-
- | Argument | Shortcut | Default | Description |
- |--------------------|------------|---------------------|--------------|
- | `--full_precision` | | `False` | Same as `--precision=fp32`|
- | `--weights ` | | `None` | Path to weights file; use `--model stable-diffusion-1.4` instead |
- | `--laion400m` | `-l` | `False` | Use older LAION400m weights; use `--model=laion400m` instead |
-
-
-
-!!! tip
-
- On Windows systems, you may run into
- problems when passing the invoke script standard backslashed path
- names because the Python interpreter treats "\" as an escape.
- You can either double your slashes (ick): `C:\\path\\to\\my\\file`, or
- use Linux/Mac style forward slashes (better): `C:/path/to/my/file`.
-
-## The .invokeai initialization file
-
-To start up invoke.py with your preferred settings, place your desired
-startup options in a file in your home directory named `.invokeai` The
-file should contain the startup options as you would type them on the
-command line (`--steps=10 --grid`), one argument per line, or a
-mixture of both using any of the accepted command switch formats:
-
-!!! example "my unmodified initialization file"
-
- ```bash title="~/.invokeai" linenums="1"
- # InvokeAI initialization file
- # This is the InvokeAI initialization file, which contains command-line default values.
- # Feel free to edit. If anything goes wrong, you can re-initialize this file by deleting
- # or renaming it and then running invokeai-configure again.
-
- # The --root option below points to the folder in which InvokeAI stores its models, configs and outputs.
- --root="/Users/mauwii/invokeai"
-
- # the --outdir option controls the default location of image files.
- --outdir="/Users/mauwii/invokeai/outputs"
-
- # You may place other frequently-used startup commands here, one or more per line.
- # Examples:
- # --web --host=0.0.0.0
- # --steps=20
- # -Ak_euler_a -C10.0
- ```
-
-!!! note
-
- The initialization file only accepts the command line arguments.
- There are additional arguments that you can provide on the `invoke>` command
- line (such as `-n` or `--iterations`) that cannot be entered into this file.
- Also be alert for empty blank lines at the end of the file, which will cause
- an arguments error at startup time.
-
-## List of prompt arguments
-
-After the invoke.py script initializes, it will present you with a `invoke>`
-prompt. Here you can enter information to generate images from text
-([txt2img](#txt2img)), to embellish an existing image or sketch
-([img2img](#img2img)), or to selectively alter chosen regions of the image
-([inpainting](#inpainting)).
-
-### txt2img
-
-!!! example ""
-
- ```bash
- invoke> waterfall and rainbow -W640 -H480
- ```
-
- This will create the requested image with the dimensions 640 (width)
- and 480 (height).
-
-Here are the invoke> command that apply to txt2img:
-
-| Argument -inpainting-1.5 not loaded Stable Diffusion inpainting model -stable-diffusion-1.5 active Stable Diffusion v1.5 -waifu-diffusion not loaded Waifu Diffusion v1.4 -- -#### `!switch
-invoke> !edit_model waifu-diffusion ->> Editing model waifu-diffusion from configuration file ./configs/models.yaml -description: Waifu diffusion v1.4beta -weights: models/ldm/stable-diffusion-v1/model-epoch10-float16.ckpt -config: configs/stable-diffusion/v1-inference.yaml -width: 512 -height: 512 - ->> New configuration: -waifu-diffusion: - config: configs/stable-diffusion/v1-inference.yaml - description: Waifu diffusion v1.4beta - weights: models/ldm/stable-diffusion-v1/model-epoch10-float16.ckpt - height: 512 - width: 512 - -OK to import [n]? y ->> Caching model stable-diffusion-1.4 in system RAM ->> Loading waifu-diffusion from models/ldm/stable-diffusion-v1/model-epoch10-float16.ckpt -... -- -### History processing - -The CLI provides a series of convenient commands for reviewing previous actions, -retrieving them, modifying them, and re-running them. - -#### `!history` - -The invoke script keeps track of all the commands you issue during a session, -allowing you to re-run them. On Mac and Linux systems, it also writes the -command-line history out to disk, giving you access to the most recent 1000 -commands issued. - -The `!history` command will return a numbered list of all the commands issued -during the session (Windows), or the most recent 1000 commands (Mac|Linux). You -can then repeat a command by using the command `!NNN`, where "NNN" is the -history line number. For example: - -!!! example "" - - ```bash - invoke> !history - ... - [14] happy woman sitting under tree wearing broad hat and flowing garment - [15] beautiful woman sitting under tree wearing broad hat and flowing garment - [18] beautiful woman sitting under tree wearing broad hat and flowing garment -v0.2 -n6 - [20] watercolor of beautiful woman sitting under tree wearing broad hat and flowing garment -v0.2 -n6 -S2878767194 - [21] surrealist painting of beautiful woman sitting under tree wearing broad hat and flowing garment -v0.2 -n6 -S2878767194 - ... - invoke> !20 - invoke> watercolor of beautiful woman sitting under tree wearing broad hat and flowing garment -v0.2 -n6 -S2878767194 - ``` - -####`!fetch` - -This command retrieves the generation parameters from a previously generated -image and either loads them into the command line (Linux|Mac), or prints them -out in a comment for copy-and-paste (Windows). You may provide either the name -of a file in the current output directory, or a full file path. Specify path to -a folder with image png files, and wildcard \*.png to retrieve the dream command -used to generate the images, and save them to a file commands.txt for further -processing. - -!!! example "load the generation command for a single png file" - - ```bash - invoke> !fetch 0000015.8929913.png - # the script returns the next line, ready for editing and running: - invoke> a fantastic alien landscape -W 576 -H 512 -s 60 -A plms -C 7.5 - ``` - -!!! example "fetch the generation commands from a batch of files and store them into `selected.txt`" - - ```bash - invoke> !fetch outputs\selected-imgs\*.png selected.txt - ``` - -#### `!replay` - -This command replays a text file generated by !fetch or created manually - -!!! example - - ```bash - invoke> !replay outputs\selected-imgs\selected.txt - ``` - -!!! note - - These commands may behave unexpectedly if given a PNG file that was - not generated by InvokeAI. - -#### `!search
Detailed Discussion
+ +Python (and PyTorch) relies on the memory allocator from the C Standard Library (`libc`). On linux, with the GNU C Standard Library implementation (`glibc`), our memory access patterns have been observed to cause severe memory fragmentation. + +This fragmentation results in large amounts of memory that has been freed but can't be released back to the OS. Loading models from disk and moving them between CPU/CUDA seem to be the operations that contribute most to the fragmentation. + +This memory fragmentation issue can result in OOM crashes during frequent model switching, even if `ram` (the max RAM cache size) is set to a reasonable value (e.g. a OOM crash with `ram=16` on a system with 32GB of RAM). + +This problem may also exist on other OSes, and other `libc` implementations. But, at the time of writing, it has only been investigated on linux with `glibc`. + +To better understand how the `glibc` memory allocator works, see these references: + +- Basics:Automatic Install
-## **[Automated Installer (Recommended)](010_INSTALL_AUTOMATED.md)** - ✅ This is the recommended installation method for first-time users. +✅ The automatic install is the best way to run InvokeAI. Check out the [installation guide] to get started. - This is a script that will install all of InvokeAI's essential - third party libraries and InvokeAI itself. +Manual Install
-🖥️ **Download the latest installer .zip file here** : https://github.com/invoke-ai/InvokeAI/releases/latest - -- *Look for the file labelled "InvokeAI-installer-v4.X.X.zip" at the bottom of the page* -- If you experience issues, read through the full [installation instructions](010_INSTALL_AUTOMATED.md) to make sure you have met all of the installation requirements. If you need more help, join the [Discord](discord.gg/invoke-ai) or create an issue on [Github](https://github.com/invoke-ai/InvokeAI). +If you are familiar with python and want more control over the packages that are installed, you can [install InvokeAI manually via PyPI]. +Developer Install
+If you want to contribute to InvokeAI, consult the [developer install guide]. -## **[Manual Installation](020_INSTALL_MANUAL.md)** -This method is recommended for experienced users and developers. +Docker Install
- In this method you will manually run the commands needed to install - InvokeAI and its dependencies. We offer two recipes: one suited to - those who prefer the `conda` tool, and one suited to those who prefer - `pip` and Python virtual environments. In our hands the pip install - is faster and more reliable, but your mileage may vary. - Note that the conda installation method is currently deprecated and - will not be supported at some point in the future. - -## **[Docker Installation](040_INSTALL_DOCKER.md)** This method is recommended for those familiar with running Docker containers. We offer a method for creating Docker containers containing InvokeAI and its dependencies. This method is recommended for individuals with experience with Docker containers and understand the pluses and minuses of a container-based install. -## Other Installation Guides - - [PyPatchMatch](060_INSTALL_PATCHMATCH.md) - - [XFormers](070_INSTALL_XFORMERS.md) - - [CUDA and ROCm Drivers](030_INSTALL_CUDA_AND_ROCM.md) - - [Installing New Models](050_INSTALLING_MODELS.md) +See the [docker installation guide]. -## :fontawesome-solid-computer: Hardware Requirements +Other Installation Guides
-### :octicons-cpu-24: System - -You wil need one of the following: - -- :simple-nvidia: An NVIDIA-based graphics card with 4 GB or more VRAM memory. -- :simple-amd: An AMD-based graphics card with 4 GB or more VRAM memory (Linux - only) -- :fontawesome-brands-apple: An Apple computer with an M1 chip. - -** SDXL 1.0 Requirements* -To use SDXL, user must have one of the following: -- :simple-nvidia: An NVIDIA-based graphics card with 8 GB or more VRAM memory. -- :simple-amd: An AMD-based graphics card with 16 GB or more VRAM memory (Linux - only) -- :fontawesome-brands-apple: An Apple computer with an M1 chip. - - -### :fontawesome-solid-memory: Memory and Disk - -- At least 12 GB Main Memory RAM. -- At least 18 GB of free disk space for the machine learning model, Python, and - all its dependencies. - -We do **not recommend** the following video cards due to issues with their -running in half-precision mode and having insufficient VRAM to render 512x512 -images in full-precision mode: - -- NVIDIA 10xx series cards such as the 1080ti -- GTX 1650 series cards -- GTX 1660 series cards +- [PyPatchMatch](060_INSTALL_PATCHMATCH.md) +- [Installing Models](050_INSTALLING_MODELS.md) +[install InvokeAI manually via PyPI]: 020_INSTALL_MANUAL.md +[developer install guide]: INSTALL_DEVELOPMENT.md +[docker installation guide]: 040_INSTALL_DOCKER.md +[installation guide]: 010_INSTALL_AUTOMATED.md +[FAQ]: ../help/FAQ.md +[discord]: discord.gg/invoke-ai +[create an issue]: https://github.com/invoke-ai/InvokeAI/issues +[installation requirements]: INSTALL_REQUIREMENTS.md diff --git a/docs/installation/INSTALL_DEVELOPMENT.md b/docs/installation/INSTALL_DEVELOPMENT.md new file mode 100644 index 0000000000..c9a15216ff --- /dev/null +++ b/docs/installation/INSTALL_DEVELOPMENT.md @@ -0,0 +1,36 @@ +# Developer Install + +!!! warning + + InvokeAI uses a SQLite database. By running on `main`, you accept responsibility for your database. This + means making regular backups (especially before pulling) and/or fixing it yourself in the event that a + PR introduces a schema change. + + If you don't need persistent backend storage, you can use an ephemeral in-memory database by setting + `use_memory_db: true` in your `invokeai.yaml` file. You'll also want to set `scan_models_on_startup: true` + so that your models are registered on startup. + + If this is untenable, you should run the application via the official installer or a manual install of the + python package from PyPI. These releases will not break your database. + +If you have an interest in how InvokeAI works, or you would like to add features or bugfixes, you are encouraged to install the source code for InvokeAI. + +!!! info "Why do I need the frontend toolchain?" + + The repo doesn't contain a build of the frontend. You'll be responsible for rebuilding it (or running it in dev mode) to use the app, as described in the [frontend dev toolchain] docs. + +Installation
+ +1. [Fork and clone] the [InvokeAI repo]. +1. Follow the [manual installation] docs to create a new virtual environment for the development install. + - When installing the InvokeAI package, add `-e` to the command so you get an [editable install]. +1. Install the [frontend dev toolchain] and do a production build of the UI as described. +1. You can now run the app as described in the [manual installation] docs. + +As described in the [frontend dev toolchain] docs, you can run the UI using a dev server. If you do this, you won't need to continually rebuild the frontend. Instead, you run the dev server and use the app with the server URL it provides. + +[Fork and clone]: https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/working-with-forks/fork-a-repo +[InvokeAI repo]: https://github.com/invoke-ai/InvokeAI +[frontend dev toolchain]: ../contributing/frontend/OVERVIEW.md +[manual installation]: installation/020_INSTALL_MANUAL.md +[editable install]: https://pip.pypa.io/en/latest/cli/pip_install/#cmdoption-e diff --git a/docs/installation/INSTALL_REQUIREMENTS.md b/docs/installation/INSTALL_REQUIREMENTS.md new file mode 100644 index 0000000000..31f60b86eb --- /dev/null +++ b/docs/installation/INSTALL_REQUIREMENTS.md @@ -0,0 +1,181 @@ +# Requirements + +## GPU + +!!! warning "Problematic Nvidia GPUs" + + We do not recommend these GPUs. They cannot operate with half precision, but have insufficient VRAM to generate 512x512 images at full precision. + + - NVIDIA 10xx series cards such as the 1080 TI + - GTX 1650 series cards + - GTX 1660 series cards + +Invoke runs best with a dedicated GPU, but will fall back to running on CPU, albeit much slower. You'll need a beefier GPU for SDXL. + +!!! example "Stable Diffusion 1.5" + + === "Nvidia" + + ``` + Any GPU with at least 4GB VRAM. + ``` + + === "AMD" + + ``` + Any GPU with at least 4GB VRAM. Linux only. + ``` + + === "Mac" + + ``` + Any Apple Silicon Mac with at least 8GB memory. + ``` + +!!! example "Stable Diffusion XL" + + === "Nvidia" + + ``` + Any GPU with at least 8GB VRAM. Linux only. + ``` + + === "AMD" + + ``` + Any GPU with at least 16GB VRAM. + ``` + + === "Mac" + + ``` + Any Apple Silicon Mac with at least 16GB memory. + ``` + +## RAM + +At least 12GB of RAM. + +## Disk + +SSDs will, of course, offer the best performance. + +The base application disk usage depends on the torch backend. + +!!! example "Disk" + + === "Nvidia (CUDA)" + + ``` + ~6.5GB + ``` + + === "AMD (ROCm)" + + ``` + ~12GB + ``` + + === "Mac (MPS)" + + ``` + ~3.5GB + ``` + +You'll need to set aside some space for images, depending on how much you generate. A couple GB is enough to get started. + +You'll need a good chunk of space for models. Even if you only install the most popular models and the usual support models (ControlNet, IP Adapter ,etc), you will quickly hit 50GB of models. + +!!! info "`tmpfs` on Linux" + + If your temporary directory is mounted as a `tmpfs`, ensure it has sufficient space. + +## Python + +Invoke requires python 3.10 or 3.11. If you don't already have one of these versions installed, we suggest installing 3.11, as it will be supported for longer. + +Check that your system has an up-to-date Python installed by running `python --version` in the terminal (Linux, macOS) or cmd/powershell (Windows). + +Installing Python (Windows)
+ +- Install python 3.11 with [an official installer]. +- The installer includes an option to add python to your PATH. Be sure to enable this. If you missed it, re-run the installer, choose to modify an existing installation, and tick that checkbox. +- You may need to install [Microsoft Visual C++ Redistributable]. + +Installing Python (macOS)
+ +- Install python 3.11 with [an official installer]. +- If model installs fail with a certificate error, you may need to run this command (changing the python version to match what you have installed): `/Applications/Python\ 3.10/Install\ Certificates.command` +- If you haven't already, you will need to install the XCode CLI Tools by running `xcode-select --install` in a terminal. + +Installing Python (Linux)
+ +- Follow the [linux install instructions], being sure to install python 3.11. +- You'll need to install `libglib2.0-0` and `libgl1-mesa-glx` for OpenCV to work. For example, on a Debian system: `sudo apt update && sudo apt install -y libglib2.0-0 libgl1-mesa-glx` + +## Drivers + +If you have an Nvidia or AMD GPU, you may need to manually install drivers or other support packages for things to work well or at all. + +### Nvidia + +Run `nvidia-smi` on your system's command line to verify that drivers and CUDA are installed. If this command fails, or doesn't report versions, you will need to install drivers. + +Go to the [CUDA Toolkit Downloads] and carefully follow the instructions for your system to get everything installed. + +Confirm that `nvidia-smi` displays driver and CUDA versions after installation. + +#### Linux - via Nvidia Container Runtime + +An alternative to installing CUDA locally is to use the [Nvidia Container Runtime] to run the application in a container. + +#### Windows - Nvidia cuDNN DLLs + +An out-of-date cuDNN library can greatly hamper performance on 30-series and 40-series cards. Check with the community on discord to compare your `it/s` if you think you may need this fix. + +First, locate the destination for the DLL files and make a quick back up: + +1. Find your InvokeAI installation folder, e.g. `C:\Users\Username\InvokeAI\`. +1. Open the `.venv` folder, e.g. `C:\Users\Username\InvokeAI\.venv` (you may need to show hidden files to see it). +1. Navigate deeper to the `torch` package, e.g. `C:\Users\Username\InvokeAI\.venv\Lib\site-packages\torch`. +1. Copy the `lib` folder inside `torch` and back it up somewhere. + +Next, download and copy the updated cuDNN DLLs: + +1. Go toValid 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..""" diff --git a/invokeai/app/services/download/download_default.py b/invokeai/app/services/download/download_default.py index bc32422a5b..f393a18dcb 100644 --- a/invokeai/app/services/download/download_default.py +++ b/invokeai/app/services/download/download_default.py @@ -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() diff --git a/invokeai/app/services/model_install/model_install_base.py b/invokeai/app/services/model_install/model_install_base.py index 0c44571eee..0ea901fb46 100644 --- a/invokeai/app/services/model_install/model_install_base.py +++ b/invokeai/app/services/model_install/model_install_base.py @@ -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: """ diff --git a/invokeai/app/services/model_install/model_install_default.py b/invokeai/app/services/model_install/model_install_default.py index 6d71caf28b..b557797a4b 100644 --- a/invokeai/app/services/model_install/model_install_default.py +++ b/invokeai/app/services/model_install/model_install_default.py @@ -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 diff --git a/invokeai/app/services/model_records/__init__.py b/invokeai/app/services/model_records/__init__.py index 7f888cf1f3..4fee477466 100644 --- a/invokeai/app/services/model_records/__init__.py +++ b/invokeai/app/services/model_records/__init__.py @@ -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", ] diff --git a/invokeai/app/services/session_processor/session_processor_default.py b/invokeai/app/services/session_processor/session_processor_default.py index 9484395780..eb868391b8 100644 --- a/invokeai/app/services/session_processor/session_processor_default.py +++ b/invokeai/app/services/session_processor/session_processor_default.py @@ -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( diff --git a/invokeai/app/services/shared/graph.py b/invokeai/app/services/shared/graph.py index e3941d9ca3..cc2ea5cedb 100644 --- a/invokeai/app/services/shared/graph.py +++ b/invokeai/app/services/shared/graph.py @@ -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] diff --git a/invokeai/app/services/shared/sqlite/sqlite_util.py b/invokeai/app/services/shared/sqlite/sqlite_util.py index 25006f5aba..7e27182f29 100644 --- a/invokeai/app/services/shared/sqlite/sqlite_util.py +++ b/invokeai/app/services/shared/sqlite/sqlite_util.py @@ -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 diff --git a/invokeai/app/services/shared/sqlite_migrator/migrations/migration_7.py b/invokeai/app/services/shared/sqlite_migrator/migrations/migration_7.py index 6f5ddbf561..fa573d63a6 100644 --- a/invokeai/app/services/shared/sqlite_migrator/migrations/migration_7.py +++ b/invokeai/app/services/shared/sqlite_migrator/migrations/migration_7.py @@ -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};") diff --git a/invokeai/app/services/shared/sqlite_migrator/migrations/migration_8.py b/invokeai/app/services/shared/sqlite_migrator/migrations/migration_8.py new file mode 100644 index 0000000000..154a5236ca --- /dev/null +++ b/invokeai/app/services/shared/sqlite_migrator/migrations/migration_8.py @@ -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 diff --git a/invokeai/app/services/workflow_records/default_workflows/ESRGAN Upscaling with Canny ControlNet.json b/invokeai/app/services/workflow_records/default_workflows/ESRGAN Upscaling with Canny ControlNet.json index 40d222f856..dd98eca18f 100644 --- a/invokeai/app/services/workflow_records/default_workflows/ESRGAN Upscaling with Canny ControlNet.json +++ b/invokeai/app/services/workflow_records/default_workflows/ESRGAN Upscaling with Canny ControlNet.json @@ -2,7 +2,7 @@ "name": "ESRGAN Upscaling with Canny ControlNet", "author": "InvokeAI", "description": "Sample workflow for using Upscaling with ControlNet with SD1.5", - "version": "1.0.1", + "version": "2.0.0", "contact": "invoke@invoke.ai", "tags": "upscale, controlnet, default", "notes": "", @@ -18,1188 +18,616 @@ { "nodeId": "771bdf6a-0813-4099-a5d8-921a138754d4", "fieldName": "image" + }, + { + "nodeId": "f7564dd2-9539-47f2-ac13-190804461f4e", + "fieldName": "model_name" + }, + { + "nodeId": "ca1d020c-89a8-4958-880a-016d28775cfa", + "fieldName": "control_model" + }, + { + "nodeId": "3ed9b2ef-f4ec-40a7-94db-92e63b583ec0", + "fieldName": "board" } ], "meta": { - "category": "default", - "version": "2.0.0" + "version": "3.0.0", + "category": "default" }, + "id": "0e71a27e-a22b-4a9b-b20a-6d789abff2bc", "nodes": [ { "id": "e8bf67fe-67de-4227-87eb-79e86afdfc74", "type": "invocation", "data": { "id": "e8bf67fe-67de-4227-87eb-79e86afdfc74", - "type": "compel", - "label": "", - "isOpen": true, - "notes": "", - "isIntermediate": true, - "useCache": true, - "version": "1.0.0", + "version": "1.1.1", "nodePack": "invokeai", + "label": "", + "notes": "", + "type": "compel", "inputs": { "prompt": { - "id": "5f762fae-d791-42d9-8ab5-2b830c33ff20", "name": "prompt", - "fieldKind": "input", "label": "", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "StringField" - }, "value": "" }, "clip": { - "id": "8ac95f40-317d-4513-bbba-b99effd3b438", "name": "clip", - "fieldKind": "input", - "label": "", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "ClipField" - } + "label": "" } }, - "outputs": { - "conditioning": { - "id": "46c65b2b-c0b5-40c2-b183-74e9451c6d56", - "name": "conditioning", - "fieldKind": "output", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "ConditioningField" - } - } - } + "isOpen": true, + "isIntermediate": true, + "useCache": true }, "position": { "x": 1250, "y": 1500 - }, - "width": 320, - "height": 219 + } }, { "id": "d8ace142-c05f-4f1d-8982-88dc7473958d", "type": "invocation", "data": { "id": "d8ace142-c05f-4f1d-8982-88dc7473958d", - "type": "main_model_loader", - "label": "", - "isOpen": true, - "notes": "", - "isIntermediate": true, - "useCache": true, - "version": "1.0.0", + "version": "1.0.2", "nodePack": "invokeai", + "label": "", + "notes": "", + "type": "main_model_loader", "inputs": { "model": { - "id": "b35ae88a-f2d2-43f6-958c-8c624391250f", "name": "model", - "fieldKind": "input", "label": "", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "MainModelField" - }, "value": { - "model_name": "stable-diffusion-v1-5", - "base_model": "sd-1", - "model_type": "main" + "key": "5cd43ca0-dd0a-418d-9f7e-35b2b9d5e106", + "hash": "blake3:6987f323017f597213cc3264250edf57056d21a40a0a85d83a1a33a7d44dc41a", + "name": "Deliberate_v5", + "base": "sd-1", + "type": "main" } } }, - "outputs": { - "unet": { - "id": "02f243cb-c6e2-42c5-8be9-ef0519d54383", - "name": "unet", - "fieldKind": "output", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "UNetField" - } - }, - "clip": { - "id": "7762ed13-5b28-40f4-85f1-710942ceb92a", - "name": "clip", - "fieldKind": "output", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "ClipField" - } - }, - "vae": { - "id": "69566153-1918-417d-a3bb-32e9e857ef6b", - "name": "vae", - "fieldKind": "output", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "VaeField" - } - } - } + "isOpen": true, + "isIntermediate": true, + "useCache": true }, "position": { "x": 700, "y": 1375 - }, - "width": 320, - "height": 193 + } }, { "id": "771bdf6a-0813-4099-a5d8-921a138754d4", "type": "invocation", "data": { "id": "771bdf6a-0813-4099-a5d8-921a138754d4", - "type": "image", - "label": "", - "isOpen": true, - "notes": "", - "isIntermediate": true, - "useCache": true, - "version": "1.0.0", + "version": "1.0.2", "nodePack": "invokeai", + "label": "", + "notes": "", + "type": "image", "inputs": { "image": { - "id": "0f6d68a2-38bd-4f65-a112-0a256c7a2678", "name": "image", - "fieldKind": "input", "label": "Image To Upscale", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "ImageField" + "value": { + "image_name": "d2e42ba6-d420-496b-82db-91c9b75956c1.png" } } }, - "outputs": { - "image": { - "id": "76f6f9b6-755b-4373-93fa-6a779998d2c8", - "name": "image", - "fieldKind": "output", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "ImageField" - } - }, - "width": { - "id": "6858e46b-707c-444f-beda-9b5f4aecfdf8", - "name": "width", - "fieldKind": "output", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "IntegerField" - } - }, - "height": { - "id": "421bdc6e-ecd1-4935-9665-d38ab8314f79", - "name": "height", - "fieldKind": "output", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "IntegerField" - } - } - } + "isOpen": true, + "isIntermediate": true, + "useCache": true }, "position": { - "x": 375, - "y": 1900 - }, - "width": 320, - "height": 189 + "x": 344.5593065887157, + "y": 1698.161491368619 + } }, { "id": "f7564dd2-9539-47f2-ac13-190804461f4e", "type": "invocation", "data": { "id": "f7564dd2-9539-47f2-ac13-190804461f4e", - "type": "esrgan", - "label": "", - "isOpen": true, - "notes": "", - "isIntermediate": true, - "useCache": true, - "version": "1.3.0", + "version": "1.3.2", "nodePack": "invokeai", + "label": "", + "notes": "", + "type": "esrgan", "inputs": { + "board": { + "name": "board", + "label": "" + }, "metadata": { - "id": "8fa0c7eb-5bd3-4575-98e7-72285c532504", "name": "metadata", - "fieldKind": "input", - "label": "", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "MetadataField" - } + "label": "" }, "image": { - "id": "3c949799-a504-41c9-b342-cff4b8146c48", "name": "image", - "fieldKind": "input", - "label": "", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "ImageField" - } + "label": "" }, "model_name": { - "id": "77cb4750-53d6-4c2c-bb5c-145981acbf17", "name": "model_name", - "fieldKind": "input", - "label": "", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "EnumField" - }, - "value": "RealESRGAN_x4plus.pth" + "label": "Upscaler Model", + "value": "RealESRGAN_x2plus.pth" }, "tile_size": { - "id": "7787b3ad-46ee-4248-995f-bc740e1f988b", "name": "tile_size", - "fieldKind": "input", "label": "", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "IntegerField" - }, "value": 400 } }, - "outputs": { - "image": { - "id": "37e6308e-e926-4e07-b0db-4e8601f495d0", - "name": "image", - "fieldKind": "output", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "ImageField" - } - }, - "width": { - "id": "c194d84a-fac7-4856-b646-d08477a5ad2b", - "name": "width", - "fieldKind": "output", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "IntegerField" - } - }, - "height": { - "id": "b2a6206c-a9c8-4271-a055-0b93a7f7d505", - "name": "height", - "fieldKind": "output", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "IntegerField" - } - } - } + "isOpen": true, + "isIntermediate": true, + "useCache": true }, "position": { - "x": 775, - "y": 1900 - }, - "width": 320, - "height": 295 + "x": 717.3863693661265, + "y": 1721.9215053134815 + } }, { "id": "1d887701-df21-4966-ae6e-a7d82307d7bd", "type": "invocation", "data": { "id": "1d887701-df21-4966-ae6e-a7d82307d7bd", - "type": "canny_image_processor", - "label": "", - "isOpen": true, - "notes": "", - "isIntermediate": true, - "useCache": true, - "version": "1.2.0", + "version": "1.3.2", "nodePack": "invokeai", + "label": "", + "notes": "", + "type": "canny_image_processor", "inputs": { + "board": { + "name": "board", + "label": "" + }, "metadata": { - "id": "52c877c8-25d9-4949-8518-f536fcdd152d", "name": "metadata", - "fieldKind": "input", - "label": "", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "MetadataField" - } + "label": "" }, "image": { - "id": "e0af11fe-4f95-4193-a599-cf40b6a963f5", "name": "image", - "fieldKind": "input", + "label": "" + }, + "detect_resolution": { + "name": "detect_resolution", "label": "", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "ImageField" - } + "value": 512 + }, + "image_resolution": { + "name": "image_resolution", + "label": "", + "value": 512 }, "low_threshold": { - "id": "ab775f7b-f556-4298-a9d6-2274f3a6c77c", "name": "low_threshold", - "fieldKind": "input", "label": "", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "IntegerField" - }, "value": 100 }, "high_threshold": { - "id": "9e58b615-06e4-417f-b0d8-63f1574cd174", "name": "high_threshold", - "fieldKind": "input", "label": "", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "IntegerField" - }, "value": 200 } }, - "outputs": { - "image": { - "id": "61feb8bf-95c9-4634-87e2-887fc43edbdf", - "name": "image", - "fieldKind": "output", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "ImageField" - } - }, - "width": { - "id": "9e203e41-73f7-4cfa-bdca-5040e5e60c55", - "name": "width", - "fieldKind": "output", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "IntegerField" - } - }, - "height": { - "id": "ec7d99dc-0d82-4495-a759-6423808bff1c", - "name": "height", - "fieldKind": "output", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - 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"_version": 1, "name": "Face Detailer with IP-Adapter & Canny (See Note in Details)", "author": "kosmoskatten", "description": "A workflow to add detail to and improve faces. This workflow is most effective when used with a model that creates realistic outputs. ", - "version": "1.0.0", + "version": "2.0.0", "contact": "invoke@invoke.ai", "tags": "face detailer, IP-Adapter, Canny", "notes": "Set this image as the blur mask: https://i.imgur.com/Gxi61zP.png", @@ -27,11 +26,19 @@ { "nodeId": "c59e815c-1f3a-4e2b-b6b8-66f4b005e955", "fieldName": "image" + }, + { + "nodeId": "f60b6161-8f26-42f6-89ff-545e6011e501", + "fieldName": "control_model" + }, + { + "nodeId": "22b750db-b85e-486b-b278-ac983e329813", + "fieldName": "ip_adapter_model" } ], "meta": { "category": "default", - "version": "2.0.0" + "version": "3.0.0" }, "nodes": [ { @@ -39,2353 +46,1019 @@ "type": "invocation", "data": { "id": "44f2c190-eb03-460d-8d11-a94d13b33f19", - "type": "compel", - "label": "", - "isOpen": true, - "notes": "", - "isIntermediate": true, - "useCache": true, - "version": "1.0.0", + "version": "1.1.1", "nodePack": "invokeai", + "label": "", + "notes": "", + "type": "compel", "inputs": { "prompt": { - "id": "916b229a-38e1-45a2-a433-cca97495b143", "name": "prompt", - 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"name": "ImageField" - } - }, - "width": { - "id": "37e228ef-cb59-4477-b18f-343609d7bb4e", - "name": "width", - "fieldKind": "output", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "IntegerField" - } - }, - "height": { - "id": "2de6080e-8bc3-42cd-bb8a-afd72f082df4", - "name": "height", - "fieldKind": "output", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "IntegerField" - } - } - } + "isOpen": true, + "isIntermediate": true, + "useCache": true }, "position": { "x": 5000, "y": 300 - }, - "width": 320, - "height": 295 + } }, { "id": "f0de6c44-4515-4f79-bcc0-dee111bcfe31", "type": "invocation", "data": { "id": "f0de6c44-4515-4f79-bcc0-dee111bcfe31", - "type": "float", - "label": "Face Detail Scale", - "isOpen": false, - "notes": "The image is cropped to the face and scaled to 512x512. This value can scale even more. Best result with value between 1-2.\n\n1 = 512\n2 = 1024\n\n", - "isIntermediate": true, - "useCache": true, - "version": "1.0.0", + "version": "1.0.1", "nodePack": "invokeai", + "label": "Face Detail Scale", + "notes": "The image is cropped to the face and scaled to 512x512. This value can scale even more. Best result with value between 1-2.\n\n1 = 512\n2 = 1024\n\n", + "type": "float", "inputs": { "value": { - "id": "9b51a26f-af3c-4caa-940a-5183234b1ed7", "name": "value", - "fieldKind": "input", "label": "Face Detail Scale", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "FloatField" - }, "value": 1.5 } }, - "outputs": { - "value": { - "id": "c7c87b77-c149-4e9c-8ed1-beb1ba013055", - "name": "value", - "fieldKind": "output", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "FloatField" - } - } - } + "isOpen": false, + "isIntermediate": true, + "useCache": true }, "position": { "x": 2578.2364832140506, "y": 78.7948456497351 - }, - "width": 320, - "height": 24 + } }, { "id": "c6359181-6479-40ec-bf3a-b7e8451683b8", "type": "invocation", "data": { "id": "c6359181-6479-40ec-bf3a-b7e8451683b8", - "type": "main_model_loader", - "version": "1.0.0", + "version": "1.0.2", "label": "", "notes": "", - "isOpen": true, - "isIntermediate": true, - "useCache": true, + "type": "main_model_loader", "inputs": { "model": { - "id": "055b0ebf-3dd6-4f49-99cb-e99b7272736e", "name": "model", - "type": { - "name": "MainModelField", - "isCollection": false, - "isCollectionOrScalar": false - }, - "label": "", - "fieldKind": "input" + "label": "" } }, - "outputs": { - "vae": { - "id": "8326ee8e-4b2c-4209-be86-44c3014b7400", - "name": "vae", - "type": { - "name": "VaeField", - "isCollection": false, - "isCollectionOrScalar": false - }, - "fieldKind": "output" - }, - "clip": { - "id": "ccce5429-6295-434e-b054-d8965ecbd88f", - "name": "clip", - "type": { - "name": "ClipField", - "isCollection": false, - "isCollectionOrScalar": false - }, - "fieldKind": "output" - }, - "unet": { - "id": "1b410439-dd40-4ac5-ae3f-7f8cfcf42043", - "name": "unet", - "type": { - "name": "UNetField", - "isCollection": false, - "isCollectionOrScalar": false - }, - "fieldKind": "output" - } - } + "isOpen": true, + "isIntermediate": true, + "useCache": true }, "position": { "x": 2031.5518710051792, "y": -492.1742944307074 - }, - "width": 320, - "height": 168 + } } ], "edges": [ diff --git a/invokeai/app/services/workflow_records/default_workflows/Multi ControlNet (Canny & Depth).json b/invokeai/app/services/workflow_records/default_workflows/Multi ControlNet (Canny & Depth).json index 8a21a87928..d859094216 100644 --- a/invokeai/app/services/workflow_records/default_workflows/Multi ControlNet (Canny & Depth).json +++ b/invokeai/app/services/workflow_records/default_workflows/Multi ControlNet (Canny & Depth).json @@ -2,7 +2,7 @@ "name": "Multi ControlNet (Canny & Depth)", "author": "InvokeAI", "description": "A sample workflow using canny & depth ControlNets to guide the generation process. ", - "version": "1.0.0", + "version": "2.0.0", "contact": "invoke@invoke.ai", "tags": "ControlNet, canny, depth", "notes": "", @@ -26,11 +26,19 @@ { "nodeId": "8e860e51-5045-456e-bf04-9a62a2a5c49e", "fieldName": "image" + }, + { + "nodeId": "d204d184-f209-4fae-a0a1-d152800844e1", + "fieldName": "control_model" + }, + { + "nodeId": "a33199c2-8340-401e-b8a2-42ffa875fc1c", + "fieldName": "control_model" } ], "meta": { "category": "default", - "version": "2.0.0" + "version": "3.0.0" }, "nodes": [ { @@ -38,1306 +46,731 @@ "type": "invocation", "data": { "id": "8e860e51-5045-456e-bf04-9a62a2a5c49e", - "type": "image", - "label": "", - "isOpen": true, - "notes": "", - "isIntermediate": true, - "useCache": true, - "version": "1.0.0", + "version": "1.0.2", "nodePack": "invokeai", + "label": "", + "notes": "", + "type": "image", "inputs": { "image": { - "id": "189c8adf-68cc-4774-a729-49da89f6fdf1", "name": "image", - "fieldKind": "input", - "label": "Depth Input Image", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "ImageField" - } + "label": "Depth Input Image" } }, - "outputs": { - "image": { - "id": "1a31cacd-9d19-4f32-b558-c5e4aa39ce73", - "name": "image", - "fieldKind": "output", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "ImageField" - } - }, - "width": { - "id": "12f298fd-1d11-4cca-9426-01240f7ec7cf", - "name": "width", - "fieldKind": "output", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "IntegerField" - } - }, - "height": { - "id": "c47dabcb-44e8-40c9-992d-81dca59f598e", - "name": "height", - "fieldKind": "output", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "IntegerField" - } - } - } + "isOpen": true, + "isIntermediate": true, + "useCache": true }, "position": { - "x": 3625, - "y": -75 - }, - "width": 320, - "height": 189 + "x": 3666.135718057363, + "y": 186.66887319822808 + } }, { "id": "a33199c2-8340-401e-b8a2-42ffa875fc1c", "type": "invocation", "data": { "id": "a33199c2-8340-401e-b8a2-42ffa875fc1c", - "type": "controlnet", - "label": "", - "isOpen": true, - "notes": "", - "isIntermediate": true, - "useCache": true, "version": "1.1.1", "nodePack": "invokeai", + "label": "", + "notes": "", + "type": "controlnet", "inputs": { "image": { - "id": "4e0a3172-d3c2-4005-a84c-fa12a404f8a0", "name": "image", - "fieldKind": "input", - "label": "", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "ImageField" - } + "label": "" }, "control_model": { - "id": "8cb2d998-4086-430a-8b13-94cbc81e3ca3", "name": "control_model", - "fieldKind": "input", - "label": "", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "ControlNetModelField" - }, + "label": "Control Model (select depth)", "value": { - "model_name": "depth", - "base_model": "sd-1" + "key": "87e8855c-671f-4c9e-bbbb-8ed47ccb4aac", + "hash": "blake3:2550bf22a53942dfa28ab2fed9d10d80851112531f44d977168992edf9d0534c", + "name": "control_v11f1p_sd15_depth", + "base": "sd-1", + "type": "controlnet" } }, "control_weight": { - "id": "5e32bd8a-9dc8-42d8-9bcc-c2b0460c0b0f", "name": "control_weight", - "fieldKind": "input", "label": "", - "type": { - "isCollection": false, - "isCollectionOrScalar": true, - "name": "FloatField" - }, "value": 1 }, "begin_step_percent": { - "id": "c258a276-352a-416c-8358-152f11005c0c", "name": "begin_step_percent", - "fieldKind": "input", "label": "", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "FloatField" - }, "value": 0 }, "end_step_percent": { - "id": "43001125-0d70-4f87-8e79-da6603ad6c33", "name": "end_step_percent", - "fieldKind": "input", "label": "", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "FloatField" - }, "value": 1 }, "control_mode": { - "id": "d2f14561-9443-4374-9270-e2f05007944e", "name": "control_mode", - "fieldKind": "input", "label": "", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "EnumField" - }, "value": "balanced" }, "resize_mode": { - "id": "727ee7d3-8bf6-4c7d-8b8a-43546b3b59cd", "name": "resize_mode", - "fieldKind": "input", "label": "", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "EnumField" - }, "value": "just_resize" } }, - "outputs": { - "control": { - "id": "b034aa0f-4d0d-46e4-b5e3-e25a9588d087", - "name": "control", - "fieldKind": "output", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "ControlField" - } - } - } + "isOpen": true, + "isIntermediate": true, + "useCache": true }, "position": { "x": 4477.604342844504, "y": -49.39005411272677 - }, - "width": 320, - "height": 451 + } }, { "id": "273e3f96-49ea-4dc5-9d5b-9660390f14e1", "type": "invocation", "data": { "id": "273e3f96-49ea-4dc5-9d5b-9660390f14e1", - "type": "compel", - "label": "", - "isOpen": true, - "notes": "", - "isIntermediate": true, - "useCache": true, - "version": "1.0.0", + "version": "1.1.1", "nodePack": "invokeai", + "label": "", + "notes": "", + "type": "compel", "inputs": { "prompt": { - "id": "7c2c4771-2161-4d77-aced-ff8c4b3f1c15", "name": "prompt", - "fieldKind": "input", "label": "Negative Prompt", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "StringField" - }, "value": "" }, "clip": { - "id": "06d59e91-9cca-411d-bf05-86b099b3e8f7", "name": "clip", - "fieldKind": "input", - "label": "", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "ClipField" - } + "label": "" } }, - "outputs": { - "conditioning": { - "id": "858bc33c-134c-4bf6-8855-f943e1d26f14", - "name": "conditioning", - "fieldKind": "output", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "ConditioningField" - } - } - } + "isOpen": true, + "isIntermediate": true, + "useCache": true }, "position": { "x": 4075, "y": -825 - }, - "width": 320, - "height": 219 + } }, { "id": "54486974-835b-4d81-8f82-05f9f32ce9e9", "type": "invocation", "data": { "id": "54486974-835b-4d81-8f82-05f9f32ce9e9", - "type": "main_model_loader", - "label": "", - "isOpen": true, - "notes": "", - "isIntermediate": true, - "useCache": true, - "version": "1.0.0", + "version": "1.0.2", "nodePack": "invokeai", + "label": "", + "notes": "", + "type": "main_model_loader", "inputs": { "model": { - "id": "f4a915a5-593e-4b6d-9198-c78eb5cefaed", "name": "model", - "fieldKind": "input", - "label": "", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "MainModelField" - }, - "value": { - "model_name": "stable-diffusion-v1-5", - "base_model": "sd-1", - "model_type": "main" - } + "label": "" } }, - "outputs": { - "unet": { - "id": "ee24fb16-da38-4c66-9fbc-e8f296ed40d2", - "name": "unet", - "fieldKind": "output", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "UNetField" - } - }, - "clip": { - "id": "f3fb0524-8803-41c1-86db-a61a13ee6a33", - "name": "clip", - "fieldKind": "output", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "ClipField" - } - }, - "vae": { - "id": "5c4878a8-b40f-44ab-b146-1c1f42c860b3", - "name": "vae", - "fieldKind": "output", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "VaeField" - } - } - } + "isOpen": true, + "isIntermediate": true, + "useCache": true }, "position": { "x": 3600, "y": -1000 - }, - "width": 320, - "height": 193 + } }, { "id": "7ce68934-3419-42d4-ac70-82cfc9397306", "type": "invocation", "data": { "id": "7ce68934-3419-42d4-ac70-82cfc9397306", - "type": "compel", - "label": "", - "isOpen": true, - "notes": "", - "isIntermediate": true, - "useCache": true, - "version": "1.0.0", + "version": "1.1.1", "nodePack": "invokeai", + "label": "", + "notes": "", + "type": "compel", "inputs": { "prompt": { - "id": "7c2c4771-2161-4d77-aced-ff8c4b3f1c15", "name": "prompt", - "fieldKind": "input", "label": "Positive Prompt", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "StringField" - }, "value": "" }, "clip": { - "id": "06d59e91-9cca-411d-bf05-86b099b3e8f7", "name": "clip", - "fieldKind": "input", - "label": "", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "ClipField" - } + "label": "" } }, - "outputs": { - "conditioning": { - "id": "858bc33c-134c-4bf6-8855-f943e1d26f14", - "name": "conditioning", - "fieldKind": "output", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "ConditioningField" - } - } - } + "isOpen": true, + "isIntermediate": true, + "useCache": true }, "position": { "x": 4075, "y": -1125 - }, - "width": 320, - "height": 219 + } }, { "id": "d204d184-f209-4fae-a0a1-d152800844e1", "type": "invocation", "data": { "id": "d204d184-f209-4fae-a0a1-d152800844e1", - "type": "controlnet", - "label": "", - "isOpen": true, - "notes": "", - "isIntermediate": true, - "useCache": true, "version": "1.1.1", "nodePack": "invokeai", + "label": "", + "notes": "", + "type": "controlnet", "inputs": { "image": { - "id": "4e0a3172-d3c2-4005-a84c-fa12a404f8a0", "name": "image", - "fieldKind": "input", - "label": "", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "ImageField" - } + "label": "" }, "control_model": { - "id": "8cb2d998-4086-430a-8b13-94cbc81e3ca3", "name": "control_model", - "fieldKind": "input", - "label": "", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "ControlNetModelField" - }, + "label": "Control Model (select canny)", "value": { - "model_name": "canny", - "base_model": "sd-1" + "key": "5bdaacf7-a7a3-4fb8-b394-cc0ffbb8941d", + "hash": "blake3:260c7f8e10aefea9868cfc68d89970e91033bd37132b14b903e70ee05ebf530e", + "name": "sd-controlnet-canny", + "base": "sd-1", + "type": "controlnet" } }, "control_weight": { - "id": 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"8d481737-42b5-48d5-9ab4-2e18bf3116e2" + }, + { + "id": "5d675ae3-e9c7-418d-96fe-09cd8763f2a2-1170017d-4c61-496f-897e-07e44725fc66-collapsed", + "type": "collapsed", + "source": "5d675ae3-e9c7-418d-96fe-09cd8763f2a2", + "target": "1170017d-4c61-496f-897e-07e44725fc66" + }, { "id": "reactflow__edge-54486974-835b-4d81-8f82-05f9f32ce9e9clip-7ce68934-3419-42d4-ac70-82cfc9397306clip", "type": "default", @@ -1474,6 +919,86 @@ "target": "2e77a0a1-db6a-47a2-a8bf-1e003be6423b", "sourceHandle": "value", "targetHandle": "seed" + }, + { + "id": "reactflow__edge-8e860e51-5045-456e-bf04-9a62a2a5c49ewidth-5d675ae3-e9c7-418d-96fe-09cd8763f2a2a", + "type": "default", + "source": "8e860e51-5045-456e-bf04-9a62a2a5c49e", + "target": "5d675ae3-e9c7-418d-96fe-09cd8763f2a2", + "sourceHandle": "width", + "targetHandle": "a" + }, + { + "id": "reactflow__edge-8e860e51-5045-456e-bf04-9a62a2a5c49eheight-5d675ae3-e9c7-418d-96fe-09cd8763f2a2b", + "type": "default", + "source": "8e860e51-5045-456e-bf04-9a62a2a5c49e", + "target": "5d675ae3-e9c7-418d-96fe-09cd8763f2a2", + "sourceHandle": "height", + "targetHandle": "b" + }, + { + "id": "reactflow__edge-5d675ae3-e9c7-418d-96fe-09cd8763f2a2value-1170017d-4c61-496f-897e-07e44725fc66value", + "type": "default", + "source": "5d675ae3-e9c7-418d-96fe-09cd8763f2a2", + "target": "1170017d-4c61-496f-897e-07e44725fc66", + "sourceHandle": "value", + "targetHandle": "value" + }, + { + "id": "reactflow__edge-1170017d-4c61-496f-897e-07e44725fc66value-018b1214-c2af-43a7-9910-fb687c6726d7detect_resolution", + "type": "default", + "source": "1170017d-4c61-496f-897e-07e44725fc66", + "target": "018b1214-c2af-43a7-9910-fb687c6726d7", + "sourceHandle": "value", + "targetHandle": "detect_resolution" + }, + { + "id": "reactflow__edge-1170017d-4c61-496f-897e-07e44725fc66value-018b1214-c2af-43a7-9910-fb687c6726d7image_resolution", + "type": "default", + "source": "1170017d-4c61-496f-897e-07e44725fc66", + "target": "018b1214-c2af-43a7-9910-fb687c6726d7", + "sourceHandle": "value", + "targetHandle": "image_resolution" + }, + { + "id": "reactflow__edge-c4b23e64-7986-40c4-9cad-46327b12e204width-6ff9f8b4-20e4-4230-8a38-37de9f756e8ca", + "type": "default", + "source": "c4b23e64-7986-40c4-9cad-46327b12e204", + "target": "6ff9f8b4-20e4-4230-8a38-37de9f756e8c", + "sourceHandle": "width", + "targetHandle": "a" + }, + { + "id": "reactflow__edge-c4b23e64-7986-40c4-9cad-46327b12e204height-6ff9f8b4-20e4-4230-8a38-37de9f756e8cb", + "type": "default", + "source": "c4b23e64-7986-40c4-9cad-46327b12e204", + "target": "6ff9f8b4-20e4-4230-8a38-37de9f756e8c", + "sourceHandle": "height", + "targetHandle": "b" + }, + { + "id": "reactflow__edge-6ff9f8b4-20e4-4230-8a38-37de9f756e8cvalue-8d481737-42b5-48d5-9ab4-2e18bf3116e2value", + "type": "default", + "source": "6ff9f8b4-20e4-4230-8a38-37de9f756e8c", + "target": "8d481737-42b5-48d5-9ab4-2e18bf3116e2", + "sourceHandle": "value", + "targetHandle": "value" + }, + { + "id": "reactflow__edge-8d481737-42b5-48d5-9ab4-2e18bf3116e2value-c826ba5e-9676-4475-b260-07b85e88753cdetect_resolution", + "type": "default", + "source": "8d481737-42b5-48d5-9ab4-2e18bf3116e2", + "target": "c826ba5e-9676-4475-b260-07b85e88753c", + "sourceHandle": "value", + "targetHandle": "detect_resolution" + }, + { + "id": "reactflow__edge-8d481737-42b5-48d5-9ab4-2e18bf3116e2value-c826ba5e-9676-4475-b260-07b85e88753cimage_resolution", + "type": "default", + "source": "8d481737-42b5-48d5-9ab4-2e18bf3116e2", + "target": "c826ba5e-9676-4475-b260-07b85e88753c", + "sourceHandle": "value", + "targetHandle": "image_resolution" } ] } \ No newline at end of file diff --git a/invokeai/app/services/workflow_records/default_workflows/Prompt from File.json b/invokeai/app/services/workflow_records/default_workflows/Prompt from File.json index 06a9a93d3f..765b236714 100644 --- a/invokeai/app/services/workflow_records/default_workflows/Prompt from File.json +++ b/invokeai/app/services/workflow_records/default_workflows/Prompt from File.json @@ -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", "data": { "id": "c2eaf1ba-5708-4679-9e15-945b8b432692", - "type": "compel", - "label": "", - "isOpen": false, - "notes": "", - "isIntermediate": true, - "useCache": true, - "version": "1.0.0", + "version": "1.1.1", "nodePack": "invokeai", + "label": "", + "notes": "", + "type": "compel", "inputs": { "prompt": { - "id": "dcdf3f6d-9b96-4bcd-9b8d-f992fefe4f62", "name": "prompt", - "fieldKind": "input", "label": "", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "StringField" - }, "value": "" }, "clip": { - "id": "3f1981c9-d8a9-42eb-a739-4f120eb80745", "name": "clip", - "fieldKind": "input", - "label": "", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "ClipField" - } + "label": "" } }, - "outputs": { - "conditioning": { - "id": "46205e6c-c5e2-44cb-9c82-1cd20b95674a", - "name": "conditioning", - "fieldKind": "output", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "ConditioningField" - } - } - } + "isOpen": false, + "isIntermediate": true, + "useCache": true }, "position": { "x": 925, "y": -200 - }, - "width": 320, - "height": 24 + } }, { "id": "1b7e0df8-8589-4915-a4ea-c0088f15d642", "type": "invocation", "data": { "id": "1b7e0df8-8589-4915-a4ea-c0088f15d642", - "type": "prompt_from_file", - "label": "Prompts from File", - "isOpen": true, - "notes": "", - "isIntermediate": true, - "useCache": true, - "version": "1.0.1", + "version": "1.0.2", "nodePack": "invokeai", + "label": "Prompts from File", + "notes": "", + "type": "prompt_from_file", "inputs": { "file_path": { - "id": "37e37684-4f30-4ec8-beae-b333e550f904", "name": "file_path", - "fieldKind": "input", "label": "Prompts File Path", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "StringField" - }, "value": "" }, "pre_prompt": { - "id": "7de02feb-819a-4992-bad3-72a30920ddea", "name": "pre_prompt", - "fieldKind": "input", "label": "", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "StringField" - }, "value": "" }, "post_prompt": { - "id": "95f191d8-a282-428e-bd65-de8cb9b7513a", "name": "post_prompt", - "fieldKind": "input", "label": "", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "StringField" - }, "value": "" }, "start_line": { - "id": "efee9a48-05ab-4829-8429-becfa64a0782", "name": "start_line", - "fieldKind": "input", "label": "", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "IntegerField" - }, "value": 1 }, "max_prompts": { - "id": "abebb428-3d3d-49fd-a482-4e96a16fff08", "name": "max_prompts", - "fieldKind": "input", "label": "", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "IntegerField" - }, "value": 1 } }, - "outputs": { - "collection": { - "id": "77d5d7f1-9877-4ab1-9a8c-33e9ffa9abf3", - "name": "collection", - "fieldKind": "output", - "type": { - "isCollection": true, - "isCollectionOrScalar": false, - "name": "StringField" - } - } - } + "isOpen": true, + "isIntermediate": true, + "useCache": true }, "position": { "x": 475, "y": -400 - }, - "width": 320, - "height": 506 + } }, { "id": "1b89067c-3f6b-42c8-991f-e3055789b251", "type": "invocation", "data": { "id": "1b89067c-3f6b-42c8-991f-e3055789b251", - "type": "iterate", - "label": "", - "isOpen": false, - "notes": "", - "isIntermediate": true, - "useCache": true, "version": "1.1.0", + "label": "", + "notes": "", + "type": "iterate", "inputs": { "collection": { - "id": "4c564bf8-5ed6-441e-ad2c-dda265d5785f", "name": "collection", - "fieldKind": "input", - "label": "", - "type": { - "isCollection": true, - "isCollectionOrScalar": false, - "name": "CollectionField" - } + "label": "" } }, - "outputs": { - "item": { - "id": "36340f9a-e7a5-4afa-b4b5-313f4e292380", - "name": "item", - "fieldKind": "output", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "CollectionItemField" - } - }, - "index": { - "id": "1beca95a-2159-460f-97ff-c8bab7d89336", - "name": "index", - "fieldKind": "output", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "IntegerField" - } - }, - "total": { - "id": "ead597b8-108e-4eda-88a8-5c29fa2f8df9", - "name": "total", - "fieldKind": "output", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "IntegerField" - } - } - } + "isOpen": false, + "isIntermediate": true, + "useCache": true }, "position": { "x": 925, "y": -400 - }, - "width": 320, - "height": 24 + } }, { "id": "d6353b7f-b447-4e17-8f2e-80a88c91d426", "type": "invocation", "data": { "id": "d6353b7f-b447-4e17-8f2e-80a88c91d426", - "type": "main_model_loader", - "label": "", - "isOpen": true, - "notes": "", - "isIntermediate": true, - "useCache": true, - "version": "1.0.0", + "version": "1.0.2", "nodePack": "invokeai", + "label": "", + "notes": "", + "type": "main_model_loader", "inputs": { "model": { - "id": "3f264259-3418-47d5-b90d-b6600e36ae46", "name": "model", - "fieldKind": "input", - "label": "", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "MainModelField" - }, - "value": { - "model_name": "stable-diffusion-v1-5", - "base_model": "sd-1", - "model_type": "main" - } + "label": "" } }, - "outputs": { - "unet": { - "id": "8e182ea2-9d0a-4c02-9407-27819288d4b5", - "name": "unet", - "fieldKind": "output", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "UNetField" - } - }, - "clip": { - "id": "d67d9d30-058c-46d5-bded-3d09d6d1aa39", - "name": "clip", - "fieldKind": "output", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "ClipField" - } - }, - "vae": { - "id": "89641601-0429-4448-98d5-190822d920d8", - "name": "vae", - "fieldKind": "output", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "VaeField" - } - } - } + "isOpen": true, + "isIntermediate": true, + "useCache": true }, "position": { "x": 0, "y": -375 - }, - "width": 320, - "height": 193 + } }, { "id": "fc9d0e35-a6de-4a19-84e1-c72497c823f6", "type": "invocation", "data": { "id": "fc9d0e35-a6de-4a19-84e1-c72497c823f6", - "type": "compel", - "label": "", - "isOpen": false, - "notes": "", - "isIntermediate": true, - "useCache": true, - "version": "1.0.0", + "version": "1.1.1", "nodePack": "invokeai", + "label": "", + "notes": "", + "type": "compel", "inputs": { "prompt": { - "id": "dcdf3f6d-9b96-4bcd-9b8d-f992fefe4f62", "name": "prompt", - "fieldKind": "input", "label": "", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "StringField" - }, "value": "" }, "clip": { - "id": "3f1981c9-d8a9-42eb-a739-4f120eb80745", "name": "clip", - "fieldKind": "input", - "label": "", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "ClipField" - } + "label": "" } }, - "outputs": { - "conditioning": { - "id": "46205e6c-c5e2-44cb-9c82-1cd20b95674a", - "name": "conditioning", - "fieldKind": "output", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "ConditioningField" - } - } - } + "isOpen": false, + "isIntermediate": true, + "useCache": true }, "position": { "x": 925, "y": -275 - }, - "width": 320, - "height": 24 + } }, { "id": "0eb5f3f5-1b91-49eb-9ef0-41d67c7eae77", "type": "invocation", "data": { "id": "0eb5f3f5-1b91-49eb-9ef0-41d67c7eae77", - "type": "noise", - "label": "", - "isOpen": false, - "notes": "", - "isIntermediate": true, - "useCache": true, - "version": "1.0.1", + "version": "1.0.2", "nodePack": "invokeai", + "label": "", + "notes": "", + "type": "noise", "inputs": { "seed": { - "id": "b722d84a-eeee-484f-bef2-0250c027cb67", "name": "seed", - "fieldKind": "input", "label": "", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "IntegerField" - }, "value": 0 }, "width": { - "id": "d5f8ce11-0502-4bfc-9a30-5757dddf1f94", 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"id": "reactflow__edge-719dabe8-8297-4749-aea1-37be301cd425value-ad8fa655-3a76-43d0-9c02-4d7644dea650string_left", - "type": "default", - "source": "719dabe8-8297-4749-aea1-37be301cd425", - "target": "ad8fa655-3a76-43d0-9c02-4d7644dea650", - "sourceHandle": "value", - "targetHandle": "string_left" - }, - { - "id": "reactflow__edge-ad8fa655-3a76-43d0-9c02-4d7644dea650value-3193ad09-a7c2-4bf4-a3a9-1c61cc33a204style", - "type": "default", - "source": "ad8fa655-3a76-43d0-9c02-4d7644dea650", - "target": "3193ad09-a7c2-4bf4-a3a9-1c61cc33a204", - "sourceHandle": "value", - "targetHandle": "style" - }, - { - "id": "reactflow__edge-ade2c0d3-0384-4157-b39b-29ce429cfa15value-3774ec24-a69e-4254-864c-097d07a6256fstring_left", - "type": "default", - "source": "ade2c0d3-0384-4157-b39b-29ce429cfa15", - "target": "3774ec24-a69e-4254-864c-097d07a6256f", - "sourceHandle": "value", - "targetHandle": "string_left" - }, - { - "id": "reactflow__edge-3774ec24-a69e-4254-864c-097d07a6256fvalue-faf965a4-7530-427b-b1f3-4ba6505c2a08style", - "type": "default", - "source": "3774ec24-a69e-4254-864c-097d07a6256f", - "target": "faf965a4-7530-427b-b1f3-4ba6505c2a08", - "sourceHandle": "value", - "targetHandle": "style" - } - ] -} \ No newline at end of file diff --git a/invokeai/app/services/workflow_records/default_workflows/Text to Image - SD1.5.json b/invokeai/app/services/workflow_records/default_workflows/Text to Image - SD1.5.json index cde361426a..d3d52150bc 100644 --- a/invokeai/app/services/workflow_records/default_workflows/Text to Image - SD1.5.json +++ b/invokeai/app/services/workflow_records/default_workflows/Text to Image - SD1.5.json @@ -2,7 +2,7 @@ "name": "Text to Image - SD1.5", "author": "InvokeAI", "description": "Sample text to image workflow for Stable Diffusion 1.5/2", - "version": "1.1.0", + "version": "2.0.0", "contact": "invoke@invoke.ai", "tags": "text2image, SD1.5, SD2, default", "notes": "", @@ -26,11 +26,15 @@ { "nodeId": "55705012-79b9-4aac-9f26-c0b10309785b", "fieldName": "height" + }, + { + "nodeId": "58c957f5-0d01-41fc-a803-b2bbf0413d4f", + "fieldName": "board" } ], "meta": { "category": "default", - "version": "2.0.0" + "version": "3.0.0" }, "nodes": [ { @@ -38,687 +42,291 @@ "type": "invocation", "data": { "id": "93dc02a4-d05b-48ed-b99c-c9b616af3402", - "type": "compel", - "label": "Negative Compel Prompt", - "isOpen": true, - "notes": "", - "isIntermediate": true, - "useCache": true, - "version": "1.0.0", + "version": "1.1.1", "nodePack": "invokeai", + "label": "Negative Compel Prompt", + "notes": "", + "type": "compel", "inputs": { "prompt": { - "id": "7739aff6-26cb-4016-8897-5a1fb2305e4e", "name": "prompt", - "fieldKind": "input", "label": "Negative Prompt", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "StringField" - }, "value": "" }, "clip": { - "id": "48d23dce-a6ae-472a-9f8c-22a714ea5ce0", "name": "clip", - "fieldKind": "input", - "label": "", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "ClipField" - } + "label": "" } }, - "outputs": { - "conditioning": { - "id": "37cf3a9d-f6b7-4b64-8ff6-2558c5ecc447", - "name": "conditioning", - "fieldKind": "output", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "ConditioningField" - } - } - } + "isOpen": true, + "isIntermediate": true, + "useCache": true }, "position": { "x": 1000, "y": 350 - }, - "width": 320, - "height": 219 + } }, { "id": "55705012-79b9-4aac-9f26-c0b10309785b", "type": "invocation", "data": { "id": "55705012-79b9-4aac-9f26-c0b10309785b", - "type": "noise", - "label": "", - "isOpen": true, - "notes": "", - "isIntermediate": true, - "useCache": true, - "version": "1.0.1", + "version": "1.0.2", "nodePack": "invokeai", + "label": "", + "notes": "", + "type": "noise", "inputs": { "seed": { - "id": "6431737c-918a-425d-a3b4-5d57e2f35d4d", "name": "seed", - "fieldKind": "input", "label": "", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "IntegerField" - }, "value": 0 }, "width": { - "id": "38fc5b66-fe6e-47c8-bba9-daf58e454ed7", "name": "width", - "fieldKind": "input", "label": "", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "IntegerField" - }, "value": 512 }, "height": { - "id": "16298330-e2bf-4872-a514-d6923df53cbb", "name": "height", - "fieldKind": "input", "label": "", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "IntegerField" - }, - "value": 512 + "value": 768 }, "use_cpu": { - "id": "c7c436d3-7a7a-4e76-91e4-c6deb271623c", "name": "use_cpu", - "fieldKind": "input", "label": "", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "BooleanField" - }, "value": true } }, - "outputs": { - "noise": { - "id": "50f650dc-0184-4e23-a927-0497a96fe954", - "name": "noise", - "fieldKind": "output", - "type": { - "isCollection": false, - 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a/invokeai/app/services/workflow_records/default_workflows/Text to Image with LoRA.json b/invokeai/app/services/workflow_records/default_workflows/Text to Image with LoRA.json index 2cc94895fc..6df02b675d 100644 --- a/invokeai/app/services/workflow_records/default_workflows/Text to Image with LoRA.json +++ b/invokeai/app/services/workflow_records/default_workflows/Text to Image with LoRA.json @@ -2,7 +2,7 @@ "name": "Text to Image with LoRA", "author": "InvokeAI", "description": "Simple text to image workflow with a LoRA", - "version": "1.0.0", + "version": "2.0.0", "contact": "invoke@invoke.ai", "tags": "text to image, lora, default", "notes": "", @@ -22,11 +22,23 @@ { "nodeId": "c3fa6872-2599-4a82-a596-b3446a66cf8b", "fieldName": "prompt" + }, + { + "nodeId": "ea18915f-2c5b-4569-b725-8e9e9122e8d3", + "fieldName": "width" + }, + { + "nodeId": "ea18915f-2c5b-4569-b725-8e9e9122e8d3", + "fieldName": "height" + }, + { + "nodeId": "a9683c0a-6b1f-4a5e-8187-c57e764b3400", + "fieldName": "board" } ], "meta": { "category": "default", - "version": "2.0.0" + "version": "3.0.0" }, "nodes": [ { @@ -34,773 +46,321 @@ "type": "invocation", "data": { "id": "85b77bb2-c67a-416a-b3e8-291abe746c44", - "type": "compel", + "version": "1.1.1", "label": "", - "isOpen": true, "notes": "", - "isIntermediate": true, - "useCache": true, - "version": "1.0.0", + "type": "compel", "inputs": { "prompt": { - "id": "39fe92c4-38eb-4cc7-bf5e-cbcd31847b11", "name": "prompt", - "fieldKind": "input", "label": "Negative Prompt", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "StringField" - }, "value": "" }, "clip": { - "id": "14313164-e5c4-4e40-a599-41b614fe3690", "name": "clip", - "fieldKind": "input", - "label": "", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "ClipField" - } + "label": "" } }, - "outputs": { - "conditioning": { - "id": "02140b9d-50f3-470b-a0b7-01fc6ed2dcd6", - "name": "conditioning", - "fieldKind": "output", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "ConditioningField" - } - } - } + "isOpen": true, + "isIntermediate": true, + "useCache": true }, "position": { "x": 3425, "y": -300 - }, - "width": 320, - "height": 219 + } }, { "id": "24e9d7ed-4836-4ec4-8f9e-e747721f9818", "type": "invocation", "data": { "id": "24e9d7ed-4836-4ec4-8f9e-e747721f9818", - "type": "main_model_loader", + "version": "1.0.2", "label": "", - "isOpen": true, "notes": "", - "isIntermediate": true, - "useCache": true, - "version": "1.0.0", + "type": "main_model_loader", "inputs": { "model": { - "id": "e2e1c177-ae39-4244-920e-d621fa156a24", "name": "model", - "fieldKind": "input", - "label": "", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "MainModelField" - }, - "value": { - "model_name": "Analog-Diffusion", - "base_model": "sd-1", - "model_type": "main" - } + "label": "" } }, - "outputs": { - "vae": { - "id": "f91410e8-9378-4298-b285-f0f40ffd9825", - "name": "vae", - "fieldKind": "output", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "VaeField" - } - }, - "clip": { - "id": "928d91bf-de0c-44a8-b0c8-4de0e2e5b438", - "name": "clip", - "fieldKind": "output", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "ClipField" - } - }, - "unet": { - "id": "eacaf530-4e7e-472e-b904-462192189fc1", - "name": "unet", - "fieldKind": "output", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "UNetField" - } - } - } + "isOpen": true, + "isIntermediate": true, + "useCache": true }, "position": { "x": 2500, "y": -600 - }, - "width": 320, - "height": 193 + } }, { "id": "c41e705b-f2e3-4d1a-83c4-e34bb9344966", "type": "invocation", "data": { "id": "c41e705b-f2e3-4d1a-83c4-e34bb9344966", - "type": "lora_loader", + "version": "1.0.2", "label": "", - "isOpen": true, "notes": "", - "isIntermediate": true, - "useCache": true, - "version": "1.0.0", + "type": 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"name": "ClipField" - } + "label": "" } }, - "outputs": { - "unet": { - "id": "c63f7825-1bcf-451d-b7a7-aa79f5c77416", - "name": "unet", - "fieldKind": "output", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "UNetField" - } - }, - "clip": { - "id": "6f79ef2d-00f7-4917-bee3-53e845bf4192", - "name": "clip", - "fieldKind": "output", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "ClipField" - } - } - } + "isOpen": true, + "isIntermediate": true, + "useCache": true }, "position": { "x": 2975, "y": -600 - }, - "width": 320, - "height": 218 + } }, { "id": "c3fa6872-2599-4a82-a596-b3446a66cf8b", "type": "invocation", "data": { "id": "c3fa6872-2599-4a82-a596-b3446a66cf8b", - "type": "compel", + "version": "1.1.1", "label": "", - "isOpen": true, "notes": "", - "isIntermediate": true, - "useCache": true, - "version": "1.0.0", + "type": "compel", "inputs": { "prompt": { - "id": "39fe92c4-38eb-4cc7-bf5e-cbcd31847b11", "name": "prompt", - "fieldKind": "input", "label": "Positive Prompt", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "StringField" - }, - "value": "cute tiger cub" + "value": "super cute tiger cub" }, "clip": { - "id": "14313164-e5c4-4e40-a599-41b614fe3690", "name": "clip", - "fieldKind": "input", - "label": "", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "ClipField" - } + "label": "" } }, - "outputs": { - "conditioning": { - "id": "02140b9d-50f3-470b-a0b7-01fc6ed2dcd6", - "name": "conditioning", - "fieldKind": "output", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "ConditioningField" - } - } - } + "isOpen": true, + "isIntermediate": true, + "useCache": true }, "position": { "x": 3425, "y": -575 - }, - "width": 320, - "height": 219 + } }, { "id": "ad487d0c-dcbb-49c5-bb8e-b28d4cbc5a63", "type": "invocation", "data": { "id": "ad487d0c-dcbb-49c5-bb8e-b28d4cbc5a63", - "type": "denoise_latents", + "version": "1.5.3", "label": "", - "isOpen": true, "notes": "", - "isIntermediate": true, - "useCache": true, - "version": "1.5.1", + "type": "denoise_latents", "inputs": { "positive_conditioning": { - "id": "025ff44b-c4c6-4339-91b4-5f461e2cadc5", "name": "positive_conditioning", - "fieldKind": "input", - "label": "", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "ConditioningField" - } + "label": "" }, "negative_conditioning": { - "id": "2d92b45a-a7fb-4541-9a47-7c7495f50f54", "name": "negative_conditioning", - "fieldKind": "input", - "label": "", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "ConditioningField" - } + "label": "" }, "noise": { - "id": "4d0deeff-24ed-4562-a1ca-7833c0649377", "name": "noise", - "fieldKind": "input", - "label": "", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "LatentsField" - } + "label": "" }, "steps": { - "id": "c9907328-aece-4af9-8a95-211b4f99a325", "name": "steps", - "fieldKind": "input", "label": "", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "IntegerField" - }, - "value": 10 + "value": 30 }, "cfg_scale": { - "id": "7cf0f031-2078-49f4-9273-bb3a64ad7130", "name": "cfg_scale", - "fieldKind": "input", "label": "", - "type": { - "isCollection": false, - "isCollectionOrScalar": true, - "name": "FloatField" - }, "value": 7.5 }, "denoising_start": { - "id": "44cec3ba-b404-4b51-ba98-add9d783279e", "name": "denoising_start", - "fieldKind": "input", "label": "", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "FloatField" - }, "value": 0 }, "denoising_end": { - "id": "3e7975f3-e438-4a13-8a14-395eba1fb7cd", "name": "denoising_end", - "fieldKind": "input", "label": "", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "FloatField" - }, "value": 1 }, "scheduler": { - "id": "a6f6509b-7bb4-477d-b5fb-74baefa38111", "name": "scheduler", - 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"type": "rand_int", "inputs": { "low": { - "id": "d25305f3-bfd6-446c-8e2c-0b025ec9e9ad", "name": "low", - "fieldKind": "input", "label": "", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "IntegerField" - }, "value": 0 }, "high": { - "id": "10376a3d-b8fe-4a51-b81a-ea46d8c12c78", "name": "high", - "fieldKind": "input", "label": "", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "IntegerField" - }, "value": 2147483647 } }, - "outputs": { - "value": { - "id": "c64878fa-53b1-4202-b88a-cfb854216a57", - "name": "value", - "fieldKind": "output", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "IntegerField" - } - } - } + "isOpen": false, + "isIntermediate": true, + "useCache": false }, "position": { "x": 3425, "y": 0 - }, - "width": 320, - "height": 24 + } }, { "id": "a9683c0a-6b1f-4a5e-8187-c57e764b3400", "type": "invocation", "data": { "id": "a9683c0a-6b1f-4a5e-8187-c57e764b3400", - "type": "l2i", + "version": "1.2.2", "label": "", - "isOpen": true, "notes": "", - "isIntermediate": false, - "useCache": true, - "version": "1.2.0", + "type": "l2i", "inputs": { + "board": { + "name": "board", + "label": "" + }, "metadata": { - "id": "b1982e8a-14ad-4029-a697-beb30af8340f", "name": "metadata", - "fieldKind": "input", - "label": "", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "MetadataField" - } + "label": "" }, "latents": { - "id": "f7669388-9f91-46cc-94fc-301fa7041c3e", "name": "latents", - "fieldKind": "input", - "label": "", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "LatentsField" - } + "label": "" }, "vae": { - "id": "c6f2d4db-4d0a-4e3d-acb4-b5c5a228a3e2", "name": "vae", - "fieldKind": "input", - "label": "", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "VaeField" - } + "label": "" }, "tiled": { - "id": "19ef7d31-d96f-4e94-b7e5-95914e9076fc", "name": "tiled", - "fieldKind": "input", "label": "", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "BooleanField" - }, "value": false }, "fp32": { - "id": "a9454533-8ab7-4225-b411-646dc5e76d00", "name": "fp32", - "fieldKind": "input", "label": "", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "BooleanField" - }, "value": false } }, - "outputs": { - "image": { - "id": "4f81274e-e216-47f3-9fb6-f97493a40e6f", - "name": "image", - "fieldKind": "output", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "ImageField" - } - }, - "width": { - "id": "61a9acfb-1547-4f1e-8214-e89bd3855ee5", - "name": "width", - "fieldKind": "output", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "IntegerField" - } - }, - "height": { - "id": "b15cc793-4172-4b07-bcf4-5627bbc7d0d7", - "name": "height", - "fieldKind": "output", - "type": { - "isCollection": false, - "isCollectionOrScalar": false, - "name": "IntegerField" - } - } - } + "isOpen": true, + "isIntermediate": false, + "useCache": true }, "position": { "x": 4450, "y": -550 - }, - "width": 320, - "height": 224 + } } ], "edges": [ diff --git a/invokeai/app/services/workflow_records/default_workflows/Tiled Upscaling (Beta).json b/invokeai/app/services/workflow_records/default_workflows/Tiled Upscaling (Beta).json index 668cb1263d..bb0e9062e4 100644 --- a/invokeai/app/services/workflow_records/default_workflows/Tiled Upscaling (Beta).json +++ b/invokeai/app/services/workflow_records/default_workflows/Tiled Upscaling (Beta).json @@ -2,7 +2,7 @@ "name": "Tiled Upscaling (Beta)", "author": "Invoke", "description": "A workflow to upscale an input image with tiled upscaling. 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"id": "b875cae6-d8a3-4fdc-b969-4d53cbd03f9a-157d5318-fbc1-43e5-9ed4-5bbeda0594b0-collapsed", + "id": "3f99d25c-6b43-44ec-a61a-c7ff91712621-338b883c-3728-4f18-b3a6-6e7190c2f850-collapsed", "type": "collapsed", - "source": "b875cae6-d8a3-4fdc-b969-4d53cbd03f9a", - "target": "157d5318-fbc1-43e5-9ed4-5bbeda0594b0" + "source": "3f99d25c-6b43-44ec-a61a-c7ff91712621", + "target": "338b883c-3728-4f18-b3a6-6e7190c2f850" }, { "id": "fad15012-0787-43a8-99dd-27f1518b5bc7-b3513fed-ed42-408d-b382-128fdb0de523-collapsed", @@ -2803,12 +1260,6 @@ "source": "fad15012-0787-43a8-99dd-27f1518b5bc7", "target": "b3513fed-ed42-408d-b382-128fdb0de523" }, - { - "id": "fad15012-0787-43a8-99dd-27f1518b5bc7-36d25df7-6408-442b-89e2-b9aba11a72c3-collapsed", - "type": "collapsed", - "source": "fad15012-0787-43a8-99dd-27f1518b5bc7", - "target": "36d25df7-6408-442b-89e2-b9aba11a72c3" - }, { "id": "fad15012-0787-43a8-99dd-27f1518b5bc7-1f86c8bf-06f9-4e28-abee-02f46f445ac4-collapsed", "type": "collapsed", @@ -2899,12 +1350,6 @@ "source": "40de95ee-ebb5-43f7-a31a-299e76c8a5d5", "target": "857eb5ce-8e5e-4bda-8a33-3e52e57db67b" }, - { - "id": "857eb5ce-8e5e-4bda-8a33-3e52e57db67b-36d25df7-6408-442b-89e2-b9aba11a72c3-collapsed", - "type": "collapsed", - "source": "857eb5ce-8e5e-4bda-8a33-3e52e57db67b", - "target": "36d25df7-6408-442b-89e2-b9aba11a72c3" - }, { "id": "reactflow__edge-fad15012-0787-43a8-99dd-27f1518b5bc7width-b3513fed-ed42-408d-b382-128fdb0de523width", "type": "default", diff --git a/invokeai/backend/model_manager/__init__.py b/invokeai/backend/model_manager/__init__.py index 966e6ae21d..98cc5054c7 100644 --- a/invokeai/backend/model_manager/__init__.py +++ b/invokeai/backend/model_manager/__init__.py @@ -33,42 +33,3 @@ __all__ = [ "SchedulerPredictionType", "SubModelType", ] - -########## to help populate the openapi_schema with format enums for each config ########### -# This code is no longer necessary? -# leave it here just in case -# -# import inspect -# from enum import Enum -# from typing import Any, Iterable, Dict, get_args, Set -# def _expand(something: Any) -> Iterable[type]: -# if isinstance(something, type): -# yield something -# else: -# for x in get_args(something): -# for y in _expand(x): -# yield y - -# def _find_format(cls: type) -> Iterable[Enum]: -# if hasattr(inspect, "get_annotations"): -# fields = inspect.get_annotations(cls) -# else: -# fields = cls.__annotations__ -# if "format" in fields: -# for x in get_args(fields["format"]): -# yield x -# for parent_class in cls.__bases__: -# for x in _find_format(parent_class): -# yield x -# return None - -# def get_model_config_formats() -> Dict[str, Set[Enum]]: -# result: Dict[str, Set[Enum]] = {} -# for model_config in _expand(AnyModelConfig): -# for field in _find_format(model_config): -# if field is None: -# continue -# if not result.get(model_config.__qualname__): -# result[model_config.__qualname__] = set() -# result[model_config.__qualname__].add(field) -# return result diff --git a/invokeai/backend/model_manager/config.py b/invokeai/backend/model_manager/config.py index 3b7f85cb16..524e39b2a1 100644 --- a/invokeai/backend/model_manager/config.py +++ b/invokeai/backend/model_manager/config.py @@ -146,11 +146,15 @@ class MainModelDefaultSettings(BaseModel): width: int | None = Field(default=None, multiple_of=8, ge=64, description="Default width for this model") height: int | None = Field(default=None, multiple_of=8, ge=64, description="Default height for this model") + model_config = ConfigDict(extra="forbid") + class ControlAdapterDefaultSettings(BaseModel): # This could be narrowed to controlnet processor nodes, but they change. Leaving this a string is safer. preprocessor: str | None + model_config = ConfigDict(extra="forbid") + class ModelConfigBase(BaseModel): """Base class for model configuration information.""" diff --git a/invokeai/backend/model_manager/convert_ckpt_to_diffusers.py b/invokeai/backend/model_manager/convert_ckpt_to_diffusers.py index 1b09e28017..4328c18319 100644 --- a/invokeai/backend/model_manager/convert_ckpt_to_diffusers.py +++ b/invokeai/backend/model_manager/convert_ckpt_to_diffusers.py @@ -3,7 +3,7 @@ """Conversion script for the Stable Diffusion checkpoints.""" from pathlib import Path -from typing import Dict +from typing import Dict, Optional import torch from diffusers import AutoencoderKL @@ -15,6 +15,8 @@ from diffusers.pipelines.stable_diffusion.convert_from_ckpt import ( ) from omegaconf import DictConfig +from . import AnyModel + def convert_ldm_vae_to_diffusers( checkpoint: Dict[str, torch.Tensor], @@ -33,11 +35,11 @@ def convert_ldm_vae_to_diffusers( def convert_ckpt_to_diffusers( checkpoint_path: str | Path, - dump_path: str | Path, + dump_path: Optional[str | Path] = None, precision: torch.dtype = torch.float16, use_safetensors: bool = True, **kwargs, -): +) -> AnyModel: """ Takes all the arguments of download_from_original_stable_diffusion_ckpt(), and in addition a path-like object indicating the location of the desired diffusers @@ -47,18 +49,20 @@ def convert_ckpt_to_diffusers( pipe = pipe.to(precision) # TO DO: save correct repo variant - pipe.save_pretrained( - dump_path, - safe_serialization=use_safetensors, - ) + if dump_path: + pipe.save_pretrained( + dump_path, + safe_serialization=use_safetensors, + ) + return pipe def convert_controlnet_to_diffusers( checkpoint_path: Path, - dump_path: Path, + dump_path: Optional[Path] = None, precision: torch.dtype = torch.float16, **kwargs, -): +) -> AnyModel: """ Takes all the arguments of download_controlnet_from_original_ckpt(), and in addition a path-like object indicating the location of the desired diffusers @@ -68,4 +72,6 @@ def convert_controlnet_to_diffusers( pipe = pipe.to(precision) # TO DO: save correct repo variant - pipe.save_pretrained(dump_path, safe_serialization=True) + if dump_path: + pipe.save_pretrained(dump_path, safe_serialization=True) + return pipe diff --git a/invokeai/backend/model_manager/load/convert_cache/convert_cache_default.py b/invokeai/backend/model_manager/load/convert_cache/convert_cache_default.py index 84f4f76299..8dc2aff74b 100644 --- a/invokeai/backend/model_manager/load/convert_cache/convert_cache_default.py +++ b/invokeai/backend/model_manager/load/convert_cache/convert_cache_default.py @@ -19,11 +19,20 @@ class ModelConvertCache(ModelConvertCacheBase): self._cache_path = cache_path self._max_size = max_size + # adjust cache size at startup in case it has been changed + if self._cache_path.exists(): + self.make_room(0.0) + @property def max_size(self) -> float: """Return the maximum size of this cache directory (GB).""" return self._max_size + @max_size.setter + def max_size(self, value: float) -> None: + """Set the maximum size of this cache directory (GB).""" + self._max_size = value + def cache_path(self, key: str) -> Path: """Return the path for a model with the indicated key.""" return self._cache_path / key diff --git a/invokeai/backend/model_manager/load/load_base.py b/invokeai/backend/model_manager/load/load_base.py index 95a681f7d2..cf2b7d767c 100644 --- a/invokeai/backend/model_manager/load/load_base.py +++ b/invokeai/backend/model_manager/load/load_base.py @@ -82,3 +82,15 @@ class ModelLoaderBase(ABC): ) -> int: """Return size in bytes of the model, calculated before loading.""" pass + + @property + @abstractmethod + def convert_cache(self) -> ModelConvertCacheBase: + """Return the convert cache associated with this loader.""" + pass + + @property + @abstractmethod + def ram_cache(self) -> ModelCacheBase[AnyModel]: + """Return the ram cache associated with this loader.""" + pass diff --git a/invokeai/backend/model_manager/load/load_default.py b/invokeai/backend/model_manager/load/load_default.py index 2727af073e..60cc1f5e6c 100644 --- a/invokeai/backend/model_manager/load/load_default.py +++ b/invokeai/backend/model_manager/load/load_default.py @@ -3,14 +3,13 @@ from logging import Logger from pathlib import Path -from typing import Optional, Tuple +from typing import Optional from invokeai.app.services.config import InvokeAIAppConfig from invokeai.backend.model_manager import ( AnyModel, AnyModelConfig, InvalidModelConfigException, - ModelRepoVariant, SubModelType, ) from invokeai.backend.model_manager.config import DiffusersConfigBase, ModelType @@ -54,51 +53,43 @@ class ModelLoader(ModelLoaderBase): if model_config.type is ModelType.Main and not submodel_type: raise InvalidModelConfigException("submodel_type is required when loading a main model") - model_path, model_config, submodel_type = self._get_model_path(model_config, submodel_type) + model_path = self._get_model_path(model_config) if not model_path.exists(): raise InvalidModelConfigException(f"Files for model '{model_config.name}' not found at {model_path}") - model_path = self._convert_if_needed(model_config, model_path, submodel_type) - locker = self._load_if_needed(model_config, model_path, submodel_type) + with skip_torch_weight_init(): + locker = self._convert_and_load(model_config, model_path, submodel_type) return LoadedModel(config=model_config, _locker=locker) - def _get_model_path( - self, config: AnyModelConfig, submodel_type: Optional[SubModelType] = None - ) -> Tuple[Path, AnyModelConfig, Optional[SubModelType]]: + @property + def convert_cache(self) -> ModelConvertCacheBase: + """Return the convert cache associated with this loader.""" + return self._convert_cache + + @property + def ram_cache(self) -> ModelCacheBase[AnyModel]: + """Return the ram cache associated with this loader.""" + return self._ram_cache + + def _get_model_path(self, config: AnyModelConfig) -> Path: model_base = self._app_config.models_path - result = (model_base / config.path).resolve(), config, submodel_type - return result + return (model_base / config.path).resolve() - def _convert_if_needed( - self, config: AnyModelConfig, model_path: Path, submodel_type: Optional[SubModelType] = None - ) -> Path: - cache_path: Path = self._convert_cache.cache_path(config.key) - - if not self._needs_conversion(config, model_path, cache_path): - return cache_path if cache_path.exists() else model_path - - self._convert_cache.make_room(self.get_size_fs(config, model_path, submodel_type)) - return self._convert_model(config, model_path, cache_path) - - def _needs_conversion(self, config: AnyModelConfig, model_path: Path, dest_path: Path) -> bool: - return False - - def _load_if_needed( + def _convert_and_load( self, config: AnyModelConfig, model_path: Path, submodel_type: Optional[SubModelType] = None ) -> ModelLockerBase: - # TO DO: This is not thread safe! try: return self._ram_cache.get(config.key, submodel_type) except IndexError: pass - model_variant = getattr(config, "repo_variant", None) - self._ram_cache.make_room(self.get_size_fs(config, model_path, submodel_type)) - - # This is where the model is actually loaded! - with skip_torch_weight_init(): - loaded_model = self._load_model(model_path, model_variant=model_variant, submodel_type=submodel_type) + cache_path: Path = self._convert_cache.cache_path(config.key) + if self._needs_conversion(config, model_path, cache_path): + loaded_model = self._do_convert(config, model_path, cache_path, submodel_type) + else: + config.path = str(cache_path) if cache_path.exists() else str(self._get_model_path(config)) + loaded_model = self._load_model(config, submodel_type) self._ram_cache.put( config.key, @@ -123,15 +114,34 @@ class ModelLoader(ModelLoaderBase): variant=config.repo_variant if isinstance(config, DiffusersConfigBase) else None, ) + def _do_convert( + self, config: AnyModelConfig, model_path: Path, cache_path: Path, submodel_type: Optional[SubModelType] = None + ) -> AnyModel: + self.convert_cache.make_room(calc_model_size_by_fs(model_path)) + pipeline = self._convert_model(config, model_path, cache_path if self.convert_cache.max_size > 0 else None) + if submodel_type: + # Proactively load the various submodels into the RAM cache so that we don't have to re-convert + # the entire pipeline every time a new submodel is needed. + for subtype in SubModelType: + if subtype == submodel_type: + continue + if submodel := getattr(pipeline, subtype.value, None): + self._ram_cache.put( + config.key, submodel_type=subtype, model=submodel, size=calc_model_size_by_data(submodel) + ) + return getattr(pipeline, submodel_type.value) if submodel_type else pipeline + + def _needs_conversion(self, config: AnyModelConfig, model_path: Path, dest_path: Path) -> bool: + return False + # This needs to be implemented in subclasses that handle checkpoints - def _convert_model(self, config: AnyModelConfig, model_path: Path, output_path: Path) -> Path: + def _convert_model(self, config: AnyModelConfig, model_path: Path, output_path: Optional[Path] = None) -> AnyModel: raise NotImplementedError # This needs to be implemented in the subclass def _load_model( self, - model_path: Path, - model_variant: Optional[ModelRepoVariant] = None, + config: AnyModelConfig, submodel_type: Optional[SubModelType] = None, ) -> AnyModel: raise NotImplementedError diff --git a/invokeai/backend/model_manager/load/model_cache/model_cache_default.py b/invokeai/backend/model_manager/load/model_cache/model_cache_default.py index 90090b522d..519a45c237 100644 --- a/invokeai/backend/model_manager/load/model_cache/model_cache_default.py +++ b/invokeai/backend/model_manager/load/model_cache/model_cache_default.py @@ -130,6 +130,11 @@ class ModelCache(ModelCacheBase[AnyModel]): """Return the cap on cache size.""" return self._max_cache_size + @max_cache_size.setter + def max_cache_size(self, value: float) -> None: + """Set the cap on cache size.""" + self._max_cache_size = value + @property def stats(self) -> Optional[CacheStats]: """Return collected CacheStats object.""" @@ -166,9 +171,10 @@ class ModelCache(ModelCacheBase[AnyModel]): """Store model under key and optional submodel_type.""" with self._lock: key = self._make_cache_key(key, submodel_type) - assert key not in self._cached_models - - cache_record = CacheRecord(key=key, model=model, size=size) + if key in self._cached_models: + return + self.make_room(size) + cache_record = CacheRecord(key, model, size) self._cached_models[key] = cache_record self._cache_stack.append(key) @@ -334,6 +340,8 @@ class ModelCache(ModelCacheBase[AnyModel]): # # Keep in mind that gc is only responsible for handling reference cycles. Most objects should be cleaned up # immediately when their reference count hits 0. + if self.stats: + self.stats.cleared = models_cleared gc.collect() torch.cuda.empty_cache() diff --git a/invokeai/backend/model_manager/load/model_loaders/controlnet.py b/invokeai/backend/model_manager/load/model_loaders/controlnet.py index 861ba700cc..0b93d8d2ca 100644 --- a/invokeai/backend/model_manager/load/model_loaders/controlnet.py +++ b/invokeai/backend/model_manager/load/model_loaders/controlnet.py @@ -2,8 +2,10 @@ """Class for ControlNet model loading in InvokeAI.""" from pathlib import Path +from typing import Optional from invokeai.backend.model_manager import ( + AnyModel, AnyModelConfig, BaseModelType, ModelFormat, @@ -33,7 +35,7 @@ class ControlNetLoader(GenericDiffusersLoader): else: return True - def _convert_model(self, config: AnyModelConfig, model_path: Path, output_path: Path) -> Path: + def _convert_model(self, config: AnyModelConfig, model_path: Path, output_path: Optional[Path] = None) -> AnyModel: assert isinstance(config, CheckpointConfigBase) image_size = ( 512 @@ -44,8 +46,8 @@ class ControlNetLoader(GenericDiffusersLoader): ) self._logger.info(f"Converting {model_path} to diffusers format") - with open(self._app_config.root_path / config.config_path, "r") as config_stream: - convert_controlnet_to_diffusers( + with open(self._app_config.legacy_conf_path / config.config_path, "r") as config_stream: + result = convert_controlnet_to_diffusers( model_path, output_path, original_config_file=config_stream, @@ -53,4 +55,4 @@ class ControlNetLoader(GenericDiffusersLoader): precision=self._torch_dtype, from_safetensors=model_path.suffix == ".safetensors", ) - return output_path + return result diff --git a/invokeai/backend/model_manager/load/model_loaders/generic_diffusers.py b/invokeai/backend/model_manager/load/model_loaders/generic_diffusers.py index a630b24d0a..a4874b33ce 100644 --- a/invokeai/backend/model_manager/load/model_loaders/generic_diffusers.py +++ b/invokeai/backend/model_manager/load/model_loaders/generic_diffusers.py @@ -10,13 +10,14 @@ from diffusers.models.modeling_utils import ModelMixin from invokeai.backend.model_manager import ( AnyModel, + AnyModelConfig, BaseModelType, InvalidModelConfigException, ModelFormat, - ModelRepoVariant, ModelType, SubModelType, ) +from invokeai.backend.model_manager.config import DiffusersConfigBase from .. import ModelLoader, ModelLoaderRegistry @@ -28,14 +29,15 @@ class GenericDiffusersLoader(ModelLoader): def _load_model( self, - model_path: Path, - model_variant: Optional[ModelRepoVariant] = None, + config: AnyModelConfig, submodel_type: Optional[SubModelType] = None, ) -> AnyModel: + model_path = Path(config.path) model_class = self.get_hf_load_class(model_path) if submodel_type is not None: raise Exception(f"There are no submodels in models of type {model_class}") - variant = model_variant.value if model_variant else None + repo_variant = config.repo_variant if isinstance(config, DiffusersConfigBase) else None + variant = repo_variant.value if repo_variant else None try: result: AnyModel = model_class.from_pretrained(model_path, torch_dtype=self._torch_dtype, variant=variant) except OSError as e: diff --git a/invokeai/backend/model_manager/load/model_loaders/ip_adapter.py b/invokeai/backend/model_manager/load/model_loaders/ip_adapter.py index 89c54948ff..89dd46e929 100644 --- a/invokeai/backend/model_manager/load/model_loaders/ip_adapter.py +++ b/invokeai/backend/model_manager/load/model_loaders/ip_adapter.py @@ -9,13 +9,14 @@ import torch from invokeai.backend.ip_adapter.ip_adapter import build_ip_adapter from invokeai.backend.model_manager import ( AnyModel, + AnyModelConfig, BaseModelType, ModelFormat, - ModelRepoVariant, ModelType, SubModelType, ) from invokeai.backend.model_manager.load import ModelLoader, ModelLoaderRegistry +from invokeai.backend.raw_model import RawModel @ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.IPAdapter, format=ModelFormat.InvokeAI) @@ -24,13 +25,13 @@ class IPAdapterInvokeAILoader(ModelLoader): def _load_model( self, - model_path: Path, - model_variant: Optional[ModelRepoVariant] = None, + config: AnyModelConfig, submodel_type: Optional[SubModelType] = None, ) -> AnyModel: if submodel_type is not None: raise ValueError("There are no submodels in an IP-Adapter model.") - model = build_ip_adapter( + model_path = Path(config.path) + model: RawModel = build_ip_adapter( ip_adapter_ckpt_path=str(model_path / "ip_adapter.bin"), device=torch.device("cpu"), dtype=self._torch_dtype, diff --git a/invokeai/backend/model_manager/load/model_loaders/lora.py b/invokeai/backend/model_manager/load/model_loaders/lora.py index 20a39e56c3..53814279ec 100644 --- a/invokeai/backend/model_manager/load/model_loaders/lora.py +++ b/invokeai/backend/model_manager/load/model_loaders/lora.py @@ -3,7 +3,7 @@ from logging import Logger from pathlib import Path -from typing import Optional, Tuple +from typing import Optional from invokeai.app.services.config import InvokeAIAppConfig from invokeai.backend.lora import LoRAModelRaw @@ -12,7 +12,6 @@ from invokeai.backend.model_manager import ( AnyModelConfig, BaseModelType, ModelFormat, - ModelRepoVariant, ModelType, SubModelType, ) @@ -41,12 +40,12 @@ class LoRALoader(ModelLoader): def _load_model( self, - model_path: Path, - model_variant: Optional[ModelRepoVariant] = None, + config: AnyModelConfig, submodel_type: Optional[SubModelType] = None, ) -> AnyModel: if submodel_type is not None: raise ValueError("There are no submodels in a LoRA model.") + model_path = Path(config.path) assert self._model_base is not None model = LoRAModelRaw.from_checkpoint( file_path=model_path, @@ -56,12 +55,9 @@ class LoRALoader(ModelLoader): return model # override - def _get_model_path( - self, config: AnyModelConfig, submodel_type: Optional[SubModelType] = None - ) -> Tuple[Path, AnyModelConfig, Optional[SubModelType]]: - self._model_base = ( - config.base - ) # cheating a little - we remember this variable for using in the subsequent call to _load_model() + def _get_model_path(self, config: AnyModelConfig) -> Path: + # cheating a little - we remember this variable for using in the subsequent call to _load_model() + self._model_base = config.base model_base_path = self._app_config.models_path model_path = model_base_path / config.path @@ -73,5 +69,4 @@ class LoRALoader(ModelLoader): model_path = path break - result = model_path.resolve(), config, submodel_type - return result + return model_path.resolve() diff --git a/invokeai/backend/model_manager/load/model_loaders/onnx.py b/invokeai/backend/model_manager/load/model_loaders/onnx.py index e771cac8eb..b43e0a1bdf 100644 --- a/invokeai/backend/model_manager/load/model_loaders/onnx.py +++ b/invokeai/backend/model_manager/load/model_loaders/onnx.py @@ -7,9 +7,9 @@ from typing import Optional from invokeai.backend.model_manager import ( AnyModel, + AnyModelConfig, BaseModelType, ModelFormat, - ModelRepoVariant, ModelType, SubModelType, ) @@ -25,18 +25,19 @@ class OnnyxDiffusersModel(GenericDiffusersLoader): def _load_model( self, - model_path: Path, - model_variant: Optional[ModelRepoVariant] = None, + config: AnyModelConfig, submodel_type: Optional[SubModelType] = None, ) -> AnyModel: if not submodel_type is not None: raise Exception("A submodel type must be provided when loading onnx pipelines.") + model_path = Path(config.path) load_class = self.get_hf_load_class(model_path, submodel_type) - variant = model_variant.value if model_variant else None + repo_variant = getattr(config, "repo_variant", None) + variant = repo_variant.value if repo_variant else None model_path = model_path / submodel_type.value result: AnyModel = load_class.from_pretrained( model_path, torch_dtype=self._torch_dtype, variant=variant, - ) # type: ignore + ) return result diff --git a/invokeai/backend/model_manager/load/model_loaders/stable_diffusion.py b/invokeai/backend/model_manager/load/model_loaders/stable_diffusion.py index e401a10eb9..3ca7a5b2e4 100644 --- a/invokeai/backend/model_manager/load/model_loaders/stable_diffusion.py +++ b/invokeai/backend/model_manager/load/model_loaders/stable_diffusion.py @@ -9,17 +9,27 @@ from invokeai.backend.model_manager import ( AnyModelConfig, BaseModelType, ModelFormat, - ModelRepoVariant, ModelType, SchedulerPredictionType, SubModelType, ) -from invokeai.backend.model_manager.config import CheckpointConfigBase, MainCheckpointConfig +from invokeai.backend.model_manager.config import ( + CheckpointConfigBase, + DiffusersConfigBase, + MainCheckpointConfig, + ModelVariantType, +) from invokeai.backend.model_manager.convert_ckpt_to_diffusers import convert_ckpt_to_diffusers from .. import ModelLoaderRegistry from .generic_diffusers import GenericDiffusersLoader +VARIANT_TO_IN_CHANNEL_MAP = { + ModelVariantType.Normal: 4, + ModelVariantType.Depth: 5, + ModelVariantType.Inpaint: 9, +} + @ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.Main, format=ModelFormat.Diffusers) @ModelLoaderRegistry.register(base=BaseModelType.Any, type=ModelType.Main, format=ModelFormat.Checkpoint) @@ -35,14 +45,15 @@ class StableDiffusionDiffusersModel(GenericDiffusersLoader): def _load_model( self, - model_path: Path, - model_variant: Optional[ModelRepoVariant] = None, + config: AnyModelConfig, submodel_type: Optional[SubModelType] = None, ) -> AnyModel: if not submodel_type is not None: raise Exception("A submodel type must be provided when loading main pipelines.") + model_path = Path(config.path) load_class = self.get_hf_load_class(model_path, submodel_type) - variant = model_variant.value if model_variant else None + repo_variant = config.repo_variant if isinstance(config, DiffusersConfigBase) else None + variant = repo_variant.value if repo_variant else None model_path = model_path / submodel_type.value try: result: AnyModel = load_class.from_pretrained( @@ -72,7 +83,7 @@ class StableDiffusionDiffusersModel(GenericDiffusersLoader): else: return True - def _convert_model(self, config: AnyModelConfig, model_path: Path, output_path: Path) -> Path: + def _convert_model(self, config: AnyModelConfig, model_path: Path, output_path: Optional[Path] = None) -> AnyModel: assert isinstance(config, MainCheckpointConfig) base = config.base @@ -87,11 +98,12 @@ class StableDiffusionDiffusersModel(GenericDiffusersLoader): ) self._logger.info(f"Converting {model_path} to diffusers format") - convert_ckpt_to_diffusers( + + loaded_model = convert_ckpt_to_diffusers( model_path, output_path, model_type=self.model_base_to_model_type[base], - original_config_file=self._app_config.root_path / config.config_path, + original_config_file=self._app_config.legacy_conf_path / config.config_path, extract_ema=True, from_safetensors=model_path.suffix == ".safetensors", precision=self._torch_dtype, @@ -99,5 +111,6 @@ class StableDiffusionDiffusersModel(GenericDiffusersLoader): image_size=image_size, upcast_attention=upcast_attention, load_safety_checker=False, + num_in_channels=VARIANT_TO_IN_CHANNEL_MAP[config.variant], ) - return output_path + return loaded_model diff --git a/invokeai/backend/model_manager/load/model_loaders/textual_inversion.py b/invokeai/backend/model_manager/load/model_loaders/textual_inversion.py index fb9204a701..cfdc689cc8 100644 --- a/invokeai/backend/model_manager/load/model_loaders/textual_inversion.py +++ b/invokeai/backend/model_manager/load/model_loaders/textual_inversion.py @@ -2,14 +2,13 @@ """Class for TI model loading in InvokeAI.""" from pathlib import Path -from typing import Optional, Tuple +from typing import Optional from invokeai.backend.model_manager import ( AnyModel, AnyModelConfig, BaseModelType, ModelFormat, - ModelRepoVariant, ModelType, SubModelType, ) @@ -27,22 +26,19 @@ class TextualInversionLoader(ModelLoader): def _load_model( self, - model_path: Path, - model_variant: Optional[ModelRepoVariant] = None, + config: AnyModelConfig, submodel_type: Optional[SubModelType] = None, ) -> AnyModel: if submodel_type is not None: raise ValueError("There are no submodels in a TI model.") model = TextualInversionModelRaw.from_checkpoint( - file_path=model_path, + file_path=config.path, dtype=self._torch_dtype, ) return model # override - def _get_model_path( - self, config: AnyModelConfig, submodel_type: Optional[SubModelType] = None - ) -> Tuple[Path, AnyModelConfig, Optional[SubModelType]]: + def _get_model_path(self, config: AnyModelConfig) -> Path: model_path = self._app_config.models_path / config.path if config.format == ModelFormat.EmbeddingFolder: @@ -53,4 +49,4 @@ class TextualInversionLoader(ModelLoader): if not path.exists(): raise OSError(f"The embedding file at {path} was not found") - return path, config, submodel_type + return path diff --git a/invokeai/backend/model_manager/load/model_loaders/vae.py b/invokeai/backend/model_manager/load/model_loaders/vae.py index 7669ff0e19..df15f3ebfe 100644 --- a/invokeai/backend/model_manager/load/model_loaders/vae.py +++ b/invokeai/backend/model_manager/load/model_loaders/vae.py @@ -44,7 +44,7 @@ class VAELoader(GenericDiffusersLoader): raise Exception(f"VAE conversion not supported for model type: {config.base}") else: assert isinstance(config, CheckpointConfigBase) - config_file = self._app_config.root_path / config.config_path + config_file = self._app_config.legacy_conf_path / config.config_path if model_path.suffix == ".safetensors": checkpoint = safetensors_load_file(model_path, device="cpu") diff --git a/invokeai/backend/model_manager/probe.py b/invokeai/backend/model_manager/probe.py index 6d71cceaea..ddd9e99eda 100644 --- a/invokeai/backend/model_manager/probe.py +++ b/invokeai/backend/model_manager/probe.py @@ -28,7 +28,7 @@ from .config import ( ) from .util.model_util import lora_token_vector_length, read_checkpoint_meta -CkptType = Dict[str, Any] +CkptType = Dict[str | int, Any] LEGACY_CONFIGS: Dict[BaseModelType, Dict[ModelVariantType, Union[str, Dict[SchedulerPredictionType, str]]]] = { BaseModelType.StableDiffusion1: { @@ -37,7 +37,6 @@ LEGACY_CONFIGS: Dict[BaseModelType, Dict[ModelVariantType, Union[str, Dict[Sched SchedulerPredictionType.VPrediction: "v1-inference-v.yaml", }, ModelVariantType.Inpaint: "v1-inpainting-inference.yaml", - ModelVariantType.Depth: "v2-midas-inference.yaml", }, BaseModelType.StableDiffusion2: { ModelVariantType.Normal: { @@ -48,6 +47,7 @@ LEGACY_CONFIGS: Dict[BaseModelType, Dict[ModelVariantType, Union[str, Dict[Sched SchedulerPredictionType.Epsilon: "v2-inpainting-inference.yaml", SchedulerPredictionType.VPrediction: "v2-inpainting-inference-v.yaml", }, + ModelVariantType.Depth: "v2-midas-inference.yaml", }, BaseModelType.StableDiffusionXL: { ModelVariantType.Normal: "sd_xl_base.yaml", @@ -219,7 +219,7 @@ class ModelProbe(object): ckpt = checkpoint if checkpoint else read_checkpoint_meta(model_path, scan=True) ckpt = ckpt.get("state_dict", ckpt) - for key in ckpt.keys(): + for key in [str(k) for k in ckpt.keys()]: if any(key.startswith(v) for v in {"cond_stage_model.", "first_stage_model.", "model.diffusion_model."}): return ModelType.Main elif any(key.startswith(v) for v in {"encoder.conv_in", "decoder.conv_in"}): @@ -227,7 +227,7 @@ class ModelProbe(object): elif any(key.startswith(v) for v in {"lora_te_", "lora_unet_"}): return ModelType.LoRA elif any(key.endswith(v) for v in {"to_k_lora.up.weight", "to_q_lora.down.weight"}): - return ModelType.Lora + return ModelType.LoRA elif any(key.startswith(v) for v in {"controlnet", "control_model", "input_blocks"}): return ModelType.ControlNet elif key in {"emb_params", "string_to_param"}: diff --git a/invokeai/backend/model_manager/search.py b/invokeai/backend/model_manager/search.py index 23cd042602..fe545bfe34 100644 --- a/invokeai/backend/model_manager/search.py +++ b/invokeai/backend/model_manager/search.py @@ -48,9 +48,8 @@ class ModelSearch: Usage: search = ModelSearch() - search.model_found = lambda path : 'anime' in path.as_posix() - found = search.list_models(['/tmp/models1','/tmp/models2']) - # returns all models that have 'anime' in the path + search.on_model_found = lambda path : 'anime' in path.as_posix() + found = search.search(Path('/tmp/models1')) """ def __init__( diff --git a/invokeai/backend/model_manager/starter_models.py b/invokeai/backend/model_manager/starter_models.py index 325216d7a8..f92f5e08d5 100644 --- a/invokeai/backend/model_manager/starter_models.py +++ b/invokeai/backend/model_manager/starter_models.py @@ -1,149 +1,156 @@ -from dataclasses import dataclass from typing import Optional +from pydantic import BaseModel + from invokeai.backend.model_manager.config import BaseModelType, ModelType -@dataclass -class StarterModel: +class StarterModelWithoutDependencies(BaseModel): description: str source: str name: str base: BaseModelType type: ModelType - # Optional list of model source dependencies that need to be installed before this model can be used - dependencies: Optional[list[str]] = None is_installed: bool = False +class StarterModel(StarterModelWithoutDependencies): + # Optional list of model source dependencies that need to be installed before this model can be used + dependencies: Optional[list[StarterModelWithoutDependencies]] = None + + +sdxl_fp16_vae_fix = StarterModel( + name="sdxl-vae-fp16-fix", + base=BaseModelType.StableDiffusionXL, + source="madebyollin/sdxl-vae-fp16-fix", + description="SDXL VAE that works with FP16.", + type=ModelType.VAE, +) + +ip_adapter_sd_image_encoder = StarterModel( + name="IP Adapter SD1.5 Image Encoder", + base=BaseModelType.StableDiffusion1, + source="InvokeAI/ip_adapter_sd_image_encoder", + description="IP Adapter SD Image Encoder", + type=ModelType.CLIPVision, +) + +ip_adapter_sdxl_image_encoder = StarterModel( + name="IP Adapter SDXL Image Encoder", + base=BaseModelType.StableDiffusionXL, + source="InvokeAI/ip_adapter_sdxl_image_encoder", + description="IP Adapter SDXL Image Encoder", + type=ModelType.CLIPVision, +) + +cyberrealistic_negative = StarterModel( + name="CyberRealistic Negative v3", + base=BaseModelType.StableDiffusion1, + source="https://huggingface.co/cyberdelia/CyberRealistic_Negative/resolve/main/CyberRealistic_Negative_v3.pt", + description="Negative embedding specifically for use with CyberRealistic.", + type=ModelType.TextualInversion, +) + # List of starter models, displayed on the frontend. # The order/sort of this list is not changed by the frontend - set it how you want it here. STARTER_MODELS: list[StarterModel] = [ # region: Main StarterModel( - name="SD 1.5 (base)", + name="CyberRealistic v4.1", base=BaseModelType.StableDiffusion1, - source="runwayml/stable-diffusion-v1-5", - description="Stable Diffusion version 1.5 diffusers model (4.27 GB)", + source="https://huggingface.co/cyberdelia/CyberRealistic/resolve/main/CyberRealistic_V4.1_FP16.safetensors", + description="Photorealistic model. See other variants in HF repo 'cyberdelia/CyberRealistic'.", + type=ModelType.Main, + dependencies=[cyberrealistic_negative], + ), + StarterModel( + name="ReV Animated", + base=BaseModelType.StableDiffusion1, + source="stablediffusionapi/rev-animated", + description="Fantasy and anime style images.", type=ModelType.Main, ), StarterModel( - name="SD 1.5 (inpainting)", + name="Dreamshaper 8", base=BaseModelType.StableDiffusion1, - source="runwayml/stable-diffusion-inpainting", - description="RunwayML SD 1.5 model optimized for inpainting, diffusers version (4.27 GB)", + source="Lykon/dreamshaper-8", + description="Popular versatile model.", type=ModelType.Main, ), StarterModel( - name="Analog Diffusion", + name="Dreamshaper 8 (inpainting)", base=BaseModelType.StableDiffusion1, - source="wavymulder/Analog-Diffusion", - description="An SD-1.5 model trained on diverse analog photographs (2.13 GB)", + source="Lykon/dreamshaper-8-inpainting", + description="Inpainting version of Dreamshaper 8.", type=ModelType.Main, ), StarterModel( name="Deliberate v5", base=BaseModelType.StableDiffusion1, source="https://huggingface.co/XpucT/Deliberate/resolve/main/Deliberate_v5.safetensors", - description="Versatile model that produces detailed images up to 768px (4.27 GB)", + description="Popular versatile model", type=ModelType.Main, ), StarterModel( - name="Dungeons and Diffusion", + name="Deliberate v5 (inpainting)", base=BaseModelType.StableDiffusion1, - source="0xJustin/Dungeons-and-Diffusion", - description="Dungeons & Dragons characters (2.13 GB)", + source="https://huggingface.co/XpucT/Deliberate/resolve/main/Deliberate_v5-inpainting.safetensors", + description="Inpainting version of Deliberate v5.", type=ModelType.Main, ), StarterModel( - name="dreamlike photoreal v2", - base=BaseModelType.StableDiffusion1, - source="dreamlike-art/dreamlike-photoreal-2.0", - description="A photorealistic model trained on 768 pixel images based on SD 1.5 (2.13 GB)", - type=ModelType.Main, - ), - StarterModel( - name="Inkpunk Diffusion", - base=BaseModelType.StableDiffusion1, - source="Envvi/Inkpunk-Diffusion", - description='Stylized illustrations inspired by Gorillaz, FLCL and Shinkawa; prompt with "nvinkpunk" (4.27 GB)', - type=ModelType.Main, - ), - StarterModel( - name="OpenJourney", - base=BaseModelType.StableDiffusion1, - source="prompthero/openjourney", - description='An SD 1.5 model fine tuned on Midjourney; prompt with "mdjrny-v4 style" (2.13 GB)', - type=ModelType.Main, - ), - StarterModel( - name="seek.art MEGA", - base=BaseModelType.StableDiffusion1, - source="coreco/seek.art_MEGA", - description='A general use SD-1.5 "anything" model that supports multiple styles (2.1 GB)', - type=ModelType.Main, - ), - StarterModel( - name="TrinArt v2", - base=BaseModelType.StableDiffusion1, - source="naclbit/trinart_stable_diffusion_v2", - description="An SD-1.5 model finetuned with ~40K assorted high resolution manga/anime-style images (2.13 GB)", - type=ModelType.Main, - ), - StarterModel( - name="SD 2.1 (base)", - base=BaseModelType.StableDiffusion2, - source="stabilityai/stable-diffusion-2-1", - description="Stable Diffusion version 2.1 diffusers model, trained on 768 pixel images (5.21 GB)", - type=ModelType.Main, - ), - StarterModel( - name="SD 2.0 (inpainting)", - base=BaseModelType.StableDiffusion2, - source="stabilityai/stable-diffusion-2-inpainting", - description="Stable Diffusion version 2.0 inpainting model (5.21 GB)", - type=ModelType.Main, - ), - StarterModel( - name="SDXL (base)", + name="Juggernaut XL v9", base=BaseModelType.StableDiffusionXL, - source="stabilityai/stable-diffusion-xl-base-1.0", - description="Stable Diffusion XL base model (12 GB)", + source="RunDiffusion/Juggernaut-XL-v9", + description="Photograph-focused model.", type=ModelType.Main, + dependencies=[sdxl_fp16_vae_fix], + ), + StarterModel( + name="Dreamshaper XL v2 Turbo", + base=BaseModelType.StableDiffusionXL, + source="Lykon/dreamshaper-xl-v2-turbo", + description="For turbo, use CFG Scale 2, 4-8 steps, DPM++ SDE Karras. For non-turbo, use CFG Scale 6, 20-40 steps, DPM++ 2M SDE Karras.", + type=ModelType.Main, + dependencies=[sdxl_fp16_vae_fix], ), StarterModel( name="SDXL Refiner", base=BaseModelType.StableDiffusionXLRefiner, source="stabilityai/stable-diffusion-xl-refiner-1.0", - description="Stable Diffusion XL refiner model (12 GB)", + description="The OG Stable Diffusion XL refiner model.", type=ModelType.Main, + dependencies=[sdxl_fp16_vae_fix], ), # endregion # region VAE - StarterModel( - name="sdxl-vae-fp16-fix", - base=BaseModelType.StableDiffusionXL, - source="madebyollin/sdxl-vae-fp16-fix", - description="Version of the SDXL-1.0 VAE that works in half precision mode", - type=ModelType.VAE, - ), + sdxl_fp16_vae_fix, # endregion # region LoRA StarterModel( - name="FlatColor", - base=BaseModelType.StableDiffusion1, - source="https://civitai.com/models/6433/loraflatcolor", - description="A LoRA that generates scenery using solid blocks of color", + name="Alien Style", + base=BaseModelType.StableDiffusionXL, + source="https://huggingface.co/RalFinger/alien-style-lora-sdxl/resolve/main/alienzkin-sdxl.safetensors", + description="Futuristic, intricate alien styles. Trigger with 'alienzkin'.", type=ModelType.LoRA, ), StarterModel( - name="Ink scenery", - base=BaseModelType.StableDiffusion1, - source="https://civitai.com/api/download/models/83390", - description="Generate india ink-like landscapes", + name="Noodles Style", + base=BaseModelType.StableDiffusionXL, + source="https://huggingface.co/RalFinger/noodles-lora-sdxl/resolve/main/noodlez-sdxl.safetensors", + description="Never-ending, no-holds-barred, noodle nightmare. Trigger with 'noodlez'.", type=ModelType.LoRA, ), # endregion + # region TI + StarterModel( + name="EasyNegative", + base=BaseModelType.StableDiffusion1, + source="https://huggingface.co/embed/EasyNegative/resolve/main/EasyNegative.safetensors", + description="A textual inversion to use in the negative prompt to reduce bad anatomy", + type=ModelType.TextualInversion, + ), + # endregion # region IP Adapter StarterModel( name="IP Adapter", @@ -151,7 +158,7 @@ STARTER_MODELS: list[StarterModel] = [ source="InvokeAI/ip_adapter_sd15", description="IP-Adapter for SD 1.5 models", type=ModelType.IPAdapter, - dependencies=["InvokeAI/ip_adapter_sd_image_encoder"], + dependencies=[ip_adapter_sd_image_encoder], ), StarterModel( name="IP Adapter Plus", @@ -159,7 +166,7 @@ STARTER_MODELS: list[StarterModel] = [ source="InvokeAI/ip_adapter_plus_sd15", description="Refined IP-Adapter for SD 1.5 models", type=ModelType.IPAdapter, - dependencies=["InvokeAI/ip_adapter_sd_image_encoder"], + dependencies=[ip_adapter_sd_image_encoder], ), StarterModel( name="IP Adapter Plus Face", @@ -167,7 +174,7 @@ STARTER_MODELS: list[StarterModel] = [ source="InvokeAI/ip_adapter_plus_face_sd15", description="Refined IP-Adapter for SD 1.5 models, adapted for faces", type=ModelType.IPAdapter, - dependencies=["InvokeAI/ip_adapter_sd_image_encoder"], + dependencies=[ip_adapter_sd_image_encoder], ), StarterModel( name="IP Adapter SDXL", @@ -175,7 +182,7 @@ STARTER_MODELS: list[StarterModel] = [ source="InvokeAI/ip_adapter_sdxl", description="IP-Adapter for SDXL models", type=ModelType.IPAdapter, - dependencies=["InvokeAI/ip_adapter_sdxl_image_encoder"], + dependencies=[ip_adapter_sdxl_image_encoder], ), # endregion # region ControlNet @@ -378,15 +385,6 @@ STARTER_MODELS: list[StarterModel] = [ type=ModelType.T2IAdapter, ), # endregion - # region TI - StarterModel( - name="EasyNegative", - base=BaseModelType.StableDiffusion1, - source="https://huggingface.co/embed/EasyNegative/resolve/main/EasyNegative.safetensors", - description="A textual inversion to use in the negative prompt to reduce bad anatomy", - type=ModelType.TextualInversion, - ), - # endregion ] assert len(STARTER_MODELS) == len({m.source for m in STARTER_MODELS}), "Duplicate starter models" diff --git a/invokeai/backend/stable_diffusion/seamless.py b/invokeai/backend/stable_diffusion/seamless.py index e5af27bf4c..2e22c19d0e 100644 --- a/invokeai/backend/stable_diffusion/seamless.py +++ b/invokeai/backend/stable_diffusion/seamless.py @@ -28,6 +28,10 @@ def _conv_forward_asymmetric(self, input, weight, bias): @contextmanager def set_seamless(model: Union[UNet2DConditionModel, AutoencoderKL, AutoencoderTiny], seamless_axes: List[str]): + if not seamless_axes: + yield + return + # Callable: (input: Tensor, weight: Tensor, bias: Optional[Tensor]) -> Tensor to_restore: list[tuple[nn.Conv2d | nn.ConvTranspose2d, Callable]] = [] try: diff --git a/invokeai/backend/util/db_maintenance.py b/invokeai/backend/util/db_maintenance.py index d5f6200ad9..e7d3432121 100644 --- a/invokeai/backend/util/db_maintenance.py +++ b/invokeai/backend/util/db_maintenance.py @@ -31,6 +31,9 @@ class ConfigMapper: YAML_FILENAME = "invokeai.yaml" DATABASE_FILENAME = "invokeai.db" + DEFAULT_OUTDIR = "outputs" + DEFAULT_DB_DIR = "databases" + database_path = None database_backup_dir = None outputs_path = None @@ -50,12 +53,18 @@ class ConfigMapper: def __load_from_root_config(self, invoke_root): """Validate a yaml path exists, confirm the user wants to use it and load config.""" yaml_path = os.path.join(invoke_root, self.YAML_FILENAME) + if not os.path.exists(yaml_path): + print(f"Unable to find invokeai.yaml at {yaml_path}!") + return False if os.path.exists(yaml_path): db_dir, outdir = self.__load_paths_from_yaml_file(yaml_path) - if db_dir is None or outdir is None: - print("The invokeai.yaml file was found but is missing the db_dir and/or outdir setting!") - return False + if db_dir is None: + db_dir = self.DEFAULT_DB_DIR + print(f"The invokeai.yaml file was found but is missing the db_dir setting! Defaulting to {db_dir}") + if outdir is None: + outdir = self.DEFAULT_OUTDIR + print(f"The invokeai.yaml file was found but is missing the outdir setting! Defaulting to {outdir}") if os.path.isabs(db_dir): self.database_path = os.path.join(db_dir, self.DATABASE_FILENAME) diff --git a/invokeai/backend/util/devices.py b/invokeai/backend/util/devices.py index e02015614e..0be53c842a 100644 --- a/invokeai/backend/util/devices.py +++ b/invokeai/backend/util/devices.py @@ -28,6 +28,11 @@ def choose_torch_device() -> torch.device: return torch.device(config.device) +def get_torch_device_name() -> str: + device = choose_torch_device() + return torch.cuda.get_device_name(device) if device.type == "cuda" else device.type.upper() + + # We are in transition here from using a single global AppConfig to allowing multiple # configurations. It is strongly recommended to pass the app_config to this function. def choose_precision( diff --git a/invokeai/configs/stable-diffusion/v2-midas-inference.yaml b/invokeai/configs/stable-diffusion/v2-midas-inference.yaml new file mode 100644 index 0000000000..f20c30f618 --- /dev/null +++ b/invokeai/configs/stable-diffusion/v2-midas-inference.yaml @@ -0,0 +1,74 @@ +model: + base_learning_rate: 5.0e-07 + target: ldm.models.diffusion.ddpm.LatentDepth2ImageDiffusion + params: + linear_start: 0.00085 + linear_end: 0.0120 + num_timesteps_cond: 1 + log_every_t: 200 + timesteps: 1000 + first_stage_key: "jpg" + cond_stage_key: "txt" + image_size: 64 + channels: 4 + cond_stage_trainable: false + conditioning_key: hybrid + scale_factor: 0.18215 + monitor: val/loss_simple_ema + finetune_keys: null + use_ema: False + + depth_stage_config: + target: ldm.modules.midas.api.MiDaSInference + params: + model_type: "dpt_hybrid" + + unet_config: + target: ldm.modules.diffusionmodules.openaimodel.UNetModel + params: + use_checkpoint: True + image_size: 32 # unused + in_channels: 5 + out_channels: 4 + model_channels: 320 + attention_resolutions: [ 4, 2, 1 ] + num_res_blocks: 2 + channel_mult: [ 1, 2, 4, 4 ] + num_head_channels: 64 # need to fix for flash-attn + use_spatial_transformer: True + use_linear_in_transformer: True + transformer_depth: 1 + context_dim: 1024 + legacy: False + + first_stage_config: + target: ldm.models.autoencoder.AutoencoderKL + params: + embed_dim: 4 + monitor: val/rec_loss + ddconfig: + #attn_type: "vanilla-xformers" + double_z: true + z_channels: 4 + resolution: 256 + in_channels: 3 + out_ch: 3 + ch: 128 + ch_mult: + - 1 + - 2 + - 4 + - 4 + num_res_blocks: 2 + attn_resolutions: [ ] + dropout: 0.0 + lossconfig: + target: torch.nn.Identity + + cond_stage_config: + target: ldm.modules.encoders.modules.FrozenOpenCLIPEmbedder + params: + freeze: True + layer: "penultimate" + + diff --git a/invokeai/frontend/install/invokeai_update.py b/invokeai/frontend/install/invokeai_update.py deleted file mode 100644 index 32a6208e6b..0000000000 --- a/invokeai/frontend/install/invokeai_update.py +++ /dev/null @@ -1,216 +0,0 @@ -""" -Minimalist updater script. Prompts user for the tag or branch to update to and runs -pip install