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fix merge conflicts

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This commit is contained in:
Lincoln Stein
2024-03-31 17:04:57 -04:00
parent 9336a076de 1badf0f32f
commit 83356ec74c
132 changed files with 6790 additions and 17235 deletions
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194
invokeai/app/api/routers/model_manager.py
View File

@ -1,16 +1,13 @@
# Copyright (c) 2023 Lincoln D. Stein
"""FastAPI route for model configuration records."""
import contextlib
import io
import pathlib
import shutil
import traceback
from copy import deepcopy
from enum import Enum
from typing import Any, Dict, List, Optional
import huggingface_hub
from fastapi import Body, Path, Query, Response, UploadFile
from fastapi.responses import FileResponse
from fastapi.routing import APIRouter
@ -21,11 +18,11 @@ from typing_extensions import Annotated
from invokeai.app.services.model_install import ModelInstallJob
from invokeai.app.services.model_records import (
DuplicateModelException,
InvalidModelException,
ModelRecordChanges,
UnknownModelException,
)
from invokeai.app.services.model_records.model_records_base import DuplicateModelException, ModelRecordChanges
from invokeai.app.util.suppress_output import SuppressOutput
from invokeai.backend.model_manager.config import (
AnyModelConfig,
BaseModelType,
@ -37,7 +34,7 @@ from invokeai.backend.model_manager.config import (
from invokeai.backend.model_manager.metadata.fetch.huggingface import HuggingFaceMetadataFetch
from invokeai.backend.model_manager.metadata.metadata_base import ModelMetadataWithFiles, UnknownMetadataException
from invokeai.backend.model_manager.search import ModelSearch
from invokeai.backend.model_manager.starter_models import STARTER_MODELS, StarterModel
from invokeai.backend.model_manager.starter_models import STARTER_MODELS, StarterModel, StarterModelWithoutDependencies
from ..dependencies import ApiDependencies
@ -309,8 +306,10 @@ async def update_model_record(
"""Update a model's config."""
logger = ApiDependencies.invoker.services.logger
record_store = ApiDependencies.invoker.services.model_manager.store
installer = ApiDependencies.invoker.services.model_manager.install
try:
model_response: AnyModelConfig = record_store.update_model(key, changes=changes)
record_store.update_model(key, changes=changes)
model_response: AnyModelConfig = installer.sync_model_path(key)
logger.info(f"Updated model: {key}")
except UnknownModelException as e:
raise HTTPException(status_code=404, detail=str(e))
@ -442,41 +441,6 @@ async def delete_model_image(
raise HTTPException(status_code=404, detail=str(e))
# @model_manager_router.post(
# "/i/",
# operation_id="add_model_record",
# responses={
# 201: {
# "description": "The model added successfully",
# "content": {"application/json": {"example": example_model_config}},
# },
# 409: {"description": "There is already a model corresponding to this path or repo_id"},
# 415: {"description": "Unrecognized file/folder format"},
# },
# status_code=201,
# )
# async def add_model_record(
# config: Annotated[
# AnyModelConfig, Body(description="Model config", discriminator="type", example=example_model_input)
# ],
# ) -> AnyModelConfig:
# """Add a model using the configuration information appropriate for its type."""
# logger = ApiDependencies.invoker.services.logger
# record_store = ApiDependencies.invoker.services.model_manager.store
# try:
# record_store.add_model(config)
# except DuplicateModelException as e:
# logger.error(str(e))
# raise HTTPException(status_code=409, detail=str(e))
# except InvalidModelException as e:
# logger.error(str(e))
# raise HTTPException(status_code=415)
# # now fetch it out
# result: AnyModelConfig = record_store.get_model(config.key)
# return result
@model_manager_router.post(
"/install",
operation_id="install_model",
@ -628,25 +592,6 @@ async def prune_model_install_jobs() -> Response:
return Response(status_code=204)
@model_manager_router.patch(
"/sync",
operation_id="sync_models_to_config",
responses={
204: {"description": "Model config record database resynced with files on disk"},
400: {"description": "Bad request"},
},
)
async def sync_models_to_config() -> Response:
"""
Traverse the models and autoimport directories.
Model files without a corresponding
record in the database are added. Orphan records without a models file are deleted.
"""
ApiDependencies.invoker.services.model_manager.install.sync_to_config()
return Response(status_code=204)
@model_manager_router.put(
"/convert/{key}",
operation_id="convert_model",
@ -669,8 +614,8 @@ async def convert_model(
The return value is the model configuration for the converted model.
"""
model_manager = ApiDependencies.invoker.services.model_manager
loader = model_manager.load
logger = ApiDependencies.invoker.services.logger
loader = ApiDependencies.invoker.services.model_manager.load
store = ApiDependencies.invoker.services.model_manager.store
installer = ApiDependencies.invoker.services.model_manager.install
@ -685,7 +630,13 @@ async def convert_model(
raise HTTPException(400, f"The model with key {key} is not a main checkpoint model.")
# loading the model will convert it into a cached diffusers file
model_manager.load.load_model(model_config, submodel_type=SubModelType.Scheduler)
try:
cc_size = loader.convert_cache.max_size
if cc_size == 0: # temporary set the convert cache to a positive number so that cached model is written
loader._convert_cache.max_size = 1.0
loader.load_model(model_config, submodel_type=SubModelType.Scheduler)
finally:
loader._convert_cache.max_size = cc_size
# Get the path of the converted model from the loader
cache_path = loader.convert_cache.cache_path(key)
@ -723,71 +674,6 @@ async def convert_model(
return new_config
# @model_manager_router.put(
# "/merge",
# operation_id="merge",
# responses={
# 200: {
# "description": "Model converted successfully",
# "content": {"application/json": {"example": example_model_config}},
# },
# 400: {"description": "Bad request"},
# 404: {"description": "Model not found"},
# 409: {"description": "There is already a model registered at this location"},
# },
# )
# async def merge(
# keys: List[str] = Body(description="Keys for two to three models to merge", min_length=2, max_length=3),
# merged_model_name: Optional[str] = Body(description="Name of destination model", default=None),
# alpha: float = Body(description="Alpha weighting strength to apply to 2d and 3d models", default=0.5),
# force: bool = Body(
# description="Force merging of models created with different versions of diffusers",
# default=False,
# ),
# interp: Optional[MergeInterpolationMethod] = Body(description="Interpolation method", default=None),
# merge_dest_directory: Optional[str] = Body(
# description="Save the merged model to the designated directory (with 'merged_model_name' appended)",
# default=None,
# ),
# ) -> AnyModelConfig:
# """
# Merge diffusers models. The process is controlled by a set parameters provided in the body of the request.
# ```
# Argument Description [default]
# -------- ----------------------
# keys List of 2-3 model keys to merge together. All models must use the same base type.
# merged_model_name Name for the merged model [Concat model names]
# alpha Alpha value (0.0-1.0). Higher values give more weight to the second model [0.5]
# force If true, force the merge even if the models were generated by different versions of the diffusers library [False]
# interp Interpolation method. One of "weighted_sum", "sigmoid", "inv_sigmoid" or "add_difference" [weighted_sum]
# merge_dest_directory Specify a directory to store the merged model in [models directory]
# ```
# """
# logger = ApiDependencies.invoker.services.logger
# try:
# logger.info(f"Merging models: {keys} into {merge_dest_directory or '<MODELS>'}/{merged_model_name}")
# dest = pathlib.Path(merge_dest_directory) if merge_dest_directory else None
# installer = ApiDependencies.invoker.services.model_manager.install
# merger = ModelMerger(installer)
# model_names = [installer.record_store.get_model(x).name for x in keys]
# response = merger.merge_diffusion_models_and_save(
# model_keys=keys,
# merged_model_name=merged_model_name or "+".join(model_names),
# alpha=alpha,
# interp=interp,
# force=force,
# merge_dest_directory=dest,
# )
# except UnknownModelException:
# raise HTTPException(
# status_code=404,
# detail=f"One or more of the models '{keys}' not found",
# )
# except ValueError as e:
# raise HTTPException(status_code=400, detail=str(e))
# return response
@model_manager_router.get("/starter_models", operation_id="get_starter_models", response_model=list[StarterModel])
async def get_starter_models() -> list[StarterModel]:
installed_models = ApiDependencies.invoker.services.model_manager.store.search_by_attr()
@ -797,58 +683,10 @@ async def get_starter_models() -> list[StarterModel]:
if model.source in installed_model_sources:
model.is_installed = True
# Remove already-installed dependencies
missing_deps: list[str] = []
missing_deps: list[StarterModelWithoutDependencies] = []
for dep in model.dependencies or []:
if dep not in installed_model_sources:
if dep.source not in installed_model_sources:
missing_deps.append(dep)
model.dependencies = missing_deps
return starter_models
class HFTokenStatus(str, Enum):
VALID = "valid"
INVALID = "invalid"
UNKNOWN = "unknown"
class HFTokenHelper:
@classmethod
def get_status(cls) -> HFTokenStatus:
try:
if huggingface_hub.get_token_permission(huggingface_hub.get_token()):
# Valid token!
return HFTokenStatus.VALID
# No token set
return HFTokenStatus.INVALID
except Exception:
return HFTokenStatus.UNKNOWN
@classmethod
def set_token(cls, token: str) -> HFTokenStatus:
with SuppressOutput(), contextlib.suppress(Exception):
huggingface_hub.login(token=token, add_to_git_credential=False)
return cls.get_status()
@model_manager_router.get("/hf_login", operation_id="get_hf_login_status", response_model=HFTokenStatus)
async def get_hf_login_status() -> HFTokenStatus:
token_status = HFTokenHelper.get_status()
if token_status is HFTokenStatus.UNKNOWN:
ApiDependencies.invoker.services.logger.warning("Unable to verify HF token")
return token_status
@model_manager_router.post("/hf_login", operation_id="do_hf_login", response_model=HFTokenStatus)
async def do_hf_login(
token: str = Body(description="Hugging Face token to use for login", embed=True),
) -> HFTokenStatus:
HFTokenHelper.set_token(token)
token_status = HFTokenHelper.get_status()
if token_status is HFTokenStatus.UNKNOWN:
ApiDependencies.invoker.services.logger.warning("Unable to verify HF token")
return token_status

24
invokeai/app/api_app.py
View File

@ -1,4 +1,5 @@
import asyncio
import logging
import mimetypes
import socket
from contextlib import asynccontextmanager
@ -6,6 +7,7 @@ from inspect import signature
from pathlib import Path
from typing import Any
import torch
import uvicorn
from fastapi import FastAPI
from fastapi.middleware.cors import CORSMiddleware
@ -26,6 +28,7 @@ from invokeai.app.api.no_cache_staticfiles import NoCacheStaticFiles
from invokeai.app.invocations.model import ModelIdentifierField
from invokeai.app.services.config.config_default import get_config
from invokeai.app.services.session_processor.session_processor_common import ProgressImage
from invokeai.backend.util.devices import get_torch_device_name
from ..backend.util.logging import InvokeAILogger
from .api.dependencies import ApiDependencies
@ -60,6 +63,9 @@ logger = InvokeAILogger.get_logger(config=app_config)
mimetypes.add_type("application/javascript", ".js")
mimetypes.add_type("text/css", ".css")
torch_device_name = get_torch_device_name()
logger.info(f"Using torch device: {torch_device_name}")
@asynccontextmanager
async def lifespan(app: FastAPI):
@ -222,6 +228,22 @@ app.mount(
) # docs favicon is in here
def check_cudnn(logger: logging.Logger) -> None:
"""Check for cuDNN issues that could be causing degraded performance."""
if torch.backends.cudnn.is_available():
try:
# Note: At the time of writing (torch 2.2.1), torch.backends.cudnn.version() only raises an error the first
# time it is called. Subsequent calls will return the version number without complaining about a mismatch.
cudnn_version = torch.backends.cudnn.version()
logger.info(f"cuDNN version: {cudnn_version}")
except RuntimeError as e:
logger.warning(
"Encountered a cuDNN version issue. This may result in degraded performance. This issue is usually "
"caused by an incompatible cuDNN version installed in your python environment, or on the host "
f"system. Full error message:\n{e}"
)
def invoke_api() -> None:
def find_port(port: int) -> int:
"""Find a port not in use starting at given port"""
@ -248,6 +270,8 @@ def invoke_api() -> None:
if port != app_config.port:
logger.warn(f"Port {app_config.port} in use, using port {port}")
check_cudnn(logger)
# Start our own event loop for eventing usage
loop = asyncio.new_event_loop()
config = uvicorn.Config(

53
invokeai/app/invocations/image.py
View File

@ -967,3 +967,56 @@ class CanvasPasteBackInvocation(BaseInvocation, WithMetadata, WithBoard):
image_dto = context.images.save(image=source_image)
return ImageOutput.build(image_dto)
@invocation(
"mask_from_id",
title="Mask from ID",
tags=["image", "mask", "id"],
category="image",
version="1.0.0",
)
class MaskFromIDInvocation(BaseInvocation, WithMetadata, WithBoard):
"""Generate a mask for a particular color in an ID Map"""
image: ImageField = InputField(description="The image to create the mask from")
color: ColorField = InputField(description="ID color to mask")
threshold: int = InputField(default=100, description="Threshold for color detection")
invert: bool = InputField(default=False, description="Whether or not to invert the mask")
def rgba_to_hex(self, rgba_color: tuple[int, int, int, int]):
r, g, b, a = rgba_color
hex_code = "#{:02X}{:02X}{:02X}{:02X}".format(r, g, b, int(a * 255))
return hex_code
def id_to_mask(self, id_mask: Image.Image, color: tuple[int, int, int, int], threshold: int = 100):
if id_mask.mode != "RGB":
id_mask = id_mask.convert("RGB")
# Can directly just use the tuple but I'll leave this rgba_to_hex here
# incase anyone prefers using hex codes directly instead of the color picker
hex_color_str = self.rgba_to_hex(color)
rgb_color = numpy.array([int(hex_color_str[i : i + 2], 16) for i in (1, 3, 5)])
# Maybe there's a faster way to calculate this distance but I can't think of any right now.
color_distance = numpy.linalg.norm(id_mask - rgb_color, axis=-1)
# Create a mask based on the threshold and the distance calculated above
binary_mask = (color_distance < threshold).astype(numpy.uint8) * 255
# Convert the mask back to PIL
binary_mask_pil = Image.fromarray(binary_mask)
return binary_mask_pil
def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.images.get_pil(self.image.image_name)
mask = self.id_to_mask(image, self.color.tuple(), self.threshold)
if self.invert:
mask = ImageOps.invert(mask)
image_dto = context.images.save(image=mask, image_category=ImageCategory.MASK)
return ImageOutput.build(image_dto)

2
invokeai/app/invocations/upscale.py
View File

@ -31,7 +31,7 @@ ESRGAN_MODELS = Literal[
ESRGAN_MODEL_URLS: dict[str, str] = {
"RealESRGAN_x4plus.pth": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth",
"RealESRGAN_x4plus_anime_6B.pth": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth",
"ESRGAN_SRx4_DF2KOST_official.pth": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth",
"ESRGAN_SRx4_DF2KOST_official-ff704c30.pth": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth",
"RealESRGAN_x2plus.pth": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth",
}

9
invokeai/app/services/config/config_default.py
View File

@ -82,7 +82,6 @@ class InvokeAIAppConfig(BaseSettings):
ssl_keyfile: SSL key file for HTTPS. See https://www.uvicorn.org/settings/#https.
log_tokenization: Enable logging of parsed prompt tokens.
patchmatch: Enable patchmatch inpaint code.
autoimport_dir: Path to a directory of models files to be imported on startup.
models_dir: Path to the models directory.
convert_cache_dir: Path to the converted models cache directory. When loading a non-diffusers model, it will be converted and store on disk at this location.
legacy_conf_dir: Path to directory of legacy checkpoint config files.
@ -114,6 +113,7 @@ class InvokeAIAppConfig(BaseSettings):
node_cache_size: How many cached nodes to keep in memory.
hashing_algorithm: Model hashing algorthim for model installs. 'blake3_multi' is best for SSDs. 'blake3_single' is best for spinning disk HDDs. 'random' disables hashing, instead assigning a UUID to models. Useful when using a memory db to reduce model installation time, or if you don't care about storing stable hashes for models. Alternatively, any other hashlib algorithm is accepted, though these are not nearly as performant as blake3.<br>Valid values: `blake3_multi`, `blake3_single`, `random`, `md5`, `sha1`, `sha224`, `sha256`, `sha384`, `sha512`, `blake2b`, `blake2s`, `sha3_224`, `sha3_256`, `sha3_384`, `sha3_512`, `shake_128`, `shake_256`
remote_api_tokens: List of regular expression and token pairs used when downloading models from URLs. The download URL is tested against the regex, and if it matches, the token is provided in as a Bearer token.
scan_models_on_startup: Scan the models directory on startup, registering orphaned models. This is typically only used in conjunction with `use_memory_db` for testing purposes.
"""
_root: Optional[Path] = PrivateAttr(default=None)
@ -141,7 +141,6 @@ class InvokeAIAppConfig(BaseSettings):
patchmatch: bool = Field(default=True, description="Enable patchmatch inpaint code.")
# PATHS
autoimport_dir: Path = Field(default=Path("autoimport"), description="Path to a directory of models files to be imported on startup.")
models_dir: Path = Field(default=Path("models"), description="Path to the models directory.")
convert_cache_dir: Path = Field(default=Path("models/.cache"), description="Path to the converted models cache directory. When loading a non-diffusers model, it will be converted and store on disk at this location.")
legacy_conf_dir: Path = Field(default=Path("configs"), description="Path to directory of legacy checkpoint config files.")
@ -188,6 +187,7 @@ class InvokeAIAppConfig(BaseSettings):
# MODEL INSTALL
hashing_algorithm: HASHING_ALGORITHMS = Field(default="blake3_single", description="Model hashing algorthim for model installs. 'blake3_multi' is best for SSDs. 'blake3_single' is best for spinning disk HDDs. 'random' disables hashing, instead assigning a UUID to models. Useful when using a memory db to reduce model installation time, or if you don't care about storing stable hashes for models. Alternatively, any other hashlib algorithm is accepted, though these are not nearly as performant as blake3.")
remote_api_tokens: Optional[list[URLRegexTokenPair]] = Field(default=None, description="List of regular expression and token pairs used when downloading models from URLs. The download URL is tested against the regex, and if it matches, the token is provided in as a Bearer token.")
scan_models_on_startup: bool = Field(default=False, description="Scan the models directory on startup, registering orphaned models. This is typically only used in conjunction with `use_memory_db` for testing purposes.")
# fmt: on
@ -270,11 +270,6 @@ class InvokeAIAppConfig(BaseSettings):
assert resolved_path is not None
return resolved_path
@property
def autoimport_path(self) -> Path:
"""Path to the autoimports directory, resolved to an absolute path.."""
return self._resolve(self.autoimport_dir)
@property
def outputs_path(self) -> Optional[Path]:
"""Path to the outputs directory, resolved to an absolute path.."""

2
invokeai/app/services/download/download_default.py
View File

@ -85,7 +85,7 @@ class DownloadQueueService(DownloadQueueServiceBase):
self._logger.info(f"Waiting for {len(active_jobs)} active download jobs to complete")
with self._queue.mutex:
self._queue.queue.clear()
self.join() # wait for all active jobs to finish
self.cancel_all_jobs()
self._stop_event.set()
for thread in self._worker_pool:
thread.join()

17
invokeai/app/services/model_install/model_install_base.py
View File

@ -455,19 +455,18 @@ class ModelInstallServiceBase(ABC):
"""
@abstractmethod
def scan_directory(self, scan_dir: Path, install: bool = False) -> List[str]:
def sync_model_path(self, key: str) -> AnyModelConfig:
"""
Recursively scan directory for new models and register or install them.
Move model into the location indicated by its basetype, type and name.
:param scan_dir: Path to the directory to scan.
:param install: Install if True, otherwise register in place.
:returns list of IDs: Returns list of IDs of models registered/installed
Call this after updating a model's attributes in order to move
the model's path into the location indicated by its basetype, type and
name. Applies only to models whose paths are within the root `models_dir`
directory.
May raise an UnknownModelException.
"""
@abstractmethod
def sync_to_config(self) -> None:
"""Synchronize models on disk to those in the model record database."""
@abstractmethod
def download_and_cache(self, source: Union[str, AnyHttpUrl], access_token: Optional[str] = None) -> Path:
"""

211
invokeai/app/services/model_install/model_install_default.py
View File

@ -2,6 +2,7 @@
import os
import re
import signal
import threading
import time
from hashlib import sha256
@ -9,7 +10,7 @@ from pathlib import Path
from queue import Empty, Queue
from shutil import copyfile, copytree, move, rmtree
from tempfile import mkdtemp
from typing import Any, Dict, List, Optional, Set, Union
from typing import Any, Dict, List, Optional, Union
import yaml
from huggingface_hub import HfFolder
@ -24,12 +25,10 @@ from invokeai.app.services.model_records import DuplicateModelException, ModelRe
from invokeai.app.services.model_records.model_records_base import ModelRecordChanges
from invokeai.backend.model_manager.config import (
AnyModelConfig,
BaseModelType,
CheckpointConfigBase,
InvalidModelConfigException,
ModelRepoVariant,
ModelSourceType,
ModelType,
)
from invokeai.backend.model_manager.metadata import (
AnyModelRepoMetadata,
@ -41,7 +40,7 @@ from invokeai.backend.model_manager.metadata import (
from invokeai.backend.model_manager.metadata.metadata_base import HuggingFaceMetadata
from invokeai.backend.model_manager.probe import ModelProbe
from invokeai.backend.model_manager.search import ModelSearch
from invokeai.backend.util import Chdir, InvokeAILogger
from invokeai.backend.util import InvokeAILogger
from invokeai.backend.util.devices import choose_precision, choose_torch_device
from .model_install_base import (
@ -83,8 +82,6 @@ class ModelInstallService(ModelInstallServiceBase):
self._logger = InvokeAILogger.get_logger(name=self.__class__.__name__)
self._install_jobs: List[ModelInstallJob] = []
self._install_queue: Queue[ModelInstallJob] = Queue()
self._cached_model_paths: Set[Path] = set()
self._models_installed: Set[str] = set()
self._lock = threading.Lock()
self._stop_event = threading.Event()
self._downloads_changed_event = threading.Event()
@ -112,25 +109,46 @@ class ModelInstallService(ModelInstallServiceBase):
# makes the installer harder to use outside the web app
def start(self, invoker: Optional[Invoker] = None) -> None:
"""Start the installer thread."""
# Yes, this is weird. When the installer thread is running, the
# thread masks the ^C signal. When we receive a
# sigINT, we stop the thread, reset sigINT, and send a new
# sigINT to the parent process.
def sigint_handler(signum, frame):
self.stop()
signal.signal(signal.SIGINT, signal.SIG_DFL)
signal.raise_signal(signal.SIGINT)
signal.signal(signal.SIGINT, sigint_handler)
with self._lock:
if self._running:
raise Exception("Attempt to start the installer service twice")
self._start_installer_thread()
self._remove_dangling_install_dirs()
self._migrate_yaml()
self.sync_to_config()
# In normal use, we do not want to scan the models directory - it should never have orphaned models.
# We should only do the scan when the flag is set (which should only be set when testing).
if self.app_config.scan_models_on_startup:
self._register_orphaned_models()
# Check all models' paths and confirm they exist. A model could be missing if it was installed on a volume
# that isn't currently mounted. In this case, we don't want to delete the model from the database, but we do
# want to alert the user.
for model in self._scan_for_missing_models():
self._logger.warning(f"Missing model file: {model.name} at {model.path}")
def stop(self, invoker: Optional[Invoker] = None) -> None:
"""Stop the installer thread; after this the object can be deleted and garbage collected."""
with self._lock:
if not self._running:
raise Exception("Attempt to stop the install service before it was started")
self._stop_event.set()
self._clear_pending_jobs()
self._download_cache.clear()
assert self._install_thread is not None
self._install_thread.join()
self._running = False
if not self._running:
raise Exception("Attempt to stop the install service before it was started")
self._logger.debug("calling stop_event.set()")
self._stop_event.set()
self._clear_pending_jobs()
self._download_cache.clear()
assert self._install_thread is not None
self._install_thread.join()
self._running = False
def _clear_pending_jobs(self) -> None:
for job in self.list_jobs():
@ -293,15 +311,6 @@ class ModelInstallService(ModelInstallServiceBase):
unfinished_jobs = [x for x in self._install_jobs if not x.in_terminal_state]
self._install_jobs = unfinished_jobs
def sync_to_config(self) -> None:
"""Synchronize models on disk to those in the config record store database."""
self._scan_models_directory()
if self._app_config.autoimport_path:
self._logger.info("Scanning autoimport directory for new models")
installed = self.scan_directory(self._app_config.autoimport_path)
self._logger.info(f"{len(installed)} new models registered")
self._logger.info("Model installer (re)initialized")
def _migrate_yaml(self) -> None:
db_models = self.record_store.all_models()
@ -339,8 +348,13 @@ class ModelInstallService(ModelInstallServiceBase):
config: dict[str, Any] = {}
config["name"] = model_name
config["description"] = stanza.get("description")
config["config_path"] = stanza.get("config")
legacy_config_path = stanza.get("config")
if legacy_config_path:
# In v3, these paths were relative to the root. Migrate them to be relative to the legacy_conf_dir.
legacy_config_path: Path = self._app_config.root_path / legacy_config_path
if legacy_config_path.is_relative_to(self._app_config.legacy_conf_path):
legacy_config_path = legacy_config_path.relative_to(self._app_config.legacy_conf_path)
config["config_path"] = str(legacy_config_path)
try:
id = self.register_path(model_path=model_path, config=config)
self._logger.info(f"Migrated {model_name} with id {id}")
@ -353,34 +367,28 @@ class ModelInstallService(ModelInstallServiceBase):
# Unset the path - we are done with it either way
self._app_config.legacy_models_yaml_path = None
def scan_directory(self, scan_dir: Path, install: bool = False) -> List[str]: # noqa D102
self._cached_model_paths = {Path(x.path).resolve() for x in self.record_store.all_models()}
callback = self._scan_install if install else self._scan_register
search = ModelSearch(on_model_found=callback)
self._models_installed.clear()
search.search(scan_dir)
return list(self._models_installed)
def unregister(self, key: str) -> None: # noqa D102
self.record_store.del_model(key)
def delete(self, key: str) -> None: # noqa D102
"""Unregister the model. Delete its files only if they are within our models directory."""
model = self.record_store.get_model(key)
models_dir = self.app_config.models_path
model_path = models_dir / Path(model.path) # handle legacy relative model paths
if model_path.is_relative_to(models_dir):
model_path = self.app_config.models_path / model.path
if model_path.is_relative_to(self.app_config.models_path):
# If the models is in the Invoke-managed models dir, we delete it
self.unconditionally_delete(key)
else:
# Else we only unregister it, leaving the file in place
self.unregister(key)
def unconditionally_delete(self, key: str) -> None: # noqa D102
model = self.record_store.get_model(key)
model_path = self.app_config.models_path / model.path
if model_path.is_dir():
rmtree(model_path)
else:
if model_path.is_file() or model_path.is_symlink():
model_path.unlink()
elif model_path.is_dir():
rmtree(model_path)
self.unregister(key)
def download_and_cache(
@ -444,11 +452,10 @@ class ModelInstallService(ModelInstallServiceBase):
elif job.waiting or job.downloads_done:
self._register_or_install(job)
except InvalidModelConfigException as excp:
self._set_error(job, excp)
except (OSError, DuplicateModelException) as excp:
self._set_error(job, excp)
except Exception as e:
# Expected errors include InvalidModelConfigException, DuplicateModelException, OSError, but we must
# gracefully handle _any_ error here.
self._set_error(job, e)
finally:
# if this is an install of a remote file, then clean up the temporary directory
@ -497,36 +504,48 @@ class ModelInstallService(ModelInstallServiceBase):
self._logger.info(f"Removing dangling temporary directory {tmpdir}")
rmtree(tmpdir)
def _scan_models_directory(self) -> None:
def _scan_for_missing_models(self) -> list[AnyModelConfig]:
"""Scan the models directory for missing models and return a list of them."""
missing_models: list[AnyModelConfig] = []
for model_config in self.record_store.all_models():
if not (self.app_config.models_path / model_config.path).resolve().exists():
missing_models.append(model_config)
return missing_models
def _register_orphaned_models(self) -> None:
"""Scan the invoke-managed models directory for orphaned models and registers them.
This is typically only used during testing with a new DB or when using the memory DB, because those are the
only situations in which we may have orphaned models in the models directory.
"""
Scan the models directory for new and missing models.
New models will be added to the storage backend. Missing models
will be deleted.
"""
defunct_models = set()
installed = set()
installed_model_paths = {
(self._app_config.models_path / x.path).resolve() for x in self.record_store.all_models()
}
with Chdir(self._app_config.models_path):
self._logger.info("Checking for models that have been moved or deleted from disk")
for model_config in self.record_store.all_models():
path = Path(model_config.path)
if not path.exists():
self._logger.info(f"{model_config.name}: path {path.as_posix()} no longer exists. Unregistering")
defunct_models.add(model_config.key)
for key in defunct_models:
self.unregister(key)
# The bool returned by this callback determines if the model is added to the list of models found by the search
def on_model_found(model_path: Path) -> bool:
resolved_path = model_path.resolve()
# Already registered models should be in the list of found models, but not re-registered.
if resolved_path in installed_model_paths:
return True
# Skip core models entirely - these aren't registered with the model manager.
if str(resolved_path).startswith(str(self.app_config.models_path / "core")):
return False
try:
model_id = self.register_path(model_path)
self._logger.info(f"Registered {model_path.name} with id {model_id}")
except DuplicateModelException:
# In case a duplicate models sneaks by, we will ignore this error - we "found" the model
pass
return True
self._logger.info(f"Scanning {self._app_config.models_path} for new and orphaned models")
for cur_base_model in BaseModelType:
for cur_model_type in ModelType:
models_dir = self._app_config.models_path / Path(cur_base_model.value, cur_model_type.value)
if not models_dir.exists():
continue
installed.update(self.scan_directory(models_dir))
self._logger.info(f"{len(installed)} new models registered; {len(defunct_models)} unregistered")
self._logger.info(f"Scanning {self._app_config.models_path} for orphaned models")
search = ModelSearch(on_model_found=on_model_found)
found_models = search.search(self._app_config.models_path)
self._logger.info(f"{len(found_models)} new models registered")
def _sync_model_path(self, key: str) -> AnyModelConfig:
def sync_model_path(self, key: str) -> AnyModelConfig:
"""
Move model into the location indicated by its basetype, type and name.
@ -538,49 +557,24 @@ class ModelInstallService(ModelInstallServiceBase):
May raise an UnknownModelException.
"""
model = self.record_store.get_model(key)
old_path = Path(model.path)
models_dir = self.app_config.models_path
old_path = self.app_config.models_path / model.path
try:
old_path.relative_to(models_dir)
if not old_path.is_relative_to(models_dir):
# The model is not in the models directory - we don't need to move it.
return model
except ValueError:
pass
new_path = models_dir / model.base.value / model.type.value / old_path.name
new_path = (models_dir / model.base.value / model.type.value / model.name).with_suffix(old_path.suffix)
if old_path == new_path or new_path.exists() and old_path == new_path.resolve():
return model
self._logger.info(f"Moving {model.name} to {new_path}.")
new_path = self._move_model(old_path, new_path)
model.path = new_path.as_posix()
model.path = new_path.relative_to(models_dir).as_posix()
self.record_store.update_model(key, ModelRecordChanges(path=model.path))
return model
def _scan_register(self, model: Path) -> bool:
if model in self._cached_model_paths:
return True
try:
id = self.register_path(model)
self._sync_model_path(id) # possibly move it to right place in `models`
self._logger.info(f"Registered {model.name} with id {id}")
self._models_installed.add(id)
except DuplicateModelException:
pass
return True
def _scan_install(self, model: Path) -> bool:
if model in self._cached_model_paths:
return True
try:
id = self.install_path(model)
self._logger.info(f"Installed {model} with id {id}")
self._models_installed.add(id)
except DuplicateModelException:
pass
return True
def _copy_model(self, old_path: Path, new_path: Path) -> Path:
if old_path == new_path:
return old_path
@ -616,12 +610,19 @@ class ModelInstallService(ModelInstallServiceBase):
model_path = model_path.resolve()
# Models in the Invoke-managed models dir should use relative paths.
if model_path.is_relative_to(self.app_config.models_path):
model_path = model_path.relative_to(self.app_config.models_path)
info.path = model_path.as_posix()
# Checkpoints have a config file needed for conversion - resolve this to an absolute path
if isinstance(info, CheckpointConfigBase):
legacy_conf = (self.app_config.legacy_conf_path / info.config_path).resolve()
info.config_path = legacy_conf.as_posix()
# Checkpoints have a config file needed for conversion. Same handling as the model weights - if it's in the
# invoke-managed legacy config dir, we use a relative path.
legacy_config_path = self.app_config.legacy_conf_path / info.config_path
if legacy_config_path.is_relative_to(self.app_config.legacy_conf_path):
legacy_config_path = legacy_config_path.relative_to(self.app_config.legacy_conf_path)
info.config_path = legacy_config_path.as_posix()
self.record_store.add_model(info)
return info.key
@ -901,7 +902,7 @@ class ModelInstallService(ModelInstallServiceBase):
self._event_bus.emit_model_install_completed(str(job.source), key, id=job.id)
def _signal_job_errored(self, job: ModelInstallJob) -> None:
self._logger.info(f"Model install error: {job.source}, {job.error_type}\n{job.error}")
self._logger.error(f"Model install error: {job.source}\n{job.error_type}: {job.error}")
if self._event_bus:
error_type = job.error_type
error = job.error

2
invokeai/app/services/model_records/__init__.py
View File

@ -6,6 +6,7 @@ from .model_records_base import ( # noqa F401
ModelRecordServiceBase,
UnknownModelException,
ModelSummary,
ModelRecordChanges,
ModelRecordOrderBy,
)
from .model_records_sql import ModelRecordServiceSQL # noqa F401
@ -17,5 +18,6 @@ __all__ = [
"InvalidModelException",
"UnknownModelException",
"ModelSummary",
"ModelRecordChanges",
"ModelRecordOrderBy",
]

267
invokeai/app/services/session_processor/session_processor_default.py
View File

@ -70,8 +70,18 @@ class DefaultSessionProcessor(SessionProcessorBase):
async def _on_queue_event(self, event: FastAPIEvent) -> None:
event_name = event[1]["event"]
if event_name == "session_canceled" or event_name == "queue_cleared":
# These both mean we should cancel the current session.
if (
event_name == "session_canceled"
and self._queue_item
and self._queue_item.item_id == event[1]["data"]["queue_item_id"]
):
self._cancel_event.set()
self._poll_now()
elif (
event_name == "queue_cleared"
and self._queue_item
and self._queue_item.queue_id == event[1]["data"]["queue_id"]
):
self._cancel_event.set()
self._poll_now()
elif event_name == "batch_enqueued":
@ -111,146 +121,153 @@ class DefaultSessionProcessor(SessionProcessorBase):
poll_now_event.clear()
# Middle processor try block; any unhandled exception is a non-fatal processor error
try:
# Get the next session to process
self._queue_item = self._invoker.services.session_queue.dequeue()
if self._queue_item is not None and resume_event.is_set():
self._invoker.services.logger.debug(f"Executing queue item {self._queue_item.item_id}")
cancel_event.clear()
# If we are paused, wait for resume event
if resume_event.is_set():
# Get the next session to process
self._queue_item = self._invoker.services.session_queue.dequeue()
# If profiling is enabled, start the profiler
if self._profiler is not None:
self._profiler.start(profile_id=self._queue_item.session_id)
if self._queue_item is not None:
self._invoker.services.logger.debug(f"Executing queue item {self._queue_item.item_id}")
cancel_event.clear()
# Prepare invocations and take the first
self._invocation = self._queue_item.session.next()
# If profiling is enabled, start the profiler
if self._profiler is not None:
self._profiler.start(profile_id=self._queue_item.session_id)
# Loop over invocations until the session is complete or canceled
while self._invocation is not None and not cancel_event.is_set():
# get the source node id to provide to clients (the prepared node id is not as useful)
source_invocation_id = self._queue_item.session.prepared_source_mapping[self._invocation.id]
# Prepare invocations and take the first
self._invocation = self._queue_item.session.next()
# Send starting event
self._invoker.services.events.emit_invocation_started(
queue_batch_id=self._queue_item.batch_id,
queue_item_id=self._queue_item.item_id,
queue_id=self._queue_item.queue_id,
graph_execution_state_id=self._queue_item.session_id,
node=self._invocation.model_dump(),
source_node_id=source_invocation_id,
)
# Loop over invocations until the session is complete or canceled
while self._invocation is not None and not cancel_event.is_set():
# get the source node id to provide to clients (the prepared node id is not as useful)
source_invocation_id = self._queue_item.session.prepared_source_mapping[
self._invocation.id
]
# Innermost processor try block; any unhandled exception is an invocation error & will fail the graph
try:
with self._invoker.services.performance_statistics.collect_stats(
self._invocation, self._queue_item.session.id
):
# Build invocation context (the node-facing API)
data = InvocationContextData(
invocation=self._invocation,
source_invocation_id=source_invocation_id,
queue_item=self._queue_item,
)
context = build_invocation_context(
data=data,
services=self._invoker.services,
cancel_event=self._cancel_event,
# Send starting event
self._invoker.services.events.emit_invocation_started(
queue_batch_id=self._queue_item.batch_id,
queue_item_id=self._queue_item.item_id,
queue_id=self._queue_item.queue_id,
graph_execution_state_id=self._queue_item.session_id,
node=self._invocation.model_dump(),
source_node_id=source_invocation_id,
)
# Innermost processor try block; any unhandled exception is an invocation error & will fail the graph
try:
with self._invoker.services.performance_statistics.collect_stats(
self._invocation, self._queue_item.session.id
):
# Build invocation context (the node-facing API)
data = InvocationContextData(
invocation=self._invocation,
source_invocation_id=source_invocation_id,
queue_item=self._queue_item,
)
context = build_invocation_context(
data=data,
services=self._invoker.services,
cancel_event=self._cancel_event,
)
# Invoke the node
outputs = self._invocation.invoke_internal(
context=context, services=self._invoker.services
)
# Save outputs and history
self._queue_item.session.complete(self._invocation.id, outputs)
# Send complete event
self._invoker.services.events.emit_invocation_complete(
queue_batch_id=self._queue_item.batch_id,
queue_item_id=self._queue_item.item_id,
queue_id=self._queue_item.queue_id,
graph_execution_state_id=self._queue_item.session.id,
node=self._invocation.model_dump(),
source_node_id=source_invocation_id,
result=outputs.model_dump(),
)
except KeyboardInterrupt:
# TODO(MM2): Create an event for this
pass
except CanceledException:
# When the user cancels the graph, we first set the cancel event. The event is checked
# between invocations, in this loop. Some invocations are long-running, and we need to
# be able to cancel them mid-execution.
#
# For example, denoising is a long-running invocation with many steps. A step callback
# is executed after each step. This step callback checks if the canceled event is set,
# then raises a CanceledException to stop execution immediately.
#
# When we get a CanceledException, we don't need to do anything - just pass and let the
# loop go to its next iteration, and the cancel event will be handled correctly.
pass
except Exception as e:
error = traceback.format_exc()
# Save error
self._queue_item.session.set_node_error(self._invocation.id, error)
self._invoker.services.logger.error(
f"Error while invoking session {self._queue_item.session_id}, invocation {self._invocation.id} ({self._invocation.get_type()}):\n{e}"
)
self._invoker.services.logger.error(error)
# Invoke the node
outputs = self._invocation.invoke_internal(
context=context, services=self._invoker.services
)
# Save outputs and history
self._queue_item.session.complete(self._invocation.id, outputs)
# Send complete event
self._invoker.services.events.emit_invocation_complete(
queue_batch_id=self._queue_item.batch_id,
# Send error event
self._invoker.services.events.emit_invocation_error(
queue_batch_id=self._queue_item.session_id,
queue_item_id=self._queue_item.item_id,
queue_id=self._queue_item.queue_id,
graph_execution_state_id=self._queue_item.session.id,
node=self._invocation.model_dump(),
source_node_id=source_invocation_id,
result=outputs.model_dump(),
error_type=e.__class__.__name__,
error=error,
)
pass
except KeyboardInterrupt:
# TODO(MM2): Create an event for this
pass
except CanceledException:
# When the user cancels the graph, we first set the cancel event. The event is checked
# between invocations, in this loop. Some invocations are long-running, and we need to
# be able to cancel them mid-execution.
#
# For example, denoising is a long-running invocation with many steps. A step callback
# is executed after each step. This step callback checks if the canceled event is set,
# then raises a CanceledException to stop execution immediately.
#
# When we get a CanceledException, we don't need to do anything - just pass and let the
# loop go to its next iteration, and the cancel event will be handled correctly.
pass
except Exception as e:
error = traceback.format_exc()
# Save error
self._queue_item.session.set_node_error(self._invocation.id, error)
self._invoker.services.logger.error(
f"Error while invoking session {self._queue_item.session_id}, invocation {self._invocation.id} ({self._invocation.get_type()}):\n{e}"
)
self._invoker.services.logger.error(error)
# Send error event
self._invoker.services.events.emit_invocation_error(
queue_batch_id=self._queue_item.session_id,
queue_item_id=self._queue_item.item_id,
queue_id=self._queue_item.queue_id,
graph_execution_state_id=self._queue_item.session.id,
node=self._invocation.model_dump(),
source_node_id=source_invocation_id,
error_type=e.__class__.__name__,
error=error,
)
pass
# The session is complete if the all invocations are complete or there was an error
if self._queue_item.session.is_complete() or cancel_event.is_set():
# Send complete event
self._invoker.services.events.emit_graph_execution_complete(
queue_batch_id=self._queue_item.batch_id,
queue_item_id=self._queue_item.item_id,
queue_id=self._queue_item.queue_id,
graph_execution_state_id=self._queue_item.session.id,
)
# If we are profiling, stop the profiler and dump the profile & stats
if self._profiler:
profile_path = self._profiler.stop()
stats_path = profile_path.with_suffix(".json")
self._invoker.services.performance_statistics.dump_stats(
graph_execution_state_id=self._queue_item.session.id, output_path=stats_path
# The session is complete if the all invocations are complete or there was an error
if self._queue_item.session.is_complete() or cancel_event.is_set():
# Send complete event
self._invoker.services.events.emit_graph_execution_complete(
queue_batch_id=self._queue_item.batch_id,
queue_item_id=self._queue_item.item_id,
queue_id=self._queue_item.queue_id,
graph_execution_state_id=self._queue_item.session.id,
)
# We'll get a GESStatsNotFoundError if we try to log stats for an untracked graph, but in the processor
# we don't care about that - suppress the error.
with suppress(GESStatsNotFoundError):
self._invoker.services.performance_statistics.log_stats(self._queue_item.session.id)
self._invoker.services.performance_statistics.reset_stats()
# If we are profiling, stop the profiler and dump the profile & stats
if self._profiler:
profile_path = self._profiler.stop()
stats_path = profile_path.with_suffix(".json")
self._invoker.services.performance_statistics.dump_stats(
graph_execution_state_id=self._queue_item.session.id, output_path=stats_path
)
# We'll get a GESStatsNotFoundError if we try to log stats for an untracked graph, but in the processor
# we don't care about that - suppress the error.
with suppress(GESStatsNotFoundError):
self._invoker.services.performance_statistics.log_stats(
self._queue_item.session.id
)
self._invoker.services.performance_statistics.reset_stats()
# Set the invocation to None to prepare for the next session
self._invocation = None
else:
# Prepare the next invocation
self._invocation = self._queue_item.session.next()
# Set the invocation to None to prepare for the next session
self._invocation = None
else:
# Prepare the next invocation
self._invocation = self._queue_item.session.next()
# The session is complete, immediately poll for next session
self._queue_item = None
poll_now_event.set()
else:
# The queue was empty, wait for next polling interval or event to try again
self._invoker.services.logger.debug("Waiting for next polling interval or event")
poll_now_event.wait(self._polling_interval)
continue
# The session is complete, immediately poll for next session
self._queue_item = None
poll_now_event.set()
else:
# The queue was empty, wait for next polling interval or event to try again
self._invoker.services.logger.debug("Waiting for next polling interval or event")
poll_now_event.wait(self._polling_interval)
continue
except Exception:
# Non-fatal error in processor
self._invoker.services.logger.error(

37
invokeai/app/services/shared/graph.py
View File

@ -1043,23 +1043,32 @@ class GraphExecutionState(BaseModel):
"""Gets the deepest node that is ready to be executed"""
g = self.execution_graph.nx_graph()
# Depth-first search with pre-order traversal is a depth-first topological sort
sorted_nodes = nx.dfs_preorder_nodes(g)
# Perform a topological sort using depth-first search
topo_order = list(nx.dfs_postorder_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
),
None,
)
# Get all IterateInvocation nodes
iterate_nodes = [n for n in topo_order if isinstance(self.execution_graph.nodes[n], IterateInvocation)]
if next_node is None:
return None
# Sort the IterateInvocation nodes based on their index attribute
iterate_nodes.sort(key=lambda x: self.execution_graph.nodes[x].index)
return self.execution_graph.nodes[next_node]
# Prioritize IterateInvocation nodes and their children
for iterate_node in iterate_nodes:
if iterate_node not in self.executed and all((e[0] in self.executed for e in g.in_edges(iterate_node))):
return self.execution_graph.nodes[iterate_node]
# Check the children of the IterateInvocation node
for child_node in nx.dfs_postorder_nodes(g, iterate_node):
if child_node not in self.executed and all((e[0] in self.executed for e in g.in_edges(child_node))):
return self.execution_graph.nodes[child_node]
# If no IterateInvocation node or its children are ready, return the first ready node in the topological order
for node in topo_order:
if node not in self.executed and all((e[0] in self.executed for e in g.in_edges(node))):
return self.execution_graph.nodes[node]
# If no node is found, return None
return None
def _prepare_inputs(self, node: BaseInvocation):
input_edges = [e for e in self.execution_graph.edges if e.destination.node_id == node.id]

2
invokeai/app/services/shared/sqlite/sqlite_util.py
View File

@ -10,6 +10,7 @@ from invokeai.app.services.shared.sqlite_migrator.migrations.migration_4 import
from invokeai.app.services.shared.sqlite_migrator.migrations.migration_5 import build_migration_5
from invokeai.app.services.shared.sqlite_migrator.migrations.migration_6 import build_migration_6
from invokeai.app.services.shared.sqlite_migrator.migrations.migration_7 import build_migration_7
from invokeai.app.services.shared.sqlite_migrator.migrations.migration_8 import build_migration_8
from invokeai.app.services.shared.sqlite_migrator.sqlite_migrator_impl import SqliteMigrator
@ -37,6 +38,7 @@ def init_db(config: InvokeAIAppConfig, logger: Logger, image_files: ImageFileSto
migrator.register_migration(build_migration_5())
migrator.register_migration(build_migration_6())
migrator.register_migration(build_migration_7())
migrator.register_migration(build_migration_8(app_config=config))
migrator.run_migrations()
return db

2
invokeai/app/services/shared/sqlite_migrator/migrations/migration_7.py
View File

@ -11,7 +11,7 @@ class Migration7Callback:
def _drop_old_models_tables(self, cursor: sqlite3.Cursor) -> None:
"""Drops the old model_records, model_metadata, model_tags and tags tables."""
tables = ["model_records", "model_metadata", "model_tags", "tags"]
tables = ["model_config", "model_metadata", "model_tags", "tags"]
for table in tables:
cursor.execute(f"DROP TABLE IF EXISTS {table};")

91
invokeai/app/services/shared/sqlite_migrator/migrations/migration_8.py Normal file
View File

@ -0,0 +1,91 @@
import sqlite3
from pathlib import Path
from invokeai.app.services.config.config_default import InvokeAIAppConfig
from invokeai.app.services.shared.sqlite_migrator.sqlite_migrator_common import Migration
class Migration8Callback:
def __init__(self, app_config: InvokeAIAppConfig) -> None:
self._app_config = app_config
def __call__(self, cursor: sqlite3.Cursor) -> None:
self._drop_model_config_table(cursor)
self._migrate_abs_models_to_rel(cursor)
def _drop_model_config_table(self, cursor: sqlite3.Cursor) -> None:
"""Drops the old model_config table. This was missed in a previous migration."""
cursor.execute("DROP TABLE IF EXISTS model_config;")
def _migrate_abs_models_to_rel(self, cursor: sqlite3.Cursor) -> None:
"""Check all model paths & legacy config paths to determine if they are inside Invoke-managed directories. If
they are, update the paths to be relative to the managed directories.
This migration is a no-op for normal users (their paths will already be relative), but is necessary for users
who have been testing the RCs with their live databases. The paths were made absolute in the initial RC, but this
change was reverted. To smooth over the revert for our tests, we can migrate the paths back to relative.
"""
models_path = self._app_config.models_path
legacy_conf_path = self._app_config.legacy_conf_path
legacy_conf_dir = self._app_config.legacy_conf_dir
stmt = """---sql
SELECT
id,
path,
json_extract(config, '$.config_path') as config_path
FROM models;
"""
all_models = cursor.execute(stmt).fetchall()
for model_id, model_path, model_config_path in all_models:
# If the model path is inside the models directory, update it to be relative to the models directory.
if Path(model_path).is_relative_to(models_path):
new_path = Path(model_path).relative_to(models_path)
cursor.execute(
"""--sql
UPDATE models
SET config = json_set(config, '$.path', ?)
WHERE id = ?;
""",
(str(new_path), model_id),
)
# If the model has a legacy config path and it is inside the legacy conf directory, update it to be
# relative to the legacy conf directory. This also fixes up cases in which the config path was
# incorrectly relativized to the root directory. It will now be relativized to the legacy conf directory.
if model_config_path:
if Path(model_config_path).is_relative_to(legacy_conf_path):
new_config_path = Path(model_config_path).relative_to(legacy_conf_path)
elif Path(model_config_path).is_relative_to(legacy_conf_dir):
new_config_path = Path(*Path(model_config_path).parts[1:])
else:
new_config_path = None
if new_config_path:
cursor.execute(
"""--sql
UPDATE models
SET config = json_set(config, '$.config_path', ?)
WHERE id = ?;
""",
(str(new_config_path), model_id),
)
def build_migration_8(app_config: InvokeAIAppConfig) -> Migration:
"""
Build the migration from database version 7 to 8.
This migration does the following:
- Removes the `model_config` table.
- Migrates absolute model & legacy config paths to be relative to the models directory.
"""
migration_8 = Migration(
from_version=7,
to_version=8,
callback=Migration8Callback(app_config),
)
return migration_8

1112
invokeai/app/services/workflow_records/default_workflows/ESRGAN Upscaling with Canny ControlNet.json
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1923
invokeai/app/services/workflow_records/default_workflows/Face Detailer with IP-Adapter & Canny (See Note in Details).json
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invokeai/app/services/workflow_records/default_workflows/Multi ControlNet (Canny & Depth).json
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700
invokeai/app/services/workflow_records/default_workflows/Prompt from File.json
View File

@ -2,7 +2,7 @@
"name": "Prompt from File",
"author": "InvokeAI",
"description": "Sample workflow using Prompt from File node",
"version": "0.1.0",
"version": "2.0.0",
"contact": "invoke@invoke.ai",
"tags": "text2image, prompt from file, default",
"notes": "",
@ -14,11 +14,31 @@
{
"nodeId": "1b7e0df8-8589-4915-a4ea-c0088f15d642",
"fieldName": "file_path"
},
{
"nodeId": "1b7e0df8-8589-4915-a4ea-c0088f15d642",
"fieldName": "pre_prompt"
},
{
"nodeId": "1b7e0df8-8589-4915-a4ea-c0088f15d642",
"fieldName": "post_prompt"
},
{
"nodeId": "0eb5f3f5-1b91-49eb-9ef0-41d67c7eae77",
"fieldName": "width"
},
{
"nodeId": "0eb5f3f5-1b91-49eb-9ef0-41d67c7eae77",
"fieldName": "height"
},
{
"nodeId": "491ec988-3c77-4c37-af8a-39a0c4e7a2a1",
"fieldName": "board"
}
],
"meta": {
"category": "default",
"version": "2.0.0"
"version": "3.0.0"
},
"nodes": [
{
@ -26,847 +46,361 @@
"type": "invocation",
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"type": {
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"isCollectionOrScalar": false,
"name": "ClipField"
}
"label": ""
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},
"outputs": {
"conditioning": {
"id": "46205e6c-c5e2-44cb-9c82-1cd20b95674a",
"name": "conditioning",
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"type": {
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"isOpen": false,
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"version": "1.0.1",
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"nodePack": "invokeai",
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"type": "prompt_from_file",
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invokeai/app/services/workflow_records/default_workflows/Text to Image with LoRA.json
View File

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invokeai/app/services/workflow_records/default_workflows/Tiled Upscaling (Beta).json
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