml file.
# If we are writing into a version 3 directory and the
@ -530,80 +524,80 @@ def do_migrate(src_directory: Path, dest_directory: Path):
# create a new empty one.
if version_3: # write into the dest directory
try:
shutil.copy(dest_directory / 'configs' / 'models.yaml', config_file)
shutil.copy(dest_directory / "configs" / "models.yaml", config_file)
except:
MigrateTo3.initialize_yaml(config_file)
mgr = ModelManager(config_file) # important to initialize BEFORE moving the models directory
(dest_directory / 'models').replace(dest_models)
mgr = ModelManager(config_file) # important to initialize BEFORE moving the models directory
(dest_directory / "models").replace(dest_models)
else:
MigrateTo3.initialize_yaml(config_file)
mgr = ModelManager(config_file)
paths = get_legacy_embeddings(src_directory)
migrator = MigrateTo3(
from_root = src_directory,
to_models = dest_models,
model_manager = mgr,
src_paths = paths
)
migrator = MigrateTo3(from_root=src_directory, to_models=dest_models, model_manager=mgr, src_paths=paths)
migrator.migrate()
print("Migration successful.")
if not version_3:
(dest_directory / 'models').replace(src_directory / 'models.orig')
print(f'Original models directory moved to {dest_directory}/models.orig')
(dest_directory / 'configs' / 'models.yaml').replace(src_directory / 'configs' / 'models.yaml.orig')
print(f'Original models.yaml file moved to {dest_directory}/configs/models.yaml.orig')
config_file.replace(config_file.with_suffix(''))
dest_models.replace(dest_models.with_suffix(''))
(dest_directory / "models").replace(src_directory / "models.orig")
print(f"Original models directory moved to {dest_directory}/models.orig")
(dest_directory / "configs" / "models.yaml").replace(src_directory / "configs" / "models.yaml.orig")
print(f"Original models.yaml file moved to {dest_directory}/configs/models.yaml.orig")
config_file.replace(config_file.with_suffix(""))
dest_models.replace(dest_models.with_suffix(""))
def main():
parser = argparse.ArgumentParser(prog="invokeai-migrate3",
description="""
parser = argparse.ArgumentParser(
prog="invokeai-migrate3",
description="""
This will copy and convert the models directory and the configs/models.yaml from the InvokeAI 2.3 format
'--from-directory' root to the InvokeAI 3.0 '--to-directory' root. These may be abbreviated '--from' and '--to'.a
The old models directory and config file will be renamed 'models.orig' and 'models.yaml.orig' respectively.
It is safe to provide the same directory for both arguments, but it is better to use the invokeai_configure
script, which will perform a full upgrade in place."""
)
parser.add_argument('--from-directory',
dest='src_root',
type=Path,
required=True,
help='Source InvokeAI 2.3 root directory (containing "invokeai.init" or "invokeai.yaml")'
)
parser.add_argument('--to-directory',
dest='dest_root',
type=Path,
required=True,
help='Destination InvokeAI 3.0 directory (containing "invokeai.yaml")'
)
script, which will perform a full upgrade in place.""",
)
parser.add_argument(
"--from-directory",
dest="src_root",
type=Path,
required=True,
help='Source InvokeAI 2.3 root directory (containing "invokeai.init" or "invokeai.yaml")',
)
parser.add_argument(
"--to-directory",
dest="dest_root",
type=Path,
required=True,
help='Destination InvokeAI 3.0 directory (containing "invokeai.yaml")',
)
args = parser.parse_args()
src_root = args.src_root
assert src_root.is_dir(), f"{src_root} is not a valid directory"
assert (src_root / 'models').is_dir(), f"{src_root} does not contain a 'models' subdirectory"
assert (src_root / 'models' / 'hub').exists(), f"{src_root} does not contain a version 2.3 models directory"
assert (src_root / 'invokeai.init').exists() or (src_root / 'invokeai.yaml').exists(), f"{src_root} does not contain an InvokeAI init file."
assert (src_root / "models").is_dir(), f"{src_root} does not contain a 'models' subdirectory"
assert (src_root / "models" / "hub").exists(), f"{src_root} does not contain a version 2.3 models directory"
assert (src_root / "invokeai.init").exists() or (
src_root / "invokeai.yaml"
).exists(), f"{src_root} does not contain an InvokeAI init file."
dest_root = args.dest_root
assert dest_root.is_dir(), f"{dest_root} is not a valid directory"
config = InvokeAIAppConfig.get_config()
config.parse_args(['--root',str(dest_root)])
config.parse_args(["--root", str(dest_root)])
# TODO: revisit - don't rely on invokeai.yaml to exist yet!
dest_is_setup = (dest_root / 'models/core').exists() and (dest_root / 'databases').exists()
dest_is_setup = (dest_root / "models/core").exists() and (dest_root / "databases").exists()
if not dest_is_setup:
import invokeai.frontend.install.invokeai_configure
from invokeai.backend.install.invokeai_configure import initialize_rootdir
initialize_rootdir(dest_root, True)
do_migrate(src_root,dest_root)
do_migrate(src_root, dest_root)
if __name__ == '__main__':
if __name__ == "__main__":
main()

345
invokeai/backend/install/model_install_backend.py
View File

@ -4,7 +4,7 @@ Utility (backend) functions used by model_install.py
import os
import shutil
import warnings
from dataclasses import dataclass,field
from dataclasses import dataclass, field
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Dict, Callable, Union, Set
@ -28,7 +28,7 @@ warnings.filterwarnings("ignore")
# --------------------------globals-----------------------
config = InvokeAIAppConfig.get_config()
logger = InvokeAILogger.getLogger(name='InvokeAI')
logger = InvokeAILogger.getLogger(name="InvokeAI")
# the initial "configs" dir is now bundled in the `invokeai.configs` package
Dataset_path = Path(configs.__path__[0]) / "INITIAL_MODELS.yaml"
@ -45,59 +45,63 @@ Config_preamble = """
LEGACY_CONFIGS = {
BaseModelType.StableDiffusion1: {
ModelVariantType.Normal: 'v1-inference.yaml',
ModelVariantType.Inpaint: 'v1-inpainting-inference.yaml',
ModelVariantType.Normal: "v1-inference.yaml",
ModelVariantType.Inpaint: "v1-inpainting-inference.yaml",
},
BaseModelType.StableDiffusion2: {
ModelVariantType.Normal: {
SchedulerPredictionType.Epsilon: 'v2-inference.yaml',
SchedulerPredictionType.VPrediction: 'v2-inference-v.yaml',
SchedulerPredictionType.Epsilon: "v2-inference.yaml",
SchedulerPredictionType.VPrediction: "v2-inference-v.yaml",
},
ModelVariantType.Inpaint: {
SchedulerPredictionType.Epsilon: 'v2-inpainting-inference.yaml',
SchedulerPredictionType.VPrediction: 'v2-inpainting-inference-v.yaml',
}
SchedulerPredictionType.Epsilon: "v2-inpainting-inference.yaml",
SchedulerPredictionType.VPrediction: "v2-inpainting-inference-v.yaml",
},
},
BaseModelType.StableDiffusionXL: {
ModelVariantType.Normal: 'sd_xl_base.yaml',
ModelVariantType.Normal: "sd_xl_base.yaml",
},
BaseModelType.StableDiffusionXLRefiner: {
ModelVariantType.Normal: 'sd_xl_refiner.yaml',
ModelVariantType.Normal: "sd_xl_refiner.yaml",
},
}
@dataclass
class ModelInstallList:
'''Class for listing models to be installed/removed'''
"""Class for listing models to be installed/removed"""
install_models: List[str] = field(default_factory=list)
remove_models: List[str] = field(default_factory=list)
@dataclass
class InstallSelections():
install_models: List[str]= field(default_factory=list)
remove_models: List[str]=field(default_factory=list)
@dataclass
class ModelLoadInfo():
class InstallSelections:
install_models: List[str] = field(default_factory=list)
remove_models: List[str] = field(default_factory=list)
@dataclass
class ModelLoadInfo:
name: str
model_type: ModelType
base_type: BaseModelType
path: Path = None
repo_id: str = None
description: str = ''
description: str = ""
installed: bool = False
recommended: bool = False
default: bool = False
class ModelInstall(object):
def __init__(self,
config:InvokeAIAppConfig,
prediction_type_helper: Callable[[Path],SchedulerPredictionType]=None,
model_manager: ModelManager = None,
access_token:str = None):
def __init__(
self,
config: InvokeAIAppConfig,
prediction_type_helper: Callable[[Path], SchedulerPredictionType] = None,
model_manager: ModelManager = None,
access_token: str = None,
):
self.config = config
self.mgr = model_manager or ModelManager(config.model_conf_path)
self.datasets = OmegaConf.load(Dataset_path)
@ -105,66 +109,66 @@ class ModelInstall(object):
self.access_token = access_token or HfFolder.get_token()
self.reverse_paths = self._reverse_paths(self.datasets)
def all_models(self)->Dict[str,ModelLoadInfo]:
'''
def all_models(self) -> Dict[str, ModelLoadInfo]:
"""
Return dict of model_key=>ModelLoadInfo objects.
This method consolidates and simplifies the entries in both
models.yaml and INITIAL_MODELS.yaml so that they can
be treated uniformly. It also sorts the models alphabetically
by their name, to improve the display somewhat.
'''
"""
model_dict = dict()
# first populate with the entries in INITIAL_MODELS.yaml
for key, value in self.datasets.items():
name,base,model_type = ModelManager.parse_key(key)
value['name'] = name
value['base_type'] = base
value['model_type'] = model_type
name, base, model_type = ModelManager.parse_key(key)
value["name"] = name
value["base_type"] = base
value["model_type"] = model_type
model_dict[key] = ModelLoadInfo(**value)
# supplement with entries in models.yaml
installed_models = self.mgr.list_models()
for md in installed_models:
base = md['base_model']
model_type = md['model_type']
name = md['model_name']
base = md["base_model"]
model_type = md["model_type"]
name = md["model_name"]
key = ModelManager.create_key(name, base, model_type)
if key in model_dict:
model_dict[key].installed = True
else:
model_dict[key] = ModelLoadInfo(
name = name,
base_type = base,
model_type = model_type,
path = value.get('path'),
installed = True,
name=name,
base_type=base,
model_type=model_type,
path=value.get("path"),
installed=True,
)
return {x : model_dict[x] for x in sorted(model_dict.keys(),key=lambda y: model_dict[y].name.lower())}
return {x: model_dict[x] for x in sorted(model_dict.keys(), key=lambda y: model_dict[y].name.lower())}
def list_models(self, model_type):
installed = self.mgr.list_models(model_type=model_type)
print(f'Installed models of type `{model_type}`:')
print(f"Installed models of type `{model_type}`:")
for i in installed:
print(f"{i['model_name']}\t{i['base_model']}\t{i['path']}")
# logic here a little reversed to maintain backward compatibility
def starter_models(self, all_models: bool=False)->Set[str]:
def starter_models(self, all_models: bool = False) -> Set[str]:
models = set()
for key, value in self.datasets.items():
name,base,model_type = ModelManager.parse_key(key)
name, base, model_type = ModelManager.parse_key(key)
if all_models or model_type in [ModelType.Main, ModelType.Vae]:
models.add(key)
return models
def recommended_models(self)->Set[str]:
def recommended_models(self) -> Set[str]:
starters = self.starter_models(all_models=True)
return set([x for x in starters if self.datasets[x].get('recommended',False)])
def default_model(self)->str:
return set([x for x in starters if self.datasets[x].get("recommended", False)])
def default_model(self) -> str:
starters = self.starter_models()
defaults = [x for x in starters if self.datasets[x].get('default',False)]
defaults = [x for x in starters if self.datasets[x].get("default", False)]
return defaults[0]
def install(self, selections: InstallSelections):
@ -173,54 +177,57 @@ class ModelInstall(object):
job = 1
jobs = len(selections.remove_models) + len(selections.install_models)
# remove requested models
for key in selections.remove_models:
name,base,mtype = self.mgr.parse_key(key)
logger.info(f'Deleting {mtype} model {name} [{job}/{jobs}]')
name, base, mtype = self.mgr.parse_key(key)
logger.info(f"Deleting {mtype} model {name} [{job}/{jobs}]")
try:
self.mgr.del_model(name,base,mtype)
self.mgr.del_model(name, base, mtype)
except FileNotFoundError as e:
logger.warning(e)
job += 1
# add requested models
for path in selections.install_models:
logger.info(f'Installing {path} [{job}/{jobs}]')
logger.info(f"Installing {path} [{job}/{jobs}]")
try:
self.heuristic_import(path)
except (ValueError, KeyError) as e:
logger.error(str(e))
job += 1
dlogging.set_verbosity(verbosity)
self.mgr.commit()
def heuristic_import(self,
model_path_id_or_url: Union[str,Path],
models_installed: Set[Path]=None,
)->Dict[str, AddModelResult]:
'''
def heuristic_import(
self,
model_path_id_or_url: Union[str, Path],
models_installed: Set[Path] = None,
) -> Dict[str, AddModelResult]:
"""
:param model_path_id_or_url: A Path to a local model to import, or a string representing its repo_id or URL
:param models_installed: Set of installed models, used for recursive invocation
Returns a set of dict objects corresponding to newly-created stanzas in models.yaml.
'''
"""
if not models_installed:
models_installed = dict()
# A little hack to allow nested routines to retrieve info on the requested ID
self.current_id = model_path_id_or_url
path = Path(model_path_id_or_url)
# checkpoint file, or similar
if path.is_file():
models_installed.update({str(path):self._install_path(path)})
models_installed.update({str(path): self._install_path(path)})
# folders style or similar
elif path.is_dir() and any([(path/x).exists() for x in \
{'config.json','model_index.json','learned_embeds.bin','pytorch_lora_weights.bin'}
]
):
elif path.is_dir() and any(
[
(path / x).exists()
for x in {"config.json", "model_index.json", "learned_embeds.bin", "pytorch_lora_weights.bin"}
]
):
models_installed.update({str(model_path_id_or_url): self._install_path(path)})
# recursive scan
@ -229,7 +236,7 @@ class ModelInstall(object):
self.heuristic_import(child, models_installed=models_installed)
# huggingface repo
elif len(str(model_path_id_or_url).split('/')) == 2:
elif len(str(model_path_id_or_url).split("/")) == 2:
models_installed.update({str(model_path_id_or_url): self._install_repo(str(model_path_id_or_url))})
# a URL
@ -237,42 +244,43 @@ class ModelInstall(object):
models_installed.update({str(model_path_id_or_url): self._install_url(model_path_id_or_url)})
else:
raise KeyError(f'{str(model_path_id_or_url)} is not recognized as a local path, repo ID or URL. Skipping')
raise KeyError(f"{str(model_path_id_or_url)} is not recognized as a local path, repo ID or URL. Skipping")
return models_installed
# install a model from a local path. The optional info parameter is there to prevent
# the model from being probed twice in the event that it has already been probed.
def _install_path(self, path: Path, info: ModelProbeInfo=None)->AddModelResult:
info = info or ModelProbe().heuristic_probe(path,self.prediction_helper)
def _install_path(self, path: Path, info: ModelProbeInfo = None) -> AddModelResult:
info = info or ModelProbe().heuristic_probe(path, self.prediction_helper)
if not info:
logger.warning(f'Unable to parse format of {path}')
logger.warning(f"Unable to parse format of {path}")
return None
model_name = path.stem if path.is_file() else path.name
if self.mgr.model_exists(model_name, info.base_type, info.model_type):
raise ValueError(f'A model named "{model_name}" is already installed.')
attributes = self._make_attributes(path,info)
return self.mgr.add_model(model_name = model_name,
base_model = info.base_type,
model_type = info.model_type,
model_attributes = attributes,
)
attributes = self._make_attributes(path, info)
return self.mgr.add_model(
model_name=model_name,
base_model=info.base_type,
model_type=info.model_type,
model_attributes=attributes,
)
def _install_url(self, url: str)->AddModelResult:
def _install_url(self, url: str) -> AddModelResult:
with TemporaryDirectory(dir=self.config.models_path) as staging:
location = download_with_resume(url,Path(staging))
location = download_with_resume(url, Path(staging))
if not location:
logger.error(f'Unable to download {url}. Skipping.')
logger.error(f"Unable to download {url}. Skipping.")
info = ModelProbe().heuristic_probe(location)
dest = self.config.models_path / info.base_type.value / info.model_type.value / location.name
models_path = shutil.move(location,dest)
models_path = shutil.move(location, dest)
# staged version will be garbage-collected at this time
return self._install_path(Path(models_path), info)
def _install_repo(self, repo_id: str)->AddModelResult:
def _install_repo(self, repo_id: str) -> AddModelResult:
hinfo = HfApi().model_info(repo_id)
# we try to figure out how to download this most economically
# list all the files in the repo
files = [x.rfilename for x in hinfo.siblings]
@ -280,42 +288,49 @@ class ModelInstall(object):
with TemporaryDirectory(dir=self.config.models_path) as staging:
staging = Path(staging)
if 'model_index.json' in files:
location = self._download_hf_pipeline(repo_id, staging) # pipeline
if "model_index.json" in files:
location = self._download_hf_pipeline(repo_id, staging) # pipeline
else:
for suffix in ['safetensors','bin']:
if f'pytorch_lora_weights.{suffix}' in files:
location = self._download_hf_model(repo_id, ['pytorch_lora_weights.bin'], staging) # LoRA
for suffix in ["safetensors", "bin"]:
if f"pytorch_lora_weights.{suffix}" in files:
location = self._download_hf_model(repo_id, ["pytorch_lora_weights.bin"], staging) # LoRA
break
elif self.config.precision=='float16' and f'diffusion_pytorch_model.fp16.{suffix}' in files: # vae, controlnet or some other standalone
files = ['config.json', f'diffusion_pytorch_model.fp16.{suffix}']
elif (
self.config.precision == "float16" and f"diffusion_pytorch_model.fp16.{suffix}" in files
): # vae, controlnet or some other standalone
files = ["config.json", f"diffusion_pytorch_model.fp16.{suffix}"]
location = self._download_hf_model(repo_id, files, staging)
break
elif f'diffusion_pytorch_model.{suffix}' in files:
files = ['config.json', f'diffusion_pytorch_model.{suffix}']
elif f"diffusion_pytorch_model.{suffix}" in files:
files = ["config.json", f"diffusion_pytorch_model.{suffix}"]
location = self._download_hf_model(repo_id, files, staging)
break
elif f'learned_embeds.{suffix}' in files:
location = self._download_hf_model(repo_id, [f'learned_embeds.{suffix}'], staging)
elif f"learned_embeds.{suffix}" in files:
location = self._download_hf_model(repo_id, [f"learned_embeds.{suffix}"], staging)
break
if not location:
logger.warning(f'Could not determine type of repo {repo_id}. Skipping install.')
logger.warning(f"Could not determine type of repo {repo_id}. Skipping install.")
return {}
info = ModelProbe().heuristic_probe(location, self.prediction_helper)
if not info:
logger.warning(f'Could not probe {location}. Skipping install.')
logger.warning(f"Could not probe {location}. Skipping install.")
return {}
dest = self.config.models_path / info.base_type.value / info.model_type.value / self._get_model_name(repo_id,location)
dest = (
self.config.models_path
/ info.base_type.value
/ info.model_type.value
/ self._get_model_name(repo_id, location)
)
if dest.exists():
shutil.rmtree(dest)
shutil.copytree(location,dest)
shutil.copytree(location, dest)
return self._install_path(dest, info)
def _get_model_name(self,path_name: str, location: Path)->str:
'''
def _get_model_name(self, path_name: str, location: Path) -> str:
"""
Calculate a name for the model - primitive implementation.
'''
"""
if key := self.reverse_paths.get(path_name):
(name, base, mtype) = ModelManager.parse_key(key)
return name
@ -324,99 +339,103 @@ class ModelInstall(object):
else:
return location.stem
def _make_attributes(self, path: Path, info: ModelProbeInfo)->dict:
def _make_attributes(self, path: Path, info: ModelProbeInfo) -> dict:
model_name = path.name if path.is_dir() else path.stem
description = f'{info.base_type.value} {info.model_type.value} model {model_name}'
description = f"{info.base_type.value} {info.model_type.value} model {model_name}"
if key := self.reverse_paths.get(self.current_id):
if key in self.datasets:
description = self.datasets[key].get('description') or description
description = self.datasets[key].get("description") or description
rel_path = self.relative_to_root(path)
attributes = dict(
path = str(rel_path),
description = str(description),
model_format = info.format,
)
path=str(rel_path),
description=str(description),
model_format=info.format,
)
legacy_conf = None
if info.model_type == ModelType.Main:
attributes.update(dict(variant = info.variant_type,))
if info.format=="checkpoint":
attributes.update(
dict(
variant=info.variant_type,
)
)
if info.format == "checkpoint":
try:
possible_conf = path.with_suffix('.yaml')
possible_conf = path.with_suffix(".yaml")
if possible_conf.exists():
legacy_conf = str(self.relative_to_root(possible_conf))
elif info.base_type == BaseModelType.StableDiffusion2:
legacy_conf = Path(self.config.legacy_conf_dir, LEGACY_CONFIGS[info.base_type][info.variant_type][info.prediction_type])
legacy_conf = Path(
self.config.legacy_conf_dir,
LEGACY_CONFIGS[info.base_type][info.variant_type][info.prediction_type],
)
else:
legacy_conf = Path(self.config.legacy_conf_dir, LEGACY_CONFIGS[info.base_type][info.variant_type])
legacy_conf = Path(
self.config.legacy_conf_dir, LEGACY_CONFIGS[info.base_type][info.variant_type]
)
except KeyError:
legacy_conf = Path(self.config.legacy_conf_dir, 'v1-inference.yaml') # best guess
if info.model_type == ModelType.ControlNet and info.format=="checkpoint":
possible_conf = path.with_suffix('.yaml')
legacy_conf = Path(self.config.legacy_conf_dir, "v1-inference.yaml") # best guess
if info.model_type == ModelType.ControlNet and info.format == "checkpoint":
possible_conf = path.with_suffix(".yaml")
if possible_conf.exists():
legacy_conf = str(self.relative_to_root(possible_conf))
if legacy_conf:
attributes.update(
dict(
config = str(legacy_conf)
)
)
attributes.update(dict(config=str(legacy_conf)))
return attributes
def relative_to_root(self, path: Path)->Path:
def relative_to_root(self, path: Path) -> Path:
root = self.config.root_path
if path.is_relative_to(root):
return path.relative_to(root)
else:
return path
def _download_hf_pipeline(self, repo_id: str, staging: Path)->Path:
'''
def _download_hf_pipeline(self, repo_id: str, staging: Path) -> Path:
"""
This retrieves a StableDiffusion model from cache or remote and then
does a save_pretrained() to the indicated staging area.
'''
_,name = repo_id.split("/")
revisions = ['fp16','main'] if self.config.precision=='float16' else ['main']
"""
_, name = repo_id.split("/")
revisions = ["fp16", "main"] if self.config.precision == "float16" else ["main"]
model = None
for revision in revisions:
try:
model = DiffusionPipeline.from_pretrained(repo_id,revision=revision,safety_checker=None)
model = DiffusionPipeline.from_pretrained(repo_id, revision=revision, safety_checker=None)
except: # most errors are due to fp16 not being present. Fix this to catch other errors
pass
if model:
break
if not model:
logger.error(f'Diffusers model {repo_id} could not be downloaded. Skipping.')
logger.error(f"Diffusers model {repo_id} could not be downloaded. Skipping.")
return None
model.save_pretrained(staging / name, safe_serialization=True)
return staging / name
def _download_hf_model(self, repo_id: str, files: List[str], staging: Path)->Path:
_,name = repo_id.split("/")
def _download_hf_model(self, repo_id: str, files: List[str], staging: Path) -> Path:
_, name = repo_id.split("/")
location = staging / name
paths = list()
for filename in files:
p = hf_download_with_resume(repo_id,
model_dir=location,
model_name=filename,
access_token = self.access_token
)
p = hf_download_with_resume(
repo_id, model_dir=location, model_name=filename, access_token=self.access_token
)
if p:
paths.append(p)
else:
logger.warning(f'Could not download {filename} from {repo_id}.')
return location if len(paths)>0 else None
logger.warning(f"Could not download {filename} from {repo_id}.")
return location if len(paths) > 0 else None
@classmethod
def _reverse_paths(cls,datasets)->dict:
'''
def _reverse_paths(cls, datasets) -> dict:
"""
Reverse mapping from repo_id/path to destination name.
'''
return {v.get('path') or v.get('repo_id') : k for k, v in datasets.items()}
"""
return {v.get("path") or v.get("repo_id"): k for k, v in datasets.items()}
# -------------------------------------
def yes_or_no(prompt: str, default_yes=True):
@ -427,13 +446,12 @@ def yes_or_no(prompt: str, default_yes=True):
else:
return response[0] in ("y", "Y")
# ---------------------------------------------
def hf_download_from_pretrained(
model_class: object, model_name: str, destination: Path, **kwargs
):
logger = InvokeAILogger.getLogger('InvokeAI')
logger.addFilter(lambda x: 'fp16 is not a valid' not in x.getMessage())
def hf_download_from_pretrained(model_class: object, model_name: str, destination: Path, **kwargs):
logger = InvokeAILogger.getLogger("InvokeAI")
logger.addFilter(lambda x: "fp16 is not a valid" not in x.getMessage())
model = model_class.from_pretrained(
model_name,
resume_download=True,
@ -442,13 +460,14 @@ def hf_download_from_pretrained(
model.save_pretrained(destination, safe_serialization=True)
return destination
# ---------------------------------------------
def hf_download_with_resume(
repo_id: str,
model_dir: str,
model_name: str,
model_dest: Path = None,
access_token: str = None,
repo_id: str,
model_dir: str,
model_name: str,
model_dest: Path = None,
access_token: str = None,
) -> Path:
model_dest = model_dest or Path(os.path.join(model_dir, model_name))
os.makedirs(model_dir, exist_ok=True)
@ -467,9 +486,7 @@ def hf_download_with_resume(
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
if resp.status_code == 416: # "range not satisfiable", which means nothing to return
logger.info(f"{model_name}: complete file found. Skipping.")
return model_dest
elif resp.status_code == 404:
@ -498,5 +515,3 @@ def hf_download_with_resume(
logger.error(f"An error occurred while downloading {model_name}: {str(e)}")
return None
return model_dest

10
invokeai/backend/model_management/__init__.py
View File

@ -3,6 +3,12 @@ Initialization file for invokeai.backend.model_management
"""
from .model_manager import ModelManager, ModelInfo, AddModelResult, SchedulerPredictionType
from .model_cache import ModelCache
from .models import BaseModelType, ModelType, SubModelType, ModelVariantType, ModelNotFoundException, DuplicateModelException
from .models import (
BaseModelType,
ModelType,
SubModelType,
ModelVariantType,
ModelNotFoundException,
DuplicateModelException,
)
from .model_merge import ModelMerger, MergeInterpolationMethod

90
invokeai/backend/model_management/convert_ckpt_to_diffusers.py
View File

@ -56,9 +56,7 @@ from diffusers.schedulers import (
)
from diffusers.utils import is_accelerate_available, is_omegaconf_available, is_safetensors_available
from diffusers.utils.import_utils import BACKENDS_MAPPING
from diffusers.pipelines.latent_diffusion.pipeline_latent_diffusion import (
LDMBertConfig, LDMBertModel
)
from diffusers.pipelines.latent_diffusion.pipeline_latent_diffusion import LDMBertConfig, LDMBertModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
@ -85,6 +83,7 @@ if is_accelerate_available():
logger = InvokeAILogger.getLogger(__name__)
CONVERT_MODEL_ROOT = InvokeAIAppConfig.get_config().root_path / MODEL_CORE / "convert"
def shave_segments(path, n_shave_prefix_segments=1):
"""
Removes segments. Positive values shave the first segments, negative shave the last segments.
@ -509,9 +508,7 @@ def convert_ldm_unet_checkpoint(
paths = renew_resnet_paths(resnets)
meta_path = {"old": f"input_blocks.{i}.0", "new": f"down_blocks.{block_id}.resnets.{layer_in_block_id}"}
assign_to_checkpoint(
paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config
)
assign_to_checkpoint(paths, new_checkpoint, unet_state_dict, additional_replacements=[meta_path], config=config)
if len(attentions):
paths = renew_attention_paths(attentions)
@ -796,7 +793,7 @@ def convert_ldm_bert_checkpoint(checkpoint, config):
def convert_ldm_clip_checkpoint(checkpoint, local_files_only=False, text_encoder=None):
if text_encoder is None:
config = CLIPTextConfig.from_pretrained(CONVERT_MODEL_ROOT / 'clip-vit-large-patch14')
config = CLIPTextConfig.from_pretrained(CONVERT_MODEL_ROOT / "clip-vit-large-patch14")
ctx = init_empty_weights if is_accelerate_available() else nullcontext
with ctx():
@ -1008,7 +1005,9 @@ def stable_unclip_image_encoder(original_config):
elif sd_clip_image_embedder_class == "FrozenOpenCLIPImageEmbedder":
feature_extractor = CLIPImageProcessor()
# InvokeAI doesn't use CLIPVisionModelWithProjection so it isn't in the core - if this code is hit a download will occur
image_encoder = CLIPVisionModelWithProjection.from_pretrained(CONVERT_MODEL_ROOT / "CLIP-ViT-H-14-laion2B-s32B-b79K")
image_encoder = CLIPVisionModelWithProjection.from_pretrained(
CONVERT_MODEL_ROOT / "CLIP-ViT-H-14-laion2B-s32B-b79K"
)
else:
raise NotImplementedError(
f"Unknown CLIP image embedder class in stable diffusion checkpoint {sd_clip_image_embedder_class}"
@ -1071,17 +1070,17 @@ def convert_controlnet_checkpoint(
extract_ema,
use_linear_projection=None,
cross_attention_dim=None,
precision: torch.dtype=torch.float32,
precision: torch.dtype = torch.float32,
):
ctrlnet_config = create_unet_diffusers_config(original_config, image_size=image_size, controlnet=True)
ctrlnet_config["upcast_attention"] = upcast_attention
ctrlnet_config.pop("sample_size")
original_config = ctrlnet_config.copy()
ctrlnet_config.pop('addition_embed_type')
ctrlnet_config.pop('addition_time_embed_dim')
ctrlnet_config.pop('transformer_layers_per_block')
ctrlnet_config.pop("addition_embed_type")
ctrlnet_config.pop("addition_time_embed_dim")
ctrlnet_config.pop("transformer_layers_per_block")
if use_linear_projection is not None:
ctrlnet_config["use_linear_projection"] = use_linear_projection
@ -1111,6 +1110,7 @@ def convert_controlnet_checkpoint(
return controlnet.to(precision)
# TO DO - PASS PRECISION
def download_from_original_stable_diffusion_ckpt(
checkpoint_path: str,
@ -1249,8 +1249,8 @@ def download_from_original_stable_diffusion_ckpt(
# "state_dict" key https://huggingface.co/thibaud/controlnet-canny-sd21
while "state_dict" in checkpoint:
checkpoint = checkpoint["state_dict"]
logger.debug(f'model_type = {model_type}; original_config_file = {original_config_file}')
logger.debug(f"model_type = {model_type}; original_config_file = {original_config_file}")
if original_config_file is None:
key_name_v2_1 = "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight"
@ -1258,7 +1258,9 @@ def download_from_original_stable_diffusion_ckpt(
key_name_sd_xl_refiner = "conditioner.embedders.0.model.transformer.resblocks.9.mlp.c_proj.bias"
# model_type = "v1"
config_url = "https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"
config_url = (
"https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml"
)
if key_name_v2_1 in checkpoint and checkpoint[key_name_v2_1].shape[-1] == 1024:
# model_type = "v2"
@ -1277,7 +1279,10 @@ def download_from_original_stable_diffusion_ckpt(
original_config_file = BytesIO(requests.get(config_url).content)
original_config = OmegaConf.load(original_config_file)
if model_version == BaseModelType.StableDiffusion2 and original_config["model"]["params"]["parameterization"] == "v":
if (
model_version == BaseModelType.StableDiffusion2
and original_config["model"]["params"]["parameterization"] == "v"
):
prediction_type = "v_prediction"
upcast_attention = True
image_size = 768
@ -1436,7 +1441,7 @@ def download_from_original_stable_diffusion_ckpt(
config_kwargs = {"subfolder": "text_encoder"}
text_model = convert_open_clip_checkpoint(checkpoint, config_name, **config_kwargs)
tokenizer = CLIPTokenizer.from_pretrained(CONVERT_MODEL_ROOT / 'stable-diffusion-2-clip', subfolder="tokenizer")
tokenizer = CLIPTokenizer.from_pretrained(CONVERT_MODEL_ROOT / "stable-diffusion-2-clip", subfolder="tokenizer")
if stable_unclip is None:
if controlnet:
@ -1491,7 +1496,9 @@ def download_from_original_stable_diffusion_ckpt(
prior = PriorTransformer.from_pretrained(karlo_model, subfolder="prior")
prior_tokenizer = CLIPTokenizer.from_pretrained(CONVERT_MODEL_ROOT / "clip-vit-large-patch14")
prior_text_model = CLIPTextModelWithProjection.from_pretrained(CONVERT_MODEL_ROOT / "clip-vit-large-patch14")
prior_text_model = CLIPTextModelWithProjection.from_pretrained(
CONVERT_MODEL_ROOT / "clip-vit-large-patch14"
)
prior_scheduler = UnCLIPScheduler.from_pretrained(karlo_model, subfolder="prior_scheduler")
prior_scheduler = DDPMScheduler.from_config(prior_scheduler.config)
@ -1533,11 +1540,19 @@ def download_from_original_stable_diffusion_ckpt(
text_model = convert_ldm_clip_checkpoint(
checkpoint, local_files_only=local_files_only, text_encoder=text_encoder
)
tokenizer = CLIPTokenizer.from_pretrained(CONVERT_MODEL_ROOT / "clip-vit-large-patch14") if tokenizer is None else tokenizer
tokenizer = (
CLIPTokenizer.from_pretrained(CONVERT_MODEL_ROOT / "clip-vit-large-patch14")
if tokenizer is None
else tokenizer
)
if load_safety_checker:
safety_checker = StableDiffusionSafetyChecker.from_pretrained(CONVERT_MODEL_ROOT / "stable-diffusion-safety-checker")
feature_extractor = AutoFeatureExtractor.from_pretrained(CONVERT_MODEL_ROOT / "stable-diffusion-safety-checker")
safety_checker = StableDiffusionSafetyChecker.from_pretrained(
CONVERT_MODEL_ROOT / "stable-diffusion-safety-checker"
)
feature_extractor = AutoFeatureExtractor.from_pretrained(
CONVERT_MODEL_ROOT / "stable-diffusion-safety-checker"
)
else:
safety_checker = None
feature_extractor = None
@ -1567,7 +1582,7 @@ def download_from_original_stable_diffusion_ckpt(
if model_type == "SDXL":
tokenizer = CLIPTokenizer.from_pretrained(CONVERT_MODEL_ROOT / "clip-vit-large-patch14")
text_encoder = convert_ldm_clip_checkpoint(checkpoint, local_files_only=local_files_only)
tokenizer_name = CONVERT_MODEL_ROOT / "CLIP-ViT-bigG-14-laion2B-39B-b160k"
tokenizer_2 = CLIPTokenizer.from_pretrained(tokenizer_name, pad_token="!")
@ -1577,7 +1592,7 @@ def download_from_original_stable_diffusion_ckpt(
checkpoint, config_name, prefix="conditioner.embedders.1.model.", has_projection=True, **config_kwargs
)
pipe = StableDiffusionXLPipeline (
pipe = StableDiffusionXLPipeline(
vae=vae.to(precision),
text_encoder=text_encoder,
tokenizer=tokenizer,
@ -1686,24 +1701,22 @@ def download_controlnet_from_original_ckpt(
return controlnet
def convert_ldm_vae_to_diffusers(checkpoint, vae_config: DictConfig, image_size: int) -> AutoencoderKL:
vae_config = create_vae_diffusers_config(
vae_config, image_size=image_size
)
converted_vae_checkpoint = convert_ldm_vae_checkpoint(
checkpoint, vae_config
)
def convert_ldm_vae_to_diffusers(checkpoint, vae_config: DictConfig, image_size: int) -> AutoencoderKL:
vae_config = create_vae_diffusers_config(vae_config, image_size=image_size)
converted_vae_checkpoint = convert_ldm_vae_checkpoint(checkpoint, vae_config)
vae = AutoencoderKL(**vae_config)
vae.load_state_dict(converted_vae_checkpoint)
return vae
def convert_ckpt_to_diffusers(
checkpoint_path: Union[str, Path],
dump_path: Union[str, Path],
use_safetensors: bool=True,
**kwargs,
checkpoint_path: Union[str, Path],
dump_path: Union[str, Path],
use_safetensors: bool = True,
**kwargs,
):
"""
Takes all the arguments of download_from_original_stable_diffusion_ckpt(),
@ -1717,10 +1730,11 @@ def convert_ckpt_to_diffusers(
safe_serialization=use_safetensors and is_safetensors_available(),
)
def convert_controlnet_to_diffusers(
checkpoint_path: Union[str, Path],
dump_path: Union[str, Path],
**kwargs,
checkpoint_path: Union[str, Path],
dump_path: Union[str, Path],
**kwargs,
):
"""
Takes all the arguments of download_controlnet_from_original_ckpt(),

138
invokeai/backend/model_management/lora.py
View File

@ -11,14 +11,15 @@ from diffusers.models import UNet2DConditionModel
from safetensors.torch import load_file
from transformers import CLIPTextModel, CLIPTokenizer
class LoRALayerBase:
#rank: Optional[int]
#alpha: Optional[float]
#bias: Optional[torch.Tensor]
#layer_key: str
#@property
#def scale(self):
class LoRALayerBase:
# rank: Optional[int]
# alpha: Optional[float]
# bias: Optional[torch.Tensor]
# layer_key: str
# @property
# def scale(self):
# return self.alpha / self.rank if (self.alpha and self.rank) else 1.0
def __init__(
@ -31,11 +32,7 @@ class LoRALayerBase:
else:
self.alpha = None
if (
"bias_indices" in values
and "bias_values" in values
and "bias_size" in values
):
if "bias_indices" in values and "bias_values" in values and "bias_size" in values:
self.bias = torch.sparse_coo_tensor(
values["bias_indices"],
values["bias_values"],
@ -45,13 +42,13 @@ class LoRALayerBase:
else:
self.bias = None
self.rank = None # set in layer implementation
self.rank = None # set in layer implementation
self.layer_key = layer_key
def forward(
self,
module: torch.nn.Module,
input_h: Any, # for real looks like Tuple[torch.nn.Tensor] but not sure
input_h: Any, # for real looks like Tuple[torch.nn.Tensor] but not sure
multiplier: float,
):
if type(module) == torch.nn.Conv2d:
@ -71,12 +68,16 @@ class LoRALayerBase:
bias = self.bias if self.bias is not None else 0
scale = self.alpha / self.rank if (self.alpha and self.rank) else 1.0
return op(
*input_h,
(weight + bias).view(module.weight.shape),
None,
**extra_args,
) * multiplier * scale
return (
op(
*input_h,
(weight + bias).view(module.weight.shape),
None,
**extra_args,
)
* multiplier
* scale
)
def get_weight(self):
raise NotImplementedError()
@ -99,9 +100,9 @@ class LoRALayerBase:
# TODO: find and debug lora/locon with bias
class LoRALayer(LoRALayerBase):
#up: torch.Tensor
#mid: Optional[torch.Tensor]
#down: torch.Tensor
# up: torch.Tensor
# mid: Optional[torch.Tensor]
# down: torch.Tensor
def __init__(
self,
@ -151,12 +152,12 @@ class LoRALayer(LoRALayerBase):
class LoHALayer(LoRALayerBase):
#w1_a: torch.Tensor
#w1_b: torch.Tensor
#w2_a: torch.Tensor
#w2_b: torch.Tensor
#t1: Optional[torch.Tensor] = None
#t2: Optional[torch.Tensor] = None
# w1_a: torch.Tensor
# w1_b: torch.Tensor
# w2_a: torch.Tensor
# w2_b: torch.Tensor
# t1: Optional[torch.Tensor] = None
# t2: Optional[torch.Tensor] = None
def __init__(
self,
@ -187,12 +188,8 @@ class LoHALayer(LoRALayerBase):
weight = (self.w1_a @ self.w1_b) * (self.w2_a @ self.w2_b)
else:
rebuild1 = torch.einsum(
"i j k l, j r, i p -> p r k l", self.t1, self.w1_b, self.w1_a
)
rebuild2 = torch.einsum(
"i j k l, j r, i p -> p r k l", self.t2, self.w2_b, self.w2_a
)
rebuild1 = torch.einsum("i j k l, j r, i p -> p r k l", self.t1, self.w1_b, self.w1_a)
rebuild2 = torch.einsum("i j k l, j r, i p -> p r k l", self.t2, self.w2_b, self.w2_a)
weight = rebuild1 * rebuild2
return weight
@ -223,20 +220,20 @@ class LoHALayer(LoRALayerBase):
class LoKRLayer(LoRALayerBase):
#w1: Optional[torch.Tensor] = None
#w1_a: Optional[torch.Tensor] = None
#w1_b: Optional[torch.Tensor] = None
#w2: Optional[torch.Tensor] = None
#w2_a: Optional[torch.Tensor] = None
#w2_b: Optional[torch.Tensor] = None
#t2: Optional[torch.Tensor] = None
# w1: Optional[torch.Tensor] = None
# w1_a: Optional[torch.Tensor] = None
# w1_b: Optional[torch.Tensor] = None
# w2: Optional[torch.Tensor] = None
# w2_a: Optional[torch.Tensor] = None
# w2_b: Optional[torch.Tensor] = None
# t2: Optional[torch.Tensor] = None
def __init__(
self,
layer_key: str,
values: dict,
):
super().__init__(layer_key, values)
super().__init__(layer_key, values)
if "lokr_w1" in values:
self.w1 = values["lokr_w1"]
@ -266,7 +263,7 @@ class LoKRLayer(LoRALayerBase):
elif "lokr_w2_b" in values:
self.rank = values["lokr_w2_b"].shape[0]
else:
self.rank = None # unscaled
self.rank = None # unscaled
def get_weight(self):
w1 = self.w1
@ -278,7 +275,7 @@ class LoKRLayer(LoRALayerBase):
if self.t2 is None:
w2 = self.w2_a @ self.w2_b
else:
w2 = torch.einsum('i j k l, i p, j r -> p r k l', self.t2, self.w2_a, self.w2_b)
w2 = torch.einsum("i j k l, i p, j r -> p r k l", self.t2, self.w2_a, self.w2_b)
if len(w2.shape) == 4:
w1 = w1.unsqueeze(2).unsqueeze(2)
@ -317,7 +314,7 @@ class LoKRLayer(LoRALayerBase):
self.t2 = self.t2.to(device=device, dtype=dtype)
class LoRAModel: #(torch.nn.Module):
class LoRAModel: # (torch.nn.Module):
_name: str
layers: Dict[str, LoRALayer]
_device: torch.device
@ -345,7 +342,7 @@ class LoRAModel: #(torch.nn.Module):
@property
def dtype(self):
return self._dtype
return self._dtype
def to(
self,
@ -380,7 +377,7 @@ class LoRAModel: #(torch.nn.Module):
model = cls(
device=device,
dtype=dtype,
name=file_path.stem, # TODO:
name=file_path.stem, # TODO:
layers=dict(),
)
@ -392,7 +389,6 @@ class LoRAModel: #(torch.nn.Module):
state_dict = cls._group_state(state_dict)
for layer_key, values in state_dict.items():
# lora and locon
if "lora_down.weight" in values:
layer = LoRALayer(layer_key, values)
@ -407,9 +403,7 @@ class LoRAModel: #(torch.nn.Module):
else:
# TODO: diff/ia3/... format
print(
f">> Encountered unknown lora layer module in {model.name}: {layer_key}"
)
print(f">> Encountered unknown lora layer module in {model.name}: {layer_key}")
return
# lower memory consumption by removing already parsed layer values
@ -443,9 +437,10 @@ with LoRAHelper.apply_lora_unet(unet, loras):
# unmodified unet
"""
# TODO: rename smth like ModelPatcher and add TI method?
class ModelPatcher:
@staticmethod
def _resolve_lora_key(model: torch.nn.Module, lora_key: str, prefix: str) -> Tuple[str, torch.nn.Module]:
assert "." not in lora_key
@ -455,10 +450,10 @@ class ModelPatcher:
module = model
module_key = ""
key_parts = lora_key[len(prefix):].split('_')
key_parts = lora_key[len(prefix) :].split("_")
submodule_name = key_parts.pop(0)
while len(key_parts) > 0:
try:
module = module.get_submodule(submodule_name)
@ -477,7 +472,6 @@ class ModelPatcher:
applied_loras: List[Tuple[LoRAModel, float]],
layer_name: str,
):
def lora_forward(module, input_h, output):
if len(applied_loras) == 0:
return output
@ -491,7 +485,6 @@ class ModelPatcher:
return lora_forward
@classmethod
@contextmanager
def apply_lora_unet(
@ -502,7 +495,6 @@ class ModelPatcher:
with cls.apply_lora(unet, loras, "lora_unet_"):
yield
@classmethod
@contextmanager
def apply_lora_text_encoder(
@ -513,7 +505,6 @@ class ModelPatcher:
with cls.apply_lora(text_encoder, loras, "lora_te_"):
yield
@classmethod
@contextmanager
def apply_lora(
@ -526,7 +517,7 @@ class ModelPatcher:
try:
with torch.no_grad():
for lora, lora_weight in loras:
#assert lora.device.type == "cpu"
# assert lora.device.type == "cpu"
for layer_key, layer in lora.layers.items():
if not layer_key.startswith(prefix):
continue
@ -536,7 +527,7 @@ class ModelPatcher:
original_weights[module_key] = module.weight.detach().to(device="cpu", copy=True)
# enable autocast to calc fp16 loras on cpu
#with torch.autocast(device_type="cpu"):
# with torch.autocast(device_type="cpu"):
layer.to(dtype=torch.float32)
layer_scale = layer.alpha / layer.rank if (layer.alpha and layer.rank) else 1.0
layer_weight = layer.get_weight() * lora_weight * layer_scale
@ -547,14 +538,13 @@ class ModelPatcher:
module.weight += layer_weight.to(device=module.weight.device, dtype=module.weight.dtype)
yield # wait for context manager exit
yield # wait for context manager exit
finally:
with torch.no_grad():
for module_key, weight in original_weights.items():
model.get_submodule(module_key).weight.copy_(weight)
@classmethod
@contextmanager
def apply_ti(
@ -602,7 +592,9 @@ class ModelPatcher:
f"Cannot load embedding for {trigger}. It was trained on a model with token dimension {embedding.shape[0]}, but the current model has token dimension {model_embeddings.weight.data[token_id].shape[0]}."
)
model_embeddings.weight.data[token_id] = embedding.to(device=text_encoder.device, dtype=text_encoder.dtype)
model_embeddings.weight.data[token_id] = embedding.to(
device=text_encoder.device, dtype=text_encoder.dtype
)
ti_tokens.append(token_id)
if len(ti_tokens) > 1:
@ -614,7 +606,6 @@ class ModelPatcher:
if init_tokens_count and new_tokens_added:
text_encoder.resize_token_embeddings(init_tokens_count)
@classmethod
@contextmanager
def apply_clip_skip(
@ -633,9 +624,10 @@ class ModelPatcher:
while len(skipped_layers) > 0:
text_encoder.text_model.encoder.layers.append(skipped_layers.pop())
class TextualInversionModel:
name: str
embedding: torch.Tensor # [n, 768]|[n, 1280]
embedding: torch.Tensor # [n, 768]|[n, 1280]
@classmethod
def from_checkpoint(
@ -647,8 +639,8 @@ class TextualInversionModel:
if not isinstance(file_path, Path):
file_path = Path(file_path)
result = cls() # TODO:
result.name = file_path.stem # TODO:
result = cls() # TODO:
result.name = file_path.stem # TODO:
if file_path.suffix == ".safetensors":
state_dict = load_file(file_path.absolute().as_posix(), device="cpu")
@ -659,7 +651,9 @@ class TextualInversionModel:
# difference mostly in metadata
if "string_to_param" in state_dict:
if len(state_dict["string_to_param"]) > 1:
print(f"Warn: Embedding \"{file_path.name}\" contains multiple tokens, which is not supported. The first token will be used.")
print(
f'Warn: Embedding "{file_path.name}" contains multiple tokens, which is not supported. The first token will be used.'
)
result.embedding = next(iter(state_dict["string_to_param"].values()))
@ -688,10 +682,7 @@ class TextualInversionManager(BaseTextualInversionManager):
self.pad_tokens = dict()
self.tokenizer = tokenizer
def expand_textual_inversion_token_ids_if_necessary(
self, token_ids: list[int]
) -> list[int]:
def expand_textual_inversion_token_ids_if_necessary(self, token_ids: list[int]) -> list[int]:
if len(self.pad_tokens) == 0:
return token_ids
@ -707,4 +698,3 @@ class TextualInversionManager(BaseTextualInversionManager):
new_token_ids.extend(self.pad_tokens[token_id])
return new_token_ids

120
invokeai/backend/model_management/model_cache.py
View File

@ -37,19 +37,22 @@ from .models import BaseModelType, ModelType, SubModelType, ModelBase
DEFAULT_MAX_CACHE_SIZE = 6.0
# amount of GPU memory to hold in reserve for use by generations (GB)
DEFAULT_MAX_VRAM_CACHE_SIZE= 2.75
DEFAULT_MAX_VRAM_CACHE_SIZE = 2.75
# actual size of a gig
GIG = 1073741824
class ModelLocker(object):
"Forward declaration"
pass
class ModelCache(object):
"Forward declaration"
pass
class _CacheRecord:
size: int
model: Any
@ -79,22 +82,22 @@ class _CacheRecord:
return self.model.device != self.cache.storage_device
else:
return False
class ModelCache(object):
def __init__(
self,
max_cache_size: float=DEFAULT_MAX_CACHE_SIZE,
max_vram_cache_size: float=DEFAULT_MAX_VRAM_CACHE_SIZE,
execution_device: torch.device=torch.device('cuda'),
storage_device: torch.device=torch.device('cpu'),
precision: torch.dtype=torch.float16,
sequential_offload: bool=False,
lazy_offloading: bool=True,
max_cache_size: float = DEFAULT_MAX_CACHE_SIZE,
max_vram_cache_size: float = DEFAULT_MAX_VRAM_CACHE_SIZE,
execution_device: torch.device = torch.device("cuda"),
storage_device: torch.device = torch.device("cpu"),
precision: torch.dtype = torch.float16,
sequential_offload: bool = False,
lazy_offloading: bool = True,
sha_chunksize: int = 16777216,
logger: types.ModuleType = logger
logger: types.ModuleType = logger,
):
'''
"""
:param max_cache_size: Maximum size of the RAM cache [6.0 GB]
:param execution_device: Torch device to load active model into [torch.device('cuda')]
:param storage_device: Torch device to save inactive model in [torch.device('cpu')]
@ -102,16 +105,16 @@ class ModelCache(object):
:param lazy_offloading: Keep model in VRAM until another model needs to be loaded
:param sequential_offload: Conserve VRAM by loading and unloading each stage of the pipeline sequentially
:param sha_chunksize: Chunksize to use when calculating sha256 model hash
'''
"""
self.model_infos: Dict[str, ModelBase] = dict()
# allow lazy offloading only when vram cache enabled
self.lazy_offloading = lazy_offloading and max_vram_cache_size > 0
self.precision: torch.dtype=precision
self.max_cache_size: float=max_cache_size
self.max_vram_cache_size: float=max_vram_cache_size
self.execution_device: torch.device=execution_device
self.storage_device: torch.device=storage_device
self.sha_chunksize=sha_chunksize
self.precision: torch.dtype = precision
self.max_cache_size: float = max_cache_size
self.max_vram_cache_size: float = max_vram_cache_size
self.execution_device: torch.device = execution_device
self.storage_device: torch.device = storage_device
self.sha_chunksize = sha_chunksize
self.logger = logger
self._cached_models = dict()
@ -124,7 +127,6 @@ class ModelCache(object):
model_type: ModelType,
submodel_type: Optional[SubModelType] = None,
):
key = f"{model_path}:{base_model}:{model_type}"
if submodel_type:
key += f":{submodel_type}"
@ -163,7 +165,6 @@ class ModelCache(object):
submodel: Optional[SubModelType] = None,
gpu_load: bool = True,
) -> Any:
if not isinstance(model_path, Path):
model_path = Path(model_path)
@ -186,7 +187,7 @@ class ModelCache(object):
# TODO: lock for no copies on simultaneous calls?
cache_entry = self._cached_models.get(key, None)
if cache_entry is None:
self.logger.info(f'Loading model {model_path}, type {base_model}:{model_type}:{submodel}')
self.logger.info(f"Loading model {model_path}, type {base_model}:{model_type}:{submodel}")
# this will remove older cached models until
# there is sufficient room to load the requested model
@ -196,7 +197,7 @@ class ModelCache(object):
gc.collect()
model = model_info.get_model(child_type=submodel, torch_dtype=self.precision)
if mem_used := model_info.get_size(submodel):
self.logger.debug(f'CPU RAM used for load: {(mem_used/GIG):.2f} GB')
self.logger.debug(f"CPU RAM used for load: {(mem_used/GIG):.2f} GB")
cache_entry = _CacheRecord(self, model, mem_used)
self._cached_models[key] = cache_entry
@ -209,13 +210,13 @@ class ModelCache(object):
class ModelLocker(object):
def __init__(self, cache, key, model, gpu_load, size_needed):
'''
"""
:param cache: The model_cache object
:param key: The key of the model to lock in GPU
:param model: The model to lock
:param gpu_load: True if load into gpu
:param size_needed: Size of the model to load
'''
"""
self.gpu_load = gpu_load
self.cache = cache
self.key = key
@ -224,7 +225,7 @@ class ModelCache(object):
self.cache_entry = self.cache._cached_models[self.key]
def __enter__(self) -> Any:
if not hasattr(self.model, 'to'):
if not hasattr(self.model, "to"):
return self.model
# NOTE that the model has to have the to() method in order for this
@ -234,22 +235,21 @@ class ModelCache(object):
try:
if self.cache.lazy_offloading:
self.cache._offload_unlocked_models(self.size_needed)
self.cache._offload_unlocked_models(self.size_needed)
if self.model.device != self.cache.execution_device:
self.cache.logger.debug(f'Moving {self.key} into {self.cache.execution_device}')
self.cache.logger.debug(f"Moving {self.key} into {self.cache.execution_device}")
with VRAMUsage() as mem:
self.model.to(self.cache.execution_device) # move into GPU
self.cache.logger.debug(f'GPU VRAM used for load: {(mem.vram_used/GIG):.2f} GB')
self.cache.logger.debug(f'Locking {self.key} in {self.cache.execution_device}')
self.cache.logger.debug(f"GPU VRAM used for load: {(mem.vram_used/GIG):.2f} GB")
self.cache.logger.debug(f"Locking {self.key} in {self.cache.execution_device}")
self.cache._print_cuda_stats()
except:
self.cache_entry.unlock()
raise
# TODO: not fully understand
# in the event that the caller wants the model in RAM, we
# move it into CPU if it is in GPU and not locked
@ -259,7 +259,7 @@ class ModelCache(object):
return self.model
def __exit__(self, type, value, traceback):
if not hasattr(self.model, 'to'):
if not hasattr(self.model, "to"):
return
self.cache_entry.unlock()
@ -277,11 +277,11 @@ class ModelCache(object):
self,
model_path: Union[str, Path],
) -> str:
'''
"""
Given the HF repo id or path to a model on disk, returns a unique
hash. Works for legacy checkpoint files, HF models on disk, and HF repo IDs
:param model_path: Path to model file/directory on disk.
'''
"""
return self._local_model_hash(model_path)
def cache_size(self) -> float:
@ -290,7 +290,7 @@ class ModelCache(object):
return current_cache_size / GIG
def _has_cuda(self) -> bool:
return self.execution_device.type == 'cuda'
return self.execution_device.type == "cuda"
def _print_cuda_stats(self):
vram = "%4.2fG" % (torch.cuda.memory_allocated() / GIG)
@ -306,18 +306,21 @@ class ModelCache(object):
if model_info.locked:
locked_models += 1
self.logger.debug(f"Current VRAM/RAM usage: {vram}/{ram}; cached_models/loaded_models/locked_models/ = {cached_models}/{loaded_models}/{locked_models}")
self.logger.debug(
f"Current VRAM/RAM usage: {vram}/{ram}; cached_models/loaded_models/locked_models/ = {cached_models}/{loaded_models}/{locked_models}"
)
def _make_cache_room(self, model_size):
# calculate how much memory this model will require
#multiplier = 2 if self.precision==torch.float32 else 1
# multiplier = 2 if self.precision==torch.float32 else 1
bytes_needed = model_size
maximum_size = self.max_cache_size * GIG # stored in GB, convert to bytes
current_size = sum([m.size for m in self._cached_models.values()])
if current_size + bytes_needed > maximum_size:
self.logger.debug(f'Max cache size exceeded: {(current_size/GIG):.2f}/{self.max_cache_size:.2f} GB, need an additional {(bytes_needed/GIG):.2f} GB')
self.logger.debug(
f"Max cache size exceeded: {(current_size/GIG):.2f}/{self.max_cache_size:.2f} GB, need an additional {(bytes_needed/GIG):.2f} GB"
)
self.logger.debug(f"Before unloading: cached_models={len(self._cached_models)}")
@ -339,7 +342,7 @@ class ModelCache(object):
with suppress(RuntimeError):
referrer.clear()
cleared = True
#break
# break
# repeat if referrers changes(due to frame clear), else exit loop
if cleared:
@ -348,13 +351,17 @@ class ModelCache(object):
break
device = cache_entry.model.device if hasattr(cache_entry.model, "device") else None
self.logger.debug(f"Model: {model_key}, locks: {cache_entry._locks}, device: {device}, loaded: {cache_entry.loaded}, refs: {refs}")
self.logger.debug(
f"Model: {model_key}, locks: {cache_entry._locks}, device: {device}, loaded: {cache_entry.loaded}, refs: {refs}"
)
# 2 refs:
# 1 from cache_entry
# 1 from getrefcount function
if not cache_entry.locked and refs <= 2:
self.logger.debug(f'Unloading model {model_key} to free {(model_size/GIG):.2f} GB (-{(cache_entry.size/GIG):.2f} GB)')
self.logger.debug(
f"Unloading model {model_key} to free {(model_size/GIG):.2f} GB (-{(cache_entry.size/GIG):.2f} GB)"
)
current_size -= cache_entry.size
del self._cache_stack[pos]
del self._cached_models[model_key]
@ -368,38 +375,36 @@ class ModelCache(object):
self.logger.debug(f"After unloading: cached_models={len(self._cached_models)}")
def _offload_unlocked_models(self, size_needed: int=0):
def _offload_unlocked_models(self, size_needed: int = 0):
reserved = self.max_vram_cache_size * GIG
vram_in_use = torch.cuda.memory_allocated()
self.logger.debug(f'{(vram_in_use/GIG):.2f}GB VRAM used for models; max allowed={(reserved/GIG):.2f}GB')
for model_key, cache_entry in sorted(self._cached_models.items(), key=lambda x:x[1].size):
self.logger.debug(f"{(vram_in_use/GIG):.2f}GB VRAM used for models; max allowed={(reserved/GIG):.2f}GB")
for model_key, cache_entry in sorted(self._cached_models.items(), key=lambda x: x[1].size):
if vram_in_use <= reserved:
break
if not cache_entry.locked and cache_entry.loaded:
self.logger.debug(f'Offloading {model_key} from {self.execution_device} into {self.storage_device}')
self.logger.debug(f"Offloading {model_key} from {self.execution_device} into {self.storage_device}")
with VRAMUsage() as mem:
cache_entry.model.to(self.storage_device)
self.logger.debug(f'GPU VRAM freed: {(mem.vram_used/GIG):.2f} GB')
self.logger.debug(f"GPU VRAM freed: {(mem.vram_used/GIG):.2f} GB")
vram_in_use += mem.vram_used # note vram_used is negative
self.logger.debug(f'{(vram_in_use/GIG):.2f}GB VRAM used for models; max allowed={(reserved/GIG):.2f}GB')
self.logger.debug(f"{(vram_in_use/GIG):.2f}GB VRAM used for models; max allowed={(reserved/GIG):.2f}GB")
gc.collect()
torch.cuda.empty_cache()
def _local_model_hash(self, model_path: Union[str, Path]) -> str:
sha = hashlib.sha256()
path = Path(model_path)
hashpath = path / "checksum.sha256"
if hashpath.exists() and path.stat().st_mtime <= hashpath.stat().st_mtime:
with open(hashpath) as f:
hash = f.read()
return hash
self.logger.debug(f'computing hash of model {path.name}')
for file in list(path.rglob("*.ckpt")) \
+ list(path.rglob("*.safetensors")) \
+ list(path.rglob("*.pth")):
self.logger.debug(f"computing hash of model {path.name}")
for file in list(path.rglob("*.ckpt")) + list(path.rglob("*.safetensors")) + list(path.rglob("*.pth")):
with open(file, "rb") as f:
while chunk := f.read(self.sha_chunksize):
sha.update(chunk)
@ -408,11 +413,12 @@ class ModelCache(object):
f.write(hash)
return hash
class VRAMUsage(object):
def __init__(self):
self.vram = None
self.vram_used = 0
def __enter__(self):
self.vram = torch.cuda.memory_allocated()
return self

267
invokeai/backend/model_management/model_manager.py
View File

@ -249,20 +249,26 @@ from invokeai.backend.util import CUDA_DEVICE, Chdir
from .model_cache import ModelCache, ModelLocker
from .model_search import ModelSearch
from .models import (
BaseModelType, ModelType, SubModelType,
ModelError, SchedulerPredictionType, MODEL_CLASSES,
BaseModelType,
ModelType,
SubModelType,
ModelError,
SchedulerPredictionType,
MODEL_CLASSES,
ModelConfigBase,
ModelNotFoundException, InvalidModelException,
ModelNotFoundException,
InvalidModelException,
DuplicateModelException,
)
# We are only starting to number the config file with release 3.
# The config file version doesn't have to start at release version, but it will help
# reduce confusion.
CONFIG_FILE_VERSION='3.0.0'
CONFIG_FILE_VERSION = "3.0.0"
@dataclass
class ModelInfo():
class ModelInfo:
context: ModelLocker
name: str
base_model: BaseModelType
@ -275,20 +281,24 @@ class ModelInfo():
def __enter__(self):
return self.context.__enter__()
def __exit__(self,*args, **kwargs):
def __exit__(self, *args, **kwargs):
self.context.__exit__(*args, **kwargs)
class AddModelResult(BaseModel):
name: str = Field(description="The name of the model after installation")
model_type: ModelType = Field(description="The type of model")
base_model: BaseModelType = Field(description="The base model")
config: ModelConfigBase = Field(description="The configuration of the model")
MAX_CACHE_SIZE = 6.0 # GB
class ConfigMeta(BaseModel):
version: str
class ModelManager(object):
"""
High-level interface to model management.
@ -315,12 +325,12 @@ class ModelManager(object):
if isinstance(config, (str, Path)):
self.config_path = Path(config)
if not self.config_path.exists():
logger.warning(f'The file {self.config_path} was not found. Initializing a new file')
logger.warning(f"The file {self.config_path} was not found. Initializing a new file")
self.initialize_model_config(self.config_path)
config = OmegaConf.load(self.config_path)
elif not isinstance(config, DictConfig):
raise ValueError('config argument must be an OmegaConf object, a Path or a string')
raise ValueError("config argument must be an OmegaConf object, a Path or a string")
self.config_meta = ConfigMeta(**config.pop("__metadata__"))
# TODO: metadata not found
@ -330,11 +340,11 @@ 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,
execution_device = device_type,
precision = precision,
sequential_offload = sequential_offload,
logger = logger,
max_vram_cache_size=self.app_config.max_vram_cache_size,
execution_device=device_type,
precision=precision,
sequential_offload=sequential_offload,
logger=logger,
)
self._read_models(config)
@ -348,7 +358,7 @@ class ModelManager(object):
self.models = dict()
for model_key, model_config in config.items():
if model_key.startswith('_'):
if model_key.startswith("_"):
continue
model_name, base_model, model_type = self.parse_key(model_key)
model_class = MODEL_CLASSES[base_model][model_type]
@ -395,7 +405,7 @@ class ModelManager(object):
@classmethod
def parse_key(cls, model_key: str) -> Tuple[str, BaseModelType, ModelType]:
base_model_str, model_type_str, model_name = model_key.split('/', 2)
base_model_str, model_type_str, model_name = model_key.split("/", 2)
try:
model_type = ModelType(model_type_str)
except:
@ -414,20 +424,16 @@ class ModelManager(object):
@classmethod
def initialize_model_config(cls, config_path: Path):
"""Create empty config file"""
with open(config_path,'w') as yaml_file:
yaml_file.write(yaml.dump({'__metadata__':
{'version':'3.0.0'}
}
)
)
with open(config_path, "w") as yaml_file:
yaml_file.write(yaml.dump({"__metadata__": {"version": "3.0.0"}}))
def get_model(
self,
model_name: str,
base_model: BaseModelType,
model_type: ModelType,
submodel_type: Optional[SubModelType] = None
)->ModelInfo:
submodel_type: Optional[SubModelType] = None,
) -> ModelInfo:
"""Given a model named identified in models.yaml, return
an ModelInfo object describing it.
:param model_name: symbolic name of the model in models.yaml
@ -451,7 +457,7 @@ class ModelManager(object):
if not model_path.exists():
if model_class.save_to_config:
self.models[model_key].error = ModelError.NotFound
raise Exception(f"Files for model \"{model_key}\" not found")
raise Exception(f'Files for model "{model_key}" not found')
else:
self.models.pop(model_key, None)
@ -473,7 +479,7 @@ class ModelManager(object):
model_path = model_class.convert_if_required(
base_model=base_model,
model_path=str(model_path), # TODO: refactor str/Path types logic
model_path=str(model_path), # TODO: refactor str/Path types logic
output_path=dst_convert_path,
config=model_config,
)
@ -490,17 +496,17 @@ class ModelManager(object):
self.cache_keys[model_key] = set()
self.cache_keys[model_key].add(model_context.key)
model_hash = "<NO_HASH>" # TODO:
model_hash = "<NO_HASH>" # TODO:
return ModelInfo(
context = model_context,
name = model_name,
base_model = base_model,
type = submodel_type or model_type,
hash = model_hash,
location = model_path, # TODO:
precision = self.cache.precision,
_cache = self.cache,
context=model_context,
name=model_name,
base_model=base_model,
type=submodel_type or model_type,
hash=model_hash,
location=model_path, # TODO:
precision=self.cache.precision,
_cache=self.cache,
)
def model_info(
@ -516,7 +522,7 @@ class ModelManager(object):
if model_key in self.models:
return self.models[model_key].dict(exclude_defaults=True)
else:
return None # TODO: None or empty dict on not found
return None # TODO: None or empty dict on not found
def model_names(self) -> List[Tuple[str, BaseModelType, ModelType]]:
"""
@ -526,16 +532,16 @@ class ModelManager(object):
return [(self.parse_key(x)) for x in self.models.keys()]
def list_model(
self,
model_name: str,
base_model: BaseModelType,
model_type: ModelType,
self,
model_name: str,
base_model: BaseModelType,
model_type: ModelType,
) -> dict:
"""
Returns a dict describing one installed model, using
the combined format of the list_models() method.
"""
models = self.list_models(base_model,model_type,model_name)
models = self.list_models(base_model, model_type, model_name)
return models[0] if models else None
def list_models(
@ -548,13 +554,17 @@ class ModelManager(object):
Return a list of models.
"""
model_keys = [self.create_key(model_name, base_model, model_type)] if model_name else sorted(self.models, key=str.casefold)
model_keys = (
[self.create_key(model_name, base_model, model_type)]
if model_name
else sorted(self.models, key=str.casefold)
)
models = []
for model_key in model_keys:
model_config = self.models.get(model_key)
if not model_config:
self.logger.error(f'Unknown model {model_name}')
raise ModelNotFoundException(f'Unknown model {model_name}')
self.logger.error(f"Unknown model {model_name}")
raise ModelNotFoundException(f"Unknown model {model_name}")
cur_model_name, cur_base_model, cur_model_type = self.parse_key(model_key)
if base_model is not None and cur_base_model != base_model:
@ -571,8 +581,8 @@ class ModelManager(object):
)
# expose paths as absolute to help web UI
if path := model_dict.get('path'):
model_dict['path'] = str(self.app_config.root_path / path)
if path := model_dict.get("path"):
model_dict["path"] = str(self.app_config.root_path / path)
models.append(model_dict)
return models
@ -641,15 +651,15 @@ class ModelManager(object):
model_info().
"""
# relativize paths as they go in - this makes it easier to move the root directory around
if path := model_attributes.get('path'):
if path := model_attributes.get("path"):
if Path(path).is_relative_to(self.app_config.root_path):
model_attributes['path'] = str(Path(path).relative_to(self.app_config.root_path))
model_attributes["path"] = str(Path(path).relative_to(self.app_config.root_path))
model_class = MODEL_CLASSES[base_model][model_type]
model_config = model_class.create_config(**model_attributes)
model_key = self.create_key(model_name, base_model, model_type)
if model_key in self.models and not clobber:
if model_key in self.models and not clobber:
raise Exception(f'Attempt to overwrite existing model definition "{model_key}"')
old_model = self.models.pop(model_key, None)
@ -675,23 +685,23 @@ class ModelManager(object):
self.commit()
return AddModelResult(
name = model_name,
model_type = model_type,
base_model = base_model,
config = model_config,
name=model_name,
model_type=model_type,
base_model=base_model,
config=model_config,
)
def rename_model(
self,
model_name: str,
base_model: BaseModelType,
model_type: ModelType,
new_name: str = None,
new_base: BaseModelType = None,
self,
model_name: str,
base_model: BaseModelType,
model_type: ModelType,
new_name: str = None,
new_base: BaseModelType = None,
):
'''
"""
Rename or rebase a model.
'''
"""
if new_name is None and new_base is None:
self.logger.error("rename_model() called with neither a new_name nor a new_base. {model_name} unchanged.")
return
@ -710,7 +720,13 @@ class ModelManager(object):
# if this is a model file/directory that we manage ourselves, we need to move it
if old_path.is_relative_to(self.app_config.models_path):
new_path = self.app_config.root_path / 'models' / BaseModelType(new_base).value / ModelType(model_type).value / new_name
new_path = (
self.app_config.root_path
/ "models"
/ BaseModelType(new_base).value
/ ModelType(model_type).value
/ new_name
)
move(old_path, new_path)
model_cfg.path = str(new_path.relative_to(self.app_config.root_path))
@ -726,18 +742,18 @@ class ModelManager(object):
for cache_id in cache_ids:
self.cache.uncache_model(cache_id)
self.models.pop(model_key, None) # delete
self.models.pop(model_key, None) # delete
self.models[new_key] = model_cfg
self.commit()
def convert_model (
self,
model_name: str,
base_model: BaseModelType,
model_type: Union[ModelType.Main,ModelType.Vae],
dest_directory: Optional[Path]=None,
def convert_model(
self,
model_name: str,
base_model: BaseModelType,
model_type: Union[ModelType.Main, ModelType.Vae],
dest_directory: Optional[Path] = None,
) -> AddModelResult:
'''
"""
Convert a checkpoint file into a diffusers folder, deleting the cached
version and deleting the original checkpoint file if it is in the models
directory.
@ -746,7 +762,7 @@ class ModelManager(object):
:param model_type: Type of model ['vae' or 'main']
This will raise a ValueError unless the model is a checkpoint.
'''
"""
info = self.model_info(model_name, base_model, model_type)
if info["model_format"] != "checkpoint":
raise ValueError(f"not a checkpoint format model: {model_name}")
@ -754,27 +770,32 @@ class ModelManager(object):
# We are taking advantage of a side effect of get_model() that converts check points
# into cached diffusers directories stored at `location`. It doesn't matter
# what submodeltype we request here, so we get the smallest.
submodel = {"submodel_type": SubModelType.Scheduler} if model_type==ModelType.Main else {}
model = self.get_model(model_name,
base_model,
model_type,
**submodel,
)
submodel = {"submodel_type": SubModelType.Scheduler} if model_type == ModelType.Main else {}
model = self.get_model(
model_name,
base_model,
model_type,
**submodel,
)
checkpoint_path = self.app_config.root_path / info["path"]
old_diffusers_path = self.app_config.models_path / model.location
new_diffusers_path = (dest_directory or self.app_config.models_path / base_model.value / model_type.value) / model_name
new_diffusers_path = (
dest_directory or self.app_config.models_path / base_model.value / model_type.value
) / model_name
if new_diffusers_path.exists():
raise ValueError(f"A diffusers model already exists at {new_diffusers_path}")
try:
move(old_diffusers_path,new_diffusers_path)
move(old_diffusers_path, new_diffusers_path)
info["model_format"] = "diffusers"
info["path"] = str(new_diffusers_path) if dest_directory else str(new_diffusers_path.relative_to(self.app_config.root_path))
info.pop('config')
info["path"] = (
str(new_diffusers_path)
if dest_directory
else str(new_diffusers_path.relative_to(self.app_config.root_path))
)
info.pop("config")
result = self.add_model(model_name, base_model, model_type,
model_attributes = info,
clobber=True)
result = self.add_model(model_name, base_model, model_type, model_attributes=info, clobber=True)
except:
# something went wrong, so don't leave dangling diffusers model in directory or it will cause a duplicate model error!
rmtree(new_diffusers_path)
@ -798,15 +819,12 @@ class ModelManager(object):
found_models = []
for file in files:
location = str(file.resolve()).replace("\\", "/")
if (
"model.safetensors" not in location
and "diffusion_pytorch_model.safetensors" not in location
):
if "model.safetensors" not in location and "diffusion_pytorch_model.safetensors" not in location:
found_models.append({"name": file.stem, "location": location})
return search_folder, found_models
def commit(self, conf_file: Path=None) -> None:
def commit(self, conf_file: Path = None) -> None:
"""
Write current configuration out to the indicated file.
"""
@ -824,7 +842,7 @@ class ModelManager(object):
yaml_str = OmegaConf.to_yaml(data_to_save)
config_file_path = conf_file or self.config_path
assert config_file_path is not None,'no config file path to write to'
assert config_file_path is not None, "no config file path to write to"
config_file_path = self.app_config.root_path / config_file_path
tmpfile = os.path.join(os.path.dirname(config_file_path), "new_config.tmp")
try:
@ -857,11 +875,10 @@ class ModelManager(object):
base_model: Optional[BaseModelType] = None,
model_type: Optional[ModelType] = None,
):
loaded_files = set()
new_models_found = False
self.logger.info(f'Scanning {self.app_config.models_path} for new models')
self.logger.info(f"Scanning {self.app_config.models_path} for new models")
with Chdir(self.app_config.root_path):
for model_key, model_config in list(self.models.items()):
model_name, cur_base_model, cur_model_type = self.parse_key(model_key)
@ -887,10 +904,10 @@ class ModelManager(object):
models_dir = self.app_config.models_path / cur_base_model.value / cur_model_type.value
if not models_dir.exists():
continue # TODO: or create all folders?
continue # TODO: or create all folders?
for model_path in models_dir.iterdir():
if model_path not in loaded_files: # TODO: check
if model_path not in loaded_files: # TODO: check
model_name = model_path.name if model_path.is_dir() else model_path.stem
model_key = self.create_key(model_name, cur_base_model, cur_model_type)
@ -900,7 +917,7 @@ class ModelManager(object):
if model_path.is_relative_to(self.app_config.root_path):
model_path = model_path.relative_to(self.app_config.root_path)
model_config: ModelConfigBase = model_class.probe_config(str(model_path))
self.models[model_key] = model_config
new_models_found = True
@ -916,11 +933,10 @@ class ModelManager(object):
if (new_models_found or imported_models) and self.config_path:
self.commit()
def autoimport(self)->Dict[str, AddModelResult]:
'''
def autoimport(self) -> Dict[str, AddModelResult]:
"""
Scan the autoimport directory (if defined) and import new models, delete defunct models.
'''
"""
# avoid circular import
from invokeai.backend.install.model_install_backend import ModelInstall
from invokeai.frontend.install.model_install import ask_user_for_prediction_type
@ -939,7 +955,9 @@ class ModelManager(object):
self.new_models_found.update(self.installer.heuristic_import(model))
def on_search_completed(self):
self.logger.info(f'Scanned {self._items_scanned} files and directories, imported {len(self.new_models_found)} models')
self.logger.info(
f"Scanned {self._items_scanned} files and directories, imported {len(self.new_models_found)} models"
)
def models_found(self):
return self.new_models_found
@ -949,31 +967,37 @@ class ModelManager(object):
# LS: hacky
# Patch in the SD VAE from core so that it is available for use by the UI
try:
self.heuristic_import({config.root_path / 'models/core/convert/sd-vae-ft-mse'})
self.heuristic_import({config.root_path / "models/core/convert/sd-vae-ft-mse"})
except:
pass
installer = ModelInstall(config = self.app_config,
model_manager = self,
prediction_type_helper = ask_user_for_prediction_type,
)
known_paths = {config.root_path / x['path'] for x in self.list_models()}
directories = {config.root_path / x for x in [config.autoimport_dir,
config.lora_dir,
config.embedding_dir,
config.controlnet_dir,
] if x
}
installer = ModelInstall(
config=self.app_config,
model_manager=self,
prediction_type_helper=ask_user_for_prediction_type,
)
known_paths = {config.root_path / x["path"] for x in self.list_models()}
directories = {
config.root_path / x
for x in [
config.autoimport_dir,
config.lora_dir,
config.embedding_dir,
config.controlnet_dir,
]
if x
}
scanner = ScanAndImport(directories, self.logger, ignore=known_paths, installer=installer)
scanner.search()
return scanner.models_found()
def heuristic_import(self,
items_to_import: Set[str],
prediction_type_helper: Callable[[Path],SchedulerPredictionType]=None,
)->Dict[str, AddModelResult]:
'''Import a list of paths, repo_ids or URLs. Returns the set of
def heuristic_import(
self,
items_to_import: Set[str],
prediction_type_helper: Callable[[Path], SchedulerPredictionType] = None,
) -> Dict[str, AddModelResult]:
"""Import a list of paths, repo_ids or URLs. Returns the set of
successfully imported items.
:param items_to_import: Set of strings corresponding to models to be imported.
:param prediction_type_helper: A callback that receives the Path of a Stable Diffusion 2 checkpoint model and returns a SchedulerPredictionType.
@ -992,14 +1016,15 @@ class ModelManager(object):
May return the following exceptions:
- ModelNotFoundException - one or more of the items to import is not a valid path, repo_id or URL
- ValueError - a corresponding model already exists
'''
"""
# avoid circular import here
from invokeai.backend.install.model_install_backend import ModelInstall
successfully_installed = dict()
installer = ModelInstall(config = self.app_config,
prediction_type_helper = prediction_type_helper,
model_manager = self)
installer = ModelInstall(
config=self.app_config, prediction_type_helper=prediction_type_helper, model_manager=self
)
for thing in items_to_import:
installed = installer.heuristic_import(thing)
successfully_installed.update(installed)

87
invokeai/backend/model_management/model_merge.py
View File

@ -17,23 +17,25 @@ import invokeai.backend.util.logging as logger
from ...backend.model_management import ModelManager, ModelType, BaseModelType, ModelVariantType, AddModelResult
class MergeInterpolationMethod(str, Enum):
WeightedSum = "weighted_sum"
Sigmoid = "sigmoid"
InvSigmoid = "inv_sigmoid"
AddDifference = "add_difference"
class ModelMerger(object):
def __init__(self, manager: ModelManager):
self.manager = manager
def merge_diffusion_models(
self,
model_paths: List[Path],
alpha: float = 0.5,
interp: MergeInterpolationMethod = None,
force: bool = False,
**kwargs,
self,
model_paths: List[Path],
alpha: float = 0.5,
interp: MergeInterpolationMethod = None,
force: bool = False,
**kwargs,
) -> DiffusionPipeline:
"""
:param model_paths: up to three models, designated by their local paths or HuggingFace repo_ids
@ -58,24 +60,23 @@ class ModelMerger(object):
merged_pipe = pipe.merge(
pretrained_model_name_or_path_list=model_paths,
alpha=alpha,
interp=interp.value if interp else None, #diffusers API treats None as "weighted sum"
interp=interp.value if interp else None, # diffusers API treats None as "weighted sum"
force=force,
**kwargs,
)
dlogging.set_verbosity(verbosity)
return merged_pipe
def merge_diffusion_models_and_save (
self,
model_names: List[str],
base_model: Union[BaseModelType,str],
merged_model_name: str,
alpha: float = 0.5,
interp: MergeInterpolationMethod = None,
force: bool = False,
merge_dest_directory: Optional[Path] = None,
**kwargs,
def merge_diffusion_models_and_save(
self,
model_names: List[str],
base_model: Union[BaseModelType, str],
merged_model_name: str,
alpha: float = 0.5,
interp: MergeInterpolationMethod = None,
force: bool = False,
merge_dest_directory: Optional[Path] = None,
**kwargs,
) -> AddModelResult:
"""
:param models: up to three models, designated by their InvokeAI models.yaml model name
@ -94,39 +95,45 @@ class ModelMerger(object):
config = self.manager.app_config
base_model = BaseModelType(base_model)
vae = None
for mod in model_names:
info = self.manager.list_model(mod, base_model=base_model, model_type=ModelType.Main)
assert info, f"model {mod}, base_model {base_model}, is unknown"
assert info["model_format"] == "diffusers", f"{mod} is not a diffusers model. It must be optimized before merging"
assert info["variant"] == "normal", f"{mod} is a {info['variant']} model, which cannot currently be merged"
assert len(model_names) <= 2 or \
interp==MergeInterpolationMethod.AddDifference, "When merging three models, only the 'add_difference' merge method is supported"
assert info, f"model {mod}, base_model {base_model}, is unknown"
assert (
info["model_format"] == "diffusers"
), f"{mod} is not a diffusers model. It must be optimized before merging"
assert info["variant"] == "normal", f"{mod} is a {info['variant']} model, which cannot currently be merged"
assert (
len(model_names) <= 2 or interp == MergeInterpolationMethod.AddDifference
), "When merging three models, only the 'add_difference' merge method is supported"
# pick up the first model's vae
if mod == model_names[0]:
vae = info.get("vae")
model_paths.extend([config.root_path / info["path"]])
merge_method = None if interp == 'weighted_sum' else MergeInterpolationMethod(interp)
logger.debug(f'interp = {interp}, merge_method={merge_method}')
merged_pipe = self.merge_diffusion_models(
model_paths, alpha, merge_method, force, **kwargs
merge_method = None if interp == "weighted_sum" else MergeInterpolationMethod(interp)
logger.debug(f"interp = {interp}, merge_method={merge_method}")
merged_pipe = self.merge_diffusion_models(model_paths, alpha, merge_method, force, **kwargs)
dump_path = (
Path(merge_dest_directory)
if merge_dest_directory
else config.models_path / base_model.value / ModelType.Main.value
)
dump_path = Path(merge_dest_directory) if merge_dest_directory else config.models_path / base_model.value / ModelType.Main.value
dump_path.mkdir(parents=True, exist_ok=True)
dump_path = dump_path / merged_model_name
merged_pipe.save_pretrained(dump_path, safe_serialization=1)
attributes = dict(
path = str(dump_path),
description = f"Merge of models {', '.join(model_names)}",
model_format = "diffusers",
variant = ModelVariantType.Normal.value,
vae = vae,
path=str(dump_path),
description=f"Merge of models {', '.join(model_names)}",
model_format="diffusers",
variant=ModelVariantType.Normal.value,
vae=vae,
)
return self.manager.add_model(
merged_model_name,
base_model=base_model,
model_type=ModelType.Main,
model_attributes=attributes,
clobber=True,
)
return self.manager.add_model(merged_model_name,
base_model = base_model,
model_type = ModelType.Main,
model_attributes = attributes,
clobber = True
)

368
invokeai/backend/model_management/model_probe.py
View File

@ -10,12 +10,16 @@ from typing import Callable, Literal, Union, Dict, Optional
from picklescan.scanner import scan_file_path
from .models import (
BaseModelType, ModelType, ModelVariantType,
SchedulerPredictionType, SilenceWarnings,
InvalidModelException
BaseModelType,
ModelType,
ModelVariantType,
SchedulerPredictionType,
SilenceWarnings,
InvalidModelException,
)
from .models.base import read_checkpoint_meta
@dataclass
class ModelProbeInfo(object):
model_type: ModelType
@ -23,70 +27,74 @@ class ModelProbeInfo(object):
variant_type: ModelVariantType
prediction_type: SchedulerPredictionType
upcast_attention: bool
format: Literal['diffusers','checkpoint', 'lycoris']
format: Literal["diffusers", "checkpoint", "lycoris"]
image_size: int
class ProbeBase(object):
'''forward declaration'''
"""forward declaration"""
pass
class ModelProbe(object):
PROBES = {
'diffusers': { },
'checkpoint': { },
"diffusers": {},
"checkpoint": {},
}
CLASS2TYPE = {
'StableDiffusionPipeline' : ModelType.Main,
'StableDiffusionInpaintPipeline' : ModelType.Main,
'StableDiffusionXLPipeline' : ModelType.Main,
'StableDiffusionXLImg2ImgPipeline' : ModelType.Main,
'AutoencoderKL' : ModelType.Vae,
'ControlNetModel' : ModelType.ControlNet,
"StableDiffusionPipeline": ModelType.Main,
"StableDiffusionInpaintPipeline": ModelType.Main,
"StableDiffusionXLPipeline": ModelType.Main,
"StableDiffusionXLImg2ImgPipeline": ModelType.Main,
"AutoencoderKL": ModelType.Vae,
"ControlNetModel": ModelType.ControlNet,
}
@classmethod
def register_probe(cls,
format: Literal['diffusers','checkpoint'],
model_type: ModelType,
probe_class: ProbeBase):
def register_probe(cls, format: Literal["diffusers", "checkpoint"], model_type: ModelType, probe_class: ProbeBase):
cls.PROBES[format][model_type] = probe_class
@classmethod
def heuristic_probe(cls,
model: Union[Dict, ModelMixin, Path],
prediction_type_helper: Callable[[Path],SchedulerPredictionType]=None,
)->ModelProbeInfo:
if isinstance(model,Path):
return cls.probe(model_path=model,prediction_type_helper=prediction_type_helper)
elif isinstance(model,(dict,ModelMixin,ConfigMixin)):
def heuristic_probe(
cls,
model: Union[Dict, ModelMixin, Path],
prediction_type_helper: Callable[[Path], SchedulerPredictionType] = None,
) -> ModelProbeInfo:
if isinstance(model, Path):
return cls.probe(model_path=model, prediction_type_helper=prediction_type_helper)
elif isinstance(model, (dict, ModelMixin, ConfigMixin)):
return cls.probe(model_path=None, model=model, prediction_type_helper=prediction_type_helper)
else:
raise InvalidModelException("model parameter {model} is neither a Path, nor a model")
@classmethod
def probe(cls,
model_path: Path,
model: Optional[Union[Dict, ModelMixin]] = None,
prediction_type_helper: Optional[Callable[[Path],SchedulerPredictionType]] = None)->ModelProbeInfo:
'''
def probe(
cls,
model_path: Path,
model: Optional[Union[Dict, ModelMixin]] = None,
prediction_type_helper: Optional[Callable[[Path], SchedulerPredictionType]] = None,
) -> ModelProbeInfo:
"""
Probe the model at model_path and return sufficient information about it
to place it somewhere in the models directory hierarchy. If the model is
already loaded into memory, you may provide it as model in order to avoid
opening it a second time. The prediction_type_helper callable is a function that receives
the path to the model and returns the BaseModelType. It is called to distinguish
between V2-Base and V2-768 SD models.
'''
"""
if model_path:
format_type = 'diffusers' if model_path.is_dir() else 'checkpoint'
format_type = "diffusers" if model_path.is_dir() else "checkpoint"
else:
format_type = 'diffusers' if isinstance(model,(ConfigMixin,ModelMixin)) else 'checkpoint'
format_type = "diffusers" if isinstance(model, (ConfigMixin, ModelMixin)) else "checkpoint"
model_info = None
try:
model_type = cls.get_model_type_from_folder(model_path, model) \
if format_type == 'diffusers' \
else cls.get_model_type_from_checkpoint(model_path, model)
model_type = (
cls.get_model_type_from_folder(model_path, model)
if format_type == "diffusers"
else cls.get_model_type_from_checkpoint(model_path, model)
)
probe_class = cls.PROBES[format_type].get(model_type)
if not probe_class:
return None
@ -96,17 +104,23 @@ class ModelProbe(object):
prediction_type = probe.get_scheduler_prediction_type()
format = probe.get_format()
model_info = ModelProbeInfo(
model_type = model_type,
base_type = base_type,
variant_type = variant_type,
prediction_type = prediction_type,
upcast_attention = (base_type==BaseModelType.StableDiffusion2 \
and prediction_type==SchedulerPredictionType.VPrediction),
format = format,
image_size = 1024 if (base_type in {BaseModelType.StableDiffusionXL,BaseModelType.StableDiffusionXLRefiner}) else \
768 if (base_type==BaseModelType.StableDiffusion2 \
and prediction_type==SchedulerPredictionType.VPrediction ) else \
512
model_type=model_type,
base_type=base_type,
variant_type=variant_type,
prediction_type=prediction_type,
upcast_attention=(
base_type == BaseModelType.StableDiffusion2
and prediction_type == SchedulerPredictionType.VPrediction
),
format=format,
image_size=1024
if (base_type in {BaseModelType.StableDiffusionXL, BaseModelType.StableDiffusionXLRefiner})
else 768
if (
base_type == BaseModelType.StableDiffusion2
and prediction_type == SchedulerPredictionType.VPrediction
)
else 512,
)
except Exception:
raise
@ -115,7 +129,7 @@ class ModelProbe(object):
@classmethod
def get_model_type_from_checkpoint(cls, model_path: Path, checkpoint: dict) -> ModelType:
if model_path.suffix not in ('.bin','.pt','.ckpt','.safetensors','.pth'):
if model_path.suffix not in (".bin", ".pt", ".ckpt", ".safetensors", ".pth"):
return None
if model_path.name == "learned_embeds.bin":
@ -142,32 +156,32 @@ class ModelProbe(object):
# diffusers-ti
if len(ckpt) < 10 and all(isinstance(v, torch.Tensor) for v in ckpt.values()):
return ModelType.TextualInversion
raise InvalidModelException(f"Unable to determine model type for {model_path}")
@classmethod
def get_model_type_from_folder(cls, folder_path: Path, model: ModelMixin)->ModelType:
'''
def get_model_type_from_folder(cls, folder_path: Path, model: ModelMixin) -> ModelType:
"""
Get the model type of a hugging-face style folder.
'''
"""
class_name = None
if model:
class_name = model.__class__.__name__
else:
if (folder_path / 'learned_embeds.bin').exists():
if (folder_path / "learned_embeds.bin").exists():
return ModelType.TextualInversion
if (folder_path / 'pytorch_lora_weights.bin').exists():
if (folder_path / "pytorch_lora_weights.bin").exists():
return ModelType.Lora
i = folder_path / 'model_index.json'
c = folder_path / 'config.json'
i = folder_path / "model_index.json"
c = folder_path / "config.json"
config_path = i if i.exists() else c if c.exists() else None
if config_path:
with open(config_path,'r') as file:
with open(config_path, "r") as file:
conf = json.load(file)
class_name = conf['_class_name']
class_name = conf["_class_name"]
if class_name and (type := cls.CLASS2TYPE.get(class_name)):
return type
@ -176,7 +190,7 @@ class ModelProbe(object):
raise InvalidModelException(f"Unable to determine model type for {folder_path}")
@classmethod
def _scan_and_load_checkpoint(cls,model_path: Path)->dict:
def _scan_and_load_checkpoint(cls, model_path: Path) -> dict:
with SilenceWarnings():
if model_path.suffix.endswith((".ckpt", ".pt", ".bin")):
cls._scan_model(model_path, model_path)
@ -186,55 +200,53 @@ class ModelProbe(object):
@classmethod
def _scan_model(cls, model_name, checkpoint):
"""
Apply picklescanner to the indicated checkpoint and issue a warning
and option to exit if an infected file is identified.
"""
# scan model
scan_result = scan_file_path(checkpoint)
if scan_result.infected_files != 0:
raise "The model {model_name} is potentially infected by malware. Aborting import."
"""
Apply picklescanner to the indicated checkpoint and issue a warning
and option to exit if an infected file is identified.
"""
# scan model
scan_result = scan_file_path(checkpoint)
if scan_result.infected_files != 0:
raise "The model {model_name} is potentially infected by malware. Aborting import."
###################################################3
# Checkpoint probing
###################################################3
class ProbeBase(object):
def get_base_type(self)->BaseModelType:
def get_base_type(self) -> BaseModelType:
pass
def get_variant_type(self)->ModelVariantType:
pass
def get_scheduler_prediction_type(self)->SchedulerPredictionType:
def get_variant_type(self) -> ModelVariantType:
pass
def get_format(self)->str:
def get_scheduler_prediction_type(self) -> SchedulerPredictionType:
pass
def get_format(self) -> str:
pass
class CheckpointProbeBase(ProbeBase):
def __init__(self,
checkpoint_path: Path,
checkpoint: dict,
helper: Callable[[Path],SchedulerPredictionType] = None
)->BaseModelType:
def __init__(
self, checkpoint_path: Path, checkpoint: dict, helper: Callable[[Path], SchedulerPredictionType] = None
) -> BaseModelType:
self.checkpoint = checkpoint or ModelProbe._scan_and_load_checkpoint(checkpoint_path)
self.checkpoint_path = checkpoint_path
self.helper = helper
def get_base_type(self)->BaseModelType:
def get_base_type(self) -> BaseModelType:
pass
def get_format(self)->str:
return 'checkpoint'
def get_format(self) -> str:
return "checkpoint"
def get_variant_type(self)-> ModelVariantType:
model_type = ModelProbe.get_model_type_from_checkpoint(self.checkpoint_path,self.checkpoint)
def get_variant_type(self) -> ModelVariantType:
model_type = ModelProbe.get_model_type_from_checkpoint(self.checkpoint_path, self.checkpoint)
if model_type != ModelType.Main:
return ModelVariantType.Normal
state_dict = self.checkpoint.get('state_dict') or self.checkpoint
in_channels = state_dict[
"model.diffusion_model.input_blocks.0.0.weight"
].shape[1]
state_dict = self.checkpoint.get("state_dict") or self.checkpoint
in_channels = state_dict["model.diffusion_model.input_blocks.0.0.weight"].shape[1]
if in_channels == 9:
return ModelVariantType.Inpaint
elif in_channels == 5:
@ -242,18 +254,21 @@ class CheckpointProbeBase(ProbeBase):
elif in_channels == 4:
return ModelVariantType.Normal
else:
raise InvalidModelException(f"Cannot determine variant type (in_channels={in_channels}) at {self.checkpoint_path}")
raise InvalidModelException(
f"Cannot determine variant type (in_channels={in_channels}) at {self.checkpoint_path}"
)
class PipelineCheckpointProbe(CheckpointProbeBase):
def get_base_type(self)->BaseModelType:
def get_base_type(self) -> BaseModelType:
checkpoint = self.checkpoint
state_dict = self.checkpoint.get('state_dict') or checkpoint
state_dict = self.checkpoint.get("state_dict") or checkpoint
key_name = "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight"
if key_name in state_dict and state_dict[key_name].shape[-1] == 768:
return BaseModelType.StableDiffusion1
if key_name in state_dict and state_dict[key_name].shape[-1] == 1024:
return BaseModelType.StableDiffusion2
key_name = 'model.diffusion_model.input_blocks.4.1.transformer_blocks.0.attn2.to_k.weight'
key_name = "model.diffusion_model.input_blocks.4.1.transformer_blocks.0.attn2.to_k.weight"
if key_name in state_dict and state_dict[key_name].shape[-1] == 2048:
return BaseModelType.StableDiffusionXL
elif key_name in state_dict and state_dict[key_name].shape[-1] == 1280:
@ -261,35 +276,38 @@ class PipelineCheckpointProbe(CheckpointProbeBase):
else:
raise InvalidModelException("Cannot determine base type")
def get_scheduler_prediction_type(self)->SchedulerPredictionType:
def get_scheduler_prediction_type(self) -> SchedulerPredictionType:
type = self.get_base_type()
if type == BaseModelType.StableDiffusion1:
return SchedulerPredictionType.Epsilon
checkpoint = self.checkpoint
state_dict = self.checkpoint.get('state_dict') or checkpoint
state_dict = self.checkpoint.get("state_dict") or checkpoint
key_name = "model.diffusion_model.input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight"
if key_name in state_dict and state_dict[key_name].shape[-1] == 1024:
if 'global_step' in checkpoint:
if checkpoint['global_step'] == 220000:
if "global_step" in checkpoint:
if checkpoint["global_step"] == 220000:
return SchedulerPredictionType.Epsilon
elif checkpoint["global_step"] == 110000:
return SchedulerPredictionType.VPrediction
if self.checkpoint_path and self.helper \
and not self.checkpoint_path.with_suffix('.yaml').exists(): # if a .yaml config file exists, then this step not needed
if (
self.checkpoint_path and self.helper and not self.checkpoint_path.with_suffix(".yaml").exists()
): # if a .yaml config file exists, then this step not needed
return self.helper(self.checkpoint_path)
else:
return None
class VaeCheckpointProbe(CheckpointProbeBase):
def get_base_type(self)->BaseModelType:
def get_base_type(self) -> BaseModelType:
# I can't find any standalone 2.X VAEs to test with!
return BaseModelType.StableDiffusion1
class LoRACheckpointProbe(CheckpointProbeBase):
def get_format(self)->str:
return 'lycoris'
def get_base_type(self)->BaseModelType:
class LoRACheckpointProbe(CheckpointProbeBase):
def get_format(self) -> str:
return "lycoris"
def get_base_type(self) -> BaseModelType:
checkpoint = self.checkpoint
key1 = "lora_te_text_model_encoder_layers_0_mlp_fc1.lora_down.weight"
key2 = "lora_te_text_model_encoder_layers_0_self_attn_k_proj.hada_w1_a"
@ -307,16 +325,17 @@ class LoRACheckpointProbe(CheckpointProbeBase):
else:
return None
class TextualInversionCheckpointProbe(CheckpointProbeBase):
def get_format(self)->str:
def get_format(self) -> str:
return None
def get_base_type(self)->BaseModelType:
def get_base_type(self) -> BaseModelType:
checkpoint = self.checkpoint
if 'string_to_token' in checkpoint:
token_dim = list(checkpoint['string_to_param'].values())[0].shape[-1]
elif 'emb_params' in checkpoint:
token_dim = checkpoint['emb_params'].shape[-1]
if "string_to_token" in checkpoint:
token_dim = list(checkpoint["string_to_param"].values())[0].shape[-1]
elif "emb_params" in checkpoint:
token_dim = checkpoint["emb_params"].shape[-1]
else:
token_dim = list(checkpoint.values())[0].shape[0]
if token_dim == 768:
@ -326,12 +345,14 @@ class TextualInversionCheckpointProbe(CheckpointProbeBase):
else:
return None
class ControlNetCheckpointProbe(CheckpointProbeBase):
def get_base_type(self)->BaseModelType:
def get_base_type(self) -> BaseModelType:
checkpoint = self.checkpoint
for key_name in ('control_model.input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight',
'input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight'
):
for key_name in (
"control_model.input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight",
"input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight",
):
if key_name not in checkpoint:
continue
if checkpoint[key_name].shape[-1] == 768:
@ -342,56 +363,54 @@ class ControlNetCheckpointProbe(CheckpointProbeBase):
return self.helper(self.checkpoint_path)
raise InvalidModelException("Unable to determine base type for {self.checkpoint_path}")
########################################################
# classes for probing folders
#######################################################
class FolderProbeBase(ProbeBase):
def __init__(self,
folder_path: Path,
model: ModelMixin = None,
helper: Callable=None # not used
):
def __init__(self, folder_path: Path, model: ModelMixin = None, helper: Callable = None): # not used
self.model = model
self.folder_path = folder_path
def get_variant_type(self)->ModelVariantType:
def get_variant_type(self) -> ModelVariantType:
return ModelVariantType.Normal
def get_format(self)->str:
return 'diffusers'
def get_format(self) -> str:
return "diffusers"
class PipelineFolderProbe(FolderProbeBase):
def get_base_type(self)->BaseModelType:
def get_base_type(self) -> BaseModelType:
if self.model:
unet_conf = self.model.unet.config
else:
with open(self.folder_path / 'unet' / 'config.json','r') as file:
with open(self.folder_path / "unet" / "config.json", "r") as file:
unet_conf = json.load(file)
if unet_conf['cross_attention_dim'] == 768:
return BaseModelType.StableDiffusion1
elif unet_conf['cross_attention_dim'] == 1024:
if unet_conf["cross_attention_dim"] == 768:
return BaseModelType.StableDiffusion1
elif unet_conf["cross_attention_dim"] == 1024:
return BaseModelType.StableDiffusion2
elif unet_conf['cross_attention_dim'] == 1280:
elif unet_conf["cross_attention_dim"] == 1280:
return BaseModelType.StableDiffusionXLRefiner
elif unet_conf['cross_attention_dim'] == 2048:
elif unet_conf["cross_attention_dim"] == 2048:
return BaseModelType.StableDiffusionXL
else:
raise InvalidModelException(f'Unknown base model for {self.folder_path}')
raise InvalidModelException(f"Unknown base model for {self.folder_path}")
def get_scheduler_prediction_type(self)->SchedulerPredictionType:
def get_scheduler_prediction_type(self) -> SchedulerPredictionType:
if self.model:
scheduler_conf = self.model.scheduler.config
else:
with open(self.folder_path / 'scheduler' / 'scheduler_config.json','r') as file:
with open(self.folder_path / "scheduler" / "scheduler_config.json", "r") as file:
scheduler_conf = json.load(file)
if scheduler_conf['prediction_type'] == "v_prediction":
if scheduler_conf["prediction_type"] == "v_prediction":
return SchedulerPredictionType.VPrediction
elif scheduler_conf['prediction_type'] == 'epsilon':
elif scheduler_conf["prediction_type"] == "epsilon":
return SchedulerPredictionType.Epsilon
else:
return None
def get_variant_type(self)->ModelVariantType:
def get_variant_type(self) -> ModelVariantType:
# This only works for pipelines! Any kind of
# exception results in our returning the
# "normal" variant type
@ -399,11 +418,11 @@ class PipelineFolderProbe(FolderProbeBase):
if self.model:
conf = self.model.unet.config
else:
config_file = self.folder_path / 'unet' / 'config.json'
with open(config_file,'r') as file:
config_file = self.folder_path / "unet" / "config.json"
with open(config_file, "r") as file:
conf = json.load(file)
in_channels = conf['in_channels']
in_channels = conf["in_channels"]
if in_channels == 9:
return ModelVariantType.Inpaint
elif in_channels == 5:
@ -414,60 +433,67 @@ class PipelineFolderProbe(FolderProbeBase):
pass
return ModelVariantType.Normal
class VaeFolderProbe(FolderProbeBase):
def get_base_type(self)->BaseModelType:
config_file = self.folder_path / 'config.json'
def get_base_type(self) -> BaseModelType:
config_file = self.folder_path / "config.json"
if not config_file.exists():
raise InvalidModelException(f"Cannot determine base type for {self.folder_path}")
with open(config_file,'r') as file:
with open(config_file, "r") as file:
config = json.load(file)
return BaseModelType.StableDiffusionXL \
if config.get('scaling_factor',0)==0.13025 and config.get('sample_size') in [512, 1024] \
return (
BaseModelType.StableDiffusionXL
if config.get("scaling_factor", 0) == 0.13025 and config.get("sample_size") in [512, 1024]
else BaseModelType.StableDiffusion1
)
class TextualInversionFolderProbe(FolderProbeBase):
def get_format(self)->str:
def get_format(self) -> str:
return None
def get_base_type(self)->BaseModelType:
path = self.folder_path / 'learned_embeds.bin'
def get_base_type(self) -> BaseModelType:
path = self.folder_path / "learned_embeds.bin"
if not path.exists():
return None
checkpoint = ModelProbe._scan_and_load_checkpoint(path)
return TextualInversionCheckpointProbe(None,checkpoint=checkpoint).get_base_type()
return TextualInversionCheckpointProbe(None, checkpoint=checkpoint).get_base_type()
class ControlNetFolderProbe(FolderProbeBase):
def get_base_type(self)->BaseModelType:
config_file = self.folder_path / 'config.json'
def get_base_type(self) -> BaseModelType:
config_file = self.folder_path / "config.json"
if not config_file.exists():
raise InvalidModelException(f"Cannot determine base type for {self.folder_path}")
with open(config_file,'r') as file:
with open(config_file, "r") as file:
config = json.load(file)
# no obvious way to distinguish between sd2-base and sd2-768
return BaseModelType.StableDiffusion1 \
if config['cross_attention_dim']==768 \
else BaseModelType.StableDiffusion2
return (
BaseModelType.StableDiffusion1 if config["cross_attention_dim"] == 768 else BaseModelType.StableDiffusion2
)
class LoRAFolderProbe(FolderProbeBase):
def get_base_type(self)->BaseModelType:
def get_base_type(self) -> BaseModelType:
model_file = None
for suffix in ['safetensors','bin']:
base_file = self.folder_path / f'pytorch_lora_weights.{suffix}'
for suffix in ["safetensors", "bin"]:
base_file = self.folder_path / f"pytorch_lora_weights.{suffix}"
if base_file.exists():
model_file = base_file
break
if not model_file:
raise InvalidModelException('Unknown LoRA format encountered')
return LoRACheckpointProbe(model_file,None).get_base_type()
raise InvalidModelException("Unknown LoRA format encountered")
return LoRACheckpointProbe(model_file, None).get_base_type()
############## register probe classes ######
ModelProbe.register_probe('diffusers', ModelType.Main, PipelineFolderProbe)
ModelProbe.register_probe('diffusers', ModelType.Vae, VaeFolderProbe)
ModelProbe.register_probe('diffusers', ModelType.Lora, LoRAFolderProbe)
ModelProbe.register_probe('diffusers', ModelType.TextualInversion, TextualInversionFolderProbe)
ModelProbe.register_probe('diffusers', ModelType.ControlNet, ControlNetFolderProbe)
ModelProbe.register_probe('checkpoint', ModelType.Main, PipelineCheckpointProbe)
ModelProbe.register_probe('checkpoint', ModelType.Vae, VaeCheckpointProbe)
ModelProbe.register_probe('checkpoint', ModelType.Lora, LoRACheckpointProbe)
ModelProbe.register_probe('checkpoint', ModelType.TextualInversion, TextualInversionCheckpointProbe)
ModelProbe.register_probe('checkpoint', ModelType.ControlNet, ControlNetCheckpointProbe)
ModelProbe.register_probe("diffusers", ModelType.Main, PipelineFolderProbe)
ModelProbe.register_probe("diffusers", ModelType.Vae, VaeFolderProbe)
ModelProbe.register_probe("diffusers", ModelType.Lora, LoRAFolderProbe)
ModelProbe.register_probe("diffusers", ModelType.TextualInversion, TextualInversionFolderProbe)
ModelProbe.register_probe("diffusers", ModelType.ControlNet, ControlNetFolderProbe)
ModelProbe.register_probe("checkpoint", ModelType.Main, PipelineCheckpointProbe)
ModelProbe.register_probe("checkpoint", ModelType.Vae, VaeCheckpointProbe)
ModelProbe.register_probe("checkpoint", ModelType.Lora, LoRACheckpointProbe)
ModelProbe.register_probe("checkpoint", ModelType.TextualInversion, TextualInversionCheckpointProbe)
ModelProbe.register_probe("checkpoint", ModelType.ControlNet, ControlNetCheckpointProbe)

21
invokeai/backend/model_management/model_search.py
View File

@ -10,8 +10,9 @@ from pathlib import Path
import invokeai.backend.util.logging as logger
class ModelSearch(ABC):
def __init__(self, directories: List[Path], logger: types.ModuleType=logger):
def __init__(self, directories: List[Path], logger: types.ModuleType = logger):
"""
Initialize a recursive model directory search.
:param directories: List of directory Paths to recurse through
@ -56,18 +57,23 @@ class ModelSearch(ABC):
def walk_directory(self, path: Path):
for root, dirs, files in os.walk(path):
if str(Path(root).name).startswith('.'):
if str(Path(root).name).startswith("."):
self._pruned_paths.add(root)
if any([Path(root).is_relative_to(x) for x in self._pruned_paths]):
continue
self._items_scanned += len(dirs) + len(files)
for d in dirs:
path = Path(root) / d
if path in self._scanned_paths or path.parent in self._scanned_dirs:
self._scanned_dirs.add(path)
continue
if any([(path/x).exists() for x in {'config.json','model_index.json','learned_embeds.bin','pytorch_lora_weights.bin'}]):
if any(
[
(path / x).exists()
for x in {"config.json", "model_index.json", "learned_embeds.bin", "pytorch_lora_weights.bin"}
]
):
try:
self.on_model_found(path)
self._models_found += 1
@ -79,18 +85,19 @@ class ModelSearch(ABC):
path = Path(root) / f
if path.parent in self._scanned_dirs:
continue
if path.suffix in {'.ckpt','.bin','.pth','.safetensors','.pt'}:
if path.suffix in {".ckpt", ".bin", ".pth", ".safetensors", ".pt"}:
try:
self.on_model_found(path)
self._models_found += 1
except Exception as e:
self.logger.warning(str(e))
class FindModels(ModelSearch):
def on_search_started(self):
self.models_found: Set[Path] = set()
def on_model_found(self,model: Path):
def on_model_found(self, model: Path):
self.models_found.add(model)
def on_search_completed(self):
@ -99,5 +106,3 @@ class FindModels(ModelSearch):
def list_models(self) -> List[Path]:
self.search()
return list(self.models_found)

49
invokeai/backend/model_management/models/__init__.py
View File

@ -3,15 +3,24 @@ from enum import Enum
from pydantic import BaseModel
from typing import Literal, get_origin
from .base import (
BaseModelType, ModelType, SubModelType, ModelBase, ModelConfigBase,
ModelVariantType, SchedulerPredictionType, ModelError, SilenceWarnings,
ModelNotFoundException, InvalidModelException, DuplicateModelException
)
BaseModelType,
ModelType,
SubModelType,
ModelBase,
ModelConfigBase,
ModelVariantType,
SchedulerPredictionType,
ModelError,
SilenceWarnings,
ModelNotFoundException,
InvalidModelException,
DuplicateModelException,
)
from .stable_diffusion import StableDiffusion1Model, StableDiffusion2Model
from .sdxl import StableDiffusionXLModel
from .vae import VaeModel
from .lora import LoRAModel
from .controlnet import ControlNetModel # TODO:
from .controlnet import ControlNetModel # TODO:
from .textual_inversion import TextualInversionModel
MODEL_CLASSES = {
@ -45,18 +54,19 @@ MODEL_CLASSES = {
ModelType.ControlNet: ControlNetModel,
ModelType.TextualInversion: TextualInversionModel,
},
#BaseModelType.Kandinsky2_1: {
# BaseModelType.Kandinsky2_1: {
# ModelType.Main: Kandinsky2_1Model,
# ModelType.MoVQ: MoVQModel,
# ModelType.Lora: LoRAModel,
# ModelType.ControlNet: ControlNetModel,
# ModelType.TextualInversion: TextualInversionModel,
#},
# },
}
MODEL_CONFIGS = list()
OPENAPI_MODEL_CONFIGS = list()
class OpenAPIModelInfoBase(BaseModel):
model_name: str
base_model: BaseModelType
@ -72,27 +82,31 @@ for base_model, models in MODEL_CLASSES.items():
# LS: sort to get the checkpoint configs first, which makes
# for a better template in the Swagger docs
for cfg in sorted(model_configs, key=lambda x: str(x)):
model_name, cfg_name = cfg.__qualname__.split('.')[-2:]
model_name, cfg_name = cfg.__qualname__.split(".")[-2:]
openapi_cfg_name = model_name + cfg_name
if openapi_cfg_name in vars():
continue
api_wrapper = type(openapi_cfg_name, (cfg, OpenAPIModelInfoBase), dict(
__annotations__ = dict(
model_type=Literal[model_type.value],
api_wrapper = type(
openapi_cfg_name,
(cfg, OpenAPIModelInfoBase),
dict(
__annotations__=dict(
model_type=Literal[model_type.value],
),
),
))
)
#globals()[openapi_cfg_name] = api_wrapper
# globals()[openapi_cfg_name] = api_wrapper
vars()[openapi_cfg_name] = api_wrapper
OPENAPI_MODEL_CONFIGS.append(api_wrapper)
def get_model_config_enums():
enums = list()
for model_config in MODEL_CONFIGS:
if hasattr(inspect,'get_annotations'):
if hasattr(inspect, "get_annotations"):
fields = inspect.get_annotations(model_config)
else:
fields = model_config.__annotations__
@ -109,7 +123,9 @@ def get_model_config_enums():
if isinstance(field, type) and issubclass(field, str) and issubclass(field, Enum):
enums.append(field)
elif get_origin(field) is Literal and all(isinstance(arg, str) and isinstance(arg, Enum) for arg in field.__args__):
elif get_origin(field) is Literal and all(
isinstance(arg, str) and isinstance(arg, Enum) for arg in field.__args__
):
enums.append(type(field.__args__[0]))
elif field is None:
@ -119,4 +135,3 @@ def get_model_config_enums():
raise Exception(f"Unsupported format definition in {model_configs.__qualname__}")
return enums

114
invokeai/backend/model_management/models/base.py
View File

@ -15,29 +15,35 @@ from contextlib import suppress
from pydantic import BaseModel, Field
from typing import List, Dict, Optional, Type, Literal, TypeVar, Generic, Callable, Any, Union
class DuplicateModelException(Exception):
pass
class InvalidModelException(Exception):
pass
class ModelNotFoundException(Exception):
pass
class BaseModelType(str, Enum):
StableDiffusion1 = "sd-1"
StableDiffusion2 = "sd-2"
StableDiffusionXL = "sdxl"
StableDiffusionXLRefiner = "sdxl-refiner"
#Kandinsky2_1 = "kandinsky-2.1"
# Kandinsky2_1 = "kandinsky-2.1"
class ModelType(str, Enum):
Main = "main"
Vae = "vae"
Lora = "lora"
ControlNet = "controlnet" # used by model_probe
ControlNet = "controlnet" # used by model_probe
TextualInversion = "embedding"
class SubModelType(str, Enum):
UNet = "unet"
TextEncoder = "text_encoder"
@ -47,23 +53,27 @@ class SubModelType(str, Enum):
Vae = "vae"
Scheduler = "scheduler"
SafetyChecker = "safety_checker"
#MoVQ = "movq"
# MoVQ = "movq"
class ModelVariantType(str, Enum):
Normal = "normal"
Inpaint = "inpaint"
Depth = "depth"
class SchedulerPredictionType(str, Enum):
Epsilon = "epsilon"
VPrediction = "v_prediction"
Sample = "sample"
class ModelError(str, Enum):
NotFound = "not_found"
class ModelConfigBase(BaseModel):
path: str # or Path
path: str # or Path
description: Optional[str] = Field(None)
model_format: Optional[str] = Field(None)
error: Optional[ModelError] = Field(None)
@ -71,13 +81,17 @@ class ModelConfigBase(BaseModel):
class Config:
use_enum_values = True
class EmptyConfigLoader(ConfigMixin):
@classmethod
def load_config(cls, *args, **kwargs):
cls.config_name = kwargs.pop("config_name")
return super().load_config(*args, **kwargs)
T_co = TypeVar('T_co', covariant=True)
T_co = TypeVar("T_co", covariant=True)
class classproperty(Generic[T_co]):
def __init__(self, fget: Callable[[Any], T_co]) -> None:
self.fget = fget
@ -86,12 +100,13 @@ class classproperty(Generic[T_co]):
return self.fget(owner)
def __set__(self, instance: Optional[Any], value: Any) -> None:
raise AttributeError('cannot set attribute')
raise AttributeError("cannot set attribute")
class ModelBase(metaclass=ABCMeta):
#model_path: str
#base_model: BaseModelType
#model_type: ModelType
# model_path: str
# base_model: BaseModelType
# model_type: ModelType
def __init__(
self,
@ -110,7 +125,7 @@ class ModelBase(metaclass=ABCMeta):
return None
elif any(t is None for t in subtypes):
raise Exception(f"Unsupported definition: {subtypes}")
if subtypes[0] in ["diffusers", "transformers"]:
res_type = sys.modules[subtypes[0]]
subtypes = subtypes[1:]
@ -119,7 +134,6 @@ class ModelBase(metaclass=ABCMeta):
res_type = sys.modules["diffusers"]
res_type = getattr(res_type, "pipelines")
for subtype in subtypes:
res_type = getattr(res_type, subtype)
return res_type
@ -128,7 +142,7 @@ class ModelBase(metaclass=ABCMeta):
def _get_configs(cls):
with suppress(Exception):
return cls.__configs
configs = dict()
for name in dir(cls):
if name.startswith("__"):
@ -138,7 +152,7 @@ class ModelBase(metaclass=ABCMeta):
if not isinstance(value, type) or not issubclass(value, ModelConfigBase):
continue
if hasattr(inspect,'get_annotations'):
if hasattr(inspect, "get_annotations"):
fields = inspect.get_annotations(value)
else:
fields = value.__annotations__
@ -151,7 +165,9 @@ class ModelBase(metaclass=ABCMeta):
for model_format in field:
configs[model_format.value] = value
elif typing.get_origin(field) is Literal and all(isinstance(arg, str) and isinstance(arg, Enum) for arg in field.__args__):
elif typing.get_origin(field) is Literal and all(
isinstance(arg, str) and isinstance(arg, Enum) for arg in field.__args__
):
for model_format in field.__args__:
configs[model_format.value] = value
@ -203,8 +219,8 @@ class ModelBase(metaclass=ABCMeta):
class DiffusersModel(ModelBase):
#child_types: Dict[str, Type]
#child_sizes: Dict[str, int]
# child_types: Dict[str, Type]
# child_sizes: Dict[str, int]
def __init__(self, model_path: str, base_model: BaseModelType, model_type: ModelType):
super().__init__(model_path, base_model, model_type)
@ -214,7 +230,7 @@ class DiffusersModel(ModelBase):
try:
config_data = DiffusionPipeline.load_config(self.model_path)
#config_data = json.loads(os.path.join(self.model_path, "model_index.json"))
# config_data = json.loads(os.path.join(self.model_path, "model_index.json"))
except:
raise Exception("Invalid diffusers model! (model_index.json not found or invalid)")
@ -228,14 +244,12 @@ class DiffusersModel(ModelBase):
self.child_types[child_name] = child_type
self.child_sizes[child_name] = calc_model_size_by_fs(self.model_path, subfolder=child_name)
def get_size(self, child_type: Optional[SubModelType] = None):
if child_type is None:
return sum(self.child_sizes.values())
else:
return self.child_sizes[child_type]
def get_model(
self,
torch_dtype: Optional[torch.dtype],
@ -245,7 +259,7 @@ class DiffusersModel(ModelBase):
if child_type is None:
raise Exception("Child model type can't be null on diffusers model")
if child_type not in self.child_types:
return None # TODO: or raise
return None # TODO: or raise
if torch_dtype == torch.float16:
variants = ["fp16", None]
@ -265,8 +279,8 @@ class DiffusersModel(ModelBase):
)
break
except Exception as e:
#print("====ERR LOAD====")
#print(f"{variant}: {e}")
# print("====ERR LOAD====")
# print(f"{variant}: {e}")
pass
else:
raise Exception(f"Failed to load {self.base_model}:{self.model_type}:{child_type} model")
@ -275,15 +289,10 @@ class DiffusersModel(ModelBase):
self.child_sizes[child_type] = calc_model_size_by_data(model)
return model
#def convert_if_required(model_path: str, cache_path: str, config: Optional[dict]) -> str:
# def convert_if_required(model_path: str, cache_path: str, config: Optional[dict]) -> str:
def calc_model_size_by_fs(
model_path: str,
subfolder: Optional[str] = None,
variant: Optional[str] = None
):
def calc_model_size_by_fs(model_path: str, subfolder: Optional[str] = None, variant: Optional[str] = None):
if subfolder is not None:
model_path = os.path.join(model_path, subfolder)
@ -325,12 +334,12 @@ def calc_model_size_by_fs(
# calculate files size if there is no index file
formats = [
(".safetensors",), # safetensors
(".bin",), # torch
(".onnx", ".pb"), # onnx
(".msgpack",), # flax
(".ckpt",), # tf
(".h5",), # tf2
(".safetensors",), # safetensors
(".bin",), # torch
(".onnx", ".pb"), # onnx
(".msgpack",), # flax
(".ckpt",), # tf
(".h5",), # tf2
]
for file_format in formats:
@ -343,9 +352,9 @@ def calc_model_size_by_fs(
file_stats = os.stat(os.path.join(model_path, model_file))
model_size += file_stats.st_size
return model_size
#raise NotImplementedError(f"Unknown model structure! Files: {all_files}")
return 0 # scheduler/feature_extractor/tokenizer - models without loading to gpu
# raise NotImplementedError(f"Unknown model structure! Files: {all_files}")
return 0 # scheduler/feature_extractor/tokenizer - models without loading to gpu
def calc_model_size_by_data(model) -> int:
@ -364,12 +373,12 @@ def _calc_pipeline_by_data(pipeline) -> int:
if submodel is not None and isinstance(submodel, torch.nn.Module):
res += _calc_model_by_data(submodel)
return res
def _calc_model_by_data(model) -> int:
mem_params = sum([param.nelement()*param.element_size() for param in model.parameters()])
mem_bufs = sum([buf.nelement()*buf.element_size() for buf in model.buffers()])
mem = mem_params + mem_bufs # in bytes
mem_params = sum([param.nelement() * param.element_size() for param in model.parameters()])
mem_bufs = sum([buf.nelement() * buf.element_size() for buf in model.buffers()])
mem = mem_params + mem_bufs # in bytes
return mem
@ -377,11 +386,15 @@ def _fast_safetensors_reader(path: str):
checkpoint = dict()
device = torch.device("meta")
with open(path, "rb") as f:
definition_len = int.from_bytes(f.read(8), 'little')
definition_len = int.from_bytes(f.read(8), "little")
definition_json = f.read(definition_len)
definition = json.loads(definition_json)
if "__metadata__" in definition and definition["__metadata__"].get("format", "pt") not in {"pt", "torch", "pytorch"}:
if "__metadata__" in definition and definition["__metadata__"].get("format", "pt") not in {
"pt",
"torch",
"pytorch",
}:
raise Exception("Supported only pytorch safetensors files")
definition.pop("__metadata__", None)
@ -400,6 +413,7 @@ def _fast_safetensors_reader(path: str):
return checkpoint
def read_checkpoint_meta(path: Union[str, Path], scan: bool = False):
if str(path).endswith(".safetensors"):
try:
@ -411,25 +425,27 @@ def read_checkpoint_meta(path: Union[str, Path], scan: bool = False):
if scan:
scan_result = scan_file_path(path)
if scan_result.infected_files != 0:
raise Exception(f"The model file \"{path}\" is potentially infected by malware. Aborting import.")
raise Exception(f'The model file "{path}" is potentially infected by malware. Aborting import.')
checkpoint = torch.load(path, map_location=torch.device("meta"))
return checkpoint
import warnings
from diffusers import logging as diffusers_logging
from transformers import logging as transformers_logging
class SilenceWarnings(object):
def __init__(self):
self.transformers_verbosity = transformers_logging.get_verbosity()
self.diffusers_verbosity = diffusers_logging.get_verbosity()
def __enter__(self):
transformers_logging.set_verbosity_error()
diffusers_logging.set_verbosity_error()
warnings.simplefilter('ignore')
warnings.simplefilter("ignore")
def __exit__(self, type, value, traceback):
transformers_logging.set_verbosity(self.transformers_verbosity)
diffusers_logging.set_verbosity(self.diffusers_verbosity)
warnings.simplefilter('default')
warnings.simplefilter("default")

60
invokeai/backend/model_management/models/controlnet.py
View File

@ -18,13 +18,15 @@ from .base import (
)
from invokeai.app.services.config import InvokeAIAppConfig
class ControlNetModelFormat(str, Enum):
Checkpoint = "checkpoint"
Diffusers = "diffusers"
class ControlNetModel(ModelBase):
#model_class: Type
#model_size: int
# model_class: Type
# model_size: int
class DiffusersConfig(ModelConfigBase):
model_format: Literal[ControlNetModelFormat.Diffusers]
@ -39,7 +41,7 @@ class ControlNetModel(ModelBase):
try:
config = EmptyConfigLoader.load_config(self.model_path, config_name="config.json")
#config = json.loads(os.path.join(self.model_path, "config.json"))
# config = json.loads(os.path.join(self.model_path, "config.json"))
except:
raise Exception("Invalid controlnet model! (config.json not found or invalid)")
@ -67,7 +69,7 @@ class ControlNetModel(ModelBase):
raise Exception("There is no child models in controlnet model")
model = None
for variant in ['fp16',None]:
for variant in ["fp16", None]:
try:
model = self.model_class.from_pretrained(
self.model_path,
@ -79,7 +81,7 @@ class ControlNetModel(ModelBase):
pass
if not model:
raise ModelNotFoundException()
# calc more accurate size
self.model_size = calc_model_size_by_data(model)
return model
@ -105,29 +107,30 @@ class ControlNetModel(ModelBase):
@classmethod
def convert_if_required(
cls,
model_path: str,
output_path: str,
config: ModelConfigBase,
base_model: BaseModelType,
) -> str:
if cls.detect_format(model_path) == ControlNetModelFormat.Checkpoint:
return _convert_controlnet_ckpt_and_cache(
model_path = model_path,
model_config = config.config,
output_path = output_path,
base_model = base_model,
)
else:
return model_path
@classmethod
def _convert_controlnet_ckpt_and_cache(
cls,
model_path: str,
output_path: str,
config: ModelConfigBase,
base_model: BaseModelType,
model_config: ControlNetModel.CheckpointConfig,
) -> str:
if cls.detect_format(model_path) == ControlNetModelFormat.Checkpoint:
return _convert_controlnet_ckpt_and_cache(
model_path=model_path,
model_config=config.config,
output_path=output_path,
base_model=base_model,
)
else:
return model_path
@classmethod
def _convert_controlnet_ckpt_and_cache(
cls,
model_path: str,
output_path: str,
base_model: BaseModelType,
model_config: ControlNetModel.CheckpointConfig,
) -> str:
"""
Convert the controlnet from checkpoint format to diffusers format,
@ -144,12 +147,13 @@ def _convert_controlnet_ckpt_and_cache(
# to avoid circular import errors
from ..convert_ckpt_to_diffusers import convert_controlnet_to_diffusers
convert_controlnet_to_diffusers(
weights,
output_path,
original_config_file = app_config.root_path / model_config,
image_size = 512,
scan_needed = True,
from_safetensors = weights.suffix == ".safetensors"
original_config_file=app_config.root_path / model_config,
image_size=512,
scan_needed=True,
from_safetensors=weights.suffix == ".safetensors",
)
return output_path

7
invokeai/backend/model_management/models/lora.py
View File

@ -12,18 +12,21 @@ from .base import (
InvalidModelException,
ModelNotFoundException,
)
# TODO: naming
from ..lora import LoRAModel as LoRAModelRaw
class LoRAModelFormat(str, Enum):
LyCORIS = "lycoris"
Diffusers = "diffusers"
class LoRAModel(ModelBase):
#model_size: int
# model_size: int
class Config(ModelConfigBase):
model_format: LoRAModelFormat # TODO:
model_format: LoRAModelFormat # TODO:
def __init__(self, model_path: str, base_model: BaseModelType, model_type: ModelType):
assert model_type == ModelType.Lora

15
invokeai/backend/model_management/models/sdxl.py
View File

@ -15,12 +15,13 @@ from .base import (
)
from omegaconf import OmegaConf
class StableDiffusionXLModelFormat(str, Enum):
Checkpoint = "checkpoint"
Diffusers = "diffusers"
class StableDiffusionXLModel(DiffusersModel):
class StableDiffusionXLModel(DiffusersModel):
# TODO: check that configs overwriten properly
class DiffusersConfig(ModelConfigBase):
model_format: Literal[StableDiffusionXLModelFormat.Diffusers]
@ -53,7 +54,7 @@ class StableDiffusionXLModel(DiffusersModel):
else:
checkpoint = read_checkpoint_meta(path)
checkpoint = checkpoint.get('state_dict', checkpoint)
checkpoint = checkpoint.get("state_dict", checkpoint)
in_channels = checkpoint["model.diffusion_model.input_blocks.0.0.weight"].shape[1]
elif model_format == StableDiffusionXLModelFormat.Diffusers:
@ -61,7 +62,7 @@ class StableDiffusionXLModel(DiffusersModel):
if os.path.exists(unet_config_path):
with open(unet_config_path, "r") as f:
unet_config = json.loads(f.read())
in_channels = unet_config['in_channels']
in_channels = unet_config["in_channels"]
else:
raise Exception("Not supported stable diffusion diffusers format(possibly onnx?)")
@ -81,11 +82,10 @@ class StableDiffusionXLModel(DiffusersModel):
if ckpt_config_path is None:
# TO DO: implement picking
pass
return cls.create_config(
path=path,
model_format=model_format,
config=ckpt_config_path,
variant=variant,
)
@ -114,11 +114,12 @@ class StableDiffusionXLModel(DiffusersModel):
# source code changes, we simply translate here
if isinstance(config, cls.CheckpointConfig):
from invokeai.backend.model_management.models.stable_diffusion import _convert_ckpt_and_cache
return _convert_ckpt_and_cache(
version=base_model,
model_config=config,
output_path=output_path,
use_safetensors=False, # corrupts sdxl models for some reason
use_safetensors=False, # corrupts sdxl models for some reason
)
else:
return model_path

63
invokeai/backend/model_management/models/stable_diffusion.py
View File

@ -26,8 +26,8 @@ class StableDiffusion1ModelFormat(str, Enum):
Checkpoint = "checkpoint"
Diffusers = "diffusers"
class StableDiffusion1Model(DiffusersModel):
class StableDiffusion1Model(DiffusersModel):
class DiffusersConfig(ModelConfigBase):
model_format: Literal[StableDiffusion1ModelFormat.Diffusers]
vae: Optional[str] = Field(None)
@ -38,7 +38,7 @@ class StableDiffusion1Model(DiffusersModel):
vae: Optional[str] = Field(None)
config: str
variant: ModelVariantType
def __init__(self, model_path: str, base_model: BaseModelType, model_type: ModelType):
assert base_model == BaseModelType.StableDiffusion1
assert model_type == ModelType.Main
@ -59,7 +59,7 @@ class StableDiffusion1Model(DiffusersModel):
else:
checkpoint = read_checkpoint_meta(path)
checkpoint = checkpoint.get('state_dict', checkpoint)
checkpoint = checkpoint.get("state_dict", checkpoint)
in_channels = checkpoint["model.diffusion_model.input_blocks.0.0.weight"].shape[1]
elif model_format == StableDiffusion1ModelFormat.Diffusers:
@ -67,7 +67,7 @@ class StableDiffusion1Model(DiffusersModel):
if os.path.exists(unet_config_path):
with open(unet_config_path, "r") as f:
unet_config = json.loads(f.read())
in_channels = unet_config['in_channels']
in_channels = unet_config["in_channels"]
else:
raise NotImplementedError(f"{path} is not a supported stable diffusion diffusers format")
@ -88,7 +88,6 @@ class StableDiffusion1Model(DiffusersModel):
return cls.create_config(
path=path,
model_format=model_format,
config=ckpt_config_path,
variant=variant,
)
@ -125,16 +124,17 @@ class StableDiffusion1Model(DiffusersModel):
version=BaseModelType.StableDiffusion1,
model_config=config,
output_path=output_path,
)
)
else:
return model_path
class StableDiffusion2ModelFormat(str, Enum):
Checkpoint = "checkpoint"
Diffusers = "diffusers"
class StableDiffusion2Model(DiffusersModel):
class StableDiffusion2Model(DiffusersModel):
# TODO: check that configs overwriten properly
class DiffusersConfig(ModelConfigBase):
model_format: Literal[StableDiffusion2ModelFormat.Diffusers]
@ -167,7 +167,7 @@ class StableDiffusion2Model(DiffusersModel):
else:
checkpoint = read_checkpoint_meta(path)
checkpoint = checkpoint.get('state_dict', checkpoint)
checkpoint = checkpoint.get("state_dict", checkpoint)
in_channels = checkpoint["model.diffusion_model.input_blocks.0.0.weight"].shape[1]
elif model_format == StableDiffusion2ModelFormat.Diffusers:
@ -175,7 +175,7 @@ class StableDiffusion2Model(DiffusersModel):
if os.path.exists(unet_config_path):
with open(unet_config_path, "r") as f:
unet_config = json.loads(f.read())
in_channels = unet_config['in_channels']
in_channels = unet_config["in_channels"]
else:
raise Exception("Not supported stable diffusion diffusers format(possibly onnx?)")
@ -198,7 +198,6 @@ class StableDiffusion2Model(DiffusersModel):
return cls.create_config(
path=path,
model_format=model_format,
config=ckpt_config_path,
variant=variant,
)
@ -239,17 +238,19 @@ class StableDiffusion2Model(DiffusersModel):
else:
return model_path
# TODO: rework
# pass precision - currently defaulting to fp16
def _convert_ckpt_and_cache(
version: BaseModelType,
model_config: Union[StableDiffusion1Model.CheckpointConfig,
StableDiffusion2Model.CheckpointConfig,
StableDiffusionXLModel.CheckpointConfig,
],
output_path: str,
use_save_model: bool=False,
**kwargs,
version: BaseModelType,
model_config: Union[
StableDiffusion1Model.CheckpointConfig,
StableDiffusion2Model.CheckpointConfig,
StableDiffusionXLModel.CheckpointConfig,
],
output_path: str,
use_save_model: bool = False,
**kwargs,
) -> str:
"""
Convert the checkpoint model indicated in mconfig into a
@ -270,13 +271,14 @@ def _convert_ckpt_and_cache(
from ..convert_ckpt_to_diffusers import convert_ckpt_to_diffusers
from ...util.devices import choose_torch_device, torch_dtype
model_base_to_model_type = {BaseModelType.StableDiffusion1: 'FrozenCLIPEmbedder',
BaseModelType.StableDiffusion2: 'FrozenOpenCLIPEmbedder',
BaseModelType.StableDiffusionXL: 'SDXL',
BaseModelType.StableDiffusionXLRefiner: 'SDXL-Refiner',
}
logger.info(f'Converting {weights} to diffusers format')
with SilenceWarnings():
model_base_to_model_type = {
BaseModelType.StableDiffusion1: "FrozenCLIPEmbedder",
BaseModelType.StableDiffusion2: "FrozenOpenCLIPEmbedder",
BaseModelType.StableDiffusionXL: "SDXL",
BaseModelType.StableDiffusionXLRefiner: "SDXL-Refiner",
}
logger.info(f"Converting {weights} to diffusers format")
with SilenceWarnings():
convert_ckpt_to_diffusers(
weights,
output_path,
@ -286,12 +288,13 @@ def _convert_ckpt_and_cache(
original_config_file=config_file,
extract_ema=True,
scan_needed=True,
from_safetensors = weights.suffix == ".safetensors",
precision = torch_dtype(choose_torch_device()),
from_safetensors=weights.suffix == ".safetensors",
precision=torch_dtype(choose_torch_device()),
**kwargs,
)
return output_path
def _select_ckpt_config(version: BaseModelType, variant: ModelVariantType):
ckpt_configs = {
BaseModelType.StableDiffusion1: {
@ -299,7 +302,7 @@ def _select_ckpt_config(version: BaseModelType, variant: ModelVariantType):
ModelVariantType.Inpaint: "v1-inpainting-inference.yaml",
},
BaseModelType.StableDiffusion2: {
ModelVariantType.Normal: "v2-inference-v.yaml", # best guess, as we can't differentiate with base(512)
ModelVariantType.Normal: "v2-inference-v.yaml", # best guess, as we can't differentiate with base(512)
ModelVariantType.Inpaint: "v2-inpainting-inference.yaml",
ModelVariantType.Depth: "v2-midas-inference.yaml",
},
@ -321,8 +324,6 @@ def _select_ckpt_config(version: BaseModelType, variant: ModelVariantType):
if config_path.is_relative_to(app_config.root_path):
config_path = config_path.relative_to(app_config.root_path)
return str(config_path)
except:
return None

6
invokeai/backend/model_management/models/textual_inversion.py
View File

@ -11,11 +11,13 @@ from .base import (
ModelNotFoundException,
InvalidModelException,
)
# TODO: naming
from ..lora import TextualInversionModel as TextualInversionModelRaw
class TextualInversionModel(ModelBase):
#model_size: int
# model_size: int
class Config(ModelConfigBase):
model_format: None
@ -65,7 +67,7 @@ class TextualInversionModel(ModelBase):
if os.path.isdir(path):
if os.path.exists(os.path.join(path, "learned_embeds.bin")):
return None # diffusers-ti
return None # diffusers-ti
if os.path.isfile(path):
if any([path.endswith(f".{ext}") for ext in ["safetensors", "ckpt", "pt", "bin"]]):

30
invokeai/backend/model_management/models/vae.py
View File

@ -22,13 +22,15 @@ from invokeai.app.services.config import InvokeAIAppConfig
from diffusers.utils import is_safetensors_available
from omegaconf import OmegaConf
class VaeModelFormat(str, Enum):
Checkpoint = "checkpoint"
Diffusers = "diffusers"
class VaeModel(ModelBase):
#vae_class: Type
#model_size: int
# vae_class: Type
# model_size: int
class Config(ModelConfigBase):
model_format: VaeModelFormat
@ -39,7 +41,7 @@ class VaeModel(ModelBase):
try:
config = EmptyConfigLoader.load_config(self.model_path, config_name="config.json")
#config = json.loads(os.path.join(self.model_path, "config.json"))
# config = json.loads(os.path.join(self.model_path, "config.json"))
except:
raise Exception("Invalid vae model! (config.json not found or invalid)")
@ -95,7 +97,7 @@ class VaeModel(ModelBase):
cls,
model_path: str,
output_path: str,
config: ModelConfigBase, # empty config or config of parent model
config: ModelConfigBase, # empty config or config of parent model
base_model: BaseModelType,
) -> str:
if cls.detect_format(model_path) == VaeModelFormat.Checkpoint:
@ -108,6 +110,7 @@ class VaeModel(ModelBase):
else:
return model_path
# TODO: rework
def _convert_vae_ckpt_and_cache(
weights_path: str,
@ -138,13 +141,14 @@ def _convert_vae_ckpt_and_cache(
2.1 - 768
"""
image_size = 512
# return cached version if it exists
if output_path.exists():
return output_path
if base_model in {BaseModelType.StableDiffusion1, BaseModelType.StableDiffusion2}:
from .stable_diffusion import _select_ckpt_config
# all sd models use same vae settings
config_file = _select_ckpt_config(base_model, ModelVariantType.Normal)
else:
@ -152,7 +156,8 @@ def _convert_vae_ckpt_and_cache(
# this avoids circular import error
from ..convert_ckpt_to_diffusers import convert_ldm_vae_to_diffusers
if weights_path.suffix == '.safetensors':
if weights_path.suffix == ".safetensors":
checkpoint = safetensors.torch.load_file(weights_path, device="cpu")
else:
checkpoint = torch.load(weights_path, map_location="cpu")
@ -161,15 +166,12 @@ def _convert_vae_ckpt_and_cache(
if "state_dict" in checkpoint:
checkpoint = checkpoint["state_dict"]
config = OmegaConf.load(app_config.root_path/config_file)
config = OmegaConf.load(app_config.root_path / config_file)
vae_model = convert_ldm_vae_to_diffusers(
checkpoint = checkpoint,
vae_config = config,
image_size = image_size,
)
vae_model.save_pretrained(
output_path,
safe_serialization=is_safetensors_available()
checkpoint=checkpoint,
vae_config=config,
image_size=image_size,
)
vae_model.save_pretrained(output_path, safe_serialization=is_safetensors_available())
return output_path

149
invokeai/backend/stable_diffusion/diffusers_pipeline.py
View File

@ -47,6 +47,7 @@ from .diffusion import (
)
from .offloading import FullyLoadedModelGroup, ModelGroup
@dataclass
class PipelineIntermediateState:
run_id: str
@ -72,7 +73,11 @@ class AddsMaskLatents:
initial_image_latents: torch.Tensor
def __call__(
self, latents: torch.Tensor, t: torch.Tensor, text_embeddings: torch.Tensor, **kwargs,
self,
latents: torch.Tensor,
t: torch.Tensor,
text_embeddings: torch.Tensor,
**kwargs,
) -> torch.Tensor:
model_input = self.add_mask_channels(latents)
return self.forward(model_input, t, text_embeddings, **kwargs)
@ -80,12 +85,8 @@ class AddsMaskLatents:
def add_mask_channels(self, latents):
batch_size = latents.size(0)
# duplicate mask and latents for each batch
mask = einops.repeat(
self.mask, "b c h w -> (repeat b) c h w", repeat=batch_size
)
image_latents = einops.repeat(
self.initial_image_latents, "b c h w -> (repeat b) c h w", repeat=batch_size
)
mask = einops.repeat(self.mask, "b c h w -> (repeat b) c h w", repeat=batch_size)
image_latents = einops.repeat(self.initial_image_latents, "b c h w -> (repeat b) c h w", repeat=batch_size)
# add mask and image as additional channels
model_input, _ = einops.pack([latents, mask, image_latents], "b * h w")
return model_input
@ -103,9 +104,7 @@ class AddsMaskGuidance:
noise: torch.Tensor
_debug: Optional[Callable] = None
def __call__(
self, step_output: Union[BaseOutput, SchedulerOutput], t: torch.Tensor, conditioning
) -> BaseOutput:
def __call__(self, step_output: Union[BaseOutput, SchedulerOutput], t: torch.Tensor, conditioning) -> BaseOutput:
output_class = step_output.__class__ # We'll create a new one with masked data.
# The problem with taking SchedulerOutput instead of the model output is that we're less certain what's in it.
@ -116,11 +115,7 @@ class AddsMaskGuidance:
# Mask anything that has the same shape as prev_sample, return others as-is.
return output_class(
{
k: (
self.apply_mask(v, self._t_for_field(k, t))
if are_like_tensors(prev_sample, v)
else v
)
k: (self.apply_mask(v, self._t_for_field(k, t)) if are_like_tensors(prev_sample, v) else v)
for k, v in step_output.items()
}
)
@ -132,9 +127,7 @@ class AddsMaskGuidance:
def apply_mask(self, latents: torch.Tensor, t) -> torch.Tensor:
batch_size = latents.size(0)
mask = einops.repeat(
self.mask, "b c h w -> (repeat b) c h w", repeat=batch_size
)
mask = einops.repeat(self.mask, "b c h w -> (repeat b) c h w", repeat=batch_size)
if t.dim() == 0:
# some schedulers expect t to be one-dimensional.
# TODO: file diffusers bug about inconsistency?
@ -144,12 +137,8 @@ class AddsMaskGuidance:
mask_latents = self.scheduler.add_noise(self.mask_latents, self.noise, t)
# TODO: Do we need to also apply scheduler.scale_model_input? Or is add_noise appropriately scaled already?
# mask_latents = self.scheduler.scale_model_input(mask_latents, t)
mask_latents = einops.repeat(
mask_latents, "b c h w -> (repeat b) c h w", repeat=batch_size
)
masked_input = torch.lerp(
mask_latents.to(dtype=latents.dtype), latents, mask.to(dtype=latents.dtype)
)
mask_latents = einops.repeat(mask_latents, "b c h w -> (repeat b) c h w", repeat=batch_size)
masked_input = torch.lerp(mask_latents.to(dtype=latents.dtype), latents, mask.to(dtype=latents.dtype))
if self._debug:
self._debug(masked_input, f"t={t} lerped")
return masked_input
@ -159,9 +148,7 @@ def trim_to_multiple_of(*args, multiple_of=8):
return tuple((x - x % multiple_of) for x in args)
def image_resized_to_grid_as_tensor(
image: PIL.Image.Image, normalize: bool = True, multiple_of=8
) -> torch.FloatTensor:
def image_resized_to_grid_as_tensor(image: PIL.Image.Image, normalize: bool = True, multiple_of=8) -> torch.FloatTensor:
"""
:param image: input image
@ -211,6 +198,7 @@ class GeneratorToCallbackinator(Generic[ParamType, ReturnType, CallbackType]):
raise AssertionError("why was that an empty generator?")
return result
@dataclass
class ControlNetData:
model: ControlNetModel = Field(default=None)
@ -341,9 +329,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
# FIXME: can't currently register control module
# control_model=control_model,
)
self.invokeai_diffuser = InvokeAIDiffuserComponent(
self.unet, self._unet_forward
)
self.invokeai_diffuser = InvokeAIDiffuserComponent(self.unet, self._unet_forward)
self._model_group = FullyLoadedModelGroup(execution_device or self.unet.device)
self._model_group.install(*self._submodels)
@ -354,11 +340,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
if xformers is available, use it, otherwise use sliced attention.
"""
config = InvokeAIAppConfig.get_config()
if (
torch.cuda.is_available()
and is_xformers_available()
and not config.disable_xformers
):
if torch.cuda.is_available() and is_xformers_available() and not config.disable_xformers:
self.enable_xformers_memory_efficient_attention()
else:
if self.device.type == "cpu" or self.device.type == "mps":
@ -369,9 +351,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
raise ValueError(f"unrecognized device {self.device}")
# input tensor of [1, 4, h/8, w/8]
# output tensor of [16, (h/8 * w/8), (h/8 * w/8)]
bytes_per_element_needed_for_baddbmm_duplication = (
latents.element_size() + 4
)
bytes_per_element_needed_for_baddbmm_duplication = latents.element_size() + 4
max_size_required_for_baddbmm = (
16
* latents.size(dim=2)
@ -380,9 +360,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
* latents.size(dim=3)
* bytes_per_element_needed_for_baddbmm_duplication
)
if max_size_required_for_baddbmm > (
mem_free * 3.0 / 4.0
): # 3.3 / 4.0 is from old Invoke code
if max_size_required_for_baddbmm > (mem_free * 3.0 / 4.0): # 3.3 / 4.0 is from old Invoke code
self.enable_attention_slicing(slice_size="max")
elif torch.backends.mps.is_available():
# diffusers recommends always enabling for mps
@ -470,7 +448,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
control_data: List[ControlNetData] = None,
) -> tuple[torch.Tensor, Optional[AttentionMapSaver]]:
if self.scheduler.config.get("cpu_only", False):
scheduler_device = torch.device('cpu')
scheduler_device = torch.device("cpu")
else:
scheduler_device = self._model_group.device_for(self.unet)
@ -488,7 +466,6 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
run_id=run_id,
additional_guidance=additional_guidance,
control_data=control_data,
callback=callback,
)
return result.latents, result.attention_map_saver
@ -511,9 +488,9 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
additional_guidance = []
extra_conditioning_info = conditioning_data.extra
with self.invokeai_diffuser.custom_attention_context(
self.invokeai_diffuser.model,
extra_conditioning_info=extra_conditioning_info,
step_count=len(self.scheduler.timesteps),
self.invokeai_diffuser.model,
extra_conditioning_info=extra_conditioning_info,
step_count=len(self.scheduler.timesteps),
):
yield PipelineIntermediateState(
run_id=run_id,
@ -607,16 +584,15 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
# that are combined at higher level to make control_mode enum
# soft_injection determines whether to do per-layer re-weighting adjustment (if True)
# or default weighting (if False)
soft_injection = (control_mode == "more_prompt" or control_mode == "more_control")
soft_injection = control_mode == "more_prompt" or control_mode == "more_control"
# cfg_injection = determines whether to apply ControlNet to only the conditional (if True)
# or the default both conditional and unconditional (if False)
cfg_injection = (control_mode == "more_control" or control_mode == "unbalanced")
cfg_injection = control_mode == "more_control" or control_mode == "unbalanced"
first_control_step = math.floor(control_datum.begin_step_percent * total_step_count)
last_control_step = math.ceil(control_datum.end_step_percent * total_step_count)
# only apply controlnet if current step is within the controlnet's begin/end step range
if step_index >= first_control_step and step_index <= last_control_step:
if cfg_injection:
control_latent_input = unet_latent_input
else:
@ -629,7 +605,10 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
encoder_hidden_states = conditioning_data.text_embeddings
encoder_attention_mask = None
else:
encoder_hidden_states, encoder_attention_mask = self.invokeai_diffuser._concat_conditionings_for_batch(
(
encoder_hidden_states,
encoder_attention_mask,
) = self.invokeai_diffuser._concat_conditionings_for_batch(
conditioning_data.unconditioned_embeddings,
conditioning_data.text_embeddings,
)
@ -646,9 +625,9 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
timestep=timestep,
encoder_hidden_states=encoder_hidden_states,
controlnet_cond=control_datum.image_tensor,
conditioning_scale=controlnet_weight, # controlnet specific, NOT the guidance scale
conditioning_scale=controlnet_weight, # controlnet specific, NOT the guidance scale
encoder_attention_mask=encoder_attention_mask,
guess_mode=soft_injection, # this is still called guess_mode in diffusers ControlNetModel
guess_mode=soft_injection, # this is still called guess_mode in diffusers ControlNetModel
return_dict=False,
)
if cfg_injection:
@ -678,13 +657,11 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
step_index=step_index,
total_step_count=total_step_count,
down_block_additional_residuals=down_block_res_samples, # from controlnet(s)
mid_block_additional_residual=mid_block_res_sample, # from controlnet(s)
mid_block_additional_residual=mid_block_res_sample, # from controlnet(s)
)
# compute the previous noisy sample x_t -> x_t-1
step_output = self.scheduler.step(
noise_pred, timestep, latents, **conditioning_data.scheduler_args
)
step_output = self.scheduler.step(noise_pred, timestep, latents, **conditioning_data.scheduler_args)
# TODO: this additional_guidance extension point feels redundant with InvokeAIDiffusionComponent.
# But the way things are now, scheduler runs _after_ that, so there was
@ -710,17 +687,16 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
# use of AddsMaskLatents.
latents = AddsMaskLatents(
self._unet_forward,
mask=torch.ones_like(
latents[:1, :1], device=latents.device, dtype=latents.dtype
),
initial_image_latents=torch.zeros_like(
latents[:1], device=latents.device, dtype=latents.dtype
),
mask=torch.ones_like(latents[:1, :1], device=latents.device, dtype=latents.dtype),
initial_image_latents=torch.zeros_like(latents[:1], device=latents.device, dtype=latents.dtype),
).add_mask_channels(latents)
# First three args should be positional, not keywords, so torch hooks can see them.
return self.unet(
latents, t, text_embeddings, cross_attention_kwargs=cross_attention_kwargs,
latents,
t,
text_embeddings,
cross_attention_kwargs=cross_attention_kwargs,
**kwargs,
).sample
@ -774,9 +750,9 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
) -> InvokeAIStableDiffusionPipelineOutput:
timesteps, _ = self.get_img2img_timesteps(num_inference_steps, strength)
result_latents, result_attention_maps = self.latents_from_embeddings(
latents=initial_latents if strength < 1.0 else torch.zeros_like(
initial_latents, device=initial_latents.device, dtype=initial_latents.dtype
),
latents=initial_latents
if strength < 1.0
else torch.zeros_like(initial_latents, device=initial_latents.device, dtype=initial_latents.dtype),
num_inference_steps=num_inference_steps,
conditioning_data=conditioning_data,
timesteps=timesteps,
@ -797,14 +773,12 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
)
return self.check_for_safety(output, dtype=conditioning_data.dtype)
def get_img2img_timesteps(
self, num_inference_steps: int, strength: float, device=None
) -> (torch.Tensor, int):
def get_img2img_timesteps(self, num_inference_steps: int, strength: float, device=None) -> (torch.Tensor, int):
img2img_pipeline = StableDiffusionImg2ImgPipeline(**self.components)
assert img2img_pipeline.scheduler is self.scheduler
if self.scheduler.config.get("cpu_only", False):
scheduler_device = torch.device('cpu')
scheduler_device = torch.device("cpu")
else:
scheduler_device = self._model_group.device_for(self.unet)
@ -849,18 +823,16 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
# 6. Prepare latent variables
# can't quite use upstream StableDiffusionImg2ImgPipeline.prepare_latents
# because we have our own noise function
init_image_latents = self.non_noised_latents_from_image(
init_image, device=device, dtype=latents_dtype
)
init_image_latents = self.non_noised_latents_from_image(init_image, device=device, dtype=latents_dtype)
if seed is not None:
set_seed(seed)
noise = noise_func(init_image_latents)
if mask.dim() == 3:
mask = mask.unsqueeze(0)
latent_mask = tv_resize(
mask, init_image_latents.shape[-2:], T.InterpolationMode.BILINEAR
).to(device=device, dtype=latents_dtype)
latent_mask = tv_resize(mask, init_image_latents.shape[-2:], T.InterpolationMode.BILINEAR).to(
device=device, dtype=latents_dtype
)
guidance: List[Callable] = []
@ -868,22 +840,20 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
# You'd think the inpainting model wouldn't be paying attention to the area it is going to repaint
# (that's why there's a mask!) but it seems to really want that blanked out.
masked_init_image = init_image * torch.where(mask < 0.5, 1, 0)
masked_latents = self.non_noised_latents_from_image(
masked_init_image, device=device, dtype=latents_dtype
)
masked_latents = self.non_noised_latents_from_image(masked_init_image, device=device, dtype=latents_dtype)
# TODO: we should probably pass this in so we don't have to try/finally around setting it.
self.invokeai_diffuser.model_forward_callback = AddsMaskLatents(
self._unet_forward, latent_mask, masked_latents
)
else:
guidance.append(
AddsMaskGuidance(latent_mask, init_image_latents, self.scheduler, noise)
)
guidance.append(AddsMaskGuidance(latent_mask, init_image_latents, self.scheduler, noise))
try:
result_latents, result_attention_maps = self.latents_from_embeddings(
latents=init_image_latents if strength < 1.0 else torch.zeros_like(
latents=init_image_latents
if strength < 1.0
else torch.zeros_like(
init_image_latents, device=init_image_latents.device, dtype=init_image_latents.dtype
),
num_inference_steps=num_inference_steps,
@ -914,18 +884,14 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
with torch.inference_mode():
self._model_group.load(self.vae)
init_latent_dist = self.vae.encode(init_image).latent_dist
init_latents = init_latent_dist.sample().to(
dtype=dtype
) # FIXME: uses torch.randn. make reproducible!
init_latents = init_latent_dist.sample().to(dtype=dtype) # FIXME: uses torch.randn. make reproducible!
init_latents = 0.18215 * init_latents
return init_latents
def check_for_safety(self, output, dtype):
with torch.inference_mode():
screened_images, has_nsfw_concept = self.run_safety_checker(
output.images, dtype=dtype
)
screened_images, has_nsfw_concept = self.run_safety_checker(output.images, dtype=dtype)
screened_attention_map_saver = None
if has_nsfw_concept is None or not has_nsfw_concept:
screened_attention_map_saver = output.attention_map_saver
@ -949,9 +915,8 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
def debug_latents(self, latents, msg):
from invokeai.backend.image_util import debug_image
with torch.inference_mode():
decoded = self.numpy_to_pil(self.decode_latents(latents))
for i, img in enumerate(decoded):
debug_image(
img, f"latents {msg} {i+1}/{len(decoded)}", debug_status=True
)
debug_image(img, f"latents {msg} {i+1}/{len(decoded)}", debug_status=True)

161
invokeai/backend/stable_diffusion/diffusion/cross_attention_control.py
View File

@ -17,6 +17,7 @@ from torch import nn
import invokeai.backend.util.logging as logger
from ...util import torch_dtype
class CrossAttentionType(enum.Enum):
SELF = 1
TOKENS = 2
@ -55,9 +56,7 @@ class Context:
if name in self.self_cross_attention_module_identifiers:
assert False, f"name {name} cannot appear more than once"
self.self_cross_attention_module_identifiers.append(name)
for name, module in get_cross_attention_modules(
model, CrossAttentionType.TOKENS
):
for name, module in get_cross_attention_modules(model, CrossAttentionType.TOKENS):
if name in self.tokens_cross_attention_module_identifiers:
assert False, f"name {name} cannot appear more than once"
self.tokens_cross_attention_module_identifiers.append(name)
@ -68,9 +67,7 @@ class Context:
else:
self.tokens_cross_attention_action = Context.Action.SAVE
def request_apply_saved_attention_maps(
self, cross_attention_type: CrossAttentionType
):
def request_apply_saved_attention_maps(self, cross_attention_type: CrossAttentionType):
if cross_attention_type == CrossAttentionType.SELF:
self.self_cross_attention_action = Context.Action.APPLY
else:
@ -139,9 +136,7 @@ class Context:
saved_attention_dict = self.saved_cross_attention_maps[identifier]
if requested_dim is None:
if saved_attention_dict["dim"] is not None:
raise RuntimeError(
f"dim mismatch: expected dim=None, have {saved_attention_dict['dim']}"
)
raise RuntimeError(f"dim mismatch: expected dim=None, have {saved_attention_dict['dim']}")
return saved_attention_dict["slices"][0]
if saved_attention_dict["dim"] == requested_dim:
@ -154,21 +149,13 @@ class Context:
if saved_attention_dict["dim"] is None:
whole_saved_attention = saved_attention_dict["slices"][0]
if requested_dim == 0:
return whole_saved_attention[
requested_offset : requested_offset + slice_size
]
return whole_saved_attention[requested_offset : requested_offset + slice_size]
elif requested_dim == 1:
return whole_saved_attention[
:, requested_offset : requested_offset + slice_size
]
return whole_saved_attention[:, requested_offset : requested_offset + slice_size]
raise RuntimeError(
f"Cannot convert dim {saved_attention_dict['dim']} to requested dim {requested_dim}"
)
raise RuntimeError(f"Cannot convert dim {saved_attention_dict['dim']} to requested dim {requested_dim}")
def get_slicing_strategy(
self, identifier: str
) -> tuple[Optional[int], Optional[int]]:
def get_slicing_strategy(self, identifier: str) -> tuple[Optional[int], Optional[int]]:
saved_attention = self.saved_cross_attention_maps.get(identifier, None)
if saved_attention is None:
return None, None
@ -201,9 +188,7 @@ class InvokeAICrossAttentionMixin:
def set_attention_slice_wrangler(
self,
wrangler: Optional[
Callable[[nn.Module, torch.Tensor, int, int, int], torch.Tensor]
],
wrangler: Optional[Callable[[nn.Module, torch.Tensor, int, int, int], torch.Tensor]],
):
"""
Set custom attention calculator to be called when attention is calculated
@ -219,14 +204,10 @@ class InvokeAICrossAttentionMixin:
"""
self.attention_slice_wrangler = wrangler
def set_slicing_strategy_getter(
self, getter: Optional[Callable[[nn.Module], tuple[int, int]]]
):
def set_slicing_strategy_getter(self, getter: Optional[Callable[[nn.Module], tuple[int, int]]]):
self.slicing_strategy_getter = getter
def set_attention_slice_calculated_callback(
self, callback: Optional[Callable[[torch.Tensor], None]]
):
def set_attention_slice_calculated_callback(self, callback: Optional[Callable[[torch.Tensor], None]]):
self.attention_slice_calculated_callback = callback
def einsum_lowest_level(self, query, key, value, dim, offset, slice_size):
@ -247,45 +228,31 @@ class InvokeAICrossAttentionMixin:
)
# calculate attention slice by taking the best scores for each latent pixel
default_attention_slice = attention_scores.softmax(
dim=-1, dtype=attention_scores.dtype
)
default_attention_slice = attention_scores.softmax(dim=-1, dtype=attention_scores.dtype)
attention_slice_wrangler = self.attention_slice_wrangler
if attention_slice_wrangler is not None:
attention_slice = attention_slice_wrangler(
self, default_attention_slice, dim, offset, slice_size
)
attention_slice = attention_slice_wrangler(self, default_attention_slice, dim, offset, slice_size)
else:
attention_slice = default_attention_slice
if self.attention_slice_calculated_callback is not None:
self.attention_slice_calculated_callback(
attention_slice, dim, offset, slice_size
)
self.attention_slice_calculated_callback(attention_slice, dim, offset, slice_size)
hidden_states = torch.bmm(attention_slice, value)
return hidden_states
def einsum_op_slice_dim0(self, q, k, v, slice_size):
r = torch.zeros(
q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype
)
r = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
for i in range(0, q.shape[0], slice_size):
end = i + slice_size
r[i:end] = self.einsum_lowest_level(
q[i:end], k[i:end], v[i:end], dim=0, offset=i, slice_size=slice_size
)
r[i:end] = self.einsum_lowest_level(q[i:end], k[i:end], v[i:end], dim=0, offset=i, slice_size=slice_size)
return r
def einsum_op_slice_dim1(self, q, k, v, slice_size):
r = torch.zeros(
q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype
)
r = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
for i in range(0, q.shape[1], slice_size):
end = i + slice_size
r[:, i:end] = self.einsum_lowest_level(
q[:, i:end], k, v, dim=1, offset=i, slice_size=slice_size
)
r[:, i:end] = self.einsum_lowest_level(q[:, i:end], k, v, dim=1, offset=i, slice_size=slice_size)
return r
def einsum_op_mps_v1(self, q, k, v):
@ -353,6 +320,7 @@ def restore_default_cross_attention(
else:
remove_attention_function(model)
def setup_cross_attention_control_attention_processors(unet: UNet2DConditionModel, context: Context):
"""
Inject attention parameters and functions into the passed in model to enable cross attention editing.
@ -372,7 +340,7 @@ def setup_cross_attention_control_attention_processors(unet: UNet2DConditionMode
indices = torch.arange(max_length, dtype=torch.long)
for name, a0, a1, b0, b1 in context.arguments.edit_opcodes:
if b0 < max_length:
if name == "equal":# or (name == "replace" and a1 - a0 == b1 - b0):
if name == "equal": # or (name == "replace" and a1 - a0 == b1 - b0):
# these tokens have not been edited
indices[b0:b1] = indices_target[a0:a1]
mask[b0:b1] = 1
@ -386,16 +354,14 @@ def setup_cross_attention_control_attention_processors(unet: UNet2DConditionMode
else:
# try to re-use an existing slice size
default_slice_size = 4
slice_size = next((p.slice_size for p in old_attn_processors.values() if type(p) is SlicedAttnProcessor), default_slice_size)
slice_size = next(
(p.slice_size for p in old_attn_processors.values() if type(p) is SlicedAttnProcessor), default_slice_size
)
unet.set_attn_processor(SlicedSwapCrossAttnProcesser(slice_size=slice_size))
def get_cross_attention_modules(
model, which: CrossAttentionType
) -> list[tuple[str, InvokeAICrossAttentionMixin]]:
cross_attention_class: type = (
InvokeAIDiffusersCrossAttention
)
def get_cross_attention_modules(model, which: CrossAttentionType) -> list[tuple[str, InvokeAICrossAttentionMixin]]:
cross_attention_class: type = InvokeAIDiffusersCrossAttention
which_attn = "attn1" if which is CrossAttentionType.SELF else "attn2"
attention_module_tuples = [
(name, module)
@ -420,9 +386,7 @@ def get_cross_attention_modules(
def inject_attention_function(unet, context: Context):
# ORIGINAL SOURCE CODE: https://github.com/huggingface/diffusers/blob/91ddd2a25b848df0fa1262d4f1cd98c7ccb87750/src/diffusers/models/attention.py#L276
def attention_slice_wrangler(
module, suggested_attention_slice: torch.Tensor, dim, offset, slice_size
):
def attention_slice_wrangler(module, suggested_attention_slice: torch.Tensor, dim, offset, slice_size):
# memory_usage = suggested_attention_slice.element_size() * suggested_attention_slice.nelement()
attention_slice = suggested_attention_slice
@ -430,9 +394,7 @@ def inject_attention_function(unet, context: Context):
if context.get_should_save_maps(module.identifier):
# print(module.identifier, "saving suggested_attention_slice of shape",
# suggested_attention_slice.shape, "dim", dim, "offset", offset)
slice_to_save = (
attention_slice.to("cpu") if dim is not None else attention_slice
)
slice_to_save = attention_slice.to("cpu") if dim is not None else attention_slice
context.save_slice(
module.identifier,
slice_to_save,
@ -442,31 +404,20 @@ def inject_attention_function(unet, context: Context):
)
elif context.get_should_apply_saved_maps(module.identifier):
# print(module.identifier, "applying saved attention slice for dim", dim, "offset", offset)
saved_attention_slice = context.get_slice(
module.identifier, dim, offset, slice_size
)
saved_attention_slice = context.get_slice(module.identifier, dim, offset, slice_size)
# slice may have been offloaded to CPU
saved_attention_slice = saved_attention_slice.to(
suggested_attention_slice.device
)
saved_attention_slice = saved_attention_slice.to(suggested_attention_slice.device)
if context.is_tokens_cross_attention(module.identifier):
index_map = context.cross_attention_index_map
remapped_saved_attention_slice = torch.index_select(
saved_attention_slice, -1, index_map
)
remapped_saved_attention_slice = torch.index_select(saved_attention_slice, -1, index_map)
this_attention_slice = suggested_attention_slice
mask = context.cross_attention_mask.to(
torch_dtype(suggested_attention_slice.device)
)
mask = context.cross_attention_mask.to(torch_dtype(suggested_attention_slice.device))
saved_mask = mask
this_mask = 1 - mask
attention_slice = (
remapped_saved_attention_slice * saved_mask
+ this_attention_slice * this_mask
)
attention_slice = remapped_saved_attention_slice * saved_mask + this_attention_slice * this_mask
else:
# just use everything
attention_slice = saved_attention_slice
@ -480,14 +431,10 @@ def inject_attention_function(unet, context: Context):
module.identifier = identifier
try:
module.set_attention_slice_wrangler(attention_slice_wrangler)
module.set_slicing_strategy_getter(
lambda module: context.get_slicing_strategy(identifier)
)
module.set_slicing_strategy_getter(lambda module: context.get_slicing_strategy(identifier))
except AttributeError as e:
if is_attribute_error_about(e, "set_attention_slice_wrangler"):
print(
f"TODO: implement set_attention_slice_wrangler for {type(module)}"
) # TODO
print(f"TODO: implement set_attention_slice_wrangler for {type(module)}") # TODO
else:
raise
@ -503,9 +450,7 @@ def remove_attention_function(unet):
module.set_slicing_strategy_getter(None)
except AttributeError as e:
if is_attribute_error_about(e, "set_attention_slice_wrangler"):
print(
f"TODO: implement set_attention_slice_wrangler for {type(module)}"
)
print(f"TODO: implement set_attention_slice_wrangler for {type(module)}")
else:
raise
@ -530,9 +475,7 @@ def get_mem_free_total(device):
return mem_free_total
class InvokeAIDiffusersCrossAttention(
diffusers.models.attention.Attention, InvokeAICrossAttentionMixin
):
class InvokeAIDiffusersCrossAttention(diffusers.models.attention.Attention, InvokeAICrossAttentionMixin):
def __init__(self, **kwargs):
super().__init__(**kwargs)
InvokeAICrossAttentionMixin.__init__(self)
@ -641,11 +584,7 @@ class SlicedSwapCrossAttnProcesser(SlicedAttnProcessor):
# kwargs
swap_cross_attn_context: SwapCrossAttnContext = None,
):
attention_type = (
CrossAttentionType.SELF
if encoder_hidden_states is None
else CrossAttentionType.TOKENS
)
attention_type = CrossAttentionType.SELF if encoder_hidden_states is None else CrossAttentionType.TOKENS
# if cross-attention control is not in play, just call through to the base implementation.
if (
@ -654,9 +593,7 @@ class SlicedSwapCrossAttnProcesser(SlicedAttnProcessor):
or not swap_cross_attn_context.wants_cross_attention_control(attention_type)
):
# print(f"SwapCrossAttnContext for {attention_type} not active - passing request to superclass")
return super().__call__(
attn, hidden_states, encoder_hidden_states, attention_mask
)
return super().__call__(attn, hidden_states, encoder_hidden_states, attention_mask)
# else:
# print(f"SwapCrossAttnContext for {attention_type} active")
@ -699,18 +636,10 @@ class SlicedSwapCrossAttnProcesser(SlicedAttnProcessor):
query_slice = query[start_idx:end_idx]
original_key_slice = original_text_key[start_idx:end_idx]
modified_key_slice = modified_text_key[start_idx:end_idx]
attn_mask_slice = (
attention_mask[start_idx:end_idx]
if attention_mask is not None
else None
)
attn_mask_slice = attention_mask[start_idx:end_idx] if attention_mask is not None else None
original_attn_slice = attn.get_attention_scores(
query_slice, original_key_slice, attn_mask_slice
)
modified_attn_slice = attn.get_attention_scores(
query_slice, modified_key_slice, attn_mask_slice
)
original_attn_slice = attn.get_attention_scores(query_slice, original_key_slice, attn_mask_slice)
modified_attn_slice = attn.get_attention_scores(query_slice, modified_key_slice, attn_mask_slice)
# because the prompt modifications may result in token sequences shifted forwards or backwards,
# the original attention probabilities must be remapped to account for token index changes in the
@ -722,9 +651,7 @@ class SlicedSwapCrossAttnProcesser(SlicedAttnProcessor):
# only some tokens taken from the original attention probabilities. this is controlled by the mask.
mask = swap_cross_attn_context.mask
inverse_mask = 1 - mask
attn_slice = (
remapped_original_attn_slice * mask + modified_attn_slice * inverse_mask
)
attn_slice = remapped_original_attn_slice * mask + modified_attn_slice * inverse_mask
del remapped_original_attn_slice, modified_attn_slice
@ -744,6 +671,4 @@ class SlicedSwapCrossAttnProcesser(SlicedAttnProcessor):
class SwapCrossAttnProcessor(SlicedSwapCrossAttnProcesser):
def __init__(self):
super(SwapCrossAttnProcessor, self).__init__(
slice_size=int(1e9)
) # massive slice size = don't slice
super(SwapCrossAttnProcessor, self).__init__(slice_size=int(1e9)) # massive slice size = don't slice

12
invokeai/backend/stable_diffusion/diffusion/cross_attention_map_saving.py
View File

@ -59,9 +59,7 @@ class AttentionMapSaver:
for key, maps in self.collated_maps.items():
# maps has shape [(H*W), N] for N tokens
# but we want [N, H, W]
this_scale_factor = math.sqrt(
maps.shape[0] / (latents_width * latents_height)
)
this_scale_factor = math.sqrt(maps.shape[0] / (latents_width * latents_height))
this_maps_height = int(float(latents_height) * this_scale_factor)
this_maps_width = int(float(latents_width) * this_scale_factor)
# and we need to do some dimension juggling
@ -72,9 +70,7 @@ class AttentionMapSaver:
# scale to output size if necessary
if this_scale_factor != 1:
maps = tv_resize(
maps, [latents_height, latents_width], InterpolationMode.BICUBIC
)
maps = tv_resize(maps, [latents_height, latents_width], InterpolationMode.BICUBIC)
# normalize
maps_min = torch.min(maps)
@ -83,9 +79,7 @@ class AttentionMapSaver:
maps_normalized = (maps - maps_min) / maps_range
# expand to (-0.1, 1.1) and clamp
maps_normalized_expanded = maps_normalized * 1.1 - 0.05
maps_normalized_expanded_clamped = torch.clamp(
maps_normalized_expanded, 0, 1
)
maps_normalized_expanded_clamped = torch.clamp(maps_normalized_expanded, 0, 1)
# merge together, producing a vertical stack
maps_stacked = torch.reshape(

206
invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py
View File

@ -31,6 +31,7 @@ ModelForwardCallback: TypeAlias = Union[
Callable[[torch.Tensor, torch.Tensor, torch.Tensor], torch.Tensor],
]
@dataclass(frozen=True)
class PostprocessingSettings:
threshold: float
@ -81,14 +82,12 @@ class InvokeAIDiffuserComponent:
@contextmanager
def custom_attention_context(
cls,
unet: UNet2DConditionModel, # note: also may futz with the text encoder depending on requested LoRAs
unet: UNet2DConditionModel, # note: also may futz with the text encoder depending on requested LoRAs
extra_conditioning_info: Optional[ExtraConditioningInfo],
step_count: int
step_count: int,
):
old_attn_processors = None
if extra_conditioning_info and (
extra_conditioning_info.wants_cross_attention_control
):
if extra_conditioning_info and (extra_conditioning_info.wants_cross_attention_control):
old_attn_processors = unet.attn_processors
# Load lora conditions into the model
if extra_conditioning_info.wants_cross_attention_control:
@ -116,27 +115,15 @@ class InvokeAIDiffuserComponent:
return
saver.add_attention_maps(slice, key)
tokens_cross_attention_modules = get_cross_attention_modules(
self.model, CrossAttentionType.TOKENS
)
tokens_cross_attention_modules = get_cross_attention_modules(self.model, CrossAttentionType.TOKENS)
for identifier, module in tokens_cross_attention_modules:
key = (
"down"
if identifier.startswith("down")
else "up"
if identifier.startswith("up")
else "mid"
)
key = "down" if identifier.startswith("down") else "up" if identifier.startswith("up") else "mid"
module.set_attention_slice_calculated_callback(
lambda slice, dim, offset, slice_size, key=key: callback(
slice, dim, offset, slice_size, key
)
lambda slice, dim, offset, slice_size, key=key: callback(slice, dim, offset, slice_size, key)
)
def remove_attention_map_saving(self):
tokens_cross_attention_modules = get_cross_attention_modules(
self.model, CrossAttentionType.TOKENS
)
tokens_cross_attention_modules = get_cross_attention_modules(self.model, CrossAttentionType.TOKENS)
for _, module in tokens_cross_attention_modules:
module.set_attention_slice_calculated_callback(None)
@ -171,10 +158,8 @@ class InvokeAIDiffuserComponent:
context: Context = self.cross_attention_control_context
if self.cross_attention_control_context is not None:
percent_through = step_index / total_step_count
cross_attention_control_types_to_do = (
context.get_active_cross_attention_control_types_for_step(
percent_through
)
cross_attention_control_types_to_do = context.get_active_cross_attention_control_types_for_step(
percent_through
)
wants_cross_attention_control = len(cross_attention_control_types_to_do) > 0
@ -182,7 +167,11 @@ class InvokeAIDiffuserComponent:
if wants_hybrid_conditioning:
unconditioned_next_x, conditioned_next_x = self._apply_hybrid_conditioning(
x, sigma, unconditioning, conditioning, **kwargs,
x,
sigma,
unconditioning,
conditioning,
**kwargs,
)
elif wants_cross_attention_control:
(
@ -201,7 +190,11 @@ class InvokeAIDiffuserComponent:
unconditioned_next_x,
conditioned_next_x,
) = self._apply_standard_conditioning_sequentially(
x, sigma, unconditioning, conditioning, **kwargs,
x,
sigma,
unconditioning,
conditioning,
**kwargs,
)
else:
@ -209,12 +202,18 @@ class InvokeAIDiffuserComponent:
unconditioned_next_x,
conditioned_next_x,
) = self._apply_standard_conditioning(
x, sigma, unconditioning, conditioning, **kwargs,
x,
sigma,
unconditioning,
conditioning,
**kwargs,
)
combined_next_x = self._combine(
# unconditioned_next_x, conditioned_next_x, unconditional_guidance_scale
unconditioned_next_x, conditioned_next_x, guidance_scale
unconditioned_next_x,
conditioned_next_x,
guidance_scale,
)
return combined_next_x
@ -229,37 +228,47 @@ class InvokeAIDiffuserComponent:
) -> torch.Tensor:
if postprocessing_settings is not None:
percent_through = step_index / total_step_count
latents = self.apply_threshold(
postprocessing_settings, latents, percent_through
)
latents = self.apply_symmetry(
postprocessing_settings, latents, percent_through
)
latents = self.apply_threshold(postprocessing_settings, latents, percent_through)
latents = self.apply_symmetry(postprocessing_settings, latents, percent_through)
return latents
def _concat_conditionings_for_batch(self, unconditioning, conditioning):
def _pad_conditioning(cond, target_len, encoder_attention_mask):
conditioning_attention_mask = torch.ones((cond.shape[0], cond.shape[1]), device=cond.device, dtype=cond.dtype)
conditioning_attention_mask = torch.ones(
(cond.shape[0], cond.shape[1]), device=cond.device, dtype=cond.dtype
)
if cond.shape[1] < max_len:
conditioning_attention_mask = torch.cat([
conditioning_attention_mask,
torch.zeros((cond.shape[0], max_len - cond.shape[1]), device=cond.device, dtype=cond.dtype),
], dim=1)
conditioning_attention_mask = torch.cat(
[
conditioning_attention_mask,
torch.zeros((cond.shape[0], max_len - cond.shape[1]), device=cond.device, dtype=cond.dtype),
],
dim=1,
)
cond = torch.cat([
cond,
torch.zeros((cond.shape[0], max_len - cond.shape[1], cond.shape[2]), device=cond.device, dtype=cond.dtype),
], dim=1)
cond = torch.cat(
[
cond,
torch.zeros(
(cond.shape[0], max_len - cond.shape[1], cond.shape[2]),
device=cond.device,
dtype=cond.dtype,
),
],
dim=1,
)
if encoder_attention_mask is None:
encoder_attention_mask = conditioning_attention_mask
else:
encoder_attention_mask = torch.cat([
encoder_attention_mask,
conditioning_attention_mask,
])
encoder_attention_mask = torch.cat(
[
encoder_attention_mask,
conditioning_attention_mask,
]
)
return cond, encoder_attention_mask
encoder_attention_mask = None
@ -277,11 +286,11 @@ class InvokeAIDiffuserComponent:
x_twice = torch.cat([x] * 2)
sigma_twice = torch.cat([sigma] * 2)
both_conditionings, encoder_attention_mask = self._concat_conditionings_for_batch(
unconditioning, conditioning
)
both_conditionings, encoder_attention_mask = self._concat_conditionings_for_batch(unconditioning, conditioning)
both_results = self.model_forward_callback(
x_twice, sigma_twice, both_conditionings,
x_twice,
sigma_twice,
both_conditionings,
encoder_attention_mask=encoder_attention_mask,
**kwargs,
)
@ -312,13 +321,17 @@ class InvokeAIDiffuserComponent:
uncond_mid_block, cond_mid_block = mid_block_additional_residual.chunk(2)
unconditioned_next_x = self.model_forward_callback(
x, sigma, unconditioning,
x,
sigma,
unconditioning,
down_block_additional_residuals=uncond_down_block,
mid_block_additional_residual=uncond_mid_block,
**kwargs,
)
conditioned_next_x = self.model_forward_callback(
x, sigma, conditioning,
x,
sigma,
conditioning,
down_block_additional_residuals=cond_down_block,
mid_block_additional_residual=cond_mid_block,
**kwargs,
@ -335,13 +348,15 @@ class InvokeAIDiffuserComponent:
for k in conditioning:
if isinstance(conditioning[k], list):
both_conditionings[k] = [
torch.cat([unconditioning[k][i], conditioning[k][i]])
for i in range(len(conditioning[k]))
torch.cat([unconditioning[k][i], conditioning[k][i]]) for i in range(len(conditioning[k]))
]
else:
both_conditionings[k] = torch.cat([unconditioning[k], conditioning[k]])
unconditioned_next_x, conditioned_next_x = self.model_forward_callback(
x_twice, sigma_twice, both_conditionings, **kwargs,
x_twice,
sigma_twice,
both_conditionings,
**kwargs,
).chunk(2)
return unconditioned_next_x, conditioned_next_x
@ -388,9 +403,7 @@ class InvokeAIDiffuserComponent:
)
# do requested cross attention types for conditioning (positive prompt)
cross_attn_processor_context.cross_attention_types_to_do = (
cross_attention_control_types_to_do
)
cross_attn_processor_context.cross_attention_types_to_do = cross_attention_control_types_to_do
conditioned_next_x = self.model_forward_callback(
x,
sigma,
@ -414,19 +427,14 @@ class InvokeAIDiffuserComponent:
latents: torch.Tensor,
percent_through: float,
) -> torch.Tensor:
if (
postprocessing_settings.threshold is None
or postprocessing_settings.threshold == 0.0
):
if postprocessing_settings.threshold is None or postprocessing_settings.threshold == 0.0:
return latents
threshold = postprocessing_settings.threshold
warmup = postprocessing_settings.warmup
if percent_through < warmup:
current_threshold = threshold + threshold * 5 * (
1 - (percent_through / warmup)
)
current_threshold = threshold + threshold * 5 * (1 - (percent_through / warmup))
else:
current_threshold = threshold
@ -440,18 +448,10 @@ class InvokeAIDiffuserComponent:
if self.debug_thresholding:
std, mean = [i.item() for i in torch.std_mean(latents)]
outside = torch.count_nonzero(
(latents < -current_threshold) | (latents > current_threshold)
)
logger.info(
f"Threshold: %={percent_through} threshold={current_threshold:.3f} (of {threshold:.3f})"
)
logger.debug(
f"min, mean, max = {minval:.3f}, {mean:.3f}, {maxval:.3f}\tstd={std}"
)
logger.debug(
f"{outside / latents.numel() * 100:.2f}% values outside threshold"
)
outside = torch.count_nonzero((latents < -current_threshold) | (latents > current_threshold))
logger.info(f"Threshold: %={percent_through} threshold={current_threshold:.3f} (of {threshold:.3f})")
logger.debug(f"min, mean, max = {minval:.3f}, {mean:.3f}, {maxval:.3f}\tstd={std}")
logger.debug(f"{outside / latents.numel() * 100:.2f}% values outside threshold")
if maxval < current_threshold and minval > -current_threshold:
return latents
@ -464,25 +464,17 @@ class InvokeAIDiffuserComponent:
latents = torch.clone(latents)
maxval = np.clip(maxval * scale, 1, current_threshold)
num_altered += torch.count_nonzero(latents > maxval)
latents[latents > maxval] = (
torch.rand_like(latents[latents > maxval]) * maxval
)
latents[latents > maxval] = torch.rand_like(latents[latents > maxval]) * maxval
if minval < -current_threshold:
latents = torch.clone(latents)
minval = np.clip(minval * scale, -current_threshold, -1)
num_altered += torch.count_nonzero(latents < minval)
latents[latents < minval] = (
torch.rand_like(latents[latents < minval]) * minval
)
latents[latents < minval] = torch.rand_like(latents[latents < minval]) * minval
if self.debug_thresholding:
logger.debug(
f"min, , max = {minval:.3f}, , {maxval:.3f}\t(scaled by {scale})"
)
logger.debug(
f"{num_altered / latents.numel() * 100:.2f}% values altered"
)
logger.debug(f"min, , max = {minval:.3f}, , {maxval:.3f}\t(scaled by {scale})")
logger.debug(f"{num_altered / latents.numel() * 100:.2f}% values altered")
return latents
@ -501,15 +493,11 @@ class InvokeAIDiffuserComponent:
# Check for out of bounds
h_symmetry_time_pct = postprocessing_settings.h_symmetry_time_pct
if h_symmetry_time_pct is not None and (
h_symmetry_time_pct <= 0.0 or h_symmetry_time_pct > 1.0
):
if h_symmetry_time_pct is not None and (h_symmetry_time_pct <= 0.0 or h_symmetry_time_pct > 1.0):
h_symmetry_time_pct = None
v_symmetry_time_pct = postprocessing_settings.v_symmetry_time_pct
if v_symmetry_time_pct is not None and (
v_symmetry_time_pct <= 0.0 or v_symmetry_time_pct > 1.0
):
if v_symmetry_time_pct is not None and (v_symmetry_time_pct <= 0.0 or v_symmetry_time_pct > 1.0):
v_symmetry_time_pct = None
dev = latents.device.type
@ -554,9 +542,7 @@ class InvokeAIDiffuserComponent:
def estimate_percent_through(self, step_index, sigma):
if step_index is not None and self.cross_attention_control_context is not None:
# percent_through will never reach 1.0 (but this is intended)
return float(step_index) / float(
self.cross_attention_control_context.step_count
)
return float(step_index) / float(self.cross_attention_control_context.step_count)
# find the best possible index of the current sigma in the sigma sequence
smaller_sigmas = torch.nonzero(self.model.sigmas <= sigma)
sigma_index = smaller_sigmas[-1].item() if smaller_sigmas.shape[0] > 0 else 0
@ -567,19 +553,13 @@ class InvokeAIDiffuserComponent:
# todo: make this work
@classmethod
def apply_conjunction(
cls, x, t, forward_func, uc, c_or_weighted_c_list, global_guidance_scale
):
def apply_conjunction(cls, x, t, forward_func, uc, c_or_weighted_c_list, global_guidance_scale):
x_in = torch.cat([x] * 2)
t_in = torch.cat([t] * 2) # aka sigmas
deltas = None
uncond_latents = None
weighted_cond_list = (
c_or_weighted_c_list
if type(c_or_weighted_c_list) is list
else [(c_or_weighted_c_list, 1)]
)
weighted_cond_list = c_or_weighted_c_list if type(c_or_weighted_c_list) is list else [(c_or_weighted_c_list, 1)]
# below is fugly omg
conditionings = [uc] + [c for c, weight in weighted_cond_list]
@ -608,15 +588,11 @@ class InvokeAIDiffuserComponent:
deltas = torch.cat((deltas, latents_b - uncond_latents))
# merge the weighted deltas together into a single merged delta
per_delta_weights = torch.tensor(
weights[1:], dtype=deltas.dtype, device=deltas.device
)
per_delta_weights = torch.tensor(weights[1:], dtype=deltas.dtype, device=deltas.device)
normalize = False
if normalize:
per_delta_weights /= torch.sum(per_delta_weights)
reshaped_weights = per_delta_weights.reshape(
per_delta_weights.shape + (1, 1, 1)
)
reshaped_weights = per_delta_weights.reshape(per_delta_weights.shape + (1, 1, 1))
deltas_merged = torch.sum(deltas * reshaped_weights, dim=0, keepdim=True)
# old_return_value = super().forward(x, sigma, uncond, cond, cond_scale)

52
invokeai/backend/stable_diffusion/image_degradation/bsrgan.py
View File

@ -261,9 +261,7 @@ def srmd_degradation(x, k, sf=3):
year={2018}
}
"""
x = ndimage.filters.convolve(
x, np.expand_dims(k, axis=2), mode="wrap"
) # 'nearest' | 'mirror'
x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode="wrap") # 'nearest' | 'mirror'
x = bicubic_degradation(x, sf=sf)
return x
@ -389,21 +387,15 @@ def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
noise_level = random.randint(noise_level1, noise_level2)
rnum = np.random.rand()
if rnum > 0.6: # add color Gaussian noise
img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(
np.float32
)
img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
elif rnum < 0.4: # add grayscale Gaussian noise
img = img + np.random.normal(
0, noise_level / 255.0, (*img.shape[:2], 1)
).astype(np.float32)
img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
else: # add noise
L = noise_level2 / 255.0
D = np.diag(np.random.rand(3))
U = orth(np.random.rand(3, 3))
conv = np.dot(np.dot(np.transpose(U), D), U)
img = img + np.random.multivariate_normal(
[0, 0, 0], np.abs(L**2 * conv), img.shape[:2]
).astype(np.float32)
img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L**2 * conv), img.shape[:2]).astype(np.float32)
img = np.clip(img, 0.0, 1.0)
return img
@ -413,21 +405,15 @@ def add_speckle_noise(img, noise_level1=2, noise_level2=25):
img = np.clip(img, 0.0, 1.0)
rnum = random.random()
if rnum > 0.6:
img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(
np.float32
)
img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
elif rnum < 0.4:
img += img * np.random.normal(
0, noise_level / 255.0, (*img.shape[:2], 1)
).astype(np.float32)
img += img * np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
else:
L = noise_level2 / 255.0
D = np.diag(np.random.rand(3))
U = orth(np.random.rand(3, 3))
conv = np.dot(np.dot(np.transpose(U), D), U)
img += img * np.random.multivariate_normal(
[0, 0, 0], np.abs(L**2 * conv), img.shape[:2]
).astype(np.float32)
img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L**2 * conv), img.shape[:2]).astype(np.float32)
img = np.clip(img, 0.0, 1.0)
return img
@ -440,9 +426,7 @@ def add_Poisson_noise(img):
else:
img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114])
img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255.0
noise_gray = (
np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray
)
noise_gray = np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray
img += noise_gray[:, :, np.newaxis]
img = np.clip(img, 0.0, 1.0)
return img
@ -451,9 +435,7 @@ def add_Poisson_noise(img):
def add_JPEG_noise(img):
quality_factor = random.randint(30, 95)
img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR)
result, encimg = cv2.imencode(
".jpg", img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor]
)
result, encimg = cv2.imencode(".jpg", img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor])
img = cv2.imdecode(encimg, 1)
img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB)
return img
@ -540,9 +522,7 @@ def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
k = fspecial("gaussian", 25, random.uniform(0.1, 0.6 * sf))
k_shifted = shift_pixel(k, sf)
k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel
img = ndimage.filters.convolve(
img, np.expand_dims(k_shifted, axis=2), mode="mirror"
)
img = ndimage.filters.convolve(img, np.expand_dims(k_shifted, axis=2), mode="mirror")
img = img[0::sf, 0::sf, ...] # nearest downsampling
img = np.clip(img, 0.0, 1.0)
@ -646,9 +626,7 @@ def degradation_bsrgan_variant(image, sf=4, isp_model=None):
k = fspecial("gaussian", 25, random.uniform(0.1, 0.6 * sf))
k_shifted = shift_pixel(k, sf)
k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel
image = ndimage.filters.convolve(
image, np.expand_dims(k_shifted, axis=2), mode="mirror"
)
image = ndimage.filters.convolve(image, np.expand_dims(k_shifted, axis=2), mode="mirror")
image = image[0::sf, 0::sf, ...] # nearest downsampling
image = np.clip(image, 0.0, 1.0)
@ -796,9 +774,7 @@ if __name__ == "__main__":
print(i)
img_lq = deg_fn(img)
print(img_lq)
img_lq_bicubic = albumentations.SmallestMaxSize(
max_size=h, interpolation=cv2.INTER_CUBIC
)(image=img)["image"]
img_lq_bicubic = albumentations.SmallestMaxSize(max_size=h, interpolation=cv2.INTER_CUBIC)(image=img)["image"]
print(img_lq.shape)
print("bicubic", img_lq_bicubic.shape)
print(img_hq.shape)
@ -812,7 +788,5 @@ if __name__ == "__main__":
(int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
interpolation=0,
)
img_concat = np.concatenate(
[lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1
)
img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1)
util.imsave(img_concat, str(i) + ".png")

54
invokeai/backend/stable_diffusion/image_degradation/bsrgan_light.py
View File

@ -261,9 +261,7 @@ def srmd_degradation(x, k, sf=3):
year={2018}
}
"""
x = ndimage.filters.convolve(
x, np.expand_dims(k, axis=2), mode="wrap"
) # 'nearest' | 'mirror'
x = ndimage.filters.convolve(x, np.expand_dims(k, axis=2), mode="wrap") # 'nearest' | 'mirror'
x = bicubic_degradation(x, sf=sf)
return x
@ -393,21 +391,15 @@ def add_Gaussian_noise(img, noise_level1=2, noise_level2=25):
noise_level = random.randint(noise_level1, noise_level2)
rnum = np.random.rand()
if rnum > 0.6: # add color Gaussian noise
img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(
np.float32
)
img = img + np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
elif rnum < 0.4: # add grayscale Gaussian noise
img = img + np.random.normal(
0, noise_level / 255.0, (*img.shape[:2], 1)
).astype(np.float32)
img = img + np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
else: # add noise
L = noise_level2 / 255.0
D = np.diag(np.random.rand(3))
U = orth(np.random.rand(3, 3))
conv = np.dot(np.dot(np.transpose(U), D), U)
img = img + np.random.multivariate_normal(
[0, 0, 0], np.abs(L**2 * conv), img.shape[:2]
).astype(np.float32)
img = img + np.random.multivariate_normal([0, 0, 0], np.abs(L**2 * conv), img.shape[:2]).astype(np.float32)
img = np.clip(img, 0.0, 1.0)
return img
@ -417,21 +409,15 @@ def add_speckle_noise(img, noise_level1=2, noise_level2=25):
img = np.clip(img, 0.0, 1.0)
rnum = random.random()
if rnum > 0.6:
img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(
np.float32
)
img += img * np.random.normal(0, noise_level / 255.0, img.shape).astype(np.float32)
elif rnum < 0.4:
img += img * np.random.normal(
0, noise_level / 255.0, (*img.shape[:2], 1)
).astype(np.float32)
img += img * np.random.normal(0, noise_level / 255.0, (*img.shape[:2], 1)).astype(np.float32)
else:
L = noise_level2 / 255.0
D = np.diag(np.random.rand(3))
U = orth(np.random.rand(3, 3))
conv = np.dot(np.dot(np.transpose(U), D), U)
img += img * np.random.multivariate_normal(
[0, 0, 0], np.abs(L**2 * conv), img.shape[:2]
).astype(np.float32)
img += img * np.random.multivariate_normal([0, 0, 0], np.abs(L**2 * conv), img.shape[:2]).astype(np.float32)
img = np.clip(img, 0.0, 1.0)
return img
@ -444,9 +430,7 @@ def add_Poisson_noise(img):
else:
img_gray = np.dot(img[..., :3], [0.299, 0.587, 0.114])
img_gray = np.clip((img_gray * 255.0).round(), 0, 255) / 255.0
noise_gray = (
np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray
)
noise_gray = np.random.poisson(img_gray * vals).astype(np.float32) / vals - img_gray
img += noise_gray[:, :, np.newaxis]
img = np.clip(img, 0.0, 1.0)
return img
@ -455,9 +439,7 @@ def add_Poisson_noise(img):
def add_JPEG_noise(img):
quality_factor = random.randint(80, 95)
img = cv2.cvtColor(util.single2uint(img), cv2.COLOR_RGB2BGR)
result, encimg = cv2.imencode(
".jpg", img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor]
)
result, encimg = cv2.imencode(".jpg", img, [int(cv2.IMWRITE_JPEG_QUALITY), quality_factor])
img = cv2.imdecode(encimg, 1)
img = cv2.cvtColor(util.uint2single(img), cv2.COLOR_BGR2RGB)
return img
@ -544,9 +526,7 @@ def degradation_bsrgan(img, sf=4, lq_patchsize=72, isp_model=None):
k = fspecial("gaussian", 25, random.uniform(0.1, 0.6 * sf))
k_shifted = shift_pixel(k, sf)
k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel
img = ndimage.filters.convolve(
img, np.expand_dims(k_shifted, axis=2), mode="mirror"
)
img = ndimage.filters.convolve(img, np.expand_dims(k_shifted, axis=2), mode="mirror")
img = img[0::sf, 0::sf, ...] # nearest downsampling
img = np.clip(img, 0.0, 1.0)
@ -653,9 +633,7 @@ def degradation_bsrgan_variant(image, sf=4, isp_model=None):
k = fspecial("gaussian", 25, random.uniform(0.1, 0.6 * sf))
k_shifted = shift_pixel(k, sf)
k_shifted = k_shifted / k_shifted.sum() # blur with shifted kernel
image = ndimage.filters.convolve(
image, np.expand_dims(k_shifted, axis=2), mode="mirror"
)
image = ndimage.filters.convolve(image, np.expand_dims(k_shifted, axis=2), mode="mirror")
image = image[0::sf, 0::sf, ...] # nearest downsampling
image = np.clip(image, 0.0, 1.0)
@ -705,9 +683,9 @@ if __name__ == "__main__":
img_lq = deg_fn(img)["image"]
img_hq, img_lq = util.uint2single(img_hq), util.uint2single(img_lq)
print(img_lq)
img_lq_bicubic = albumentations.SmallestMaxSize(
max_size=h, interpolation=cv2.INTER_CUBIC
)(image=img_hq)["image"]
img_lq_bicubic = albumentations.SmallestMaxSize(max_size=h, interpolation=cv2.INTER_CUBIC)(image=img_hq)[
"image"
]
print(img_lq.shape)
print("bicubic", img_lq_bicubic.shape)
print(img_hq.shape)
@ -721,7 +699,5 @@ if __name__ == "__main__":
(int(sf * img_lq.shape[1]), int(sf * img_lq.shape[0])),
interpolation=0,
)
img_concat = np.concatenate(
[lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1
)
img_concat = np.concatenate([lq_bicubic_nearest, lq_nearest, util.single2uint(img_hq)], axis=1)
util.imsave(img_concat, str(i) + ".png")

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