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Merge branch 'main' into lstein/feat/multi-gpu

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Lincoln Stein 2024-03-21 20:29:38 -04:00
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67
.github/pull_request_template.md vendored
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@ -1,66 +1,21 @@
## What type of PR is this? (check all applicable)
## Summary
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
<!--A description of the changes in this PR. Include the kind of change (fix, feature, docs, etc), the "why" and the "how". Screenshots or videos are useful for frontend changes.-->
## Related Issues / Discussions
## Have you discussed this change with the InvokeAI team?
- [ ] Yes
- [ ] No, because:
<!--WHEN APPLICABLE: List any related issues or discussions on github or discord. If this PR closes an issue, please use the "Closes #1234" format, so that the issue will be automatically closed when the PR merges.-->
## QA Instructions
## Have you updated all relevant documentation?
- [ ] Yes
- [ ] No
## Description
## Related Tickets & Documents
<!--
For pull requests that relate or close an issue, please include them
below.
For example having the text: "closes #1234" would connect the current pull
request to issue 1234. And when we merge the pull request, Github will
automatically close the issue.
-->
- Related Issue #
- Closes #
## QA Instructions, Screenshots, Recordings
<!--
Please provide steps on how to test changes, any hardware or
software specifications as well as any other pertinent information.
-->
<!--WHEN APPLICABLE: Describe how we can test the changes in this PR.-->
## Merge Plan
<!--
A merge plan describes how this PR should be handled after it is approved.
<!--WHEN APPLICABLE: Large PRs, or PRs that touch sensitive things like DB schemas, may need some care when merging. For example, a careful rebase by the change author, timing to not interfere with a pending release, or a message to contributors on discord after merging.-->
Example merge plans:
- "This PR can be merged when approved"
- "This must be squash-merged when approved"
- "DO NOT MERGE - I will rebase and tidy commits before merging"
- "#dev-chat on discord needs to be advised of this change when it is merged"
## Checklist
A merge plan is particularly important for large PRs or PRs that touch the
database in any way.
-->
## Added/updated tests?
- [ ] Yes
- [ ] No : _please replace this line with details on why tests
have not been included_
## [optional] Are there any post deployment tasks we need to perform?
- [ ] _The PR has a short but descriptive title, suitable for a changelog_
- [ ] _Tests added / updated (if applicable)_
- [ ] _Documentation added / updated (if applicable)_

26
docker/.env.sample
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@ -2,17 +2,25 @@
## Any environment variables supported by InvokeAI can be specified here,
## in addition to the examples below.
# HOST_INVOKEAI_ROOT is the path on the docker host's filesystem where InvokeAI will store data.
# Outputs will also be stored here by default.
# If relative, it will be relative to the docker directory in which the docker-compose.yml file is located
#HOST_INVOKEAI_ROOT=../../invokeai-data
# INVOKEAI_ROOT is the path to the root of the InvokeAI repository within the container.
## INVOKEAI_ROOT is the path *on the host system* where Invoke will store its data.
## It is mounted into the container and allows both containerized and non-containerized usage of Invoke.
# Usually this is the only variable you need to set. It can be relative or absolute.
# INVOKEAI_ROOT=~/invokeai
# Get this value from your HuggingFace account settings page.
# HUGGING_FACE_HUB_TOKEN=
## HOST_INVOKEAI_ROOT and CONTAINER_INVOKEAI_ROOT can be used to control the on-host
## and in-container paths separately, if needed.
## HOST_INVOKEAI_ROOT is the path on the docker host's filesystem where Invoke will store data.
## If relative, it will be relative to the docker directory in which the docker-compose.yml file is located
## CONTAINER_INVOKEAI_ROOT is the path within the container where Invoke will expect to find the runtime directory.
## It MUST be absolute. There is usually no need to change this.
# HOST_INVOKEAI_ROOT=../../invokeai-data
# CONTAINER_INVOKEAI_ROOT=/invokeai
## optional variables specific to the docker setup.
## INVOKEAI_PORT is the port on which the InvokeAI web interface will be available
# INVOKEAI_PORT=9090
## GPU_DRIVER can be set to either `nvidia` or `rocm` to enable GPU support in the container accordingly.
# GPU_DRIVER=nvidia #| rocm
## CONTAINER_UID can be set to the UID of the user on the host system that should own the files in the container.
# CONTAINER_UID=1000

25
docker/Dockerfile
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@ -18,8 +18,6 @@ ENV INVOKEAI_SRC=/opt/invokeai
ENV VIRTUAL_ENV=/opt/venv/invokeai
ENV PATH="$VIRTUAL_ENV/bin:$PATH"
ARG TORCH_VERSION=2.1.2
ARG TORCHVISION_VERSION=0.16.2
ARG GPU_DRIVER=cuda
ARG TARGETPLATFORM="linux/amd64"
# unused but available
@ -27,7 +25,12 @@ ARG BUILDPLATFORM
WORKDIR ${INVOKEAI_SRC}
# Install pytorch before all other pip packages
COPY invokeai ./invokeai
COPY pyproject.toml ./
# Editable mode helps use the same image for development:
# the local working copy can be bind-mounted into the image
# at path defined by ${INVOKEAI_SRC}
# NOTE: there are no pytorch builds for arm64 + cuda, only cpu
# x86_64/CUDA is default
RUN --mount=type=cache,target=/root/.cache/pip \
@ -39,20 +42,10 @@ RUN --mount=type=cache,target=/root/.cache/pip \
else \
extra_index_url_arg="--extra-index-url https://download.pytorch.org/whl/cu121"; \
fi &&\
pip install $extra_index_url_arg \
torch==$TORCH_VERSION \
torchvision==$TORCHVISION_VERSION
# Install the local package.
# Editable mode helps use the same image for development:
# the local working copy can be bind-mounted into the image
# at path defined by ${INVOKEAI_SRC}
COPY invokeai ./invokeai
COPY pyproject.toml ./
RUN --mount=type=cache,target=/root/.cache/pip \
# xformers + triton fails to install on arm64
if [ "$GPU_DRIVER" = "cuda" ] && [ "$TARGETPLATFORM" = "linux/amd64" ]; then \
pip install -e ".[xformers]"; \
pip install $extra_index_url_arg -e ".[xformers]"; \
else \
pip install $extra_index_url_arg -e "."; \
fi
@ -101,6 +94,8 @@ RUN apt update && apt install -y --no-install-recommends \
ENV INVOKEAI_SRC=/opt/invokeai
ENV VIRTUAL_ENV=/opt/venv/invokeai
ENV INVOKEAI_ROOT=/invokeai
ENV INVOKEAI_HOST=0.0.0.0
ENV INVOKEAI_PORT=9090
ENV PATH="$VIRTUAL_ENV/bin:$INVOKEAI_SRC:$PATH"
ENV CONTAINER_UID=${CONTAINER_UID:-1000}
ENV CONTAINER_GID=${CONTAINER_GID:-1000}
@ -125,4 +120,4 @@ RUN mkdir -p ${INVOKEAI_ROOT} && chown -R ${CONTAINER_UID}:${CONTAINER_GID} ${IN
COPY docker/docker-entrypoint.sh ./
ENTRYPOINT ["/opt/invokeai/docker-entrypoint.sh"]
CMD ["invokeai-web", "--host", "0.0.0.0"]
CMD ["invokeai-web"]

25
docker/docker-compose.yml
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@ -8,35 +8,28 @@ x-invokeai: &invokeai
context: ..
dockerfile: docker/Dockerfile
# variables without a default will automatically inherit from the host environment
environment:
- INVOKEAI_ROOT
- HF_HOME
# Create a .env file in the same directory as this docker-compose.yml file
# and populate it with environment variables. See .env.sample
env_file:
- .env
# variables without a default will automatically inherit from the host environment
environment:
# if set, CONTAINER_INVOKEAI_ROOT will override the Invoke runtime directory location *inside* the container
- INVOKEAI_ROOT=${CONTAINER_INVOKEAI_ROOT:-/invokeai}
- HF_HOME
ports:
- "${INVOKEAI_PORT:-9090}:9090"
- "${INVOKEAI_PORT:-9090}:${INVOKEAI_PORT:-9090}"
volumes:
- type: bind
source: ${HOST_INVOKEAI_ROOT:-${INVOKEAI_ROOT:-~/invokeai}}
target: ${INVOKEAI_ROOT:-/invokeai}
target: ${CONTAINER_INVOKEAI_ROOT:-/invokeai}
bind:
create_host_path: true
- ${HF_HOME:-~/.cache/huggingface}:${HF_HOME:-/invokeai/.cache/huggingface}
# - ${INVOKEAI_MODELS_DIR:-${INVOKEAI_ROOT:-/invokeai/models}}
# - ${INVOKEAI_MODELS_CONFIG_PATH:-${INVOKEAI_ROOT:-/invokeai/configs/models.yaml}}
tty: true
stdin_open: true
# # Example of running alternative commands/scripts in the container
# command:
# - bash
# - -c
# - |
# invokeai-model-install --yes --default-only --config_file ${INVOKEAI_ROOT}/config_custom.yaml
# invokeai-nodes-web --host 0.0.0.0
services:
invokeai-nvidia:

28
docker/docker-entrypoint.sh
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@ -9,10 +9,6 @@ set -e -o pipefail
### Set INVOKEAI_ROOT pointing to a valid runtime directory
# Otherwise configure the runtime dir first.
### Configure the InvokeAI runtime directory (done by default)):
# docker run --rm -it <this image> --configure
# or skip with --no-configure
### Set the CONTAINER_UID envvar to match your user.
# Ensures files created in the container are owned by you:
# docker run --rm -it -v /some/path:/invokeai -e CONTAINER_UID=$(id -u) <this image>
@ -22,27 +18,6 @@ USER_ID=${CONTAINER_UID:-1000}
USER=ubuntu
usermod -u ${USER_ID} ${USER} 1>/dev/null
configure() {
# Configure the runtime directory
if [[ -f ${INVOKEAI_ROOT}/invokeai.yaml ]]; then
echo "${INVOKEAI_ROOT}/invokeai.yaml exists. InvokeAI is already configured."
echo "To reconfigure InvokeAI, delete the above file."
echo "======================================================================"
else
mkdir -p "${INVOKEAI_ROOT}"
chown --recursive ${USER} "${INVOKEAI_ROOT}"
gosu ${USER} invokeai-configure --yes --default_only
fi
}
## Skip attempting to configure.
## Must be passed first, before any other args.
if [[ $1 != "--no-configure" ]]; then
configure
else
shift
fi
### Set the $PUBLIC_KEY env var to enable SSH access.
# We do not install openssh-server in the image by default to avoid bloat.
# but it is useful to have the full SSH server e.g. on Runpod.
@ -58,7 +33,8 @@ if [[ -v "PUBLIC_KEY" ]] && [[ ! -d "${HOME}/.ssh" ]]; then
service ssh start
fi
mkdir -p "${INVOKEAI_ROOT}"
chown --recursive ${USER} "${INVOKEAI_ROOT}"
cd "${INVOKEAI_ROOT}"
# Run the CMD as the Container User (not root).

37
docs/features/CONFIGURATION.md
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@ -18,9 +18,6 @@ Settings sources are used in this order:
- `invokeai.yaml` settings
- Fallback: defaults
The most commonly changed settings are also accessible
graphically via the `invokeai-configure` script.
### InvokeAI Root Directory
On startup, InvokeAI searches for its "root" directory. This is the directory
@ -42,10 +39,9 @@ It has two sections - one for internal use and one for user settings:
```yaml
# Internal metadata - do not edit:
meta:
schema_version: 4
schema_version: 4
# Put user settings here:
# Put user settings here - see https://invoke-ai.github.io/InvokeAI/features/CONFIGURATION/:
host: 0.0.0.0 # serve the app on your local network
models_dir: D:\invokeai\models # store models on an external drive
precision: float16 # always use fp16 precision
@ -62,6 +58,12 @@ You can fix a broken `invokeai.yaml` by deleting it and running the
configuration script again -- option [6] in the launcher, "Re-run the
configure script".
#### Custom Config File Location
You can use any config file with the `--config` CLI arg. Pass in the path to the `invokeai.yaml` file you want to use.
Note that environment variables will trump any settings in the config file.
### Environment Variables
All settings may be set via environment variables by prefixing `INVOKEAI_`
@ -81,13 +83,10 @@ We suggest using `invokeai.yaml`, as it is more user-friendly.
A subset of settings may be specified using CLI args:
- `--root`: specify the root directory
- `--ignore_missing_core-models`: if set, do not check for models needed
to convert checkpoint/safetensor models to diffusers
- `--config`: override the default `invokeai.yaml` file location
### All Settings
The config is managed by the `InvokeAIAppConfig` class. The below docs are autogenerated from the class.
Following the table are additional explanations for certain settings.
<!-- prettier-ignore-start -->
@ -120,21 +119,21 @@ The provided token will be added as a `Bearer` token to the network requests to
#### Model Hashing
Models are hashed during installation, providing a stable identifier for models across all platforms. The default algorithm is `blake3`, with a multi-threaded implementation.
If your models are stored on a spinning hard drive, we suggest using `blake3_single`, the single-threaded implementation. The hashes are the same, but it's much faster on spinning disks.
Models are hashed during installation, providing a stable identifier for models across all platforms. Hashing is a one-time operation.
```yaml
hashing_algorithm: blake3_single
hashing_algorithm: blake3_single # default value
```
Model hashing is a one-time operation, but it may take a couple minutes to hash a large model collection. You may opt out of model hashing entirely by setting the algorithm to `random`.
You might want to change this setting, depending on your system:
```yaml
hashing_algorithm: random
```
- `blake3_single` (default): Single-threaded - best for spinning HDDs, still OK for SSDs
- `blake3_multi`: Parallelized, memory-mapped implementation - best for SSDs, terrible for spinning disks
- `random`: Skip hashing entirely - fastest but of course no hash
Most common algorithms are supported, like `md5`, `sha256`, and `sha512`. These are typically much, much slower than `blake3`.
During the first startup after upgrading to v4, all of your models will be hashed. This can take a few minutes.
Most common algorithms are supported, like `md5`, `sha256`, and `sha512`. These are typically much, much slower than either of the BLAKE3 variants.
#### Path Settings

180
docs/installation/010_INSTALL_AUTOMATED.md
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@ -122,9 +122,9 @@ experimental versions later.
[latest release](https://github.com/invoke-ai/InvokeAI/releases/latest),
and look for a file named:
- InvokeAI-installer-v3.X.X.zip
- InvokeAI-installer-v4.X.X.zip
where "3.X.X" is the latest released version. The file is located
where "4.X.X" is the latest released version. The file is located
at the very bottom of the release page, under **Assets**.
4. **Unpack the installer**: Unpack the zip file into a convenient directory. This will create a new
@ -199,136 +199,7 @@ experimental versions later.
![initial-settings-screenshot](../assets/installer-walkthrough/settings-form.png)
</figure>
10. **Post-install Configuration**: After installation completes, the
installer will launch the configuration form, which will guide you
through the first-time process of adjusting some of InvokeAI's
startup settings. To move around this form use ctrl-N for
&lt;N&gt;ext and ctrl-P for &lt;P&gt;revious, or use &lt;tab&gt;
and shift-&lt;tab&gt; to move forward and back. Once you are in a
multi-checkbox field use the up and down cursor keys to select the
item you want, and &lt;space&gt; to toggle it on and off. Within
a directory field, pressing &lt;tab&gt; will provide autocomplete
options.
Generally the defaults are fine, and you can come back to this screen at
any time to tweak your system. Here are the options you can adjust:
- ***HuggingFace Access Token***
InvokeAI has the ability to download embedded styles and subjects
from the HuggingFace Concept Library on-demand. However, some of
the concept library files are password protected. To make download
smoother, you can set up an account at huggingface.co, obtain an
access token, and paste it into this field. Note that you paste
to this screen using ctrl-shift-V
- ***Free GPU memory after each generation***
This is useful for low-memory machines and helps minimize the
amount of GPU VRAM used by InvokeAI.
- ***Enable xformers support if available***
If the xformers library was successfully installed, this will activate
it to reduce memory consumption and increase rendering speed noticeably.
Note that xformers has the side effect of generating slightly different
images even when presented with the same seed and other settings.
- ***Force CPU to be used on GPU systems***
This will use the (slow) CPU rather than the accelerated GPU. This
can be used to generate images on systems that don't have a compatible
GPU.
- ***Precision***
This controls whether to use float32 or float16 arithmetic.
float16 uses less memory but is also slightly less accurate.
Ordinarily the right arithmetic is picked automatically ("auto"),
but you may have to use float32 to get images on certain systems
and graphics cards. The "autocast" option is deprecated and
shouldn't be used unless you are asked to by a member of the team.
- **Size of the RAM cache used for fast model switching***
This allows you to keep models in memory and switch rapidly among
them rather than having them load from disk each time. This slider
controls how many models to keep loaded at once. A typical SD-1 or SD-2 model
uses 2-3 GB of memory. A typical SDXL model uses 6-7 GB. Providing more
RAM will allow more models to be co-resident.
- ***Output directory for images***
This is the path to a directory in which InvokeAI will store all its
generated images.
- ***Autoimport Folder***
This is the directory in which you can place models you have
downloaded and wish to load into InvokeAI. You can place a variety
of models in this directory, including diffusers folders, .ckpt files,
.safetensors files, as well as LoRAs, ControlNet and Textual Inversion
files (both folder and file versions). To help organize this folder,
you can create several levels of subfolders and drop your models into
whichever ones you want.
- ***LICENSE***
At the bottom of the screen you will see a checkbox for accepting
the CreativeML Responsible AI Licenses. You need to accept the license
in order to download Stable Diffusion models from the next screen.
_You can come back to the startup options form_ as many times as you like.
From the `invoke.sh` or `invoke.bat` launcher, select option (6) to relaunch
this script. On the command line, it is named `invokeai-configure`.
11. **Downloading Models**: After you press `[NEXT]` on the screen, you will be taken
to another screen that prompts you to download a series of starter models. The ones
we recommend are preselected for you, but you are encouraged to use the checkboxes to
pick and choose.
You will probably wish to download `autoencoder-840000` for use with models that
were trained with an older version of the Stability VAE.
<figure markdown>
![select-models-screenshot](../assets/installer-walkthrough/installing-models.png)
</figure>
Below the preselected list of starter models is a large text field which you can use
to specify a series of models to import. You can specify models in a variety of formats,
each separated by a space or newline. The formats accepted are:
- The path to a .ckpt or .safetensors file. On most systems, you can drag a file from
the file browser to the textfield to automatically paste the path. Be sure to remove
extraneous quotation marks and other things that come along for the ride.
- The path to a directory containing a combination of `.ckpt` and `.safetensors` files.
The directory will be scanned from top to bottom (including subfolders) and any
file that can be imported will be.
- A URL pointing to a `.ckpt` or `.safetensors` file. You can cut
and paste directly from a web page, or simply drag the link from the web page
or navigation bar. (You can also use ctrl-shift-V to paste into this field)
The file will be downloaded and installed.
- The HuggingFace repository ID (repo_id) for a `diffusers` model. These IDs have
the format _author_name/model_name_, as in `andite/anything-v4.0`
- The path to a local directory containing a `diffusers`
model. These directories always have the file `model_index.json`
at their top level.
_Select a directory for models to import_ You may select a local
directory for autoimporting at startup time. If you select this
option, the directory you choose will be scanned for new
.ckpt/.safetensors files each time InvokeAI starts up, and any new
files will be automatically imported and made available for your
use.
_Convert imported models into diffusers_ When legacy checkpoint
files are imported, you may select to use them unmodified (the
default) or to convert them into `diffusers` models. The latter
load much faster and have slightly better rendering performance,
but not all checkpoint files can be converted. Note that Stable Diffusion
Version 2.X files are **only** supported in `diffusers` format and will
be converted regardless.
_You can come back to the model install form_ as many times as you like.
From the `invoke.sh` or `invoke.bat` launcher, select option (5) to relaunch
this script. On the command line, it is named `invokeai-model-install`.
12. **Running InvokeAI for the first time**: The script will now exit and you'll be ready to generate some images. Look
10. **Running InvokeAI for the first time**: The script will now exit and you'll be ready to generate some images. Look
for the directory `invokeai` installed in the location you chose at the
beginning of the install session. Look for a shell script named `invoke.sh`
(Linux/Mac) or `invoke.bat` (Windows). Launch the script by double-clicking
@ -349,14 +220,14 @@ experimental versions later.
http://localhost:9090. Click on this link to open up a browser
and start exploring InvokeAI's features.
12. **InvokeAI Options**: You can launch InvokeAI with several different command-line arguments that
customize its behavior. For example, you can change the location of the
12. **InvokeAI Options**: You can configure using the `invokeai.yaml` config file.
For example, you can change the location of the
image output directory or balance memory usage vs performance. See
[Configuration](../features/CONFIGURATION.md) for a full list of the options.
- To set defaults that will take effect every time you launch InvokeAI,
use a text editor (e.g. Notepad) to exit the file
`invokeai\invokeai.init`. It contains a variety of examples that you can
`invokeai\invokeai.yaml`. It contains a variety of examples that you can
follow to add and modify launch options.
- The launcher script also offers you an option labeled "open the developer
@ -394,7 +265,6 @@ rm .\.venv -r -force
python -mvenv .venv
.\.venv\Scripts\activate
pip install invokeai
invokeai-configure --yes --root .
```
If you see anything marked as an error during this process please stop
@ -426,16 +296,10 @@ error messages:
This failure mode occurs when there is a network glitch during
downloading the very large SDXL model.
To address this, first go to the Web Model Manager and delete the
Stable-Diffusion-XL-base-1.X model. Then navigate to HuggingFace and
manually download the .safetensors version of the model. The 1.0
version is located at
https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/tree/main
and the file is named `sd_xl_base_1.0.safetensors`.
Save this file to disk and then reenter the Model Manager. Navigate to
Import Models->Add Model, then type (or drag-and-drop) the path to the
.safetensors file. Press "Add Model".
To address this, first go to the Model Manager and delete the
Stable-Diffusion-XL-base-1.X model. Then, click the HuggingFace tab,
paste the Repo ID stabilityai/stable-diffusion-xl-base-1.0 and install
the model.
### _Package dependency conflicts_
@ -488,15 +352,7 @@ download models, etc), but this doesn't fix the problem.
This issue is often caused by a misconfigured configuration directive in the
`invokeai\invokeai.init` initialization file that contains startup settings. The
easiest way to fix the problem is to move the file out of the way and re-run
`invokeai-configure`. Enter the developer's console (option 3 of the launcher
script) and run this command:
```cmd
invokeai-configure --root=.
```
Note the dot (.) after `--root`. It is part of the command.
easiest way to fix the problem is to move the file out of the way and restart the app.
_If none of these maneuvers fixes the problem_ then please report the problem to
the [InvokeAI Issues](https://github.com/invoke-ai/InvokeAI/issues) section, or
@ -565,16 +421,4 @@ This distribution is changing rapidly, and we add new features
regularly. Releases are announced at
http://github.com/invoke-ai/InvokeAI/releases, and at
https://pypi.org/project/InvokeAI/ To update to the latest released
version (recommended), follow these steps:
1. Start the `invoke.sh`/`invoke.bat` launch script from within the
`invokeai` root directory.
2. Choose menu item (10) "Update InvokeAI".
3. This will launch a menu that gives you the option of:
1. Updating to the latest official release;
2. Updating to the bleeding-edge development version; or
3. Manually entering the tag or branch name of a version of
InvokeAI you wish to try out.
version (recommended), download the latest release and run the installer.

2
docs/installation/INSTALLATION.md
View File

@ -26,7 +26,7 @@ driver).
🖥️ **Download the latest installer .zip file here** : https://github.com/invoke-ai/InvokeAI/releases/latest
- *Look for the file labelled "InvokeAI-installer-v3.X.X.zip" at the bottom of the page*
- *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).

50
installer/lib/installer.py
View File

@ -149,9 +149,6 @@ class Installer:
# install the launch/update scripts into the runtime directory
self.instance.install_user_scripts()
# run through the configuration flow
self.instance.configure()
class InvokeAiInstance:
"""
@ -242,53 +239,6 @@ class InvokeAiInstance:
)
sys.exit(1)
def configure(self):
"""
Configure the InvokeAI runtime directory
"""
auto_install = False
# set sys.argv to a consistent state
new_argv = [sys.argv[0]]
for i in range(1, len(sys.argv)):
el = sys.argv[i]
if el in ["-r", "--root"]:
new_argv.append(el)
new_argv.append(sys.argv[i + 1])
elif el in ["-y", "--yes", "--yes-to-all"]:
auto_install = True
sys.argv = new_argv
import messages
import requests # to catch download exceptions
auto_install = auto_install or messages.user_wants_auto_configuration()
if auto_install:
sys.argv.append("--yes")
else:
messages.introduction()
from invokeai.frontend.install.invokeai_configure import invokeai_configure
# NOTE: currently the config script does its own arg parsing! this means the command-line switches
# from the installer will also automatically propagate down to the config script.
# this may change in the future with config refactoring!
succeeded = False
try:
invokeai_configure()
succeeded = True
except requests.exceptions.ConnectionError as e:
print(f"\nA network error was encountered during configuration and download: {str(e)}")
except OSError as e:
print(f"\nAn OS error was encountered during configuration and download: {str(e)}")
except Exception as e:
print(f"\nA problem was encountered during the configuration and download steps: {str(e)}")
finally:
if not succeeded:
print('To try again, find the "invokeai" directory, run the script "invoke.sh" or "invoke.bat"')
print("and choose option 7 to fix a broken install, optionally followed by option 5 to install models.")
print("Alternatively you can relaunch the installer.")
def install_user_scripts(self):
"""
Copy the launch and update scripts to the runtime dir

63
installer/lib/messages.py
View File

@ -8,7 +8,7 @@ import platform
from enum import Enum
from pathlib import Path
from prompt_toolkit import HTML, prompt
from prompt_toolkit import prompt
from prompt_toolkit.completion import FuzzyWordCompleter, PathCompleter
from prompt_toolkit.validation import Validator
from rich import box, print
@ -98,39 +98,6 @@ def choose_version(available_releases: tuple | None = None) -> str:
return "stable" if response == "" else response
def user_wants_auto_configuration() -> bool:
"""Prompt the user to choose between manual and auto configuration."""
console.rule("InvokeAI Configuration Section")
console.print(
Panel(
Group(
"\n".join(
[
"Libraries are installed and InvokeAI will now set up its root directory and configuration. Choose between:",
"",
" * AUTOMATIC configuration: install reasonable defaults and a minimal set of starter models.",
" * MANUAL configuration: manually inspect and adjust configuration options and pick from a larger set of starter models.",
"",
"Later you can fine tune your configuration by selecting option [6] 'Change InvokeAI startup options' from the invoke.bat/invoke.sh launcher script.",
]
),
),
box=box.MINIMAL,
padding=(1, 1),
)
)
choice = (
prompt(
HTML("Choose <b>&lt;a&gt;</b>utomatic or <b>&lt;m&gt;</b>anual configuration [a/m] (a): "),
validator=Validator.from_callable(
lambda n: n == "" or n.startswith(("a", "A", "m", "M")), error_message="Please select 'a' or 'm'"
),
)
or "a"
)
return choice.lower().startswith("a")
def confirm_install(dest: Path) -> bool:
if dest.exists():
print(f":stop_sign: Directory {dest} already exists!")
@ -351,34 +318,6 @@ def windows_long_paths_registry() -> None:
)
def introduction() -> None:
"""
Display a banner when starting configuration of the InvokeAI application
"""
console.rule()
console.print(
Panel(
title=":art: Configuring InvokeAI :art:",
renderable=Group(
"",
"[b]This script will:",
"",
"1. Configure the InvokeAI application directory",
"2. Help download the Stable Diffusion weight files",
" and other large models that are needed for text to image generation",
"3. Create initial configuration files.",
"",
"[i]At any point you may interrupt this program and resume later.",
"",
"[b]For the best user experience, please enlarge or maximize this window",
),
)
)
console.line(2)
def _platform_specific_help() -> Text | None:
if OS == "Darwin":
text = Text.from_markup(

34
installer/templates/invoke.bat.in
View File

@ -9,15 +9,10 @@ set INVOKEAI_ROOT=.
:start
echo Desired action:
echo 1. Generate images with the browser-based interface
echo 2. Run textual inversion training
echo 3. Merge models (diffusers type only)
echo 4. Download and install models
echo 5. Change InvokeAI startup options
echo 6. Re-run the configure script to fix a broken install or to complete a major upgrade
echo 7. Open the developer console
echo 8. Update InvokeAI (DEPRECATED - please use the installer)
echo 9. Run the InvokeAI image database maintenance script
echo 10. Command-line help
echo 2. Open the developer console
echo 3. Update InvokeAI (DEPRECATED - please use the installer)
echo 4. Run the InvokeAI image database maintenance script
echo 5. Command-line help
echo Q - Quit
set /P choice="Please enter 1-10, Q: [1] "
if not defined choice set choice=1
@ -25,21 +20,6 @@ IF /I "%choice%" == "1" (
echo Starting the InvokeAI browser-based UI..
python .venv\Scripts\invokeai-web.exe %*
) ELSE IF /I "%choice%" == "2" (
echo Starting textual inversion training..
python .venv\Scripts\invokeai-ti.exe --gui
) ELSE IF /I "%choice%" == "3" (
echo Starting model merging script..
python .venv\Scripts\invokeai-merge.exe --gui
) ELSE IF /I "%choice%" == "4" (
echo Running invokeai-model-install...
python .venv\Scripts\invokeai-model-install.exe
) ELSE IF /I "%choice%" == "5" (
echo Running invokeai-configure...
python .venv\Scripts\invokeai-configure.exe --skip-sd-weight --skip-support-models
) ELSE IF /I "%choice%" == "6" (
echo Running invokeai-configure...
python .venv\Scripts\invokeai-configure.exe --yes --skip-sd-weight
) ELSE IF /I "%choice%" == "7" (
echo Developer Console
echo Python command is:
where python
@ -51,15 +31,15 @@ IF /I "%choice%" == "1" (
echo *************************
echo *** Type `exit` to quit this shell and deactivate the Python virtual environment ***
call cmd /k
) ELSE IF /I "%choice%" == "8" (
) ELSE IF /I "%choice%" == "3" (
echo UPDATING FROM WITHIN THE APP IS BEING DEPRECATED.
echo Please download the installer from https://github.com/invoke-ai/InvokeAI/releases/latest and run it to update your installation.
timeout 4
python -m invokeai.frontend.install.invokeai_update
) ELSE IF /I "%choice%" == "9" (
) ELSE IF /I "%choice%" == "4" (
echo Running the db maintenance script...
python .venv\Scripts\invokeai-db-maintenance.exe
) ELSE IF /I "%choice%" == "10" (
) ELSE IF /I "%choice%" == "5" (
echo Displaying command line help...
python .venv\Scripts\invokeai-web.exe --help %*
pause

57
installer/templates/invoke.sh.in
View File

@ -58,49 +58,24 @@ do_choice() {
invokeai-web $PARAMS
;;
2)
clear
printf "Textual inversion training\n"
invokeai-ti --gui $PARAMS
;;
3)
clear
printf "Merge models (diffusers type only)\n"
invokeai-merge --gui $PARAMS
;;
4)
clear
printf "Download and install models\n"
invokeai-model-install --root ${INVOKEAI_ROOT}
;;
5)
clear
printf "Change InvokeAI startup options\n"
invokeai-configure --root ${INVOKEAI_ROOT} --skip-sd-weights --skip-support-models
;;
6)
clear
printf "Re-run the configure script to fix a broken install or to complete a major upgrade\n"
invokeai-configure --root ${INVOKEAI_ROOT} --yes --default_only --skip-sd-weights
;;
7)
clear
printf "Open the developer console\n"
file_name=$(basename "${BASH_SOURCE[0]}")
bash --init-file "$file_name"
;;
8)
3)
clear
printf "UPDATING FROM WITHIN THE APP IS BEING DEPRECATED\n"
printf "Please download the installer from https://github.com/invoke-ai/InvokeAI/releases/latest and run it to update your installation.\n"
sleep 4
python -m invokeai.frontend.install.invokeai_update
;;
9)
4)
clear
printf "Running the db maintenance script\n"
invokeai-db-maintenance --root ${INVOKEAI_ROOT}
;;
10)
5)
clear
printf "Command-line help\n"
invokeai-web --help
@ -118,15 +93,10 @@ do_choice() {
do_dialog() {
options=(
1 "Generate images with a browser-based interface"
2 "Textual inversion training"
3 "Merge models (diffusers type only)"
4 "Download and install models"
5 "Change InvokeAI startup options"
6 "Re-run the configure script to fix a broken install or to complete a major upgrade"
7 "Open the developer console"
8 "Update InvokeAI (DEPRECATED - please use the installer)"
9 "Run the InvokeAI image database maintenance script"
10 "Command-line help"
2 "Open the developer console"
3 "Update InvokeAI (DEPRECATED - please use the installer)"
4 "Run the InvokeAI image database maintenance script"
5 "Command-line help"
)
choice=$(dialog --clear \
@ -151,15 +121,10 @@ do_line_input() {
printf " ** For a more attractive experience, please install the 'dialog' utility using your package manager. **\n\n"
printf "What would you like to do?\n"
printf "1: Generate images using the browser-based interface\n"
printf "2: Run textual inversion training\n"
printf "3: Merge models (diffusers type only)\n"
printf "4: Download and install models\n"
printf "5: Change InvokeAI startup options\n"
printf "6: Re-run the configure script to fix a broken install\n"
printf "7: Open the developer console\n"
printf "8: Update InvokeAI\n"
printf "9: Run the InvokeAI image database maintenance script\n"
printf "10: Command-line help\n"
printf "2: Open the developer console\n"
printf "3: Update InvokeAI\n"
printf "4: Run the InvokeAI image database maintenance script\n"
printf "5: Command-line help\n"
printf "Q: Quit\n\n"
read -p "Please enter 1-10, Q: [1] " yn
choice=${yn:='1'}

11
invokeai/README
View File

@ -1,11 +0,0 @@
Organization of the source tree:
app -- Home of nodes invocations and services
assets -- Images and other data files used by InvokeAI
backend -- Non-user facing libraries, including the rendering
core.
configs -- Configuration files used at install and run times
frontend -- User-facing scripts, including the CLI and the WebUI
version -- Current InvokeAI version string, stored
in version/invokeai_version.py

72
invokeai/app/api/routers/model_manager.py
View File

@ -1,12 +1,16 @@
# Copyright (c) 2023 Lincoln D. Stein
"""FastAPI route for model configuration records."""
import contextlib
import io
import pathlib
import shutil
import traceback
from copy import deepcopy
from enum import Enum
from typing import Any, Dict, List, Optional
import huggingface_hub
from fastapi import Body, Path, Query, Response, UploadFile
from fastapi.responses import FileResponse
from fastapi.routing import APIRouter
@ -21,6 +25,7 @@ from invokeai.app.services.model_records import (
UnknownModelException,
)
from invokeai.app.services.model_records.model_records_base import DuplicateModelException, ModelRecordChanges
from invokeai.app.util.suppress_output import SuppressOutput
from invokeai.backend.model_manager.config import (
AnyModelConfig,
BaseModelType,
@ -32,6 +37,7 @@ from invokeai.backend.model_manager.config import (
from invokeai.backend.model_manager.metadata.fetch.huggingface import HuggingFaceMetadataFetch
from invokeai.backend.model_manager.metadata.metadata_base import ModelMetadataWithFiles, UnknownMetadataException
from invokeai.backend.model_manager.search import ModelSearch
from invokeai.backend.model_manager.starter_models import STARTER_MODELS, StarterModel
from ..dependencies import ApiDependencies
@ -780,3 +786,69 @@ async def convert_model(
# except ValueError as e:
# raise HTTPException(status_code=400, detail=str(e))
# return response
@model_manager_router.get("/starter_models", operation_id="get_starter_models", response_model=list[StarterModel])
async def get_starter_models() -> list[StarterModel]:
installed_models = ApiDependencies.invoker.services.model_manager.store.search_by_attr()
installed_model_sources = {m.source for m in installed_models}
starter_models = deepcopy(STARTER_MODELS)
for model in starter_models:
if model.source in installed_model_sources:
model.is_installed = True
# Remove already-installed dependencies
missing_deps: list[str] = []
for dep in model.dependencies or []:
if dep not in installed_model_sources:
missing_deps.append(dep)
model.dependencies = missing_deps
return starter_models
class HFTokenStatus(str, Enum):
VALID = "valid"
INVALID = "invalid"
UNKNOWN = "unknown"
class HFTokenHelper:
@classmethod
def get_status(cls) -> HFTokenStatus:
try:
if huggingface_hub.get_token_permission(huggingface_hub.get_token()):
# Valid token!
return HFTokenStatus.VALID
# No token set
return HFTokenStatus.INVALID
except Exception:
return HFTokenStatus.UNKNOWN
@classmethod
def set_token(cls, token: str) -> HFTokenStatus:
with SuppressOutput(), contextlib.suppress(Exception):
huggingface_hub.login(token=token, add_to_git_credential=False)
return cls.get_status()
@model_manager_router.get("/hf_login", operation_id="get_hf_login_status", response_model=HFTokenStatus)
async def get_hf_login_status() -> HFTokenStatus:
token_status = HFTokenHelper.get_status()
if token_status is HFTokenStatus.UNKNOWN:
ApiDependencies.invoker.services.logger.warning("Unable to verify HF token")
return token_status
@model_manager_router.post("/hf_login", operation_id="do_hf_login", response_model=HFTokenStatus)
async def do_hf_login(
token: str = Body(description="Hugging Face token to use for login", embed=True),
) -> HFTokenStatus:
HFTokenHelper.set_token(token)
token_status = HFTokenHelper.get_status()
if token_status is HFTokenStatus.UNKNOWN:
ApiDependencies.invoker.services.logger.warning("Unable to verify HF token")
return token_status

4
invokeai/app/api_app.py
View File

@ -233,10 +233,6 @@ def invoke_api() -> None:
else:
return port
from invokeai.backend.install.check_directories import check_directories
check_directories(app_config) # note, may exit with an exception if root not set up
if app_config.dev_reload:
try:
import jurigged

8
invokeai/app/invocations/compel.py
View File

@ -36,7 +36,7 @@ from .model import CLIPField
title="Prompt",
tags=["prompt", "compel"],
category="conditioning",
version="1.0.1",
version="1.1.1",
)
class CompelInvocation(BaseInvocation):
"""Parse prompt using compel package to conditioning."""
@ -232,7 +232,7 @@ class SDXLPromptInvocationBase:
title="SDXL Prompt",
tags=["sdxl", "compel", "prompt"],
category="conditioning",
version="1.0.1",
version="1.1.1",
)
class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
"""Parse prompt using compel package to conditioning."""
@ -325,7 +325,7 @@ class SDXLCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
title="SDXL Refiner Prompt",
tags=["sdxl", "compel", "prompt"],
category="conditioning",
version="1.0.1",
version="1.1.1",
)
class SDXLRefinerCompelPromptInvocation(BaseInvocation, SDXLPromptInvocationBase):
"""Parse prompt using compel package to conditioning."""
@ -381,7 +381,7 @@ class CLIPSkipInvocationOutput(BaseInvocationOutput):
title="CLIP Skip",
tags=["clipskip", "clip", "skip"],
category="conditioning",
version="1.0.0",
version="1.1.0",
)
class CLIPSkipInvocation(BaseInvocation):
"""Skip layers in clip text_encoder model."""

81
invokeai/app/invocations/controlnet_image_processors.py
View File

@ -7,12 +7,8 @@ from typing import Dict, List, Literal, Union
import cv2
import numpy as np
from controlnet_aux import (
CannyDetector,
ContentShuffleDetector,
HEDdetector,
LeresDetector,
LineartAnimeDetector,
LineartDetector,
MediapipeFaceDetector,
MidasDetector,
MLSDdetector,
@ -39,8 +35,12 @@ from invokeai.app.invocations.model import ModelIdentifierField
from invokeai.app.invocations.primitives import ImageOutput
from invokeai.app.invocations.util import validate_begin_end_step, validate_weights
from invokeai.app.services.shared.invocation_context import InvocationContext
from invokeai.backend.image_util.canny import get_canny_edges
from invokeai.backend.image_util.depth_anything import DepthAnythingDetector
from invokeai.backend.image_util.dw_openpose import DWOpenposeDetector
from invokeai.backend.image_util.hed import HEDProcessor
from invokeai.backend.image_util.lineart import LineartProcessor
from invokeai.backend.image_util.lineart_anime import LineartAnimeProcessor
from .baseinvocation import BaseInvocation, BaseInvocationOutput, invocation, invocation_output
@ -171,11 +171,12 @@ class ImageProcessorInvocation(BaseInvocation, WithMetadata, WithBoard):
title="Canny Processor",
tags=["controlnet", "canny"],
category="controlnet",
version="1.2.1",
version="1.3.2",
)
class CannyImageProcessorInvocation(ImageProcessorInvocation):
"""Canny edge detection for ControlNet"""
detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
low_threshold: int = InputField(
default=100, ge=0, le=255, description="The low threshold of the Canny pixel gradient (0-255)"
@ -188,12 +189,12 @@ class CannyImageProcessorInvocation(ImageProcessorInvocation):
# Keep alpha channel for Canny processing to detect edges of transparent areas
return context.images.get_pil(self.image.image_name, "RGBA")
def run_processor(self, image):
canny_processor = CannyDetector()
processed_image = canny_processor(
def run_processor(self, image: Image.Image) -> Image.Image:
processed_image = get_canny_edges(
image,
self.low_threshold,
self.high_threshold,
detect_resolution=self.detect_resolution,
image_resolution=self.image_resolution,
)
return processed_image
@ -204,7 +205,7 @@ class CannyImageProcessorInvocation(ImageProcessorInvocation):
title="HED (softedge) Processor",
tags=["controlnet", "hed", "softedge"],
category="controlnet",
version="1.2.1",
version="1.2.2",
)
class HedImageProcessorInvocation(ImageProcessorInvocation):
"""Applies HED edge detection to image"""
@ -215,9 +216,9 @@ class HedImageProcessorInvocation(ImageProcessorInvocation):
# safe: bool = InputField(default=False, description=FieldDescriptions.safe_mode)
scribble: bool = InputField(default=False, description=FieldDescriptions.scribble_mode)
def run_processor(self, image):
hed_processor = HEDdetector.from_pretrained("lllyasviel/Annotators")
processed_image = hed_processor(
def run_processor(self, image: Image.Image) -> Image.Image:
hed_processor = HEDProcessor()
processed_image = hed_processor.run(
image,
detect_resolution=self.detect_resolution,
image_resolution=self.image_resolution,
@ -233,7 +234,7 @@ class HedImageProcessorInvocation(ImageProcessorInvocation):
title="Lineart Processor",
tags=["controlnet", "lineart"],
category="controlnet",
version="1.2.1",
version="1.2.2",
)
class LineartImageProcessorInvocation(ImageProcessorInvocation):
"""Applies line art processing to image"""
@ -242,9 +243,9 @@ class LineartImageProcessorInvocation(ImageProcessorInvocation):
image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
coarse: bool = InputField(default=False, description="Whether to use coarse mode")
def run_processor(self, image):
lineart_processor = LineartDetector.from_pretrained("lllyasviel/Annotators")
processed_image = lineart_processor(
def run_processor(self, image: Image.Image) -> Image.Image:
lineart_processor = LineartProcessor()
processed_image = lineart_processor.run(
image, detect_resolution=self.detect_resolution, image_resolution=self.image_resolution, coarse=self.coarse
)
return processed_image
@ -255,7 +256,7 @@ class LineartImageProcessorInvocation(ImageProcessorInvocation):
title="Lineart Anime Processor",
tags=["controlnet", "lineart", "anime"],
category="controlnet",
version="1.2.1",
version="1.2.2",
)
class LineartAnimeImageProcessorInvocation(ImageProcessorInvocation):
"""Applies line art anime processing to image"""
@ -263,9 +264,9 @@ class LineartAnimeImageProcessorInvocation(ImageProcessorInvocation):
detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
def run_processor(self, image):
processor = LineartAnimeDetector.from_pretrained("lllyasviel/Annotators")
processed_image = processor(
def run_processor(self, image: Image.Image) -> Image.Image:
processor = LineartAnimeProcessor()
processed_image = processor.run(
image,
detect_resolution=self.detect_resolution,
image_resolution=self.image_resolution,
@ -278,13 +279,14 @@ class LineartAnimeImageProcessorInvocation(ImageProcessorInvocation):
title="Midas Depth Processor",
tags=["controlnet", "midas"],
category="controlnet",
version="1.2.1",
version="1.2.3",
)
class MidasDepthImageProcessorInvocation(ImageProcessorInvocation):
"""Applies Midas depth processing to image"""
a_mult: float = InputField(default=2.0, ge=0, description="Midas parameter `a_mult` (a = a_mult * PI)")
bg_th: float = InputField(default=0.1, ge=0, description="Midas parameter `bg_th`")
detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
# depth_and_normal not supported in controlnet_aux v0.0.3
# depth_and_normal: bool = InputField(default=False, description="whether to use depth and normal mode")
@ -296,6 +298,7 @@ class MidasDepthImageProcessorInvocation(ImageProcessorInvocation):
a=np.pi * self.a_mult,
bg_th=self.bg_th,
image_resolution=self.image_resolution,
detect_resolution=self.detect_resolution,
# dept_and_normal not supported in controlnet_aux v0.0.3
# depth_and_normal=self.depth_and_normal,
)
@ -307,7 +310,7 @@ class MidasDepthImageProcessorInvocation(ImageProcessorInvocation):
title="Normal BAE Processor",
tags=["controlnet"],
category="controlnet",
version="1.2.1",
version="1.2.2",
)
class NormalbaeImageProcessorInvocation(ImageProcessorInvocation):
"""Applies NormalBae processing to image"""
@ -324,7 +327,7 @@ class NormalbaeImageProcessorInvocation(ImageProcessorInvocation):
@invocation(
"mlsd_image_processor", title="MLSD Processor", tags=["controlnet", "mlsd"], category="controlnet", version="1.2.1"
"mlsd_image_processor", title="MLSD Processor", tags=["controlnet", "mlsd"], category="controlnet", version="1.2.2"
)
class MlsdImageProcessorInvocation(ImageProcessorInvocation):
"""Applies MLSD processing to image"""
@ -347,7 +350,7 @@ class MlsdImageProcessorInvocation(ImageProcessorInvocation):
@invocation(
"pidi_image_processor", title="PIDI Processor", tags=["controlnet", "pidi"], category="controlnet", version="1.2.1"
"pidi_image_processor", title="PIDI Processor", tags=["controlnet", "pidi"], category="controlnet", version="1.2.2"
)
class PidiImageProcessorInvocation(ImageProcessorInvocation):
"""Applies PIDI processing to image"""
@ -374,7 +377,7 @@ class PidiImageProcessorInvocation(ImageProcessorInvocation):
title="Content Shuffle Processor",
tags=["controlnet", "contentshuffle"],
category="controlnet",
version="1.2.1",
version="1.2.2",
)
class ContentShuffleImageProcessorInvocation(ImageProcessorInvocation):
"""Applies content shuffle processing to image"""
@ -404,7 +407,7 @@ class ContentShuffleImageProcessorInvocation(ImageProcessorInvocation):
title="Zoe (Depth) Processor",
tags=["controlnet", "zoe", "depth"],
category="controlnet",
version="1.2.1",
version="1.2.2",
)
class ZoeDepthImageProcessorInvocation(ImageProcessorInvocation):
"""Applies Zoe depth processing to image"""
@ -420,19 +423,24 @@ class ZoeDepthImageProcessorInvocation(ImageProcessorInvocation):
title="Mediapipe Face Processor",
tags=["controlnet", "mediapipe", "face"],
category="controlnet",
version="1.2.1",
version="1.2.3",
)
class MediapipeFaceProcessorInvocation(ImageProcessorInvocation):
"""Applies mediapipe face processing to image"""
max_faces: int = InputField(default=1, ge=1, description="Maximum number of faces to detect")
min_confidence: float = InputField(default=0.5, ge=0, le=1, description="Minimum confidence for face detection")
detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
def run_processor(self, image):
mediapipe_face_processor = MediapipeFaceDetector()
processed_image = mediapipe_face_processor(
image, max_faces=self.max_faces, min_confidence=self.min_confidence, image_resolution=self.image_resolution
image,
max_faces=self.max_faces,
min_confidence=self.min_confidence,
image_resolution=self.image_resolution,
detect_resolution=self.detect_resolution,
)
return processed_image
@ -442,7 +450,7 @@ class MediapipeFaceProcessorInvocation(ImageProcessorInvocation):
title="Leres (Depth) Processor",
tags=["controlnet", "leres", "depth"],
category="controlnet",
version="1.2.1",
version="1.2.2",
)
class LeresImageProcessorInvocation(ImageProcessorInvocation):
"""Applies leres processing to image"""
@ -471,7 +479,7 @@ class LeresImageProcessorInvocation(ImageProcessorInvocation):
title="Tile Resample Processor",
tags=["controlnet", "tile"],
category="controlnet",
version="1.2.1",
version="1.2.2",
)
class TileResamplerProcessorInvocation(ImageProcessorInvocation):
"""Tile resampler processor"""
@ -511,11 +519,12 @@ class TileResamplerProcessorInvocation(ImageProcessorInvocation):
title="Segment Anything Processor",
tags=["controlnet", "segmentanything"],
category="controlnet",
version="1.2.1",
version="1.2.3",
)
class SegmentAnythingProcessorInvocation(ImageProcessorInvocation):
"""Applies segment anything processing to image"""
detect_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.detect_res)
image_resolution: int = InputField(default=512, ge=0, description=FieldDescriptions.image_res)
def run_processor(self, image):
@ -524,7 +533,9 @@ class SegmentAnythingProcessorInvocation(ImageProcessorInvocation):
"ybelkada/segment-anything", subfolder="checkpoints"
)
np_img = np.array(image, dtype=np.uint8)
processed_image = segment_anything_processor(np_img, image_resolution=self.image_resolution)
processed_image = segment_anything_processor(
np_img, image_resolution=self.image_resolution, detect_resolution=self.detect_resolution
)
return processed_image
@ -555,7 +566,7 @@ class SamDetectorReproducibleColors(SamDetector):
title="Color Map Processor",
tags=["controlnet"],
category="controlnet",
version="1.2.1",
version="1.2.2",
)
class ColorMapImageProcessorInvocation(ImageProcessorInvocation):
"""Generates a color map from the provided image"""
@ -587,7 +598,7 @@ DEPTH_ANYTHING_MODEL_SIZES = Literal["large", "base", "small"]
title="Depth Anything Processor",
tags=["controlnet", "depth", "depth anything"],
category="controlnet",
version="1.0.1",
version="1.1.1",
)
class DepthAnythingImageProcessorInvocation(ImageProcessorInvocation):
"""Generates a depth map based on the Depth Anything algorithm"""
@ -610,7 +621,7 @@ class DepthAnythingImageProcessorInvocation(ImageProcessorInvocation):
title="DW Openpose Image Processor",
tags=["controlnet", "dwpose", "openpose"],
category="controlnet",
version="1.0.0",
version="1.1.0",
)
class DWOpenposeImageProcessorInvocation(ImageProcessorInvocation):
"""Generates an openpose pose from an image using DWPose"""

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

@ -13,7 +13,7 @@ from .baseinvocation import BaseInvocation, invocation
from .fields import InputField, WithBoard, WithMetadata
@invocation("cv_inpaint", title="OpenCV Inpaint", tags=["opencv", "inpaint"], category="inpaint", version="1.2.1")
@invocation("cv_inpaint", title="OpenCV Inpaint", tags=["opencv", "inpaint"], category="inpaint", version="1.3.1")
class CvInpaintInvocation(BaseInvocation, WithMetadata, WithBoard):
"""Simple inpaint using opencv."""

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

@ -435,7 +435,7 @@ def get_faces_list(
return all_faces
@invocation("face_off", title="FaceOff", tags=["image", "faceoff", "face", "mask"], category="image", version="1.2.1")
@invocation("face_off", title="FaceOff", tags=["image", "faceoff", "face", "mask"], category="image", version="1.2.2")
class FaceOffInvocation(BaseInvocation, WithMetadata):
"""Bound, extract, and mask a face from an image using MediaPipe detection"""
@ -514,7 +514,7 @@ class FaceOffInvocation(BaseInvocation, WithMetadata):
return output
@invocation("face_mask_detection", title="FaceMask", tags=["image", "face", "mask"], category="image", version="1.2.1")
@invocation("face_mask_detection", title="FaceMask", tags=["image", "face", "mask"], category="image", version="1.2.2")
class FaceMaskInvocation(BaseInvocation, WithMetadata):
"""Face mask creation using mediapipe face detection"""
@ -617,7 +617,7 @@ class FaceMaskInvocation(BaseInvocation, WithMetadata):
@invocation(
"face_identifier", title="FaceIdentifier", tags=["image", "face", "identifier"], category="image", version="1.2.1"
"face_identifier", title="FaceIdentifier", tags=["image", "face", "identifier"], category="image", version="1.2.2"
)
class FaceIdentifierInvocation(BaseInvocation, WithMetadata, WithBoard):
"""Outputs an image with detected face IDs printed on each face. For use with other FaceTools."""

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

@ -49,7 +49,7 @@ class ShowImageInvocation(BaseInvocation):
title="Blank Image",
tags=["image"],
category="image",
version="1.2.1",
version="1.2.2",
)
class BlankImageInvocation(BaseInvocation, WithMetadata, WithBoard):
"""Creates a blank image and forwards it to the pipeline"""
@ -72,7 +72,7 @@ class BlankImageInvocation(BaseInvocation, WithMetadata, WithBoard):
title="Crop Image",
tags=["image", "crop"],
category="image",
version="1.2.1",
version="1.2.2",
)
class ImageCropInvocation(BaseInvocation, WithMetadata, WithBoard):
"""Crops an image to a specified box. The box can be outside of the image."""
@ -143,7 +143,7 @@ class CenterPadCropInvocation(BaseInvocation):
title="Paste Image",
tags=["image", "paste"],
category="image",
version="1.2.1",
version="1.2.2",
)
class ImagePasteInvocation(BaseInvocation, WithMetadata, WithBoard):
"""Pastes an image into another image."""
@ -190,7 +190,7 @@ class ImagePasteInvocation(BaseInvocation, WithMetadata, WithBoard):
title="Mask from Alpha",
tags=["image", "mask"],
category="image",
version="1.2.1",
version="1.2.2",
)
class MaskFromAlphaInvocation(BaseInvocation, WithMetadata, WithBoard):
"""Extracts the alpha channel of an image as a mask."""
@ -215,7 +215,7 @@ class MaskFromAlphaInvocation(BaseInvocation, WithMetadata, WithBoard):
title="Multiply Images",
tags=["image", "multiply"],
category="image",
version="1.2.1",
version="1.2.2",
)
class ImageMultiplyInvocation(BaseInvocation, WithMetadata, WithBoard):
"""Multiplies two images together using `PIL.ImageChops.multiply()`."""
@ -242,7 +242,7 @@ IMAGE_CHANNELS = Literal["A", "R", "G", "B"]
title="Extract Image Channel",
tags=["image", "channel"],
category="image",
version="1.2.1",
version="1.2.2",
)
class ImageChannelInvocation(BaseInvocation, WithMetadata, WithBoard):
"""Gets a channel from an image."""
@ -265,7 +265,7 @@ class ImageChannelInvocation(BaseInvocation, WithMetadata, WithBoard):
title="Convert Image Mode",
tags=["image", "convert"],
category="image",
version="1.2.1",
version="1.2.2",
)
class ImageConvertInvocation(BaseInvocation, WithMetadata, WithBoard):
"""Converts an image to a different mode."""
@ -288,7 +288,7 @@ class ImageConvertInvocation(BaseInvocation, WithMetadata, WithBoard):
title="Blur Image",
tags=["image", "blur"],
category="image",
version="1.2.1",
version="1.2.2",
)
class ImageBlurInvocation(BaseInvocation, WithMetadata, WithBoard):
"""Blurs an image"""
@ -316,7 +316,7 @@ class ImageBlurInvocation(BaseInvocation, WithMetadata, WithBoard):
title="Unsharp Mask",
tags=["image", "unsharp_mask"],
category="image",
version="1.2.1",
version="1.2.2",
classification=Classification.Beta,
)
class UnsharpMaskInvocation(BaseInvocation, WithMetadata, WithBoard):
@ -385,7 +385,7 @@ PIL_RESAMPLING_MAP = {
title="Resize Image",
tags=["image", "resize"],
category="image",
version="1.2.1",
version="1.2.2",
)
class ImageResizeInvocation(BaseInvocation, WithMetadata, WithBoard):
"""Resizes an image to specific dimensions"""
@ -415,7 +415,7 @@ class ImageResizeInvocation(BaseInvocation, WithMetadata, WithBoard):
title="Scale Image",
tags=["image", "scale"],
category="image",
version="1.2.1",
version="1.2.2",
)
class ImageScaleInvocation(BaseInvocation, WithMetadata, WithBoard):
"""Scales an image by a factor"""
@ -450,7 +450,7 @@ class ImageScaleInvocation(BaseInvocation, WithMetadata, WithBoard):
title="Lerp Image",
tags=["image", "lerp"],
category="image",
version="1.2.1",
version="1.2.2",
)
class ImageLerpInvocation(BaseInvocation, WithMetadata, WithBoard):
"""Linear interpolation of all pixels of an image"""
@ -477,7 +477,7 @@ class ImageLerpInvocation(BaseInvocation, WithMetadata, WithBoard):
title="Inverse Lerp Image",
tags=["image", "ilerp"],
category="image",
version="1.2.1",
version="1.2.2",
)
class ImageInverseLerpInvocation(BaseInvocation, WithMetadata, WithBoard):
"""Inverse linear interpolation of all pixels of an image"""
@ -504,7 +504,7 @@ class ImageInverseLerpInvocation(BaseInvocation, WithMetadata, WithBoard):
title="Blur NSFW Image",
tags=["image", "nsfw"],
category="image",
version="1.2.1",
version="1.2.2",
)
class ImageNSFWBlurInvocation(BaseInvocation, WithMetadata, WithBoard):
"""Add blur to NSFW-flagged images"""
@ -539,7 +539,7 @@ class ImageNSFWBlurInvocation(BaseInvocation, WithMetadata, WithBoard):
title="Add Invisible Watermark",
tags=["image", "watermark"],
category="image",
version="1.2.1",
version="1.2.2",
)
class ImageWatermarkInvocation(BaseInvocation, WithMetadata, WithBoard):
"""Add an invisible watermark to an image"""
@ -560,7 +560,7 @@ class ImageWatermarkInvocation(BaseInvocation, WithMetadata, WithBoard):
title="Mask Edge",
tags=["image", "mask", "inpaint"],
category="image",
version="1.2.1",
version="1.2.2",
)
class MaskEdgeInvocation(BaseInvocation, WithMetadata, WithBoard):
"""Applies an edge mask to an image"""
@ -599,7 +599,7 @@ class MaskEdgeInvocation(BaseInvocation, WithMetadata, WithBoard):
title="Combine Masks",
tags=["image", "mask", "multiply"],
category="image",
version="1.2.1",
version="1.2.2",
)
class MaskCombineInvocation(BaseInvocation, WithMetadata, WithBoard):
"""Combine two masks together by multiplying them using `PIL.ImageChops.multiply()`."""
@ -623,7 +623,7 @@ class MaskCombineInvocation(BaseInvocation, WithMetadata, WithBoard):
title="Color Correct",
tags=["image", "color"],
category="image",
version="1.2.1",
version="1.2.2",
)
class ColorCorrectInvocation(BaseInvocation, WithMetadata, WithBoard):
"""
@ -727,7 +727,7 @@ class ColorCorrectInvocation(BaseInvocation, WithMetadata, WithBoard):
title="Adjust Image Hue",
tags=["image", "hue"],
category="image",
version="1.2.1",
version="1.2.2",
)
class ImageHueAdjustmentInvocation(BaseInvocation, WithMetadata, WithBoard):
"""Adjusts the Hue of an image."""
@ -816,7 +816,7 @@ CHANNEL_FORMATS = {
"value",
],
category="image",
version="1.2.1",
version="1.2.2",
)
class ImageChannelOffsetInvocation(BaseInvocation, WithMetadata, WithBoard):
"""Add or subtract a value from a specific color channel of an image."""
@ -872,7 +872,7 @@ class ImageChannelOffsetInvocation(BaseInvocation, WithMetadata, WithBoard):
"value",
],
category="image",
version="1.2.1",
version="1.2.2",
)
class ImageChannelMultiplyInvocation(BaseInvocation, WithMetadata, WithBoard):
"""Scale a specific color channel of an image."""
@ -916,7 +916,7 @@ class ImageChannelMultiplyInvocation(BaseInvocation, WithMetadata, WithBoard):
title="Save Image",
tags=["primitives", "image"],
category="primitives",
version="1.2.1",
version="1.2.2",
use_cache=False,
)
class SaveImageInvocation(BaseInvocation, WithMetadata, WithBoard):

18
invokeai/app/invocations/infill.py
View File

@ -9,6 +9,7 @@ from PIL import Image, ImageOps
from invokeai.app.invocations.fields import ColorField, ImageField
from invokeai.app.invocations.primitives import ImageOutput
from invokeai.app.services.shared.invocation_context import InvocationContext
from invokeai.app.util.download_with_progress import download_with_progress_bar
from invokeai.app.util.misc import SEED_MAX
from invokeai.backend.image_util.cv2_inpaint import cv2_inpaint
from invokeai.backend.image_util.lama import LaMA
@ -120,7 +121,7 @@ def tile_fill_missing(im: Image.Image, tile_size: int = 16, seed: Optional[int]
return si
@invocation("infill_rgba", title="Solid Color Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.1")
@invocation("infill_rgba", title="Solid Color Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.2")
class InfillColorInvocation(BaseInvocation, WithMetadata, WithBoard):
"""Infills transparent areas of an image with a solid color"""
@ -143,7 +144,7 @@ class InfillColorInvocation(BaseInvocation, WithMetadata, WithBoard):
return ImageOutput.build(image_dto)
@invocation("infill_tile", title="Tile Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.2")
@invocation("infill_tile", title="Tile Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.3")
class InfillTileInvocation(BaseInvocation, WithMetadata, WithBoard):
"""Infills transparent areas of an image with tiles of the image"""
@ -168,7 +169,7 @@ class InfillTileInvocation(BaseInvocation, WithMetadata, WithBoard):
@invocation(
"infill_patchmatch", title="PatchMatch Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.1"
"infill_patchmatch", title="PatchMatch Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.2"
)
class InfillPatchMatchInvocation(BaseInvocation, WithMetadata, WithBoard):
"""Infills transparent areas of an image using the PatchMatch algorithm"""
@ -208,7 +209,7 @@ class InfillPatchMatchInvocation(BaseInvocation, WithMetadata, WithBoard):
return ImageOutput.build(image_dto)
@invocation("infill_lama", title="LaMa Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.1")
@invocation("infill_lama", title="LaMa Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.2")
class LaMaInfillInvocation(BaseInvocation, WithMetadata, WithBoard):
"""Infills transparent areas of an image using the LaMa model"""
@ -217,6 +218,13 @@ class LaMaInfillInvocation(BaseInvocation, WithMetadata, WithBoard):
def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.images.get_pil(self.image.image_name)
# Downloads the LaMa model if it doesn't already exist
download_with_progress_bar(
name="LaMa Inpainting Model",
url="https://github.com/Sanster/models/releases/download/add_big_lama/big-lama.pt",
dest_path=context.config.get().models_path / "core/misc/lama/lama.pt",
)
infilled = infill_lama(image.copy())
image_dto = context.images.save(image=infilled)
@ -224,7 +232,7 @@ class LaMaInfillInvocation(BaseInvocation, WithMetadata, WithBoard):
return ImageOutput.build(image_dto)
@invocation("infill_cv2", title="CV2 Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.1")
@invocation("infill_cv2", title="CV2 Infill", tags=["image", "inpaint"], category="inpaint", version="1.2.2")
class CV2InfillInvocation(BaseInvocation, WithMetadata, WithBoard):
"""Infills transparent areas of an image using OpenCV Inpainting"""

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

@ -48,7 +48,7 @@ class IPAdapterOutput(BaseInvocationOutput):
ip_adapter: IPAdapterField = OutputField(description=FieldDescriptions.ip_adapter, title="IP-Adapter")
@invocation("ip_adapter", title="IP-Adapter", tags=["ip_adapter", "control"], category="ip_adapter", version="1.1.2")
@invocation("ip_adapter", title="IP-Adapter", tags=["ip_adapter", "control"], category="ip_adapter", version="1.2.2")
class IPAdapterInvocation(BaseInvocation):
"""Collects IP-Adapter info to pass to other nodes."""

18
invokeai/app/invocations/latent.py
View File

@ -113,7 +113,7 @@ class SchedulerInvocation(BaseInvocation):
title="Create Denoise Mask",
tags=["mask", "denoise"],
category="latents",
version="1.0.1",
version="1.0.2",
)
class CreateDenoiseMaskInvocation(BaseInvocation):
"""Creates mask for denoising model run."""
@ -279,7 +279,7 @@ def get_scheduler(
title="Denoise Latents",
tags=["latents", "denoise", "txt2img", "t2i", "t2l", "img2img", "i2i", "l2l"],
category="latents",
version="1.5.2",
version="1.5.3",
)
class DenoiseLatentsInvocation(BaseInvocation):
"""Denoises noisy latents to decodable images"""
@ -816,7 +816,7 @@ class DenoiseLatentsInvocation(BaseInvocation):
title="Latents to Image",
tags=["latents", "image", "vae", "l2i"],
category="latents",
version="1.2.1",
version="1.2.2",
)
class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
"""Generates an image from latents."""
@ -903,7 +903,7 @@ LATENTS_INTERPOLATION_MODE = Literal["nearest", "linear", "bilinear", "bicubic",
title="Resize Latents",
tags=["latents", "resize"],
category="latents",
version="1.0.1",
version="1.0.2",
)
class ResizeLatentsInvocation(BaseInvocation):
"""Resizes latents to explicit width/height (in pixels). Provided dimensions are floor-divided by 8."""
@ -953,7 +953,7 @@ class ResizeLatentsInvocation(BaseInvocation):
title="Scale Latents",
tags=["latents", "resize"],
category="latents",
version="1.0.1",
version="1.0.2",
)
class ScaleLatentsInvocation(BaseInvocation):
"""Scales latents by a given factor."""
@ -995,7 +995,7 @@ class ScaleLatentsInvocation(BaseInvocation):
title="Image to Latents",
tags=["latents", "image", "vae", "i2l"],
category="latents",
version="1.0.1",
version="1.0.2",
)
class ImageToLatentsInvocation(BaseInvocation):
"""Encodes an image into latents."""
@ -1094,7 +1094,7 @@ class ImageToLatentsInvocation(BaseInvocation):
title="Blend Latents",
tags=["latents", "blend"],
category="latents",
version="1.0.1",
version="1.0.2",
)
class BlendLatentsInvocation(BaseInvocation):
"""Blend two latents using a given alpha. Latents must have same size."""
@ -1185,7 +1185,7 @@ class BlendLatentsInvocation(BaseInvocation):
title="Crop Latents",
tags=["latents", "crop"],
category="latents",
version="1.0.1",
version="1.0.2",
)
# TODO(ryand): Named `CropLatentsCoreInvocation` to prevent a conflict with custom node `CropLatentsInvocation`.
# Currently, if the class names conflict then 'GET /openapi.json' fails.
@ -1246,7 +1246,7 @@ class IdealSizeOutput(BaseInvocationOutput):
"ideal_size",
title="Ideal Size",
tags=["latents", "math", "ideal_size"],
version="1.0.2",
version="1.0.3",
)
class IdealSizeInvocation(BaseInvocation):
"""Calculates the ideal size for generation to avoid duplication"""

18
invokeai/app/invocations/math.py
View File

@ -12,7 +12,7 @@ from invokeai.app.services.shared.invocation_context import InvocationContext
from .baseinvocation import BaseInvocation, invocation
@invocation("add", title="Add Integers", tags=["math", "add"], category="math", version="1.0.0")
@invocation("add", title="Add Integers", tags=["math", "add"], category="math", version="1.0.1")
class AddInvocation(BaseInvocation):
"""Adds two numbers"""
@ -23,7 +23,7 @@ class AddInvocation(BaseInvocation):
return IntegerOutput(value=self.a + self.b)
@invocation("sub", title="Subtract Integers", tags=["math", "subtract"], category="math", version="1.0.0")
@invocation("sub", title="Subtract Integers", tags=["math", "subtract"], category="math", version="1.0.1")
class SubtractInvocation(BaseInvocation):
"""Subtracts two numbers"""
@ -34,7 +34,7 @@ class SubtractInvocation(BaseInvocation):
return IntegerOutput(value=self.a - self.b)
@invocation("mul", title="Multiply Integers", tags=["math", "multiply"], category="math", version="1.0.0")
@invocation("mul", title="Multiply Integers", tags=["math", "multiply"], category="math", version="1.0.1")
class MultiplyInvocation(BaseInvocation):
"""Multiplies two numbers"""
@ -45,7 +45,7 @@ class MultiplyInvocation(BaseInvocation):
return IntegerOutput(value=self.a * self.b)
@invocation("div", title="Divide Integers", tags=["math", "divide"], category="math", version="1.0.0")
@invocation("div", title="Divide Integers", tags=["math", "divide"], category="math", version="1.0.1")
class DivideInvocation(BaseInvocation):
"""Divides two numbers"""
@ -61,7 +61,7 @@ class DivideInvocation(BaseInvocation):
title="Random Integer",
tags=["math", "random"],
category="math",
version="1.0.0",
version="1.0.1",
use_cache=False,
)
class RandomIntInvocation(BaseInvocation):
@ -100,7 +100,7 @@ class RandomFloatInvocation(BaseInvocation):
title="Float To Integer",
tags=["math", "round", "integer", "float", "convert"],
category="math",
version="1.0.0",
version="1.0.1",
)
class FloatToIntegerInvocation(BaseInvocation):
"""Rounds a float number to (a multiple of) an integer."""
@ -122,7 +122,7 @@ class FloatToIntegerInvocation(BaseInvocation):
return IntegerOutput(value=int(self.value / self.multiple) * self.multiple)
@invocation("round_float", title="Round Float", tags=["math", "round"], category="math", version="1.0.0")
@invocation("round_float", title="Round Float", tags=["math", "round"], category="math", version="1.0.1")
class RoundInvocation(BaseInvocation):
"""Rounds a float to a specified number of decimal places."""
@ -176,7 +176,7 @@ INTEGER_OPERATIONS_LABELS = {
"max",
],
category="math",
version="1.0.0",
version="1.0.1",
)
class IntegerMathInvocation(BaseInvocation):
"""Performs integer math."""
@ -250,7 +250,7 @@ FLOAT_OPERATIONS_LABELS = {
title="Float Math",
tags=["math", "float", "add", "subtract", "multiply", "divide", "power", "root", "absolute value", "min", "max"],
category="math",
version="1.0.0",
version="1.0.1",
)
class FloatMathInvocation(BaseInvocation):
"""Performs floating point math."""

8
invokeai/app/invocations/metadata.py
View File

@ -84,7 +84,7 @@ class MetadataItemOutput(BaseInvocationOutput):
item: MetadataItemField = OutputField(description="Metadata Item")
@invocation("metadata_item", title="Metadata Item", tags=["metadata"], category="metadata", version="1.0.0")
@invocation("metadata_item", title="Metadata Item", tags=["metadata"], category="metadata", version="1.0.1")
class MetadataItemInvocation(BaseInvocation):
"""Used to create an arbitrary metadata item. Provide "label" and make a connection to "value" to store that data as the value."""
@ -100,7 +100,7 @@ class MetadataOutput(BaseInvocationOutput):
metadata: MetadataField = OutputField(description="Metadata Dict")
@invocation("metadata", title="Metadata", tags=["metadata"], category="metadata", version="1.0.0")
@invocation("metadata", title="Metadata", tags=["metadata"], category="metadata", version="1.0.1")
class MetadataInvocation(BaseInvocation):
"""Takes a MetadataItem or collection of MetadataItems and outputs a MetadataDict."""
@ -121,7 +121,7 @@ class MetadataInvocation(BaseInvocation):
return MetadataOutput(metadata=MetadataField.model_validate(data))
@invocation("merge_metadata", title="Metadata Merge", tags=["metadata"], category="metadata", version="1.0.0")
@invocation("merge_metadata", title="Metadata Merge", tags=["metadata"], category="metadata", version="1.0.1")
class MergeMetadataInvocation(BaseInvocation):
"""Merged a collection of MetadataDict into a single MetadataDict."""
@ -140,7 +140,7 @@ GENERATION_MODES = Literal[
]
@invocation("core_metadata", title="Core Metadata", tags=["metadata"], category="metadata", version="1.1.1")
@invocation("core_metadata", title="Core Metadata", tags=["metadata"], category="metadata", version="2.0.0")
class CoreMetadataInvocation(BaseInvocation):
"""Collects core generation metadata into a MetadataField"""

12
invokeai/app/invocations/model.py
View File

@ -98,7 +98,7 @@ class ModelLoaderOutput(UNetOutput, CLIPOutput, VAEOutput):
title="Main Model",
tags=["model"],
category="model",
version="1.0.1",
version="1.0.2",
)
class MainModelLoaderInvocation(BaseInvocation):
"""Loads a main model, outputting its submodels."""
@ -134,7 +134,7 @@ class LoRALoaderOutput(BaseInvocationOutput):
clip: Optional[CLIPField] = OutputField(default=None, description=FieldDescriptions.clip, title="CLIP")
@invocation("lora_loader", title="LoRA", tags=["model"], category="model", version="1.0.1")
@invocation("lora_loader", title="LoRA", tags=["model"], category="model", version="1.0.2")
class LoRALoaderInvocation(BaseInvocation):
"""Apply selected lora to unet and text_encoder."""
@ -204,7 +204,7 @@ class SDXLLoRALoaderOutput(BaseInvocationOutput):
title="SDXL LoRA",
tags=["lora", "model"],
category="model",
version="1.0.1",
version="1.0.2",
)
class SDXLLoRALoaderInvocation(BaseInvocation):
"""Apply selected lora to unet and text_encoder."""
@ -279,7 +279,7 @@ class SDXLLoRALoaderInvocation(BaseInvocation):
return output
@invocation("vae_loader", title="VAE", tags=["vae", "model"], category="model", version="1.0.1")
@invocation("vae_loader", title="VAE", tags=["vae", "model"], category="model", version="1.0.2")
class VAELoaderInvocation(BaseInvocation):
"""Loads a VAE model, outputting a VaeLoaderOutput"""
@ -309,7 +309,7 @@ class SeamlessModeOutput(BaseInvocationOutput):
title="Seamless",
tags=["seamless", "model"],
category="model",
version="1.0.0",
version="1.0.1",
)
class SeamlessModeInvocation(BaseInvocation):
"""Applies the seamless transformation to the Model UNet and VAE."""
@ -349,7 +349,7 @@ class SeamlessModeInvocation(BaseInvocation):
return SeamlessModeOutput(unet=unet, vae=vae)
@invocation("freeu", title="FreeU", tags=["freeu"], category="unet", version="1.0.0")
@invocation("freeu", title="FreeU", tags=["freeu"], category="unet", version="1.0.1")
class FreeUInvocation(BaseInvocation):
"""
Applies FreeU to the UNet. Suggested values (b1/b2/s1/s2):

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

@ -81,7 +81,7 @@ class NoiseOutput(BaseInvocationOutput):
title="Noise",
tags=["latents", "noise"],
category="latents",
version="1.0.1",
version="1.0.2",
)
class NoiseInvocation(BaseInvocation):
"""Generates latent noise."""

4
invokeai/app/invocations/param_easing.py
View File

@ -51,7 +51,7 @@ from .fields import InputField
title="Float Range",
tags=["math", "range"],
category="math",
version="1.0.0",
version="1.0.1",
)
class FloatLinearRangeInvocation(BaseInvocation):
"""Creates a range"""
@ -111,7 +111,7 @@ EASING_FUNCTION_KEYS = Literal[tuple(EASING_FUNCTIONS_MAP.keys())]
title="Step Param Easing",
tags=["step", "easing"],
category="step",
version="1.0.1",
version="1.0.2",
)
class StepParamEasingInvocation(BaseInvocation):
"""Experimental per-step parameter easing for denoising steps"""

30
invokeai/app/invocations/primitives.py
View File

@ -54,7 +54,7 @@ class BooleanCollectionOutput(BaseInvocationOutput):
@invocation(
"boolean", title="Boolean Primitive", tags=["primitives", "boolean"], category="primitives", version="1.0.0"
"boolean", title="Boolean Primitive", tags=["primitives", "boolean"], category="primitives", version="1.0.1"
)
class BooleanInvocation(BaseInvocation):
"""A boolean primitive value"""
@ -70,7 +70,7 @@ class BooleanInvocation(BaseInvocation):
title="Boolean Collection Primitive",
tags=["primitives", "boolean", "collection"],
category="primitives",
version="1.0.1",
version="1.0.2",
)
class BooleanCollectionInvocation(BaseInvocation):
"""A collection of boolean primitive values"""
@ -103,7 +103,7 @@ class IntegerCollectionOutput(BaseInvocationOutput):
@invocation(
"integer", title="Integer Primitive", tags=["primitives", "integer"], category="primitives", version="1.0.0"
"integer", title="Integer Primitive", tags=["primitives", "integer"], category="primitives", version="1.0.1"
)
class IntegerInvocation(BaseInvocation):
"""An integer primitive value"""
@ -119,7 +119,7 @@ class IntegerInvocation(BaseInvocation):
title="Integer Collection Primitive",
tags=["primitives", "integer", "collection"],
category="primitives",
version="1.0.1",
version="1.0.2",
)
class IntegerCollectionInvocation(BaseInvocation):
"""A collection of integer primitive values"""
@ -151,7 +151,7 @@ class FloatCollectionOutput(BaseInvocationOutput):
)
@invocation("float", title="Float Primitive", tags=["primitives", "float"], category="primitives", version="1.0.0")
@invocation("float", title="Float Primitive", tags=["primitives", "float"], category="primitives", version="1.0.1")
class FloatInvocation(BaseInvocation):
"""A float primitive value"""
@ -166,7 +166,7 @@ class FloatInvocation(BaseInvocation):
title="Float Collection Primitive",
tags=["primitives", "float", "collection"],
category="primitives",
version="1.0.1",
version="1.0.2",
)
class FloatCollectionInvocation(BaseInvocation):
"""A collection of float primitive values"""
@ -198,7 +198,7 @@ class StringCollectionOutput(BaseInvocationOutput):
)
@invocation("string", title="String Primitive", tags=["primitives", "string"], category="primitives", version="1.0.0")
@invocation("string", title="String Primitive", tags=["primitives", "string"], category="primitives", version="1.0.1")
class StringInvocation(BaseInvocation):
"""A string primitive value"""
@ -213,7 +213,7 @@ class StringInvocation(BaseInvocation):
title="String Collection Primitive",
tags=["primitives", "string", "collection"],
category="primitives",
version="1.0.1",
version="1.0.2",
)
class StringCollectionInvocation(BaseInvocation):
"""A collection of string primitive values"""
@ -255,7 +255,7 @@ class ImageCollectionOutput(BaseInvocationOutput):
)
@invocation("image", title="Image Primitive", tags=["primitives", "image"], category="primitives", version="1.0.1")
@invocation("image", title="Image Primitive", tags=["primitives", "image"], category="primitives", version="1.0.2")
class ImageInvocation(BaseInvocation):
"""An image primitive value"""
@ -276,7 +276,7 @@ class ImageInvocation(BaseInvocation):
title="Image Collection Primitive",
tags=["primitives", "image", "collection"],
category="primitives",
version="1.0.0",
version="1.0.1",
)
class ImageCollectionInvocation(BaseInvocation):
"""A collection of image primitive values"""
@ -341,7 +341,7 @@ class LatentsCollectionOutput(BaseInvocationOutput):
@invocation(
"latents", title="Latents Primitive", tags=["primitives", "latents"], category="primitives", version="1.0.1"
"latents", title="Latents Primitive", tags=["primitives", "latents"], category="primitives", version="1.0.2"
)
class LatentsInvocation(BaseInvocation):
"""A latents tensor primitive value"""
@ -359,7 +359,7 @@ class LatentsInvocation(BaseInvocation):
title="Latents Collection Primitive",
tags=["primitives", "latents", "collection"],
category="primitives",
version="1.0.0",
version="1.0.1",
)
class LatentsCollectionInvocation(BaseInvocation):
"""A collection of latents tensor primitive values"""
@ -393,7 +393,7 @@ class ColorCollectionOutput(BaseInvocationOutput):
)
@invocation("color", title="Color Primitive", tags=["primitives", "color"], category="primitives", version="1.0.0")
@invocation("color", title="Color Primitive", tags=["primitives", "color"], category="primitives", version="1.0.1")
class ColorInvocation(BaseInvocation):
"""A color primitive value"""
@ -433,7 +433,7 @@ class ConditioningCollectionOutput(BaseInvocationOutput):
title="Conditioning Primitive",
tags=["primitives", "conditioning"],
category="primitives",
version="1.0.0",
version="1.0.1",
)
class ConditioningInvocation(BaseInvocation):
"""A conditioning tensor primitive value"""
@ -449,7 +449,7 @@ class ConditioningInvocation(BaseInvocation):
title="Conditioning Collection Primitive",
tags=["primitives", "conditioning", "collection"],
category="primitives",
version="1.0.1",
version="1.0.2",
)
class ConditioningCollectionInvocation(BaseInvocation):
"""A collection of conditioning tensor primitive values"""

4
invokeai/app/invocations/prompt.py
View File

@ -17,7 +17,7 @@ from .fields import InputField, UIComponent
title="Dynamic Prompt",
tags=["prompt", "collection"],
category="prompt",
version="1.0.0",
version="1.0.1",
use_cache=False,
)
class DynamicPromptInvocation(BaseInvocation):
@ -46,7 +46,7 @@ class DynamicPromptInvocation(BaseInvocation):
title="Prompts from File",
tags=["prompt", "file"],
category="prompt",
version="1.0.1",
version="1.0.2",
)
class PromptsFromFileInvocation(BaseInvocation):
"""Loads prompts from a text file"""

4
invokeai/app/invocations/sdxl.py
View File

@ -30,7 +30,7 @@ class SDXLRefinerModelLoaderOutput(BaseInvocationOutput):
vae: VAEField = OutputField(description=FieldDescriptions.vae, title="VAE")
@invocation("sdxl_model_loader", title="SDXL Main Model", tags=["model", "sdxl"], category="model", version="1.0.1")
@invocation("sdxl_model_loader", title="SDXL Main Model", tags=["model", "sdxl"], category="model", version="1.0.2")
class SDXLModelLoaderInvocation(BaseInvocation):
"""Loads an sdxl base model, outputting its submodels."""
@ -67,7 +67,7 @@ class SDXLModelLoaderInvocation(BaseInvocation):
title="SDXL Refiner Model",
tags=["model", "sdxl", "refiner"],
category="model",
version="1.0.1",
version="1.0.2",
)
class SDXLRefinerModelLoaderInvocation(BaseInvocation):
"""Loads an sdxl refiner model, outputting its submodels."""

10
invokeai/app/invocations/strings.py
View File

@ -27,7 +27,7 @@ class StringPosNegOutput(BaseInvocationOutput):
title="String Split Negative",
tags=["string", "split", "negative"],
category="string",
version="1.0.0",
version="1.0.1",
)
class StringSplitNegInvocation(BaseInvocation):
"""Splits string into two strings, inside [] goes into negative string everthing else goes into positive string. Each [ and ] character is replaced with a space"""
@ -69,7 +69,7 @@ class String2Output(BaseInvocationOutput):
string_2: str = OutputField(description="string 2")
@invocation("string_split", title="String Split", tags=["string", "split"], category="string", version="1.0.0")
@invocation("string_split", title="String Split", tags=["string", "split"], category="string", version="1.0.1")
class StringSplitInvocation(BaseInvocation):
"""Splits string into two strings, based on the first occurance of the delimiter. The delimiter will be removed from the string"""
@ -89,7 +89,7 @@ class StringSplitInvocation(BaseInvocation):
return String2Output(string_1=part1, string_2=part2)
@invocation("string_join", title="String Join", tags=["string", "join"], category="string", version="1.0.0")
@invocation("string_join", title="String Join", tags=["string", "join"], category="string", version="1.0.1")
class StringJoinInvocation(BaseInvocation):
"""Joins string left to string right"""
@ -100,7 +100,7 @@ class StringJoinInvocation(BaseInvocation):
return StringOutput(value=((self.string_left or "") + (self.string_right or "")))
@invocation("string_join_three", title="String Join Three", tags=["string", "join"], category="string", version="1.0.0")
@invocation("string_join_three", title="String Join Three", tags=["string", "join"], category="string", version="1.0.1")
class StringJoinThreeInvocation(BaseInvocation):
"""Joins string left to string middle to string right"""
@ -113,7 +113,7 @@ class StringJoinThreeInvocation(BaseInvocation):
@invocation(
"string_replace", title="String Replace", tags=["string", "replace", "regex"], category="string", version="1.0.0"
"string_replace", title="String Replace", tags=["string", "replace", "regex"], category="string", version="1.0.1"
)
class StringReplaceInvocation(BaseInvocation):
"""Replaces the search string with the replace string"""

2
invokeai/app/invocations/t2i_adapter.py
View File

@ -45,7 +45,7 @@ class T2IAdapterOutput(BaseInvocationOutput):
@invocation(
"t2i_adapter", title="T2I-Adapter", tags=["t2i_adapter", "control"], category="t2i_adapter", version="1.0.1"
"t2i_adapter", title="T2I-Adapter", tags=["t2i_adapter", "control"], category="t2i_adapter", version="1.0.2"
)
class T2IAdapterInvocation(BaseInvocation):
"""Collects T2I-Adapter info to pass to other nodes."""

12
invokeai/app/invocations/tiles.py
View File

@ -39,7 +39,7 @@ class CalculateImageTilesOutput(BaseInvocationOutput):
title="Calculate Image Tiles",
tags=["tiles"],
category="tiles",
version="1.0.0",
version="1.0.1",
classification=Classification.Beta,
)
class CalculateImageTilesInvocation(BaseInvocation):
@ -73,7 +73,7 @@ class CalculateImageTilesInvocation(BaseInvocation):
title="Calculate Image Tiles Even Split",
tags=["tiles"],
category="tiles",
version="1.1.0",
version="1.1.1",
classification=Classification.Beta,
)
class CalculateImageTilesEvenSplitInvocation(BaseInvocation):
@ -116,7 +116,7 @@ class CalculateImageTilesEvenSplitInvocation(BaseInvocation):
title="Calculate Image Tiles Minimum Overlap",
tags=["tiles"],
category="tiles",
version="1.0.0",
version="1.0.1",
classification=Classification.Beta,
)
class CalculateImageTilesMinimumOverlapInvocation(BaseInvocation):
@ -167,7 +167,7 @@ class TileToPropertiesOutput(BaseInvocationOutput):
title="Tile to Properties",
tags=["tiles"],
category="tiles",
version="1.0.0",
version="1.0.1",
classification=Classification.Beta,
)
class TileToPropertiesInvocation(BaseInvocation):
@ -200,7 +200,7 @@ class PairTileImageOutput(BaseInvocationOutput):
title="Pair Tile with Image",
tags=["tiles"],
category="tiles",
version="1.0.0",
version="1.0.1",
classification=Classification.Beta,
)
class PairTileImageInvocation(BaseInvocation):
@ -229,7 +229,7 @@ BLEND_MODES = Literal["Linear", "Seam"]
title="Merge Tiles to Image",
tags=["tiles"],
category="tiles",
version="1.1.0",
version="1.1.1",
classification=Classification.Beta,
)
class MergeTilesToImageInvocation(BaseInvocation, WithMetadata, WithBoard):

20
invokeai/app/invocations/upscale.py
View File

@ -11,6 +11,7 @@ from pydantic import ConfigDict
from invokeai.app.invocations.fields import ImageField
from invokeai.app.invocations.primitives import ImageOutput
from invokeai.app.services.shared.invocation_context import InvocationContext
from invokeai.app.util.download_with_progress import download_with_progress_bar
from invokeai.backend.image_util.basicsr.rrdbnet_arch import RRDBNet
from invokeai.backend.image_util.realesrgan.realesrgan import RealESRGAN
from invokeai.backend.util.devices import choose_torch_device
@ -27,11 +28,18 @@ ESRGAN_MODELS = Literal[
"RealESRGAN_x2plus.pth",
]
ESRGAN_MODEL_URLS: dict[str, str] = {
"RealESRGAN_x4plus.pth": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth",
"RealESRGAN_x4plus_anime_6B.pth": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth",
"ESRGAN_SRx4_DF2KOST_official.pth": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth",
"RealESRGAN_x2plus.pth": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth",
}
if choose_torch_device() == torch.device("mps"):
from torch import mps
@invocation("esrgan", title="Upscale (RealESRGAN)", tags=["esrgan", "upscale"], category="esrgan", version="1.3.1")
@invocation("esrgan", title="Upscale (RealESRGAN)", tags=["esrgan", "upscale"], category="esrgan", version="1.3.2")
class ESRGANInvocation(BaseInvocation, WithMetadata, WithBoard):
"""Upscales an image using RealESRGAN."""
@ -45,7 +53,6 @@ class ESRGANInvocation(BaseInvocation, WithMetadata, WithBoard):
def invoke(self, context: InvocationContext) -> ImageOutput:
image = context.images.get_pil(self.image.image_name)
models_path = context.config.get().models_path
rrdbnet_model = None
netscale = None
@ -92,11 +99,16 @@ class ESRGANInvocation(BaseInvocation, WithMetadata, WithBoard):
context.logger.error(msg)
raise ValueError(msg)
esrgan_model_path = Path(f"core/upscaling/realesrgan/{self.model_name}")
esrgan_model_path = Path(context.config.get().models_path, f"core/upscaling/realesrgan/{self.model_name}")
# Downloads the ESRGAN model if it doesn't already exist
download_with_progress_bar(
name=self.model_name, url=ESRGAN_MODEL_URLS[self.model_name], dest_path=esrgan_model_path
)
upscaler = RealESRGAN(
scale=netscale,
model_path=models_path / esrgan_model_path,
model_path=esrgan_model_path,
model=rrdbnet_model,
half=False,
tile=self.tile_size,

176
invokeai/app/services/config/config_default.py
View File

@ -10,10 +10,12 @@ from functools import lru_cache
from pathlib import Path
from typing import Any, Literal, Optional
import psutil
import yaml
from pydantic import BaseModel, Field, PrivateAttr, field_validator
from pydantic_settings import BaseSettings, SettingsConfigDict
from pydantic_settings import BaseSettings, PydanticBaseSettingsSource, SettingsConfigDict
import invokeai.configs as model_configs
from invokeai.backend.model_hash.model_hash import HASHING_ALGORITHMS
from invokeai.frontend.cli.arg_parser import InvokeAIArgs
@ -29,7 +31,25 @@ ATTENTION_TYPE = Literal["auto", "normal", "xformers", "sliced", "torch-sdp"]
ATTENTION_SLICE_SIZE = Literal["auto", "balanced", "max", 1, 2, 3, 4, 5, 6, 7, 8]
LOG_FORMAT = Literal["plain", "color", "syslog", "legacy"]
LOG_LEVEL = Literal["debug", "info", "warning", "error", "critical"]
CONFIG_SCHEMA_VERSION = 4
CONFIG_SCHEMA_VERSION = "4.0.0"
def get_default_ram_cache_size() -> float:
"""Run a heuristic for the default RAM cache based on installed RAM."""
# On some machines, psutil.virtual_memory().total gives a value that is slightly less than the actual RAM, so the
# limits are set slightly lower than than what we expect the actual RAM to be.
GB = 1024**3
max_ram = psutil.virtual_memory().total / GB
if max_ram >= 60:
return 15.0
if max_ram >= 30:
return 7.5
if max_ram >= 14:
return 4.0
return 2.1 # 2.1 is just large enough for sd 1.5 ;-)
class URLRegexTokenPair(BaseModel):
@ -63,7 +83,6 @@ class InvokeAIAppConfig(BaseSettings):
ssl_keyfile: SSL key file for HTTPS. See https://www.uvicorn.org/settings/#https.
log_tokenization: Enable logging of parsed prompt tokens.
patchmatch: Enable patchmatch inpaint code.
ignore_missing_core_models: Ignore missing core models on startup. If `True`, the app will attempt to download missing models on startup.
autoimport_dir: Path to a directory of models files to be imported on startup.
models_dir: Path to the models directory.
convert_cache_dir: Path to the converted models cache directory. When loading a non-diffusers model, it will be converted and store on disk at this location.
@ -96,16 +115,18 @@ class InvokeAIAppConfig(BaseSettings):
allow_nodes: List of nodes to allow. Omit to allow all.
deny_nodes: List of nodes to deny. Omit to deny none.
node_cache_size: How many cached nodes to keep in memory.
hashing_algorithm: Model hashing algorthim for model installs. 'blake3' is best for SSDs. 'blake3_single' is best for spinning disk HDDs. 'random' disables hashing, instead assigning a UUID to models. Useful when using a memory db to reduce model installation time, or if you don't care about storing stable hashes for models. Alternatively, any other hashlib algorithm is accepted, though these are not nearly as performant as blake3.<br>Valid values: `md5`, `sha1`, `sha224`, `sha256`, `sha384`, `sha512`, `blake2b`, `blake2s`, `sha3_224`, `sha3_256`, `sha3_384`, `sha3_512`, `shake_128`, `shake_256`, `blake3`, `blake3_single`, `random`
hashing_algorithm: Model hashing algorthim for model installs. 'blake3_multi' is best for SSDs. 'blake3_single' is best for spinning disk HDDs. 'random' disables hashing, instead assigning a UUID to models. Useful when using a memory db to reduce model installation time, or if you don't care about storing stable hashes for models. Alternatively, any other hashlib algorithm is accepted, though these are not nearly as performant as blake3.<br>Valid values: `blake3_multi`, `blake3_single`, `random`, `md5`, `sha1`, `sha224`, `sha256`, `sha384`, `sha512`, `blake2b`, `blake2s`, `sha3_224`, `sha3_256`, `sha3_384`, `sha3_512`, `shake_128`, `shake_256`
remote_api_tokens: List of regular expression and token pairs used when downloading models from URLs. The download URL is tested against the regex, and if it matches, the token is provided in as a Bearer token.
"""
_root: Optional[Path] = PrivateAttr(default=None)
_config_file: Optional[Path] = PrivateAttr(default=None)
# fmt: off
# INTERNAL
schema_version: int = Field(default=CONFIG_SCHEMA_VERSION, description="Schema version of the config file. This is not a user-configurable setting.")
schema_version: str = Field(default=CONFIG_SCHEMA_VERSION, description="Schema version of the config file. This is not a user-configurable setting.")
# This is only used during v3 models.yaml migration
legacy_models_yaml_path: Optional[Path] = Field(default=None, description="Path to the legacy models.yaml file. This is not a user-configurable setting.")
# WEB
@ -121,13 +142,12 @@ class InvokeAIAppConfig(BaseSettings):
# MISC FEATURES
log_tokenization: bool = Field(default=False, description="Enable logging of parsed prompt tokens.")
patchmatch: bool = Field(default=True, description="Enable patchmatch inpaint code.")
ignore_missing_core_models: bool = Field(default=False, description="Ignore missing core models on startup. If `True`, the app will attempt to download missing models on startup.")
# 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/stable-diffusion"), description="Path to directory of legacy checkpoint config files.")
legacy_conf_dir: Path = Field(default=Path("configs"), description="Path to directory of legacy checkpoint config files.")
db_dir: Path = Field(default=Path("databases"), description="Path to InvokeAI databases directory.")
outputs_dir: Path = Field(default=Path("outputs"), description="Path to directory for outputs.")
custom_nodes_dir: Path = Field(default=Path("nodes"), description="Path to directory for custom nodes.")
@ -147,7 +167,7 @@ class InvokeAIAppConfig(BaseSettings):
profiles_dir: Path = Field(default=Path("profiles"), description="Path to profiles output directory.")
# CACHE
ram: float = Field(default=DEFAULT_RAM_CACHE, gt=0, description="Maximum memory amount used by memory model cache for rapid switching (GB).")
ram: float = Field(default_factory=get_default_ram_cache_size, gt=0, description="Maximum memory amount used by memory model cache for rapid switching (GB).")
vram: float = Field(default=DEFAULT_VRAM_CACHE, ge=0, description="Amount of VRAM reserved for model storage (GB).")
convert_cache: float = Field(default=DEFAULT_CONVERT_CACHE, ge=0, description="Maximum size of on-disk converted models cache (GB).")
lazy_offload: bool = Field(default=True, description="Keep models in VRAM until their space is needed.")
@ -171,7 +191,7 @@ class InvokeAIAppConfig(BaseSettings):
node_cache_size: int = Field(default=512, description="How many cached nodes to keep in memory.")
# MODEL INSTALL
hashing_algorithm: HASHING_ALGORITHMS = Field(default="blake3", description="Model hashing algorthim for model installs. 'blake3' 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.")
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.")
# fmt: on
@ -202,7 +222,7 @@ class InvokeAIAppConfig(BaseSettings):
if new_value != current_value:
setattr(self, field_name, new_value)
def write_file(self, dest_path: Path) -> None:
def write_file(self, dest_path: Path, as_example: bool = False) -> None:
"""Write the current configuration to file. This will overwrite the existing file.
A `meta` stanza is added to the top of the file, containing metadata about the config file. This is not stored in the config object.
@ -210,46 +230,31 @@ class InvokeAIAppConfig(BaseSettings):
Args:
dest_path: Path to write the config to.
"""
dest_path.parent.mkdir(parents=True, exist_ok=True)
with open(dest_path, "w") as file:
# Meta fields should be written in a separate stanza
# Meta fields should be written in a separate stanza - skip legacy_models_yaml_path
meta_dict = self.model_dump(mode="json", include={"schema_version"})
# Only include the legacy_models_yaml_path if it's set
if self.legacy_models_yaml_path:
meta_dict.update(self.model_dump(mode="json", include={"legacy_models_yaml_path"}))
# User settings
config_dict = self.model_dump(
mode="json",
exclude_unset=True,
exclude_defaults=True,
exclude_unset=False if as_example else True,
exclude_defaults=False if as_example else True,
exclude_none=True if as_example else False,
exclude={"schema_version", "legacy_models_yaml_path"},
)
if as_example:
file.write(
"# This is an example file with default and example settings. Use the values here as a baseline.\n\n"
)
file.write("# Internal metadata - do not edit:\n")
file.write(yaml.dump(meta_dict, sort_keys=False))
file.write("\n")
file.write("# Put user settings here:\n")
file.write("# Put user settings here - see https://invoke-ai.github.io/InvokeAI/features/CONFIGURATION/:\n")
if len(config_dict) > 0:
file.write(yaml.dump(config_dict, sort_keys=False))
def merge_from_file(self, source_path: Optional[Path] = None) -> None:
"""Read the config from the `invokeai.yaml` file, migrating it if necessary and merging it into the singleton config.
This function will write to the `invokeai.yaml` file if the config is migrated.
Args:
source_path: Path to the config file. If not provided, the default path is used.
"""
path = source_path or self.init_file_path
config_from_file = load_and_migrate_config(path)
# Clobbering here will overwrite any settings that were set via environment variables
self.update_config(config_from_file, clobber=False)
def set_root(self, root: Path) -> None:
"""Set the runtime root directory. This is typically set using a CLI arg."""
assert isinstance(root, Path)
self._root = root
def _resolve(self, partial_path: Path) -> Path:
return (self.root_path / partial_path).resolve()
@ -264,9 +269,9 @@ class InvokeAIAppConfig(BaseSettings):
return root.resolve()
@property
def init_file_path(self) -> Path:
def config_file_path(self) -> Path:
"""Path to invokeai.yaml, resolved to an absolute path.."""
resolved_path = self._resolve(INIT_FILE)
resolved_path = self._resolve(self._config_file or INIT_FILE)
assert resolved_path is not None
return resolved_path
@ -327,6 +332,27 @@ class InvokeAIAppConfig(BaseSettings):
return root
class DefaultInvokeAIAppConfig(InvokeAIAppConfig):
"""A version of `InvokeAIAppConfig` that does not automatically parse any settings from environment variables
or any file.
This is useful for writing out a default config file.
Note that init settings are set if provided.
"""
@classmethod
def settings_customise_sources(
cls,
settings_cls: type[BaseSettings],
init_settings: PydanticBaseSettingsSource,
env_settings: PydanticBaseSettingsSource,
dotenv_settings: PydanticBaseSettingsSource,
file_secret_settings: PydanticBaseSettingsSource,
) -> tuple[PydanticBaseSettingsSource, ...]:
return (init_settings,)
def migrate_v3_config_dict(config_dict: dict[str, Any]) -> InvokeAIAppConfig:
"""Migrate a v3 config dictionary to a current config object.
@ -351,10 +377,19 @@ def migrate_v3_config_dict(config_dict: dict[str, Any]) -> InvokeAIAppConfig:
parsed_config_dict["vram"] = v
if k == "conf_path":
parsed_config_dict["legacy_models_yaml_path"] = v
if k == "legacy_conf_dir":
# The old default for this was "configs/stable-diffusion". If if the incoming config has that as the value, we won't set it.
# Else if the path ends in "stable-diffusion", we assume the parent is the new correct path.
# Else we do not attempt to migrate this setting
if v != "configs/stable-diffusion":
parsed_config_dict["legacy_conf_dir"] = v
elif Path(v).name == "stable-diffusion":
parsed_config_dict["legacy_conf_dir"] = str(Path(v).parent)
elif k in InvokeAIAppConfig.model_fields:
# skip unknown fields
parsed_config_dict[k] = v
config = InvokeAIAppConfig.model_validate(parsed_config_dict)
# When migrating the config file, we should not include currently-set environment variables.
config = DefaultInvokeAIAppConfig.model_validate(parsed_config_dict)
return config
@ -378,13 +413,13 @@ def load_and_migrate_config(config_path: Path) -> InvokeAIAppConfig:
# This is a v3 config file, attempt to migrate it
shutil.copy(config_path, config_path.with_suffix(".yaml.bak"))
try:
config = migrate_v3_config_dict(loaded_config_dict)
# loaded_config_dict could be the wrong shape, but we will catch all exceptions below
migrated_config = migrate_v3_config_dict(loaded_config_dict) # pyright: ignore [reportUnknownArgumentType]
except Exception as e:
shutil.copy(config_path.with_suffix(".yaml.bak"), config_path)
raise RuntimeError(f"Failed to load and migrate v3 config file {config_path}: {e}") from e
# By excluding defaults, we ensure that the new config file only contains the settings that were explicitly set
config.write_file(config_path)
return config
migrated_config.write_file(config_path)
return migrated_config
else:
# Attempt to load as a v4 config file
try:
@ -400,32 +435,53 @@ def load_and_migrate_config(config_path: Path) -> InvokeAIAppConfig:
@lru_cache(maxsize=1)
def get_config() -> InvokeAIAppConfig:
"""Return the global singleton app config.
"""Get the global singleton app config.
When called, this function will parse the CLI args and merge in config from the `invokeai.yaml` config file.
When first called, this function:
- Creates a config object. `pydantic-settings` handles merging of settings from environment variables, but not the init file.
- Retrieves any provided CLI args from the InvokeAIArgs class. It does not _parse_ the CLI args; that is done in the main entrypoint.
- Sets the root dir, if provided via CLI args.
- Logs in to HF if there is no valid token already.
- Copies all legacy configs to the legacy conf dir (needed for conversion from ckpt to diffusers).
- Reads and merges in settings from the config file if it exists, else writes out a default config file.
On subsequent calls, the object is returned from the cache.
"""
# This object includes environment variables, as parsed by pydantic-settings
config = InvokeAIAppConfig()
args = InvokeAIArgs.args
# CLI args trump environment variables
# This flag serves as a proxy for whether the config was retrieved in the context of the full application or not.
# If it is False, we should just return a default config and not set the root, log in to HF, etc.
if not InvokeAIArgs.did_parse:
return config
# Set CLI args
if root := getattr(args, "root", None):
config.set_root(Path(root))
if ignore_missing_core_models := getattr(args, "ignore_missing_core_models", None):
config.ignore_missing_core_models = ignore_missing_core_models
config._root = Path(root)
if config_file := getattr(args, "config_file", None):
config._config_file = Path(config_file)
# TODO(psyche): This shouldn't be wrapped in a try/catch. The configuration script imports a number of classes
# from throughout the app, which in turn call get_config(). At that time, there may not be a config file to
# read from, and this raises.
#
# Once we move all* model installation to the web app, the configure script will not be reaching into the main app
# and we can make this an unhandled error, which feels correct.
#
# *all user-facing models. Core model installation will still be handled by the configure/install script.
# Create the example config file, with some extra example values provided
example_config = DefaultInvokeAIAppConfig()
example_config.remote_api_tokens = [
URLRegexTokenPair(url_regex="cool-models.com", token="my_secret_token"),
URLRegexTokenPair(url_regex="nifty-models.com", token="some_other_token"),
]
example_config.write_file(config.config_file_path.with_suffix(".example.yaml"), as_example=True)
try:
config.merge_from_file()
except FileNotFoundError:
pass
# Copy all legacy configs - We know `__path__[0]` is correct here
configs_src = Path(model_configs.__path__[0]) # pyright: ignore [reportUnknownMemberType, reportUnknownArgumentType, reportAttributeAccessIssue]
shutil.copytree(configs_src, config.legacy_conf_path, dirs_exist_ok=True)
if config.config_file_path.exists():
config_from_file = load_and_migrate_config(config.config_file_path)
# Clobbering here will overwrite any settings that were set via environment variables
config.update_config(config_from_file, clobber=False)
else:
# We should never write env vars to the config file
default_config = DefaultInvokeAIAppConfig()
default_config.write_file(config.config_file_path, as_example=False)
return config

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

@ -87,6 +87,8 @@ class DownloadQueueService(DownloadQueueServiceBase):
self._queue.queue.clear()
self.join() # wait for all active jobs to finish
self._stop_event.set()
for thread in self._worker_pool:
thread.join()
self._worker_pool.clear()
def submit_download_job(

4
invokeai/app/services/model_install/model_install_base.py
View File

@ -114,8 +114,10 @@ class HFModelSource(StringLikeSource):
def __str__(self) -> str:
"""Return string version of repoid when string rep needed."""
base: str = self.repo_id
if self.variant:
base += f":{self.variant or ''}"
base += f":{self.subfolder}" if self.subfolder else ""
if self.subfolder:
base += f":{self.subfolder}"
return base

86
invokeai/app/services/model_install/model_install_default.py
View File

@ -34,6 +34,7 @@ from invokeai.backend.model_manager.config import (
from invokeai.backend.model_manager.metadata import (
AnyModelRepoMetadata,
HuggingFaceMetadataFetch,
ModelMetadataFetchBase,
ModelMetadataWithFiles,
RemoteModelFile,
)
@ -92,6 +93,7 @@ class ModelInstallService(ModelInstallServiceBase):
self._download_cache: Dict[AnyHttpUrl, ModelInstallJob] = {}
self._running = False
self._session = session
self._install_thread: Optional[threading.Thread] = None
self._next_job_id = 0
@property
@ -126,6 +128,8 @@ class ModelInstallService(ModelInstallServiceBase):
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:
@ -275,6 +279,7 @@ class ModelInstallService(ModelInstallServiceBase):
if timeout > 0 and time.time() - start > timeout:
raise TimeoutError("Timeout exceeded")
self._install_queue.join()
return self._install_jobs
def cancel_job(self, job: ModelInstallJob) -> None:
@ -304,6 +309,10 @@ class ModelInstallService(ModelInstallServiceBase):
self._app_config.legacy_models_yaml_path or self._app_config.root_path / "configs" / "models.yaml"
)
# The old path may be relative to the root path
if not legacy_models_yaml_path.exists():
legacy_models_yaml_path = Path(self._app_config.root_path, legacy_models_yaml_path)
if legacy_models_yaml_path.exists():
legacy_models_yaml = yaml.safe_load(legacy_models_yaml_path.read_text())
@ -341,9 +350,8 @@ class ModelInstallService(ModelInstallServiceBase):
# Rename `models.yaml` to `models.yaml.bak` to prevent re-migration
legacy_models_yaml_path.rename(legacy_models_yaml_path.with_suffix(".yaml.bak"))
# Remove `legacy_models_yaml_path` from the config file - we are done with it either way
# Unset the path - we are done with it either way
self._app_config.legacy_models_yaml_path = None
self._app_config.write_file(self._app_config.init_file_path)
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()}
@ -411,15 +419,16 @@ class ModelInstallService(ModelInstallServiceBase):
# Internal functions that manage the installer threads
# --------------------------------------------------------------------------------------------
def _start_installer_thread(self) -> None:
threading.Thread(target=self._install_next_item, daemon=True).start()
self._install_thread = threading.Thread(target=self._install_next_item, daemon=True)
self._install_thread.start()
self._running = True
def _install_next_item(self) -> None:
done = False
while not done:
self._logger.debug(f"Installer thread {threading.get_ident()} starting")
while True:
if self._stop_event.is_set():
done = True
continue
break
self._logger.debug(f"Installer thread {threading.get_ident()} polling")
try:
job = self._install_queue.get(timeout=1)
except Empty:
@ -432,9 +441,25 @@ class ModelInstallService(ModelInstallServiceBase):
elif job.errored:
self._signal_job_errored(job)
elif (
job.waiting or job.downloads_done
): # local jobs will be in waiting state, remote jobs will be downloading state
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)
finally:
# if this is an install of a remote file, then clean up the temporary directory
if job._install_tmpdir is not None:
rmtree(job._install_tmpdir)
self._install_completed_event.set()
self._install_queue.task_done()
self._logger.info(f"Installer thread {threading.get_ident()} exiting")
def _register_or_install(self, job: ModelInstallJob) -> None:
# local jobs will be in waiting state, remote jobs will be downloading state
job.total_bytes = self._stat_size(job.local_path)
job.bytes = job.total_bytes
self._signal_job_running(job)
@ -451,7 +476,7 @@ class ModelInstallService(ModelInstallServiceBase):
job.config_out = self.record_store.get_model(key)
self._signal_job_completed(job)
except InvalidModelConfigException as excp:
def _set_error(self, job: ModelInstallJob, excp: Exception) -> None:
if any(x.content_type is not None and "text/html" in x.content_type for x in job.download_parts):
job.set_error(
InvalidModelConfigException(
@ -462,17 +487,6 @@ class ModelInstallService(ModelInstallServiceBase):
job.set_error(excp)
self._signal_job_errored(job)
except (OSError, DuplicateModelException) as excp:
job.set_error(excp)
self._signal_job_errored(job)
finally:
# if this is an install of a remote file, then clean up the temporary directory
if job._install_tmpdir is not None:
rmtree(job._install_tmpdir)
self._install_completed_event.set()
self._install_queue.task_done()
# --------------------------------------------------------------------------------------------
# Internal functions that manage the models directory
# --------------------------------------------------------------------------------------------
@ -604,7 +618,7 @@ class ModelInstallService(ModelInstallServiceBase):
info.path = model_path.as_posix()
# add 'main' specific fields
# 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()
@ -718,12 +732,13 @@ class ModelInstallService(ModelInstallServiceBase):
install_job._install_tmpdir = tmpdir
assert install_job.total_bytes is not None # to avoid type checking complaints in the loop below
self._logger.info(f"Queuing {source} for downloading")
files_string = "file" if len(remote_files) == 1 else "file"
self._logger.info(f"Queuing model install: {source} ({len(remote_files)} {files_string})")
self._logger.debug(f"remote_files={remote_files}")
for model_file in remote_files:
url = model_file.url
path = root / model_file.path.relative_to(subfolder)
self._logger.info(f"Downloading {url} => {path}")
self._logger.debug(f"Downloading {url} => {path}")
install_job.total_bytes += model_file.size
assert hasattr(source, "access_token")
dest = tmpdir / path.parent
@ -759,7 +774,7 @@ class ModelInstallService(ModelInstallServiceBase):
# Callbacks are executed by the download queue in a separate thread
# ------------------------------------------------------------------
def _download_started_callback(self, download_job: DownloadJob) -> None:
self._logger.info(f"{download_job.source}: model download started")
self._logger.info(f"Model download started: {download_job.source}")
with self._lock:
install_job = self._download_cache[download_job.source]
install_job.status = InstallStatus.DOWNLOADING
@ -785,7 +800,7 @@ class ModelInstallService(ModelInstallServiceBase):
self._signal_job_downloading(install_job)
def _download_complete_callback(self, download_job: DownloadJob) -> None:
self._logger.info(f"{download_job.source}: model download complete")
self._logger.info(f"Model download complete: {download_job.source}")
with self._lock:
install_job = self._download_cache[download_job.source]
@ -818,7 +833,7 @@ class ModelInstallService(ModelInstallServiceBase):
if not install_job:
return
self._downloads_changed_event.set()
self._logger.warning(f"{download_job.source}: model download cancelled")
self._logger.warning(f"Model download canceled: {download_job.source}")
# if install job has already registered an error, then do not replace its status with cancelled
if not install_job.errored:
install_job.cancel()
@ -842,7 +857,7 @@ class ModelInstallService(ModelInstallServiceBase):
# ------------------------------------------------------------------------------------------------
def _signal_job_running(self, job: ModelInstallJob) -> None:
job.status = InstallStatus.RUNNING
self._logger.info(f"{job.source}: model installation started")
self._logger.info(f"Model install started: {job.source}")
if self._event_bus:
self._event_bus.emit_model_install_running(str(job.source))
@ -870,16 +885,15 @@ class ModelInstallService(ModelInstallServiceBase):
def _signal_job_downloads_done(self, job: ModelInstallJob) -> None:
job.status = InstallStatus.DOWNLOADS_DONE
self._logger.info(f"{job.source}: all parts of this model are downloaded")
self._logger.info(f"Model download complete: {job.source}")
if self._event_bus:
self._event_bus.emit_model_install_downloads_done(str(job.source))
def _signal_job_completed(self, job: ModelInstallJob) -> None:
job.status = InstallStatus.COMPLETED
assert job.config_out
self._logger.info(
f"{job.source}: model installation completed. {job.local_path} registered key {job.config_out.key}"
)
self._logger.info(f"Model install complete: {job.source}")
self._logger.debug(f"{job.local_path} registered key {job.config_out.key}")
if self._event_bus:
assert job.local_path is not None
assert job.config_out is not None
@ -887,7 +901,7 @@ 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"{job.source}: model installation encountered an exception: {job.error_type}\n{job.error}")
self._logger.info(f"Model install error: {job.source}, {job.error_type}\n{job.error}")
if self._event_bus:
error_type = job.error_type
error = job.error
@ -896,12 +910,12 @@ class ModelInstallService(ModelInstallServiceBase):
self._event_bus.emit_model_install_error(str(job.source), error_type, error, id=job.id)
def _signal_job_cancelled(self, job: ModelInstallJob) -> None:
self._logger.info(f"{job.source}: model installation was cancelled")
self._logger.info(f"Model install canceled: {job.source}")
if self._event_bus:
self._event_bus.emit_model_install_cancelled(str(job.source), id=job.id)
@staticmethod
def get_fetcher_from_url(url: str):
def get_fetcher_from_url(url: str) -> ModelMetadataFetchBase:
if re.match(r"^https?://huggingface.co/[^/]+/[^/]+$", url.lower()):
return HuggingFaceMetadataFetch
raise ValueError(f"Unsupported model source: '{url}'")

51
invokeai/app/util/download_with_progress.py Normal file
View File

@ -0,0 +1,51 @@
from pathlib import Path
from urllib import request
from tqdm import tqdm
from invokeai.backend.util.logging import InvokeAILogger
class ProgressBar:
"""Simple progress bar for urllib.request.urlretrieve using tqdm."""
def __init__(self, model_name: str = "file"):
self.pbar = None
self.name = model_name
def __call__(self, block_num: int, block_size: int, total_size: int):
if not self.pbar:
self.pbar = tqdm(
desc=self.name,
initial=0,
unit="iB",
unit_scale=True,
unit_divisor=1000,
total=total_size,
)
self.pbar.update(block_size)
def download_with_progress_bar(name: str, url: str, dest_path: Path) -> bool:
"""Download a file from a URL to a destination path, with a progress bar.
If the file already exists, it will not be downloaded again.
Exceptions are not caught.
Args:
name (str): Name of the file being downloaded.
url (str): URL to download the file from.
dest_path (Path): Destination path to save the file to.
Returns:
bool: True if the file was downloaded, False if it already existed.
"""
if dest_path.exists():
return False # already downloaded
InvokeAILogger.get_logger().info(f"Downloading {name}...")
dest_path.parent.mkdir(parents=True, exist_ok=True)
request.urlretrieve(url, dest_path, ProgressBar(name))
return True

24
invokeai/app/util/suppress_output.py Normal file
View File

@ -0,0 +1,24 @@
import io
import sys
from typing import Any
class SuppressOutput:
"""Context manager to suppress stdout.
Example:
```
with SuppressOutput():
print("This will not be printed")
```
"""
def __enter__(self):
# Save the original stdout
self._original_stdout = sys.stdout
# Redirect stdout to a dummy StringIO object
sys.stdout = io.StringIO()
def __exit__(self, *args: Any, **kwargs: Any):
# Restore stdout
sys.stdout = self._original_stdout

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

@ -1,4 +0,0 @@
"""Initialization file for invokeai.backend.embeddings modules."""
# from .model_patcher import ModelPatcher
# __all__ = ["ModelPatcher"]

12
invokeai/backend/embeddings/embedding_base.py
View File

@ -1,12 +0,0 @@
"""Base class for LoRA and Textual Inversion models.
The EmbeddingRaw class is the base class of LoRAModelRaw and TextualInversionModelRaw,
and is used for type checking of calls to the model patcher.
The use of "Raw" here is a historical artifact, and carried forward in
order to avoid confusion.
"""
class EmbeddingModelRaw:
"""Base class for LoRA and Textual Inversion models."""

17
invokeai/backend/image_util/__init__.py
View File

@ -5,21 +5,4 @@ Initialization file for invokeai.backend.image_util methods.
from .patchmatch import PatchMatch # noqa: F401
from .pngwriter import PngWriter, PromptFormatter, retrieve_metadata, write_metadata # noqa: F401
from .seamless import configure_model_padding # noqa: F401
from .txt2mask import Txt2Mask # noqa: F401
from .util import InitImageResizer, make_grid # noqa: F401
def debug_image(debug_image, debug_text, debug_show=True, debug_result=False, debug_status=False):
from PIL import ImageDraw
if not debug_status:
return
image_copy = debug_image.copy().convert("RGBA")
ImageDraw.Draw(image_copy).text((5, 5), debug_text, (255, 0, 0))
if debug_show:
image_copy.show()
if debug_result:
return image_copy

41
invokeai/backend/image_util/canny.py Normal file
View File

@ -0,0 +1,41 @@
import cv2
from PIL import Image
from invokeai.backend.image_util.util import (
cv2_to_pil,
normalize_image_channel_count,
pil_to_cv2,
resize_image_to_resolution,
)
def get_canny_edges(
image: Image.Image, low_threshold: int, high_threshold: int, detect_resolution: int, image_resolution: int
) -> Image.Image:
"""Returns the edges of an image using the Canny edge detection algorithm.
Adapted from https://github.com/huggingface/controlnet_aux (Apache-2.0 license).
Args:
image: The input image.
low_threshold: The lower threshold for the hysteresis procedure.
high_threshold: The upper threshold for the hysteresis procedure.
input_resolution: The resolution of the input image. The image will be resized to this resolution before edge detection.
output_resolution: The resolution of the output image. The edges will be resized to this resolution before returning.
Returns:
The Canny edges of the input image.
"""
if image.mode != "RGB":
image = image.convert("RGB")
np_image = pil_to_cv2(image)
np_image = normalize_image_channel_count(np_image)
np_image = resize_image_to_resolution(np_image, detect_resolution)
edge_map = cv2.Canny(np_image, low_threshold, high_threshold)
edge_map = normalize_image_channel_count(edge_map)
edge_map = resize_image_to_resolution(edge_map, image_resolution)
return cv2_to_pil(edge_map)

11
invokeai/backend/image_util/depth_anything/__init__.py
View File

@ -10,11 +10,11 @@ from PIL import Image
from torchvision.transforms import Compose
from invokeai.app.services.config.config_default import get_config
from invokeai.app.util.download_with_progress import download_with_progress_bar
from invokeai.backend.image_util.depth_anything.model.dpt import DPT_DINOv2
from invokeai.backend.image_util.depth_anything.utilities.util import NormalizeImage, PrepareForNet, Resize
from invokeai.backend.util.devices import choose_torch_device
from invokeai.backend.util.logging import InvokeAILogger
from invokeai.backend.util.util import download_with_progress_bar
config = get_config()
logger = InvokeAILogger.get_logger(config=config)
@ -59,9 +59,12 @@ class DepthAnythingDetector:
self.device = choose_torch_device()
def load_model(self, model_size: Literal["large", "base", "small"] = "small"):
DEPTH_ANYTHING_MODEL_PATH = pathlib.Path(config.models_path / DEPTH_ANYTHING_MODELS[model_size]["local"])
if not DEPTH_ANYTHING_MODEL_PATH.exists():
download_with_progress_bar(DEPTH_ANYTHING_MODELS[model_size]["url"], DEPTH_ANYTHING_MODEL_PATH)
DEPTH_ANYTHING_MODEL_PATH = config.models_path / DEPTH_ANYTHING_MODELS[model_size]["local"]
download_with_progress_bar(
pathlib.Path(DEPTH_ANYTHING_MODELS[model_size]["url"]).name,
DEPTH_ANYTHING_MODELS[model_size]["url"],
DEPTH_ANYTHING_MODEL_PATH,
)
if not self.model or model_size != self.model_size:
del self.model

17
invokeai/backend/image_util/dw_openpose/wholebody.py
View File

@ -1,14 +1,13 @@
# Code from the original DWPose Implementation: https://github.com/IDEA-Research/DWPose
# Modified pathing to suit Invoke
import pathlib
import numpy as np
import onnxruntime as ort
from invokeai.app.services.config.config_default import get_config
from invokeai.app.util.download_with_progress import download_with_progress_bar
from invokeai.backend.util.devices import choose_torch_device
from invokeai.backend.util.util import download_with_progress_bar
from .onnxdet import inference_detector
from .onnxpose import inference_pose
@ -24,7 +23,7 @@ DWPOSE_MODELS = {
},
}
config = get_config
config = get_config()
class Wholebody:
@ -33,13 +32,13 @@ class Wholebody:
providers = ["CUDAExecutionProvider"] if device == "cuda" else ["CPUExecutionProvider"]
DET_MODEL_PATH = pathlib.Path(config.models_path / DWPOSE_MODELS["yolox_l.onnx"]["local"])
if not DET_MODEL_PATH.exists():
download_with_progress_bar(DWPOSE_MODELS["yolox_l.onnx"]["url"], DET_MODEL_PATH)
DET_MODEL_PATH = config.models_path / DWPOSE_MODELS["yolox_l.onnx"]["local"]
download_with_progress_bar("yolox_l.onnx", DWPOSE_MODELS["yolox_l.onnx"]["url"], DET_MODEL_PATH)
POSE_MODEL_PATH = pathlib.Path(config.models_path / DWPOSE_MODELS["dw-ll_ucoco_384.onnx"]["local"])
if not POSE_MODEL_PATH.exists():
download_with_progress_bar(DWPOSE_MODELS["dw-ll_ucoco_384.onnx"]["url"], POSE_MODEL_PATH)
POSE_MODEL_PATH = config.models_path / DWPOSE_MODELS["dw-ll_ucoco_384.onnx"]["local"]
download_with_progress_bar(
"dw-ll_ucoco_384.onnx", DWPOSE_MODELS["dw-ll_ucoco_384.onnx"]["url"], POSE_MODEL_PATH
)
onnx_det = DET_MODEL_PATH
onnx_pose = POSE_MODEL_PATH

142
invokeai/backend/image_util/hed.py Normal file
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@ -0,0 +1,142 @@
"""Adapted from https://github.com/huggingface/controlnet_aux (Apache-2.0 license)."""
import cv2
import numpy as np
import torch
from einops import rearrange
from huggingface_hub import hf_hub_download
from PIL import Image
from invokeai.backend.image_util.util import (
non_maximum_suppression,
normalize_image_channel_count,
np_to_pil,
pil_to_np,
resize_image_to_resolution,
safe_step,
)
class DoubleConvBlock(torch.nn.Module):
def __init__(self, input_channel, output_channel, layer_number):
super().__init__()
self.convs = torch.nn.Sequential()
self.convs.append(
torch.nn.Conv2d(
in_channels=input_channel, out_channels=output_channel, kernel_size=(3, 3), stride=(1, 1), padding=1
)
)
for _i in range(1, layer_number):
self.convs.append(
torch.nn.Conv2d(
in_channels=output_channel,
out_channels=output_channel,
kernel_size=(3, 3),
stride=(1, 1),
padding=1,
)
)
self.projection = torch.nn.Conv2d(
in_channels=output_channel, out_channels=1, kernel_size=(1, 1), stride=(1, 1), padding=0
)
def __call__(self, x, down_sampling=False):
h = x
if down_sampling:
h = torch.nn.functional.max_pool2d(h, kernel_size=(2, 2), stride=(2, 2))
for conv in self.convs:
h = conv(h)
h = torch.nn.functional.relu(h)
return h, self.projection(h)
class ControlNetHED_Apache2(torch.nn.Module):
def __init__(self):
super().__init__()
self.norm = torch.nn.Parameter(torch.zeros(size=(1, 3, 1, 1)))
self.block1 = DoubleConvBlock(input_channel=3, output_channel=64, layer_number=2)
self.block2 = DoubleConvBlock(input_channel=64, output_channel=128, layer_number=2)
self.block3 = DoubleConvBlock(input_channel=128, output_channel=256, layer_number=3)
self.block4 = DoubleConvBlock(input_channel=256, output_channel=512, layer_number=3)
self.block5 = DoubleConvBlock(input_channel=512, output_channel=512, layer_number=3)
def __call__(self, x):
h = x - self.norm
h, projection1 = self.block1(h)
h, projection2 = self.block2(h, down_sampling=True)
h, projection3 = self.block3(h, down_sampling=True)
h, projection4 = self.block4(h, down_sampling=True)
h, projection5 = self.block5(h, down_sampling=True)
return projection1, projection2, projection3, projection4, projection5
class HEDProcessor:
"""Holistically-Nested Edge Detection.
On instantiation, loads the HED model from the HuggingFace Hub.
"""
def __init__(self):
model_path = hf_hub_download("lllyasviel/Annotators", "ControlNetHED.pth")
self.network = ControlNetHED_Apache2()
self.network.load_state_dict(torch.load(model_path, map_location="cpu"))
self.network.float().eval()
def to(self, device: torch.device):
self.network.to(device)
return self
def run(
self,
input_image: Image.Image,
detect_resolution: int = 512,
image_resolution: int = 512,
safe: bool = False,
scribble: bool = False,
) -> Image.Image:
"""Processes an image and returns the detected edges.
Args:
input_image: The input image.
detect_resolution: The resolution to fit the image to before edge detection.
image_resolution: The resolution to fit the edges to before returning.
safe: Whether to apply safe step to the detected edges.
scribble: Whether to apply non-maximum suppression and Gaussian blur to the detected edges.
Returns:
The detected edges.
"""
device = next(iter(self.network.parameters())).device
np_image = pil_to_np(input_image)
np_image = normalize_image_channel_count(np_image)
np_image = resize_image_to_resolution(np_image, detect_resolution)
assert np_image.ndim == 3
height, width, _channels = np_image.shape
with torch.no_grad():
image_hed = torch.from_numpy(np_image.copy()).float().to(device)
image_hed = rearrange(image_hed, "h w c -> 1 c h w")
edges = self.network(image_hed)
edges = [e.detach().cpu().numpy().astype(np.float32)[0, 0] for e in edges]
edges = [cv2.resize(e, (width, height), interpolation=cv2.INTER_LINEAR) for e in edges]
edges = np.stack(edges, axis=2)
edge = 1 / (1 + np.exp(-np.mean(edges, axis=2).astype(np.float64)))
if safe:
edge = safe_step(edge)
edge = (edge * 255.0).clip(0, 255).astype(np.uint8)
detected_map = edge
detected_map = normalize_image_channel_count(detected_map)
img = resize_image_to_resolution(np_image, image_resolution)
height, width, _channels = img.shape
detected_map = cv2.resize(detected_map, (width, height), interpolation=cv2.INTER_LINEAR)
if scribble:
detected_map = non_maximum_suppression(detected_map, 127, 3.0)
detected_map = cv2.GaussianBlur(detected_map, (0, 0), 3.0)
detected_map[detected_map > 4] = 255
detected_map[detected_map < 255] = 0
return np_to_pil(detected_map)

46
invokeai/backend/image_util/invoke_metadata.py
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@ -1,46 +0,0 @@
# Copyright (c) 2023 Lincoln D. Stein and the InvokeAI Development Team
"""Very simple functions to fetch and print metadata from InvokeAI-generated images."""
import json
import sys
from pathlib import Path
from typing import Any, Dict
from PIL import Image
def get_invokeai_metadata(image_path: Path) -> Dict[str, Any]:
"""
Retrieve "invokeai_metadata" field from png image.
:param image_path: Path to the image to read metadata from.
May raise:
OSError -- image path not found
KeyError -- image doesn't contain the metadata field
"""
image: Image = Image.open(image_path)
return json.loads(image.text["invokeai_metadata"])
def print_invokeai_metadata(image_path: Path):
"""Pretty-print the metadata."""
try:
metadata = get_invokeai_metadata(image_path)
print(f"{image_path}:\n{json.dumps(metadata, sort_keys=True, indent=4)}")
except OSError:
print(f"{image_path}:\nNo file found.")
except KeyError:
print(f"{image_path}:\nNo metadata found.")
print()
def main():
"""Run the command-line utility."""
image_paths = sys.argv[1:]
if not image_paths:
print(f"Usage: {Path(sys.argv[0]).name} image1 image2 image3 ...")
print("\nPretty-print InvokeAI image metadata from the listed png files.")
sys.exit(-1)
for img in image_paths:
print_invokeai_metadata(img)

158
invokeai/backend/image_util/lineart.py Normal file
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@ -0,0 +1,158 @@
"""Adapted from https://github.com/huggingface/controlnet_aux (Apache-2.0 license)."""
import cv2
import numpy as np
import torch
import torch.nn as nn
from einops import rearrange
from huggingface_hub import hf_hub_download
from PIL import Image
from invokeai.backend.image_util.util import (
normalize_image_channel_count,
np_to_pil,
pil_to_np,
resize_image_to_resolution,
)
class ResidualBlock(nn.Module):
def __init__(self, in_features):
super(ResidualBlock, self).__init__()
conv_block = [
nn.ReflectionPad2d(1),
nn.Conv2d(in_features, in_features, 3),
nn.InstanceNorm2d(in_features),
nn.ReLU(inplace=True),
nn.ReflectionPad2d(1),
nn.Conv2d(in_features, in_features, 3),
nn.InstanceNorm2d(in_features),
]
self.conv_block = nn.Sequential(*conv_block)
def forward(self, x):
return x + self.conv_block(x)
class Generator(nn.Module):
def __init__(self, input_nc, output_nc, n_residual_blocks=9, sigmoid=True):
super(Generator, self).__init__()
# Initial convolution block
model0 = [nn.ReflectionPad2d(3), nn.Conv2d(input_nc, 64, 7), nn.InstanceNorm2d(64), nn.ReLU(inplace=True)]
self.model0 = nn.Sequential(*model0)
# Downsampling
model1 = []
in_features = 64
out_features = in_features * 2
for _ in range(2):
model1 += [
nn.Conv2d(in_features, out_features, 3, stride=2, padding=1),
nn.InstanceNorm2d(out_features),
nn.ReLU(inplace=True),
]
in_features = out_features
out_features = in_features * 2
self.model1 = nn.Sequential(*model1)
model2 = []
# Residual blocks
for _ in range(n_residual_blocks):
model2 += [ResidualBlock(in_features)]
self.model2 = nn.Sequential(*model2)
# Upsampling
model3 = []
out_features = in_features // 2
for _ in range(2):
model3 += [
nn.ConvTranspose2d(in_features, out_features, 3, stride=2, padding=1, output_padding=1),
nn.InstanceNorm2d(out_features),
nn.ReLU(inplace=True),
]
in_features = out_features
out_features = in_features // 2
self.model3 = nn.Sequential(*model3)
# Output layer
model4 = [nn.ReflectionPad2d(3), nn.Conv2d(64, output_nc, 7)]
if sigmoid:
model4 += [nn.Sigmoid()]
self.model4 = nn.Sequential(*model4)
def forward(self, x, cond=None):
out = self.model0(x)
out = self.model1(out)
out = self.model2(out)
out = self.model3(out)
out = self.model4(out)
return out
class LineartProcessor:
"""Processor for lineart detection."""
def __init__(self):
model_path = hf_hub_download("lllyasviel/Annotators", "sk_model.pth")
self.model = Generator(3, 1, 3)
self.model.load_state_dict(torch.load(model_path, map_location=torch.device("cpu")))
self.model.eval()
coarse_model_path = hf_hub_download("lllyasviel/Annotators", "sk_model2.pth")
self.model_coarse = Generator(3, 1, 3)
self.model_coarse.load_state_dict(torch.load(coarse_model_path, map_location=torch.device("cpu")))
self.model_coarse.eval()
def to(self, device: torch.device):
self.model.to(device)
self.model_coarse.to(device)
return self
def run(
self, input_image: Image.Image, coarse: bool = False, detect_resolution: int = 512, image_resolution: int = 512
) -> Image.Image:
"""Processes an image to detect lineart.
Args:
input_image: The input image.
coarse: Whether to use the coarse model.
detect_resolution: The resolution to fit the image to before edge detection.
image_resolution: The resolution of the output image.
Returns:
The detected lineart.
"""
device = next(iter(self.model.parameters())).device
np_image = pil_to_np(input_image)
np_image = normalize_image_channel_count(np_image)
np_image = resize_image_to_resolution(np_image, detect_resolution)
model = self.model_coarse if coarse else self.model
assert np_image.ndim == 3
image = np_image
with torch.no_grad():
image = torch.from_numpy(image).float().to(device)
image = image / 255.0
image = rearrange(image, "h w c -> 1 c h w")
line = model(image)[0][0]
line = line.cpu().numpy()
line = (line * 255.0).clip(0, 255).astype(np.uint8)
detected_map = line
detected_map = normalize_image_channel_count(detected_map)
img = resize_image_to_resolution(np_image, image_resolution)
H, W, C = img.shape
detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_LINEAR)
detected_map = 255 - detected_map
return np_to_pil(detected_map)

203
invokeai/backend/image_util/lineart_anime.py Normal file
View File

@ -0,0 +1,203 @@
"""Adapted from https://github.com/huggingface/controlnet_aux (Apache-2.0 license)."""
import functools
from typing import Optional
import cv2
import numpy as np
import torch
import torch.nn as nn
from einops import rearrange
from huggingface_hub import hf_hub_download
from PIL import Image
from invokeai.backend.image_util.util import (
normalize_image_channel_count,
np_to_pil,
pil_to_np,
resize_image_to_resolution,
)
class UnetGenerator(nn.Module):
"""Create a Unet-based generator"""
def __init__(
self,
input_nc: int,
output_nc: int,
num_downs: int,
ngf: int = 64,
norm_layer=nn.BatchNorm2d,
use_dropout: bool = False,
):
"""Construct a Unet generator
Parameters:
input_nc (int) -- the number of channels in input images
output_nc (int) -- the number of channels in output images
num_downs (int) -- the number of downsamplings in UNet. For example, # if |num_downs| == 7,
image of size 128x128 will become of size 1x1 # at the bottleneck
ngf (int) -- the number of filters in the last conv layer
norm_layer -- normalization layer
We construct the U-Net from the innermost layer to the outermost layer.
It is a recursive process.
"""
super(UnetGenerator, self).__init__()
# construct unet structure
unet_block = UnetSkipConnectionBlock(
ngf * 8, ngf * 8, input_nc=None, submodule=None, norm_layer=norm_layer, innermost=True
) # add the innermost layer
for _ in range(num_downs - 5): # add intermediate layers with ngf * 8 filters
unet_block = UnetSkipConnectionBlock(
ngf * 8, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer, use_dropout=use_dropout
)
# gradually reduce the number of filters from ngf * 8 to ngf
unet_block = UnetSkipConnectionBlock(
ngf * 4, ngf * 8, input_nc=None, submodule=unet_block, norm_layer=norm_layer
)
unet_block = UnetSkipConnectionBlock(
ngf * 2, ngf * 4, input_nc=None, submodule=unet_block, norm_layer=norm_layer
)
unet_block = UnetSkipConnectionBlock(ngf, ngf * 2, input_nc=None, submodule=unet_block, norm_layer=norm_layer)
self.model = UnetSkipConnectionBlock(
output_nc, ngf, input_nc=input_nc, submodule=unet_block, outermost=True, norm_layer=norm_layer
) # add the outermost layer
def forward(self, input):
"""Standard forward"""
return self.model(input)
class UnetSkipConnectionBlock(nn.Module):
"""Defines the Unet submodule with skip connection.
X -------------------identity----------------------
|-- downsampling -- |submodule| -- upsampling --|
"""
def __init__(
self,
outer_nc: int,
inner_nc: int,
input_nc: Optional[int] = None,
submodule=None,
outermost: bool = False,
innermost: bool = False,
norm_layer=nn.BatchNorm2d,
use_dropout: bool = False,
):
"""Construct a Unet submodule with skip connections.
Parameters:
outer_nc (int) -- the number of filters in the outer conv layer
inner_nc (int) -- the number of filters in the inner conv layer
input_nc (int) -- the number of channels in input images/features
submodule (UnetSkipConnectionBlock) -- previously defined submodules
outermost (bool) -- if this module is the outermost module
innermost (bool) -- if this module is the innermost module
norm_layer -- normalization layer
use_dropout (bool) -- if use dropout layers.
"""
super(UnetSkipConnectionBlock, self).__init__()
self.outermost = outermost
if type(norm_layer) == functools.partial:
use_bias = norm_layer.func == nn.InstanceNorm2d
else:
use_bias = norm_layer == nn.InstanceNorm2d
if input_nc is None:
input_nc = outer_nc
downconv = nn.Conv2d(input_nc, inner_nc, kernel_size=4, stride=2, padding=1, bias=use_bias)
downrelu = nn.LeakyReLU(0.2, True)
downnorm = norm_layer(inner_nc)
uprelu = nn.ReLU(True)
upnorm = norm_layer(outer_nc)
if outermost:
upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc, kernel_size=4, stride=2, padding=1)
down = [downconv]
up = [uprelu, upconv, nn.Tanh()]
model = down + [submodule] + up
elif innermost:
upconv = nn.ConvTranspose2d(inner_nc, outer_nc, kernel_size=4, stride=2, padding=1, bias=use_bias)
down = [downrelu, downconv]
up = [uprelu, upconv, upnorm]
model = down + up
else:
upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc, kernel_size=4, stride=2, padding=1, bias=use_bias)
down = [downrelu, downconv, downnorm]
up = [uprelu, upconv, upnorm]
if use_dropout:
model = down + [submodule] + up + [nn.Dropout(0.5)]
else:
model = down + [submodule] + up
self.model = nn.Sequential(*model)
def forward(self, x):
if self.outermost:
return self.model(x)
else: # add skip connections
return torch.cat([x, self.model(x)], 1)
class LineartAnimeProcessor:
"""Processes an image to detect lineart."""
def __init__(self):
model_path = hf_hub_download("lllyasviel/Annotators", "netG.pth")
norm_layer = functools.partial(nn.InstanceNorm2d, affine=False, track_running_stats=False)
self.model = UnetGenerator(3, 1, 8, 64, norm_layer=norm_layer, use_dropout=False)
ckpt = torch.load(model_path)
for key in list(ckpt.keys()):
if "module." in key:
ckpt[key.replace("module.", "")] = ckpt[key]
del ckpt[key]
self.model.load_state_dict(ckpt)
self.model.eval()
def to(self, device: torch.device):
self.model.to(device)
return self
def run(self, input_image: Image.Image, detect_resolution: int = 512, image_resolution: int = 512) -> Image.Image:
"""Processes an image to detect lineart.
Args:
input_image: The input image.
detect_resolution: The resolution to use for detection.
image_resolution: The resolution to use for the output image.
Returns:
The detected lineart.
"""
device = next(iter(self.model.parameters())).device
np_image = pil_to_np(input_image)
np_image = normalize_image_channel_count(np_image)
np_image = resize_image_to_resolution(np_image, detect_resolution)
H, W, C = np_image.shape
Hn = 256 * int(np.ceil(float(H) / 256.0))
Wn = 256 * int(np.ceil(float(W) / 256.0))
img = cv2.resize(np_image, (Wn, Hn), interpolation=cv2.INTER_CUBIC)
with torch.no_grad():
image_feed = torch.from_numpy(img).float().to(device)
image_feed = image_feed / 127.5 - 1.0
image_feed = rearrange(image_feed, "h w c -> 1 c h w")
line = self.model(image_feed)[0, 0] * 127.5 + 127.5
line = line.cpu().numpy()
line = cv2.resize(line, (W, H), interpolation=cv2.INTER_CUBIC)
line = line.clip(0, 255).astype(np.uint8)
detected_map = line
detected_map = normalize_image_channel_count(detected_map)
img = resize_image_to_resolution(np_image, image_resolution)
H, W, C = img.shape
detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_LINEAR)
detected_map = 255 - detected_map
return np_to_pil(detected_map)

13
invokeai/backend/image_util/safety_checker.py
View File

@ -4,6 +4,8 @@ wraps the safety_checker model. It respects the global "nsfw_checker"
configuration variable, that allows the checker to be supressed.
"""
from pathlib import Path
import numpy as np
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from PIL import Image
@ -34,22 +36,21 @@ class SafetyChecker:
try:
cls.safety_checker = StableDiffusionSafetyChecker.from_pretrained(get_config().models_path / CHECKER_PATH)
cls.feature_extractor = AutoFeatureExtractor.from_pretrained(get_config().models_path / CHECKER_PATH)
logger.info("NSFW checker initialized")
except Exception as e:
logger.warning(f"Could not load NSFW checker: {str(e)}")
cls.tried_load = True
@classmethod
def safety_checker_available(cls) -> bool:
cls._load_safety_checker()
return cls.safety_checker is not None
return Path(get_config().models_path, CHECKER_PATH).exists()
@classmethod
def has_nsfw_concept(cls, image: Image.Image) -> bool:
if not cls.safety_checker_available():
if not cls.safety_checker_available() and cls.tried_load:
return False
cls._load_safety_checker()
if cls.safety_checker is None or cls.feature_extractor is None:
return False
assert cls.safety_checker is not None
assert cls.feature_extractor is not None
device = choose_torch_device()
features = cls.feature_extractor([image], return_tensors="pt")
features.to(device)

114
invokeai/backend/image_util/txt2mask.py
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@ -1,114 +0,0 @@
"""Makes available the Txt2Mask class, which assists in the automatic
assignment of masks via text prompt using clipseg.
Here is typical usage:
from invokeai.backend.image_util.txt2mask import Txt2Mask, SegmentedGrayscale
from PIL import Image
txt2mask = Txt2Mask(self.device)
segmented = txt2mask.segment(Image.open('/path/to/img.png'),'a bagel')
# this will return a grayscale Image of the segmented data
grayscale = segmented.to_grayscale()
# this will return a semi-transparent image in which the
# selected object(s) are opaque and the rest is at various
# levels of transparency
transparent = segmented.to_transparent()
# this will return a masked image suitable for use in inpainting:
mask = segmented.to_mask(threshold=0.5)
The threshold used in the call to to_mask() selects pixels for use in
the mask that exceed the indicated confidence threshold. Values range
from 0.0 to 1.0. The higher the threshold, the more confident the
algorithm is. In limited testing, I have found that values around 0.5
work fine.
"""
import numpy as np
import torch
from PIL import Image, ImageOps
from transformers import AutoProcessor, CLIPSegForImageSegmentation
import invokeai.backend.util.logging as logger
from invokeai.app.services.config.config_default import get_config
CLIPSEG_MODEL = "CIDAS/clipseg-rd64-refined"
CLIPSEG_SIZE = 352
class SegmentedGrayscale(object):
def __init__(self, image: Image.Image, heatmap: torch.Tensor):
self.heatmap = heatmap
self.image = image
def to_grayscale(self, invert: bool = False) -> Image.Image:
return self._rescale(Image.fromarray(np.uint8(255 - self.heatmap * 255 if invert else self.heatmap * 255)))
def to_mask(self, threshold: float = 0.5) -> Image.Image:
discrete_heatmap = self.heatmap.lt(threshold).int()
return self._rescale(Image.fromarray(np.uint8(discrete_heatmap * 255), mode="L"))
def to_transparent(self, invert: bool = False) -> Image.Image:
transparent_image = self.image.copy()
# For img2img, we want the selected regions to be transparent,
# but to_grayscale() returns the opposite. Thus invert.
gs = self.to_grayscale(not invert)
transparent_image.putalpha(gs)
return transparent_image
# unscales and uncrops the 352x352 heatmap so that it matches the image again
def _rescale(self, heatmap: Image.Image) -> Image.Image:
size = self.image.width if (self.image.width > self.image.height) else self.image.height
resized_image = heatmap.resize((size, size), resample=Image.Resampling.LANCZOS)
return resized_image.crop((0, 0, self.image.width, self.image.height))
class Txt2Mask(object):
"""
Create new Txt2Mask object. The optional device argument can be one of
'cuda', 'mps' or 'cpu'.
"""
def __init__(self, device="cpu", refined=False):
logger.info("Initializing clipseg model for text to mask inference")
# BUG: we are not doing anything with the device option at this time
self.device = device
self.processor = AutoProcessor.from_pretrained(CLIPSEG_MODEL, cache_dir=get_config().cache_dir)
self.model = CLIPSegForImageSegmentation.from_pretrained(CLIPSEG_MODEL, cache_dir=get_config().cache_dir)
@torch.no_grad()
def segment(self, image: Image.Image, prompt: str) -> SegmentedGrayscale:
"""
Given a prompt string such as "a bagel", tries to identify the object in the
provided image and returns a SegmentedGrayscale object in which the brighter
pixels indicate where the object is inferred to be.
"""
if isinstance(image, str):
image = Image.open(image).convert("RGB")
image = ImageOps.exif_transpose(image)
img = self._scale_and_crop(image)
inputs = self.processor(text=[prompt], images=[img], padding=True, return_tensors="pt")
outputs = self.model(**inputs)
heatmap = torch.sigmoid(outputs.logits)
return SegmentedGrayscale(image, heatmap)
def _scale_and_crop(self, image: Image.Image) -> Image.Image:
scaled_image = Image.new("RGB", (CLIPSEG_SIZE, CLIPSEG_SIZE))
if image.width > image.height: # width is constraint
scale = CLIPSEG_SIZE / image.width
else:
scale = CLIPSEG_SIZE / image.height
scaled_image.paste(
image.resize(
(int(scale * image.width), int(scale * image.height)),
resample=Image.Resampling.LANCZOS,
),
box=(0, 0),
)
return scaled_image

133
invokeai/backend/image_util/util.py
View File

@ -1,5 +1,7 @@
from math import ceil, floor, sqrt
import cv2
import numpy as np
from PIL import Image
@ -69,3 +71,134 @@ def make_grid(image_list, rows=None, cols=None):
i = i + 1
return grid_img
def pil_to_np(image: Image.Image) -> np.ndarray:
"""Converts a PIL image to a numpy array."""
return np.array(image, dtype=np.uint8)
def np_to_pil(image: np.ndarray) -> Image.Image:
"""Converts a numpy array to a PIL image."""
return Image.fromarray(image)
def pil_to_cv2(image: Image.Image) -> np.ndarray:
"""Converts a PIL image to a CV2 image."""
return cv2.cvtColor(np.array(image, dtype=np.uint8), cv2.COLOR_RGB2BGR)
def cv2_to_pil(image: np.ndarray) -> Image.Image:
"""Converts a CV2 image to a PIL image."""
return Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
def normalize_image_channel_count(image: np.ndarray) -> np.ndarray:
"""Normalizes an image to have 3 channels.
If the image has 1 channel, it will be duplicated 3 times.
If the image has 1 channel, a third empty channel will be added.
If the image has 4 channels, the alpha channel will be used to blend the image with a white background.
Adapted from https://github.com/huggingface/controlnet_aux (Apache-2.0 license).
Args:
image: The input image.
Returns:
The normalized image.
"""
assert image.dtype == np.uint8
if image.ndim == 2:
image = image[:, :, None]
assert image.ndim == 3
_height, _width, channels = image.shape
assert channels == 1 or channels == 3 or channels == 4
if channels == 3:
return image
if channels == 1:
return np.concatenate([image, image, image], axis=2)
if channels == 4:
color = image[:, :, 0:3].astype(np.float32)
alpha = image[:, :, 3:4].astype(np.float32) / 255.0
normalized = color * alpha + 255.0 * (1.0 - alpha)
normalized = normalized.clip(0, 255).astype(np.uint8)
return normalized
raise ValueError("Invalid number of channels.")
def resize_image_to_resolution(input_image: np.ndarray, resolution: int) -> np.ndarray:
"""Resizes an image, fitting it to the given resolution.
Adapted from https://github.com/huggingface/controlnet_aux (Apache-2.0 license).
Args:
input_image: The input image.
resolution: The resolution to fit the image to.
Returns:
The resized image.
"""
h = float(input_image.shape[0])
w = float(input_image.shape[1])
scaling_factor = float(resolution) / min(h, w)
h *= scaling_factor
w *= scaling_factor
h = int(np.round(h / 64.0)) * 64
w = int(np.round(w / 64.0)) * 64
if scaling_factor > 1:
return cv2.resize(input_image, (w, h), interpolation=cv2.INTER_LANCZOS4)
else:
return cv2.resize(input_image, (w, h), interpolation=cv2.INTER_AREA)
def non_maximum_suppression(image: np.ndarray, threshold: int, sigma: float):
"""
Apply non-maximum suppression to an image.
This function is adapted from https://github.com/lllyasviel/ControlNet.
Args:
image: The input image.
threshold: The threshold value for the suppression. Pixels with values greater than this will be set to 255.
sigma: The standard deviation for the Gaussian blur applied to the image.
Returns:
The image after non-maximum suppression.
"""
image = cv2.GaussianBlur(image.astype(np.float32), (0, 0), sigma)
filter_1 = np.array([[0, 0, 0], [1, 1, 1], [0, 0, 0]], dtype=np.uint8)
filter_2 = np.array([[0, 1, 0], [0, 1, 0], [0, 1, 0]], dtype=np.uint8)
filter_3 = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]], dtype=np.uint8)
filter_4 = np.array([[0, 0, 1], [0, 1, 0], [1, 0, 0]], dtype=np.uint8)
y = np.zeros_like(image)
for f in [filter_1, filter_2, filter_3, filter_4]:
np.putmask(y, cv2.dilate(image, kernel=f) == image, image)
z = np.zeros_like(y, dtype=np.uint8)
z[y > threshold] = 255
return z
def safe_step(x: np.ndarray, step: int = 2) -> np.ndarray:
"""Apply the safe step operation to an array.
I don't fully understand the purpose of this function, but it appears to be normalizing/quantizing the array.
Adapted from https://github.com/huggingface/controlnet_aux (Apache-2.0 license).
Args:
x: The input array.
step: The step value.
Returns:
The array after the safe step operation.
"""
y = x.astype(np.float32) * float(step + 1)
y = y.astype(np.int32).astype(np.float32) / float(step)
return y

0
invokeai/backend/install/__init__.py
View File

30
invokeai/backend/install/check_directories.py
View File

@ -1,30 +0,0 @@
"""
Check that the invokeai_root is correctly configured and exit if not.
"""
import sys
from invokeai.app.services.config import InvokeAIAppConfig
# TODO(psyche): Should this also check for things like ESRGAN models, database, etc?
def validate_directories(config: InvokeAIAppConfig) -> None:
assert config.db_path.parent.exists(), f"{config.db_path.parent} not found"
assert config.models_path.exists(), f"{config.models_path} not found"
def check_directories(config: InvokeAIAppConfig):
try:
validate_directories(config)
except Exception as e:
print()
print(f"An exception has occurred: {str(e)}")
print("== STARTUP ABORTED ==")
print("** One or more necessary files is missing from your InvokeAI directories **")
print("** Please rerun the configuration script to fix this problem. **")
print("** From the launcher, selection option [6]. **")
print(
'** From the command line, activate the virtual environment and run "invokeai-configure --yes --skip-sd-weights" **'
)
input("Press any key to continue...")
sys.exit(0)

267
invokeai/backend/install/install_helper.py
View File

@ -1,267 +0,0 @@
"""Utility (backend) functions used by model_install.py"""
from logging import Logger
from pathlib import Path
from typing import Any, Dict, List, Optional
import omegaconf
from pydantic import BaseModel, Field
from pydantic.dataclasses import dataclass
from requests import HTTPError
from tqdm import tqdm
import invokeai.configs as configs
from invokeai.app.services.config import InvokeAIAppConfig
from invokeai.app.services.download import DownloadQueueService
from invokeai.app.services.events.events_base import EventServiceBase
from invokeai.app.services.image_files.image_files_disk import DiskImageFileStorage
from invokeai.app.services.model_install import (
ModelInstallService,
ModelInstallServiceBase,
)
from invokeai.app.services.model_records import ModelRecordServiceBase, ModelRecordServiceSQL
from invokeai.app.services.shared.sqlite.sqlite_util import init_db
from invokeai.backend.model_manager import (
BaseModelType,
InvalidModelConfigException,
ModelType,
)
from invokeai.backend.model_manager.metadata import UnknownMetadataException
from invokeai.backend.util.logging import InvokeAILogger
# name of the starter models file
INITIAL_MODELS = "INITIAL_MODELS.yaml"
def initialize_record_store(app_config: InvokeAIAppConfig) -> ModelRecordServiceBase:
"""Return an initialized ModelConfigRecordServiceBase object."""
logger = InvokeAILogger.get_logger(config=app_config)
image_files = DiskImageFileStorage(f"{app_config.outputs_path}/images")
db = init_db(config=app_config, logger=logger, image_files=image_files)
obj: ModelRecordServiceBase = ModelRecordServiceSQL(db)
return obj
def initialize_installer(
app_config: InvokeAIAppConfig, event_bus: Optional[EventServiceBase] = None
) -> ModelInstallServiceBase:
"""Return an initialized ModelInstallService object."""
record_store = initialize_record_store(app_config)
download_queue = DownloadQueueService()
installer = ModelInstallService(
app_config=app_config,
record_store=record_store,
download_queue=download_queue,
event_bus=event_bus,
)
download_queue.start()
installer.start()
return installer
class UnifiedModelInfo(BaseModel):
"""Catchall class for information in INITIAL_MODELS2.yaml."""
name: Optional[str] = None
base: Optional[BaseModelType] = None
type: Optional[ModelType] = None
source: Optional[str] = None
subfolder: Optional[str] = None
description: Optional[str] = None
recommended: bool = False
installed: bool = False
default: bool = False
requires: List[str] = Field(default_factory=list)
@dataclass
class InstallSelections:
"""Lists of models to install and remove."""
install_models: List[UnifiedModelInfo] = Field(default_factory=list)
remove_models: List[str] = Field(default_factory=list)
class TqdmEventService(EventServiceBase):
"""An event service to track downloads."""
def __init__(self) -> None:
"""Create a new TqdmEventService object."""
super().__init__()
self._bars: Dict[str, tqdm] = {}
self._last: Dict[str, int] = {}
self._logger = InvokeAILogger.get_logger(__name__)
def dispatch(self, event_name: str, payload: Any) -> None:
"""Dispatch an event by appending it to self.events."""
data = payload["data"]
source = data["source"]
if payload["event"] == "model_install_downloading":
dest = data["local_path"]
total_bytes = data["total_bytes"]
bytes = data["bytes"]
if dest not in self._bars:
self._bars[dest] = tqdm(desc=Path(dest).name, initial=0, total=total_bytes, unit="iB", unit_scale=True)
self._last[dest] = 0
self._bars[dest].update(bytes - self._last[dest])
self._last[dest] = bytes
elif payload["event"] == "model_install_completed":
self._logger.info(f"{source}: installed successfully.")
elif payload["event"] == "model_install_error":
self._logger.warning(f"{source}: installation failed with error {data['error']}")
elif payload["event"] == "model_install_cancelled":
self._logger.warning(f"{source}: installation cancelled")
class InstallHelper(object):
"""Capture information stored jointly in INITIAL_MODELS.yaml and the installed models db."""
def __init__(self, app_config: InvokeAIAppConfig, logger: Logger):
"""Create new InstallHelper object."""
self._app_config = app_config
self.all_models: Dict[str, UnifiedModelInfo] = {}
omega = omegaconf.OmegaConf.load(Path(configs.__path__[0]) / INITIAL_MODELS)
assert isinstance(omega, omegaconf.dictconfig.DictConfig)
self._installer = initialize_installer(app_config, TqdmEventService())
self._initial_models = omega
self._installed_models: List[str] = []
self._starter_models: List[str] = []
self._default_model: Optional[str] = None
self._logger = logger
self._initialize_model_lists()
@property
def installer(self) -> ModelInstallServiceBase:
"""Return the installer object used internally."""
return self._installer
def _initialize_model_lists(self) -> None:
"""
Initialize our model slots.
Set up the following:
installed_models -- list of installed model keys
starter_models -- list of starter model keys from INITIAL_MODELS
all_models -- dict of key => UnifiedModelInfo
default_model -- key to default model
"""
# previously-installed models
for model in self._installer.record_store.all_models():
info = UnifiedModelInfo.model_validate(model.model_dump())
info.installed = True
model_key = f"{model.base.value}/{model.type.value}/{model.name}"
self.all_models[model_key] = info
self._installed_models.append(model_key)
for key in self._initial_models.keys():
assert isinstance(key, str)
if key in self.all_models:
# we want to preserve the description
description = self.all_models[key].description or self._initial_models[key].get("description")
self.all_models[key].description = description
else:
base_model, model_type, model_name = key.split("/")
info = UnifiedModelInfo(
name=model_name,
type=ModelType(model_type),
base=BaseModelType(base_model),
source=self._initial_models[key].source,
description=self._initial_models[key].get("description"),
recommended=self._initial_models[key].get("recommended", False),
default=self._initial_models[key].get("default", False),
subfolder=self._initial_models[key].get("subfolder"),
requires=list(self._initial_models[key].get("requires", [])),
)
self.all_models[key] = info
if not self.default_model():
self._default_model = key
elif self._initial_models[key].get("default", False):
self._default_model = key
self._starter_models.append(key)
# previously-installed models
for model in self._installer.record_store.all_models():
info = UnifiedModelInfo.model_validate(model.model_dump())
info.installed = True
model_key = f"{model.base.value}/{model.type.value}/{model.name}"
self.all_models[model_key] = info
self._installed_models.append(model_key)
def recommended_models(self) -> List[UnifiedModelInfo]:
"""List of the models recommended in INITIAL_MODELS.yaml."""
return [self._to_model(x) for x in self._starter_models if self._to_model(x).recommended]
def installed_models(self) -> List[UnifiedModelInfo]:
"""List of models already installed."""
return [self._to_model(x) for x in self._installed_models]
def starter_models(self) -> List[UnifiedModelInfo]:
"""List of starter models."""
return [self._to_model(x) for x in self._starter_models]
def default_model(self) -> Optional[UnifiedModelInfo]:
"""Return the default model."""
return self._to_model(self._default_model) if self._default_model else None
def _to_model(self, key: str) -> UnifiedModelInfo:
return self.all_models[key]
def _add_required_models(self, model_list: List[UnifiedModelInfo]) -> None:
installed = {x.source for x in self.installed_models()}
reverse_source = {x.source: x for x in self.all_models.values()}
additional_models: List[UnifiedModelInfo] = []
for model_info in model_list:
for requirement in model_info.requires:
if requirement not in installed and reverse_source.get(requirement):
additional_models.append(reverse_source[requirement])
model_list.extend(additional_models)
def add_or_delete(self, selections: InstallSelections) -> None:
"""Add or delete selected models."""
installer = self._installer
self._add_required_models(selections.install_models)
for model in selections.install_models:
assert model.source
model_path_id_or_url = model.source.strip("\"' ")
config = (
{
"description": model.description,
"name": model.name,
}
if model.name
else None
)
try:
installer.heuristic_import(
source=model_path_id_or_url,
config=config,
)
except (UnknownMetadataException, InvalidModelConfigException, HTTPError, OSError) as e:
self._logger.warning(f"{model.source}: {e}")
for model_to_remove in selections.remove_models:
parts = model_to_remove.split("/")
if len(parts) == 1:
base_model, model_type, model_name = (None, None, model_to_remove)
else:
base_model, model_type, model_name = parts
matches = installer.record_store.search_by_attr(
base_model=BaseModelType(base_model) if base_model else None,
model_type=ModelType(model_type) if model_type else None,
model_name=model_name,
)
if len(matches) > 1:
self._logger.error(
"{model_to_remove} is ambiguous. Please use model_base/model_type/model_name (e.g. sd-1/main/my_model) to disambiguate"
)
elif not matches:
self._logger.error(f"{model_to_remove}: unknown model")
else:
for m in matches:
self._logger.info(f"Deleting {m.type}:{m.name}")
installer.delete(m.key)
installer.wait_for_installs()

837
invokeai/backend/install/invokeai_configure.py
View File

@ -1,837 +0,0 @@
#!/usr/bin/env python
# Copyright (c) 2022 Lincoln D. Stein (https://github.com/lstein)
# Before running stable-diffusion on an internet-isolated machine,
# run this script from one with internet connectivity. The
# two machines must share a common .cache directory.
#
# Coauthor: Kevin Turner http://github.com/keturn
#
import io
import os
import shutil
import sys
import textwrap
import traceback
import warnings
from argparse import Namespace
from enum import Enum
from pathlib import Path
from shutil import copy, get_terminal_size, move
from typing import Any, Optional, Tuple, Type, get_args, get_type_hints
from urllib import request
import npyscreen
import psutil
import torch
import transformers
from diffusers import ModelMixin
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from huggingface_hub import HfFolder
from huggingface_hub import login as hf_hub_login
from tqdm import tqdm
from transformers import AutoFeatureExtractor
import invokeai.configs as model_configs
from invokeai.app.services.config import InvokeAIAppConfig, get_config
from invokeai.backend.install.install_helper import InstallHelper, InstallSelections
from invokeai.backend.model_manager import ModelType
from invokeai.backend.util import choose_precision, choose_torch_device
from invokeai.backend.util.logging import InvokeAILogger
from invokeai.frontend.install.model_install import addModelsForm
# TO DO - Move all the frontend code into invokeai.frontend.install
from invokeai.frontend.install.widgets import (
MIN_COLS,
MIN_LINES,
CenteredButtonPress,
CyclingForm,
FileBox,
MultiSelectColumns,
SingleSelectColumnsSimple,
WindowTooSmallException,
set_min_terminal_size,
)
warnings.filterwarnings("ignore")
transformers.logging.set_verbosity_error()
def get_literal_fields(field: str) -> Tuple[Any]:
return get_args(get_type_hints(InvokeAIAppConfig).get(field))
# --------------------------globals-----------------------
config = None
PRECISION_CHOICES = get_literal_fields("precision")
DEVICE_CHOICES = get_literal_fields("device")
ATTENTION_CHOICES = get_literal_fields("attention_type")
ATTENTION_SLICE_CHOICES = get_literal_fields("attention_slice_size")
GENERATION_OPT_CHOICES = ["sequential_guidance", "force_tiled_decode", "lazy_offload"]
GB = 1073741824 # GB in bytes
HAS_CUDA = torch.cuda.is_available()
_, MAX_VRAM = torch.cuda.mem_get_info() if HAS_CUDA else (0.0, 0.0)
MAX_VRAM /= GB
MAX_RAM = psutil.virtual_memory().total / GB
FORCE_FULL_PRECISION = False
INIT_FILE_PREAMBLE = """# 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.
"""
logger = InvokeAILogger.get_logger()
class DummyWidgetValue(Enum):
"""Dummy widget values."""
zero = 0
true = True
false = False
# --------------------------------------------
def postscript(errors: set[str]) -> None:
if not any(errors):
message = f"""
** INVOKEAI INSTALLATION SUCCESSFUL **
If you installed manually from source or with 'pip install': activate the virtual environment
then run one of the following commands to start InvokeAI.
Web UI:
invokeai-web
If you installed using an installation script, run:
{config.root_path}/invoke.{"bat" if sys.platform == "win32" else "sh"}
Add the '--help' argument to see all of the command-line switches available for use.
"""
else:
message = (
"\n** There were errors during installation. It is possible some of the models were not fully downloaded.\n"
)
for err in errors:
message += f"\t - {err}\n"
message += "Please check the logs above and correct any issues."
print(message)
# ---------------------------------------------
def yes_or_no(prompt: str, default_yes=True):
default = "y" if default_yes else "n"
response = input(f"{prompt} [{default}] ") or default
if default_yes:
return response[0] not in ("n", "N")
else:
return response[0] in ("y", "Y")
# ---------------------------------------------
def HfLogin(access_token) -> None:
"""
Helper for logging in to Huggingface
The stdout capture is needed to hide the irrelevant "git credential helper" warning
"""
capture = io.StringIO()
sys.stdout = capture
try:
hf_hub_login(token=access_token, add_to_git_credential=False)
sys.stdout = sys.__stdout__
except Exception as exc:
sys.stdout = sys.__stdout__
print(exc)
raise exc
# -------------------------------------
class ProgressBar:
def __init__(self, model_name: str = "file"):
self.pbar = None
self.name = model_name
def __call__(self, block_num, block_size, total_size):
if not self.pbar:
self.pbar = tqdm(
desc=self.name,
initial=0,
unit="iB",
unit_scale=True,
unit_divisor=1000,
total=total_size,
)
self.pbar.update(block_size)
# ---------------------------------------------
def hf_download_from_pretrained(model_class: Type[ModelMixin], model_name: str, destination: Path, **kwargs: Any):
filter = lambda x: "fp16 is not a valid" not in x.getMessage() # noqa E731
logger.addFilter(filter)
try:
model = model_class.from_pretrained(
model_name,
resume_download=True,
**kwargs,
)
model.save_pretrained(destination, safe_serialization=True)
finally:
logger.removeFilter(filter)
return destination
# ---------------------------------------------
def download_with_progress_bar(model_url: str, model_dest: str | Path, label: str = "the"):
try:
logger.info(f"Installing {label} model file {model_url}...")
if not os.path.exists(model_dest):
os.makedirs(os.path.dirname(model_dest), exist_ok=True)
request.urlretrieve(model_url, model_dest, ProgressBar(os.path.basename(model_dest)))
logger.info("...downloaded successfully")
else:
logger.info("...exists")
except Exception:
logger.info("...download failed")
logger.info(f"Error downloading {label} model")
print(traceback.format_exc(), file=sys.stderr)
def download_safety_checker():
target_dir = config.models_path / "core/convert"
kwargs = {} # for future use
try:
# safety checking
logger.info("Downloading safety checker")
repo_id = "CompVis/stable-diffusion-safety-checker"
pipeline = AutoFeatureExtractor.from_pretrained(repo_id, **kwargs)
pipeline.save_pretrained(target_dir / "stable-diffusion-safety-checker", safe_serialization=True)
pipeline = StableDiffusionSafetyChecker.from_pretrained(repo_id, **kwargs)
pipeline.save_pretrained(target_dir / "stable-diffusion-safety-checker", safe_serialization=True)
except KeyboardInterrupt:
raise
except Exception as e:
logger.error(str(e))
# ---------------------------------------------
# TO DO: use the download queue here.
def download_realesrgan():
logger.info("Installing ESRGAN Upscaling models...")
URLs = [
{
"url": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth",
"dest": "core/upscaling/realesrgan/RealESRGAN_x4plus.pth",
"description": "RealESRGAN_x4plus.pth",
},
{
"url": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.2.4/RealESRGAN_x4plus_anime_6B.pth",
"dest": "core/upscaling/realesrgan/RealESRGAN_x4plus_anime_6B.pth",
"description": "RealESRGAN_x4plus_anime_6B.pth",
},
{
"url": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.1/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth",
"dest": "core/upscaling/realesrgan/ESRGAN_SRx4_DF2KOST_official-ff704c30.pth",
"description": "ESRGAN_SRx4_DF2KOST_official.pth",
},
{
"url": "https://github.com/xinntao/Real-ESRGAN/releases/download/v0.2.1/RealESRGAN_x2plus.pth",
"dest": "core/upscaling/realesrgan/RealESRGAN_x2plus.pth",
"description": "RealESRGAN_x2plus.pth",
},
]
for model in URLs:
download_with_progress_bar(model["url"], config.models_path / model["dest"], model["description"])
# ---------------------------------------------
def download_lama():
logger.info("Installing lama infill model")
download_with_progress_bar(
"https://github.com/Sanster/models/releases/download/add_big_lama/big-lama.pt",
config.models_path / "core/misc/lama/lama.pt",
"lama infill model",
)
# ---------------------------------------------
def download_support_models() -> None:
download_realesrgan()
download_lama()
download_safety_checker()
# -------------------------------------
def get_root(root: Optional[str] = None) -> str:
if root:
return root
elif root := os.environ.get("INVOKEAI_ROOT"):
assert root is not None
return root
else:
return str(config.root_path)
# -------------------------------------
class editOptsForm(CyclingForm, npyscreen.FormMultiPage):
# for responsive resizing - disabled
# FIX_MINIMUM_SIZE_WHEN_CREATED = False
def create(self):
program_opts = self.parentApp.program_opts
old_opts: InvokeAIAppConfig = self.parentApp.invokeai_opts
first_time = not (config.root_path / "invokeai.yaml").exists()
access_token = HfFolder.get_token()
window_width, window_height = get_terminal_size()
label = """Configure startup settings. You can come back and change these later.
Use ctrl-N and ctrl-P to move to the <N>ext and <P>revious fields.
Use cursor arrows to make a checkbox selection, and space to toggle.
"""
self.nextrely -= 1
for i in textwrap.wrap(label, width=window_width - 6):
self.add_widget_intelligent(
npyscreen.FixedText,
value=i,
editable=False,
color="CONTROL",
)
self.nextrely += 1
label = """HuggingFace access token (OPTIONAL) for automatic model downloads. See https://huggingface.co/settings/tokens."""
for line in textwrap.wrap(label, width=window_width - 6):
self.add_widget_intelligent(
npyscreen.FixedText,
value=line,
editable=False,
color="CONTROL",
)
self.hf_token = self.add_widget_intelligent(
npyscreen.TitlePassword,
name="Access Token (ctrl-shift-V pastes):",
value=access_token,
begin_entry_at=42,
use_two_lines=False,
scroll_exit=True,
)
# old settings for defaults
precision = old_opts.precision or ("float32" if program_opts.full_precision else "auto")
device = old_opts.device
attention_type = old_opts.attention_type
attention_slice_size = old_opts.attention_slice_size
self.nextrely += 1
self.add_widget_intelligent(
npyscreen.TitleFixedText,
name="Image Generation Options:",
editable=False,
color="CONTROL",
scroll_exit=True,
)
self.nextrely -= 2
self.generation_options = self.add_widget_intelligent(
MultiSelectColumns,
columns=3,
values=GENERATION_OPT_CHOICES,
value=[GENERATION_OPT_CHOICES.index(x) for x in GENERATION_OPT_CHOICES if getattr(old_opts, x)],
relx=30,
max_height=2,
max_width=80,
scroll_exit=True,
)
self.add_widget_intelligent(
npyscreen.TitleFixedText,
name="Floating Point Precision:",
begin_entry_at=0,
editable=False,
color="CONTROL",
scroll_exit=True,
)
self.nextrely -= 2
self.precision = self.add_widget_intelligent(
SingleSelectColumnsSimple,
columns=len(PRECISION_CHOICES),
name="Precision",
values=PRECISION_CHOICES,
value=PRECISION_CHOICES.index(precision),
begin_entry_at=3,
max_height=2,
relx=30,
max_width=80,
scroll_exit=True,
)
self.add_widget_intelligent(
npyscreen.TitleFixedText,
name="Generation Device:",
begin_entry_at=0,
editable=False,
color="CONTROL",
scroll_exit=True,
)
self.nextrely -= 2
self.device = self.add_widget_intelligent(
SingleSelectColumnsSimple,
columns=len(DEVICE_CHOICES),
values=DEVICE_CHOICES,
value=[DEVICE_CHOICES.index(device)],
begin_entry_at=3,
relx=30,
max_height=2,
max_width=60,
scroll_exit=True,
)
self.add_widget_intelligent(
npyscreen.TitleFixedText,
name="Attention Type:",
begin_entry_at=0,
editable=False,
color="CONTROL",
scroll_exit=True,
)
self.nextrely -= 2
self.attention_type = self.add_widget_intelligent(
SingleSelectColumnsSimple,
columns=len(ATTENTION_CHOICES),
values=ATTENTION_CHOICES,
value=[ATTENTION_CHOICES.index(attention_type)],
begin_entry_at=3,
max_height=2,
relx=30,
max_width=80,
scroll_exit=True,
)
self.attention_type.on_changed = self.show_hide_slice_sizes
self.attention_slice_label = self.add_widget_intelligent(
npyscreen.TitleFixedText,
name="Attention Slice Size:",
relx=5,
editable=False,
hidden=attention_type != "sliced",
color="CONTROL",
scroll_exit=True,
)
self.nextrely -= 2
self.attention_slice_size = self.add_widget_intelligent(
SingleSelectColumnsSimple,
columns=len(ATTENTION_SLICE_CHOICES),
values=ATTENTION_SLICE_CHOICES,
value=[ATTENTION_SLICE_CHOICES.index(attention_slice_size)],
relx=30,
hidden=attention_type != "sliced",
max_height=2,
max_width=110,
scroll_exit=True,
)
self.add_widget_intelligent(
npyscreen.TitleFixedText,
name="Model disk conversion cache size (GB). This is used to cache safetensors files that need to be converted to diffusers..",
begin_entry_at=0,
editable=False,
color="CONTROL",
scroll_exit=True,
)
self.nextrely -= 1
self.disk = self.add_widget_intelligent(
npyscreen.Slider,
value=clip(old_opts.convert_cache, range=(0, 100), step=0.5),
out_of=100,
lowest=0.0,
step=0.5,
relx=8,
scroll_exit=True,
)
self.nextrely += 1
self.add_widget_intelligent(
npyscreen.TitleFixedText,
name="Model RAM cache size (GB). Make this at least large enough to hold a single full model (2GB for SD-1, 6GB for SDXL).",
begin_entry_at=0,
editable=False,
color="CONTROL",
scroll_exit=True,
)
self.nextrely -= 1
self.ram = self.add_widget_intelligent(
npyscreen.Slider,
value=clip(old_opts.ram, range=(3.0, MAX_RAM), step=0.5),
out_of=round(MAX_RAM),
lowest=0.0,
step=0.5,
relx=8,
scroll_exit=True,
)
if HAS_CUDA:
self.nextrely += 1
self.add_widget_intelligent(
npyscreen.TitleFixedText,
name="Model VRAM cache size (GB). Reserving a small amount of VRAM will modestly speed up the start of image generation.",
begin_entry_at=0,
editable=False,
color="CONTROL",
scroll_exit=True,
)
self.nextrely -= 1
self.vram = self.add_widget_intelligent(
npyscreen.Slider,
value=clip(old_opts.vram, range=(0, MAX_VRAM), step=0.25),
out_of=round(MAX_VRAM * 2) / 2,
lowest=0.0,
relx=8,
step=0.25,
scroll_exit=True,
)
else:
self.vram = DummyWidgetValue.zero
self.nextrely += 1
self.add_widget_intelligent(
npyscreen.FixedText,
value="Location of the database used to store model path and configuration information:",
editable=False,
color="CONTROL",
)
self.nextrely += 1
self.outdir = self.add_widget_intelligent(
FileBox,
name="Output directory for images (<tab> autocompletes, ctrl-N advances):",
value=str(default_output_dir()),
select_dir=True,
must_exist=False,
use_two_lines=False,
labelColor="GOOD",
begin_entry_at=40,
max_height=3,
max_width=127,
scroll_exit=True,
)
self.autoimport_dirs = {}
self.autoimport_dirs["autoimport_dir"] = self.add_widget_intelligent(
FileBox,
name="Optional folder to scan for new checkpoints, ControlNets, LoRAs and TI models",
value=str(config.autoimport_path),
select_dir=True,
must_exist=False,
use_two_lines=False,
labelColor="GOOD",
begin_entry_at=32,
max_height=3,
max_width=127,
scroll_exit=True,
)
self.nextrely += 1
label = """BY DOWNLOADING THE STABLE DIFFUSION WEIGHT FILES, YOU AGREE TO HAVE READ
AND ACCEPTED THE CREATIVEML RESPONSIBLE AI LICENSES LOCATED AT
https://huggingface.co/spaces/CompVis/stable-diffusion-license and
https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0/blob/main/LICENSE.md
"""
for i in textwrap.wrap(label, width=window_width - 6):
self.add_widget_intelligent(
npyscreen.FixedText,
value=i,
editable=False,
color="CONTROL",
)
self.license_acceptance = self.add_widget_intelligent(
npyscreen.Checkbox,
name="I accept the CreativeML Responsible AI Licenses",
value=not first_time,
relx=2,
scroll_exit=True,
)
self.nextrely += 1
label = "DONE" if program_opts.skip_sd_weights or program_opts.default_only else "NEXT"
self.ok_button = self.add_widget_intelligent(
CenteredButtonPress,
name=label,
relx=(window_width - len(label)) // 2,
when_pressed_function=self.on_ok,
)
def show_hide_slice_sizes(self, value):
show = ATTENTION_CHOICES[value[0]] == "sliced"
self.attention_slice_label.hidden = not show
self.attention_slice_size.hidden = not show
def show_hide_model_conf_override(self, value):
self.model_conf_override.hidden = value
self.model_conf_override.display()
def on_ok(self):
options = self.marshall_arguments()
if self.validate_field_values(options):
self.parentApp.new_opts = options
if hasattr(self.parentApp, "model_select"):
self.parentApp.setNextForm("MODELS")
else:
self.parentApp.setNextForm(None)
self.editing = False
else:
self.editing = True
def validate_field_values(self, opt: Namespace) -> bool:
bad_fields = []
if not opt.license_acceptance:
bad_fields.append("Please accept the license terms before proceeding to model downloads")
if not Path(opt.outdir).parent.exists():
bad_fields.append(
f"The output directory does not seem to be valid. Please check that {str(Path(opt.outdir).parent)} is an existing directory."
)
if len(bad_fields) > 0:
message = "The following problems were detected and must be corrected:\n"
for problem in bad_fields:
message += f"* {problem}\n"
npyscreen.notify_confirm(message)
return False
else:
return True
def marshall_arguments(self) -> Namespace:
new_opts = Namespace()
for attr in [
"ram",
"vram",
"convert_cache",
"outdir",
]:
if hasattr(self, attr):
setattr(new_opts, attr, getattr(self, attr).value)
for attr in self.autoimport_dirs:
if not self.autoimport_dirs[attr].value:
continue
directory = Path(self.autoimport_dirs[attr].value)
if directory.is_relative_to(config.root_path):
directory = directory.relative_to(config.root_path)
setattr(new_opts, attr, directory)
new_opts.hf_token = self.hf_token.value
new_opts.license_acceptance = self.license_acceptance.value
new_opts.precision = PRECISION_CHOICES[self.precision.value[0]]
new_opts.device = DEVICE_CHOICES[self.device.value[0]]
new_opts.attention_type = ATTENTION_CHOICES[self.attention_type.value[0]]
new_opts.attention_slice_size = ATTENTION_SLICE_CHOICES[self.attention_slice_size.value[0]]
generation_options = [GENERATION_OPT_CHOICES[x] for x in self.generation_options.value]
for v in GENERATION_OPT_CHOICES:
setattr(new_opts, v, v in generation_options)
return new_opts
class EditOptApplication(npyscreen.NPSAppManaged):
def __init__(self, program_opts: Namespace, invokeai_opts: InvokeAIAppConfig, install_helper: InstallHelper):
super().__init__()
self.program_opts = program_opts
self.invokeai_opts = invokeai_opts
self.user_cancelled = False
self.autoload_pending = True
self.install_helper = install_helper
self.install_selections = default_user_selections(program_opts, install_helper)
def onStart(self):
npyscreen.setTheme(npyscreen.Themes.DefaultTheme)
self.options = self.addForm(
"MAIN",
editOptsForm,
name="InvokeAI Startup Options",
cycle_widgets=False,
)
if not (self.program_opts.skip_sd_weights or self.program_opts.default_only):
self.model_select = self.addForm(
"MODELS",
addModelsForm,
name="Install Stable Diffusion Models",
multipage=True,
cycle_widgets=False,
)
def get_default_ram_cache_size() -> float:
"""Run a heuristic for the default RAM cache based on installed RAM."""
# Note that on my 64 GB machine, psutil.virtual_memory().total gives 62 GB,
# So we adjust everthing down a bit.
return (
15.0 if MAX_RAM >= 60 else 7.5 if MAX_RAM >= 30 else 4 if MAX_RAM >= 14 else 2.1
) # 2.1 is just large enough for sd 1.5 ;-)
def get_default_config() -> InvokeAIAppConfig:
"""Builds a new config object, setting the ram and precision using the appropriate heuristic."""
config = InvokeAIAppConfig()
config.ram = get_default_ram_cache_size()
config.precision = "float32" if FORCE_FULL_PRECISION else choose_precision(torch.device(choose_torch_device()))
return config
def default_user_selections(program_opts: Namespace, install_helper: InstallHelper) -> InstallSelections:
default_model = install_helper.default_model()
assert default_model is not None
default_models = [default_model] if program_opts.default_only else install_helper.recommended_models()
return InstallSelections(
install_models=default_models if program_opts.yes_to_all else [],
)
# -------------------------------------
def clip(value: float, range: tuple[float, float], step: float) -> float:
minimum, maximum = range
if value < minimum:
value = minimum
if value > maximum:
value = maximum
return round(value / step) * step
# -------------------------------------
def initialize_rootdir(root: Path, yes_to_all: bool = False):
logger.info("Initializing InvokeAI runtime directory")
for name in ("models", "databases", "text-inversion-output", "text-inversion-training-data", "configs"):
os.makedirs(os.path.join(root, name), exist_ok=True)
for model_type in ModelType:
Path(root, "autoimport", model_type.value).mkdir(parents=True, exist_ok=True)
configs_src = Path(model_configs.__path__[0])
configs_dest = root / "configs"
if not os.path.samefile(configs_src, configs_dest):
shutil.copytree(configs_src, configs_dest, dirs_exist_ok=True)
dest = root / "models"
dest.mkdir(parents=True, exist_ok=True)
# -------------------------------------
def run_console_ui(
program_opts: Namespace, install_helper: InstallHelper
) -> Tuple[Optional[Namespace], Optional[InstallSelections]]:
first_time = not config.init_file_path.exists()
config_opts = get_default_config() if first_time else config
if program_opts.root:
config_opts.set_root(Path(program_opts.root))
if not set_min_terminal_size(MIN_COLS, MIN_LINES):
raise WindowTooSmallException(
"Could not increase terminal size. Try running again with a larger window or smaller font size."
)
editApp = EditOptApplication(program_opts, config_opts, install_helper)
editApp.run()
if editApp.user_cancelled:
return (None, None)
else:
return (editApp.new_opts, editApp.install_selections)
# -------------------------------------
def default_output_dir() -> Path:
return config.root_path / "outputs"
def is_v2_install(root: Path) -> bool:
# We check for to see if the runtime directory is correctly initialized.
old_init_file = root / "invokeai.init"
new_init_file = root / "invokeai.yaml"
old_hub = root / "models/hub"
is_v2 = (old_init_file.exists() and not new_init_file.exists()) and old_hub.exists()
return is_v2
# -------------------------------------
def main(opt: Namespace) -> None:
global FORCE_FULL_PRECISION # FIXME
global config
updates: dict[str, Any] = {}
config = get_config()
if opt.full_precision:
updates["precision"] = "float32"
try:
# Attempt to read the config file into the config object
config.merge_from_file()
except FileNotFoundError:
# No config file, first time running the app
pass
config.update_config(updates)
logger = InvokeAILogger().get_logger(config=config)
errors: set[str] = set()
FORCE_FULL_PRECISION = opt.full_precision # FIXME global
# Before we write anything else, make a backup of the existing init file
new_init_file = config.init_file_path
backup_init_file = new_init_file.with_suffix(".bak")
if new_init_file.exists():
copy(new_init_file, backup_init_file)
try:
# v2.3 -> v4.0.0 upgrade is no longer supported
if is_v2_install(config.root_path):
logger.error("Migration from v2.3 to v4.0.0 is no longer supported. Please install a fresh copy.")
sys.exit(0)
# run this unconditionally in case new directories need to be added
initialize_rootdir(config.root_path, opt.yes_to_all)
# this will initialize and populate the models tables if not present
install_helper = InstallHelper(config, logger)
models_to_download = default_user_selections(opt, install_helper)
if opt.yes_to_all:
# We will not show the UI - just write the default config to the file and move on to installing models.
get_default_config().write_file(new_init_file)
else:
# Run the UI to get the user's options & model choices
user_opts, models_to_download = run_console_ui(opt, install_helper)
if user_opts:
# Create a dict of the user's opts, omitting any fields that are not config settings (like `hf_token`)
user_opts_dict = {k: v for k, v in vars(user_opts).items() if k in config.model_fields}
# Merge the user's opts back into the config object & write it
config.update_config(user_opts_dict)
config.write_file(config.init_file_path)
if hasattr(user_opts, "hf_token") and user_opts.hf_token:
HfLogin(user_opts.hf_token)
else:
logger.info('\n** CANCELLED AT USER\'S REQUEST. USE THE "invoke.sh" LAUNCHER TO RUN LATER **\n')
sys.exit(0)
if opt.skip_support_models:
logger.info("Skipping support models at user's request")
else:
logger.info("Installing support models")
download_support_models()
if opt.skip_sd_weights:
logger.warning("Skipping diffusion weights download per user request")
elif models_to_download:
install_helper.add_or_delete(models_to_download)
postscript(errors=errors)
if not opt.yes_to_all:
input("Press any key to continue...")
except WindowTooSmallException as e:
logger.error(str(e))
if backup_init_file.exists():
move(backup_init_file, new_init_file)
except KeyboardInterrupt:
print("\nGoodbye! Come back soon.")
if backup_init_file.exists():
move(backup_init_file, new_init_file)
except Exception:
print("An error occurred during installation.")
if backup_init_file.exists():
move(backup_init_file, new_init_file)
print(traceback.format_exc(), file=sys.stderr)
# -------------------------------------
if __name__ == "__main__":
main()

379
invokeai/backend/install/legacy_arg_parsing.py
View File

@ -1,379 +0,0 @@
# Copyright 2023 Lincoln D. Stein and the InvokeAI Team
import argparse
import shlex
from argparse import ArgumentParser
# note that this includes both old sampler names and new scheduler names
# in order to be able to parse both 2.0 and 3.0-pre-nodes versions of invokeai.init
SAMPLER_CHOICES = [
"ddim",
"ddpm",
"deis",
"lms",
"lms_k",
"pndm",
"heun",
"heun_k",
"euler",
"euler_k",
"euler_a",
"kdpm_2",
"kdpm_2_a",
"dpmpp_2s",
"dpmpp_2s_k",
"dpmpp_2m",
"dpmpp_2m_k",
"dpmpp_2m_sde",
"dpmpp_2m_sde_k",
"dpmpp_sde",
"dpmpp_sde_k",
"unipc",
"k_dpm_2_a",
"k_dpm_2",
"k_dpmpp_2_a",
"k_dpmpp_2",
"k_euler_a",
"k_euler",
"k_heun",
"k_lms",
"plms",
"lcm",
]
PRECISION_CHOICES = [
"auto",
"float32",
"autocast",
"float16",
]
class FileArgumentParser(ArgumentParser):
"""
Supports reading defaults from an init file.
"""
def convert_arg_line_to_args(self, arg_line):
return shlex.split(arg_line, comments=True)
legacy_parser = FileArgumentParser(
description="""
Generate images using Stable Diffusion.
Use --web to launch the web interface.
Use --from_file to load prompts from a file path or standard input ("-").
Otherwise you will be dropped into an interactive command prompt (type -h for help.)
Other command-line arguments are defaults that can usually be overridden
prompt the command prompt.
""",
fromfile_prefix_chars="@",
)
general_group = legacy_parser.add_argument_group("General")
model_group = legacy_parser.add_argument_group("Model selection")
file_group = legacy_parser.add_argument_group("Input/output")
web_server_group = legacy_parser.add_argument_group("Web server")
render_group = legacy_parser.add_argument_group("Rendering")
postprocessing_group = legacy_parser.add_argument_group("Postprocessing")
deprecated_group = legacy_parser.add_argument_group("Deprecated options")
deprecated_group.add_argument("--laion400m")
deprecated_group.add_argument("--weights") # deprecated
general_group.add_argument("--version", "-V", action="store_true", help="Print InvokeAI version number")
model_group.add_argument(
"--root_dir",
default=None,
help='Path to directory containing "models", "outputs" and "configs". If not present will read from environment variable INVOKEAI_ROOT. Defaults to ~/invokeai.',
)
model_group.add_argument(
"--config",
"-c",
"-config",
dest="conf",
default="./configs/models.yaml",
help="Path to configuration file for alternate models.",
)
model_group.add_argument(
"--model",
help='Indicates which diffusion model to load (defaults to "default" stanza in configs/models.yaml)',
)
model_group.add_argument(
"--weight_dirs",
nargs="+",
type=str,
help="List of one or more directories that will be auto-scanned for new model weights to import",
)
model_group.add_argument(
"--png_compression",
"-z",
type=int,
default=6,
choices=range(0, 9),
dest="png_compression",
help="level of PNG compression, from 0 (none) to 9 (maximum). Default is 6.",
)
model_group.add_argument(
"-F",
"--full_precision",
dest="full_precision",
action="store_true",
help="Deprecated way to set --precision=float32",
)
model_group.add_argument(
"--max_loaded_models",
dest="max_loaded_models",
type=int,
default=2,
help="Maximum number of models to keep in memory for fast switching, including the one in GPU",
)
model_group.add_argument(
"--free_gpu_mem",
dest="free_gpu_mem",
action="store_true",
help="Force free gpu memory before final decoding",
)
model_group.add_argument(
"--sequential_guidance",
dest="sequential_guidance",
action="store_true",
help="Calculate guidance in serial instead of in parallel, lowering memory requirement " "at the expense of speed",
)
model_group.add_argument(
"--xformers",
action=argparse.BooleanOptionalAction,
default=True,
help="Enable/disable xformers support (default enabled if installed)",
)
model_group.add_argument(
"--always_use_cpu", dest="always_use_cpu", action="store_true", help="Force use of CPU even if GPU is available"
)
model_group.add_argument(
"--precision",
dest="precision",
type=str,
choices=PRECISION_CHOICES,
metavar="PRECISION",
help=f'Set model precision. Defaults to auto selected based on device. Options: {", ".join(PRECISION_CHOICES)}',
default="auto",
)
model_group.add_argument(
"--ckpt_convert",
action=argparse.BooleanOptionalAction,
dest="ckpt_convert",
default=True,
help="Deprecated option. Legacy ckpt files are now always converted to diffusers when loaded.",
)
model_group.add_argument(
"--internet",
action=argparse.BooleanOptionalAction,
dest="internet_available",
default=True,
help="Indicate whether internet is available for just-in-time model downloading (default: probe automatically).",
)
model_group.add_argument(
"--nsfw_checker",
"--safety_checker",
action=argparse.BooleanOptionalAction,
dest="safety_checker",
default=False,
help="Check for and blur potentially NSFW images. Use --no-nsfw_checker to disable.",
)
model_group.add_argument(
"--autoimport",
default=None,
type=str,
help="Check the indicated directory for .ckpt/.safetensors weights files at startup and import directly",
)
model_group.add_argument(
"--autoconvert",
default=None,
type=str,
help="Check the indicated directory for .ckpt/.safetensors weights files at startup and import as optimized diffuser models",
)
model_group.add_argument(
"--patchmatch",
action=argparse.BooleanOptionalAction,
default=True,
help="Load the patchmatch extension for outpainting. Use --no-patchmatch to disable.",
)
file_group.add_argument(
"--from_file",
dest="infile",
type=str,
help="If specified, load prompts from this file",
)
file_group.add_argument(
"--outdir",
"-o",
type=str,
help="Directory to save generated images and a log of prompts and seeds. Default: ROOTDIR/outputs",
default="outputs",
)
file_group.add_argument(
"--prompt_as_dir",
"-p",
action="store_true",
help="Place images in subdirectories named after the prompt.",
)
render_group.add_argument(
"--fnformat",
default="{prefix}.{seed}.png",
type=str,
help="Overwrite the filename format. You can use any argument as wildcard enclosed in curly braces. Default is {prefix}.{seed}.png",
)
render_group.add_argument("-s", "--steps", type=int, default=50, help="Number of steps")
render_group.add_argument(
"-W",
"--width",
type=int,
help="Image width, multiple of 64",
)
render_group.add_argument(
"-H",
"--height",
type=int,
help="Image height, multiple of 64",
)
render_group.add_argument(
"-C",
"--cfg_scale",
default=7.5,
type=float,
help='Classifier free guidance (CFG) scale - higher numbers cause generator to "try" harder.',
)
render_group.add_argument(
"--sampler",
"-A",
"-m",
dest="sampler_name",
type=str,
choices=SAMPLER_CHOICES,
metavar="SAMPLER_NAME",
help=f'Set the default sampler. Supported samplers: {", ".join(SAMPLER_CHOICES)}',
default="k_lms",
)
render_group.add_argument(
"--log_tokenization", "-t", action="store_true", help="shows how the prompt is split into tokens"
)
render_group.add_argument(
"-f",
"--strength",
type=float,
help="img2img strength for noising/unnoising. 0.0 preserves image exactly, 1.0 replaces it completely",
)
render_group.add_argument(
"-T",
"-fit",
"--fit",
action=argparse.BooleanOptionalAction,
help="If specified, will resize the input image to fit within the dimensions of width x height (512x512 default)",
)
render_group.add_argument("--grid", "-g", action=argparse.BooleanOptionalAction, help="generate a grid")
render_group.add_argument(
"--embedding_directory",
"--embedding_path",
dest="embedding_path",
default="embeddings",
type=str,
help="Path to a directory containing .bin and/or .pt files, or a single .bin/.pt file. You may use subdirectories. (default is ROOTDIR/embeddings)",
)
render_group.add_argument(
"--lora_directory",
dest="lora_path",
default="loras",
type=str,
help="Path to a directory containing LoRA files; subdirectories are not supported. (default is ROOTDIR/loras)",
)
render_group.add_argument(
"--embeddings",
action=argparse.BooleanOptionalAction,
default=True,
help="Enable embedding directory (default). Use --no-embeddings to disable.",
)
render_group.add_argument("--enable_image_debugging", action="store_true", help="Generates debugging image to display")
render_group.add_argument(
"--karras_max",
type=int,
default=None,
help="control the point at which the K* samplers will shift from using the Karras noise schedule (good for low step counts) to the LatentDiffusion noise schedule (good for high step counts). Set to 0 to use LatentDiffusion for all step values, and to a high value (e.g. 1000) to use Karras for all step values. [29].",
)
# Restoration related args
postprocessing_group.add_argument(
"--no_restore",
dest="restore",
action="store_false",
help="Disable face restoration with GFPGAN or codeformer",
)
postprocessing_group.add_argument(
"--no_upscale",
dest="esrgan",
action="store_false",
help="Disable upscaling with ESRGAN",
)
postprocessing_group.add_argument(
"--esrgan_bg_tile",
type=int,
default=400,
help="Tile size for background sampler, 0 for no tile during testing. Default: 400.",
)
postprocessing_group.add_argument(
"--esrgan_denoise_str",
type=float,
default=0.75,
help="esrgan denoise str. 0 is no denoise, 1 is max denoise. Default: 0.75",
)
postprocessing_group.add_argument(
"--gfpgan_model_path",
type=str,
default="./models/gfpgan/GFPGANv1.4.pth",
help="Indicates the path to the GFPGAN model",
)
web_server_group.add_argument(
"--web",
dest="web",
action="store_true",
help="Start in web server mode.",
)
web_server_group.add_argument(
"--web_develop",
dest="web_develop",
action="store_true",
help="Start in web server development mode.",
)
web_server_group.add_argument(
"--web_verbose",
action="store_true",
help="Enables verbose logging",
)
web_server_group.add_argument(
"--cors",
nargs="*",
type=str,
help="Additional allowed origins, comma-separated",
)
web_server_group.add_argument(
"--host",
type=str,
default="127.0.0.1",
help="Web server: Host or IP to listen on. Set to 0.0.0.0 to accept traffic from other devices on your network.",
)
web_server_group.add_argument("--port", type=int, default="9090", help="Web server: Port to listen on")
web_server_group.add_argument(
"--certfile",
type=str,
default=None,
help="Web server: Path to certificate file to use for SSL. Use together with --keyfile",
)
web_server_group.add_argument(
"--keyfile",
type=str,
default=None,
help="Web server: Path to private key file to use for SSL. Use together with --certfile",
)
web_server_group.add_argument(
"--gui",
dest="gui",
action="store_true",
help="Start InvokeAI GUI",
)

26
invokeai/backend/model_hash/model_hash.py
View File

@ -6,10 +6,14 @@ from pathlib import Path
from typing import Callable, Literal, Optional, Union
from blake3 import blake3
from tqdm import tqdm
from invokeai.app.util.misc import uuid_string
HASHING_ALGORITHMS = Literal[
"blake3_multi",
"blake3_single",
"random",
"md5",
"sha1",
"sha224",
@ -24,9 +28,6 @@ HASHING_ALGORITHMS = Literal[
"sha3_512",
"shake_128",
"shake_256",
"blake3",
"blake3_single",
"random",
]
MODEL_FILE_EXTENSIONS = (".ckpt", ".safetensors", ".bin", ".pt", ".pth")
@ -60,10 +61,10 @@ class ModelHash:
"""
def __init__(
self, algorithm: HASHING_ALGORITHMS = "blake3", file_filter: Optional[Callable[[str], bool]] = None
self, algorithm: HASHING_ALGORITHMS = "blake3_single", file_filter: Optional[Callable[[str], bool]] = None
) -> None:
self.algorithm: HASHING_ALGORITHMS = algorithm
if algorithm == "blake3":
if algorithm == "blake3_multi":
self._hash_file = self._blake3
elif algorithm == "blake3_single":
self._hash_file = self._blake3_single
@ -94,7 +95,14 @@ class ModelHash:
# blake3_single is a single-threaded version of blake3, prefix should still be "blake3:"
prefix = self._get_prefix(self.algorithm)
if model_path.is_file():
return prefix + self._hash_file(model_path)
hash_ = None
# To give a similar user experience for single files and directories, we use a progress bar even for single files
pbar = tqdm([model_path], desc=f"Hashing {model_path.name}", unit="file")
for component in pbar:
pbar.set_description(f"Hashing {component.name}")
hash_ = prefix + self._hash_file(model_path)
assert hash_ is not None
return hash_
elif model_path.is_dir():
return prefix + self._hash_dir(model_path)
else:
@ -112,7 +120,9 @@ class ModelHash:
model_component_paths = self._get_file_paths(dir, self._file_filter)
component_hashes: list[str] = []
for component in sorted(model_component_paths):
pbar = tqdm(sorted(model_component_paths), desc=f"Hashing {dir.name}", unit="file")
for component in pbar:
pbar.set_description(f"Hashing {component.name}")
component_hashes.append(self._hash_file(component))
# BLAKE3 is cryptographically secure. We may as well fall back on a secure algorithm
@ -216,4 +226,4 @@ class ModelHash:
def _get_prefix(algorithm: HASHING_ALGORITHMS) -> str:
"""Return the prefix for the given algorithm, e.g. \"blake3:\" or \"md5:\"."""
# blake3_single is a single-threaded version of blake3, prefix should still be "blake3:"
return "blake3:" if algorithm == "blake3_single" else f"{algorithm}:"
return "blake3:" if algorithm == "blake3_single" or algorithm == "blake3_multi" else f"{algorithm}:"

4
invokeai/backend/model_manager/load/model_loaders/controlnet.py
View File

@ -35,8 +35,6 @@ class ControlNetLoader(GenericDiffusersLoader):
def _convert_model(self, config: AnyModelConfig, model_path: Path, output_path: Path) -> Path:
assert isinstance(config, CheckpointConfigBase)
config_file = config.config_path
image_size = (
512
if config.base == BaseModelType.StableDiffusion1
@ -46,7 +44,7 @@ class ControlNetLoader(GenericDiffusersLoader):
)
self._logger.info(f"Converting {model_path} to diffusers format")
with open(self._app_config.root_path / config_file, "r") as config_stream:
with open(self._app_config.root_path / config.config_path, "r") as config_stream:
convert_controlnet_to_diffusers(
model_path,
output_path,

3
invokeai/backend/model_manager/load/model_loaders/stable_diffusion.py
View File

@ -76,7 +76,6 @@ class StableDiffusionDiffusersModel(GenericDiffusersLoader):
assert isinstance(config, MainCheckpointConfig)
base = config.base
config_file = config.config_path
prediction_type = config.prediction_type.value
upcast_attention = config.upcast_attention
image_size = (
@ -92,7 +91,7 @@ class StableDiffusionDiffusersModel(GenericDiffusersLoader):
model_path,
output_path,
model_type=self.model_base_to_model_type[base],
original_config_file=self._app_config.root_path / config_file,
original_config_file=self._app_config.root_path / config.config_path,
extract_ema=True,
from_safetensors=model_path.suffix == ".safetensors",
precision=self._torch_dtype,

4
invokeai/backend/model_manager/load/model_loaders/vae.py
View File

@ -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 = config.config_path
config_file = self._app_config.root_path / config.config_path
if model_path.suffix == ".safetensors":
checkpoint = safetensors_load_file(model_path, device="cpu")
@ -55,7 +55,7 @@ class VAELoader(GenericDiffusersLoader):
if "state_dict" in checkpoint:
checkpoint = checkpoint["state_dict"]
ckpt_config = OmegaConf.load(self._app_config.root_path / config_file)
ckpt_config = OmegaConf.load(config_file)
assert isinstance(ckpt_config, DictConfig)
self._logger.info(f"Converting {model_path} to diffusers format")
vae_model = convert_ldm_vae_to_diffusers(

34
invokeai/backend/model_manager/probe.py
View File

@ -114,7 +114,7 @@ class ModelProbe(object):
@classmethod
def probe(
cls, model_path: Path, fields: Optional[Dict[str, Any]] = None, hash_algo: HASHING_ALGORITHMS = "blake3"
cls, model_path: Path, fields: Optional[Dict[str, Any]] = None, hash_algo: HASHING_ALGORITHMS = "blake3_single"
) -> AnyModelConfig:
"""
Probe the model at model_path and return its configuration record.
@ -178,13 +178,14 @@ class ModelProbe(object):
fields["type"] in [ModelType.Main, ModelType.ControlNet, ModelType.VAE]
and fields["format"] is ModelFormat.Checkpoint
):
fields["config_path"] = cls._get_checkpoint_config_path(
ckpt_config_path = cls._get_checkpoint_config_path(
model_path,
model_type=fields["type"],
base_type=fields["base"],
variant_type=fields["variant"],
prediction_type=fields["prediction_type"],
).as_posix()
)
fields["config_path"] = str(ckpt_config_path)
# additional fields needed for main non-checkpoint models
elif fields["type"] == ModelType.Main and fields["format"] in [
@ -226,8 +227,8 @@ 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
elif any(key.startswith(v) for v in {"control_model", "input_blocks"}):
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"}:
return ModelType.TextualInversion
@ -298,23 +299,23 @@ class ModelProbe(object):
config_file = LEGACY_CONFIGS[base_type][variant_type]
if isinstance(config_file, dict): # need another tier for sd-2.x models
config_file = config_file[prediction_type]
config_file = f"stable-diffusion/{config_file}"
elif model_type is ModelType.ControlNet:
config_file = (
"../controlnet/cldm_v15.yaml"
"controlnet/cldm_v15.yaml"
if base_type is BaseModelType.StableDiffusion1
else "../controlnet/cldm_v21.yaml"
else "controlnet/cldm_v21.yaml"
)
elif model_type is ModelType.VAE:
config_file = (
"../stable-diffusion/v1-inference.yaml"
"stable-diffusion/v1-inference.yaml"
if base_type is BaseModelType.StableDiffusion1
else "../stable-diffusion/v2-inference.yaml"
else "stable-diffusion/v2-inference.yaml"
)
else:
raise InvalidModelConfigException(
f"{model_path}: Unrecognized combination of model_type={model_type}, base_type={base_type}"
)
assert isinstance(config_file, str)
return Path(config_file)
@classmethod
@ -507,15 +508,22 @@ class ControlNetCheckpointProbe(CheckpointProbeBase):
checkpoint = self.checkpoint
for key_name in (
"control_model.input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight",
"controlnet_mid_block.bias",
"input_blocks.2.1.transformer_blocks.0.attn2.to_k.weight",
"down_blocks.1.attentions.0.transformer_blocks.0.attn2.to_k.weight",
):
if key_name not in checkpoint:
continue
if checkpoint[key_name].shape[-1] == 768:
width = checkpoint[key_name].shape[-1]
if width == 768:
return BaseModelType.StableDiffusion1
elif checkpoint[key_name].shape[-1] == 1024:
elif width == 1024:
return BaseModelType.StableDiffusion2
raise InvalidModelConfigException("{self.model_path}: Unable to determine base type")
elif width == 2048:
return BaseModelType.StableDiffusionXL
elif width == 1280:
return BaseModelType.StableDiffusionXL
raise InvalidModelConfigException(f"{self.model_path}: Unable to determine base type")
class IPAdapterCheckpointProbe(CheckpointProbeBase):

392
invokeai/backend/model_manager/starter_models.py Normal file
View File

@ -0,0 +1,392 @@
from dataclasses import dataclass
from typing import Optional
from invokeai.backend.model_manager.config import BaseModelType, ModelType
@dataclass
class StarterModel:
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
# 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)",
base=BaseModelType.StableDiffusion1,
source="runwayml/stable-diffusion-v1-5",
description="Stable Diffusion version 1.5 diffusers model (4.27 GB)",
type=ModelType.Main,
),
StarterModel(
name="SD 1.5 (inpainting)",
base=BaseModelType.StableDiffusion1,
source="runwayml/stable-diffusion-inpainting",
description="RunwayML SD 1.5 model optimized for inpainting, diffusers version (4.27 GB)",
type=ModelType.Main,
),
StarterModel(
name="Analog Diffusion",
base=BaseModelType.StableDiffusion1,
source="wavymulder/Analog-Diffusion",
description="An SD-1.5 model trained on diverse analog photographs (2.13 GB)",
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)",
type=ModelType.Main,
),
StarterModel(
name="Dungeons and Diffusion",
base=BaseModelType.StableDiffusion1,
source="0xJustin/Dungeons-and-Diffusion",
description="Dungeons & Dragons characters (2.13 GB)",
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)",
base=BaseModelType.StableDiffusionXL,
source="stabilityai/stable-diffusion-xl-base-1.0",
description="Stable Diffusion XL base model (12 GB)",
type=ModelType.Main,
),
StarterModel(
name="SDXL Refiner",
base=BaseModelType.StableDiffusionXLRefiner,
source="stabilityai/stable-diffusion-xl-refiner-1.0",
description="Stable Diffusion XL refiner model (12 GB)",
type=ModelType.Main,
),
# 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,
),
# 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",
type=ModelType.LoRA,
),
StarterModel(
name="Ink scenery",
base=BaseModelType.StableDiffusion1,
source="https://civitai.com/api/download/models/83390",
description="Generate india ink-like landscapes",
type=ModelType.LoRA,
),
# endregion
# region IP Adapter
StarterModel(
name="IP Adapter",
base=BaseModelType.StableDiffusion1,
source="InvokeAI/ip_adapter_sd15",
description="IP-Adapter for SD 1.5 models",
type=ModelType.IPAdapter,
dependencies=["InvokeAI/ip_adapter_sd_image_encoder"],
),
StarterModel(
name="IP Adapter Plus",
base=BaseModelType.StableDiffusion1,
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"],
),
StarterModel(
name="IP Adapter Plus Face",
base=BaseModelType.StableDiffusion1,
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"],
),
StarterModel(
name="IP Adapter SDXL",
base=BaseModelType.StableDiffusionXL,
source="InvokeAI/ip_adapter_sdxl",
description="IP-Adapter for SDXL models",
type=ModelType.IPAdapter,
dependencies=["InvokeAI/ip_adapter_sdxl_image_encoder"],
),
# endregion
# region ControlNet
StarterModel(
name="QRCode Monster",
base=BaseModelType.StableDiffusion1,
source="monster-labs/control_v1p_sd15_qrcode_monster",
description="Controlnet model that generates scannable creative QR codes",
type=ModelType.ControlNet,
),
StarterModel(
name="canny",
base=BaseModelType.StableDiffusion1,
source="lllyasviel/control_v11p_sd15_canny",
description="Controlnet weights trained on sd-1.5 with canny conditioning.",
type=ModelType.ControlNet,
),
StarterModel(
name="inpaint",
base=BaseModelType.StableDiffusion1,
source="lllyasviel/control_v11p_sd15_inpaint",
description="Controlnet weights trained on sd-1.5 with canny conditioning, inpaint version",
type=ModelType.ControlNet,
),
StarterModel(
name="mlsd",
base=BaseModelType.StableDiffusion1,
source="lllyasviel/control_v11p_sd15_mlsd",
description="Controlnet weights trained on sd-1.5 with canny conditioning, MLSD version",
type=ModelType.ControlNet,
),
StarterModel(
name="depth",
base=BaseModelType.StableDiffusion1,
source="lllyasviel/control_v11f1p_sd15_depth",
description="Controlnet weights trained on sd-1.5 with depth conditioning",
type=ModelType.ControlNet,
),
StarterModel(
name="normal_bae",
base=BaseModelType.StableDiffusion1,
source="lllyasviel/control_v11p_sd15_normalbae",
description="Controlnet weights trained on sd-1.5 with normalbae image conditioning",
type=ModelType.ControlNet,
),
StarterModel(
name="seg",
base=BaseModelType.StableDiffusion1,
source="lllyasviel/control_v11p_sd15_seg",
description="Controlnet weights trained on sd-1.5 with seg image conditioning",
type=ModelType.ControlNet,
),
StarterModel(
name="lineart",
base=BaseModelType.StableDiffusion1,
source="lllyasviel/control_v11p_sd15_lineart",
description="Controlnet weights trained on sd-1.5 with lineart image conditioning",
type=ModelType.ControlNet,
),
StarterModel(
name="lineart_anime",
base=BaseModelType.StableDiffusion1,
source="lllyasviel/control_v11p_sd15s2_lineart_anime",
description="Controlnet weights trained on sd-1.5 with anime image conditioning",
type=ModelType.ControlNet,
),
StarterModel(
name="openpose",
base=BaseModelType.StableDiffusion1,
source="lllyasviel/control_v11p_sd15_openpose",
description="Controlnet weights trained on sd-1.5 with openpose image conditioning",
type=ModelType.ControlNet,
),
StarterModel(
name="scribble",
base=BaseModelType.StableDiffusion1,
source="lllyasviel/control_v11p_sd15_scribble",
description="Controlnet weights trained on sd-1.5 with scribble image conditioning",
type=ModelType.ControlNet,
),
StarterModel(
name="softedge",
base=BaseModelType.StableDiffusion1,
source="lllyasviel/control_v11p_sd15_softedge",
description="Controlnet weights trained on sd-1.5 with soft edge conditioning",
type=ModelType.ControlNet,
),
StarterModel(
name="shuffle",
base=BaseModelType.StableDiffusion1,
source="lllyasviel/control_v11e_sd15_shuffle",
description="Controlnet weights trained on sd-1.5 with shuffle image conditioning",
type=ModelType.ControlNet,
),
StarterModel(
name="tile",
base=BaseModelType.StableDiffusion1,
source="lllyasviel/control_v11f1e_sd15_tile",
description="Controlnet weights trained on sd-1.5 with tiled image conditioning",
type=ModelType.ControlNet,
),
StarterModel(
name="ip2p",
base=BaseModelType.StableDiffusion1,
source="lllyasviel/control_v11e_sd15_ip2p",
description="Controlnet weights trained on sd-1.5 with ip2p conditioning.",
type=ModelType.ControlNet,
),
StarterModel(
name="canny-sdxl",
base=BaseModelType.StableDiffusionXL,
source="diffusers/controlnet-canny-sdxl-1.0",
description="Controlnet weights trained on sdxl-1.0 with canny conditioning.",
type=ModelType.ControlNet,
),
StarterModel(
name="depth-sdxl",
base=BaseModelType.StableDiffusionXL,
source="diffusers/controlnet-depth-sdxl-1.0",
description="Controlnet weights trained on sdxl-1.0 with depth conditioning.",
type=ModelType.ControlNet,
),
StarterModel(
name="softedge-dexined-sdxl",
base=BaseModelType.StableDiffusionXL,
source="SargeZT/controlnet-sd-xl-1.0-softedge-dexined",
description="Controlnet weights trained on sdxl-1.0 with dexined soft edge preprocessing.",
type=ModelType.ControlNet,
),
StarterModel(
name="depth-16bit-zoe-sdxl",
base=BaseModelType.StableDiffusionXL,
source="SargeZT/controlnet-sd-xl-1.0-depth-16bit-zoe",
description="Controlnet weights trained on sdxl-1.0 with Zoe's preprocessor (16 bits).",
type=ModelType.ControlNet,
),
StarterModel(
name="depth-zoe-sdxl",
base=BaseModelType.StableDiffusionXL,
source="diffusers/controlnet-zoe-depth-sdxl-1.0",
description="Controlnet weights trained on sdxl-1.0 with Zoe's preprocessor (32 bits).",
type=ModelType.ControlNet,
),
# endregion
# region T2I Adapter
StarterModel(
name="canny-sd15",
base=BaseModelType.StableDiffusion1,
source="TencentARC/t2iadapter_canny_sd15v2",
description="T2I Adapter weights trained on sd-1.5 with canny conditioning.",
type=ModelType.T2IAdapter,
),
StarterModel(
name="sketch-sd15",
base=BaseModelType.StableDiffusion1,
source="TencentARC/t2iadapter_sketch_sd15v2",
description="T2I Adapter weights trained on sd-1.5 with sketch conditioning.",
type=ModelType.T2IAdapter,
),
StarterModel(
name="depth-sd15",
base=BaseModelType.StableDiffusion1,
source="TencentARC/t2iadapter_depth_sd15v2",
description="T2I Adapter weights trained on sd-1.5 with depth conditioning.",
type=ModelType.T2IAdapter,
),
StarterModel(
name="zoedepth-sd15",
base=BaseModelType.StableDiffusion1,
source="TencentARC/t2iadapter_zoedepth_sd15v1",
description="T2I Adapter weights trained on sd-1.5 with zoe depth conditioning.",
type=ModelType.T2IAdapter,
),
StarterModel(
name="canny-sdxl",
base=BaseModelType.StableDiffusionXL,
source="TencentARC/t2i-adapter-canny-sdxl-1.0",
description="T2I Adapter weights trained on sdxl-1.0 with canny conditioning.",
type=ModelType.T2IAdapter,
),
StarterModel(
name="zoedepth-sdxl",
base=BaseModelType.StableDiffusionXL,
source="TencentARC/t2i-adapter-depth-zoe-sdxl-1.0",
description="T2I Adapter weights trained on sdxl-1.0 with zoe depth conditioning.",
type=ModelType.T2IAdapter,
),
StarterModel(
name="lineart-sdxl",
base=BaseModelType.StableDiffusionXL,
source="TencentARC/t2i-adapter-lineart-sdxl-1.0",
description="T2I Adapter weights trained on sdxl-1.0 with lineart conditioning.",
type=ModelType.T2IAdapter,
),
StarterModel(
name="sketch-sdxl",
base=BaseModelType.StableDiffusionXL,
source="TencentARC/t2i-adapter-sketch-sdxl-1.0",
description="T2I Adapter weights trained on sdxl-1.0 with sketch conditioning.",
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"

69
invokeai/backend/stable_diffusion/diffusers_pipeline.py
View File

@ -15,9 +15,8 @@ from diffusers.models.controlnet import ControlNetModel
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import KarrasDiffusionSchedulers
from diffusers.schedulers.scheduling_utils import SchedulerMixin, SchedulerOutput
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.outputs import BaseOutput
from pydantic import Field
from transformers import CLIPFeatureExtractor, CLIPTextModel, CLIPTokenizer
@ -26,8 +25,8 @@ from invokeai.backend.ip_adapter.ip_adapter import IPAdapter
from invokeai.backend.ip_adapter.unet_patcher import UNetPatcher
from invokeai.backend.stable_diffusion.diffusion.conditioning_data import ConditioningData
from invokeai.backend.stable_diffusion.diffusion.shared_invokeai_diffusion import InvokeAIDiffuserComponent
from ..util import auto_detect_slice_size, normalize_device
from invokeai.backend.util.attention import auto_detect_slice_size
from invokeai.backend.util.devices import normalize_device
@dataclass
@ -86,26 +85,8 @@ class AddsMaskGuidance:
noise: torch.Tensor
gradient_mask: bool
def __call__(self, step_output: Union[BaseOutput, SchedulerOutput], t: torch.Tensor, conditioning) -> BaseOutput:
output_class = step_output.__class__ # We'll create a new one with masked data.
# The problem with taking SchedulerOutput instead of the model output is that we're less certain what's in it.
# It's reasonable to assume the first thing is prev_sample, but then does it have other things
# like pred_original_sample? Should we apply the mask to them too?
# But what if there's just some other random field?
prev_sample = step_output[0]
# Mask anything that has the same shape as prev_sample, return others as-is.
return output_class(
{
k: self.apply_mask(v, self._t_for_field(k, t)) if are_like_tensors(prev_sample, v) else v
for k, v in step_output.items()
}
)
def _t_for_field(self, field_name: str, t):
if field_name == "pred_original_sample":
return self.scheduler.timesteps[-1]
return t
def __call__(self, latents: torch.Tensor, t: torch.Tensor) -> torch.Tensor:
return self.apply_mask(latents, t)
def apply_mask(self, latents: torch.Tensor, t) -> torch.Tensor:
batch_size = latents.size(0)
@ -383,9 +364,15 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
finally:
self.invokeai_diffuser.model_forward_callback = self._unet_forward
# restore unmasked part
if mask is not None and not gradient_mask:
latents = torch.lerp(orig_latents, latents.to(dtype=orig_latents.dtype), mask.to(dtype=orig_latents.dtype))
# restore unmasked part after the last step is completed
# in-process masking happens before each step
if mask is not None:
if gradient_mask:
latents = torch.where(mask > 0, latents, orig_latents)
else:
latents = torch.lerp(
orig_latents, latents.to(dtype=orig_latents.dtype), mask.to(dtype=orig_latents.dtype)
)
return latents
@ -490,6 +477,10 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
if additional_guidance is None:
additional_guidance = []
# one day we will expand this extension point, but for now it just does denoise masking
for guidance in additional_guidance:
latents = guidance(latents, timestep)
# TODO: should this scaling happen here or inside self._unet_forward?
# i.e. before or after passing it to InvokeAIDiffuserComponent
latent_model_input = self.scheduler.scale_model_input(latents, timestep)
@ -580,21 +571,17 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
# compute the previous noisy sample x_t -> x_t-1
step_output = self.scheduler.step(noise_pred, timestep, latents, **conditioning_data.scheduler_args)
# TODO: issue to diffusers?
# undo internal counter increment done by scheduler.step, so timestep can be resolved as before call
# this needed to be able call scheduler.add_noise with current timestep
if self.scheduler.order == 2:
self.scheduler._index_counter[timestep.item()] -= 1
# TODO: this additional_guidance extension point feels redundant with InvokeAIDiffusionComponent.
# But the way things are now, scheduler runs _after_ that, so there was
# no way to use it to apply an operation that happens after the last scheduler.step.
# TODO: discuss injection point options. For now this is a patch to get progress images working with inpainting again.
for guidance in additional_guidance:
step_output = guidance(step_output, timestep, conditioning_data)
# restore internal counter
if self.scheduler.order == 2:
self.scheduler._index_counter[timestep.item()] += 1
# apply the mask to any "denoised" or "pred_original_sample" fields
if hasattr(step_output, "denoised"):
step_output.pred_original_sample = guidance(step_output.denoised, self.scheduler.timesteps[-1])
elif hasattr(step_output, "pred_original_sample"):
step_output.pred_original_sample = guidance(
step_output.pred_original_sample, self.scheduler.timesteps[-1]
)
else:
step_output.pred_original_sample = guidance(latents, self.scheduler.timesteps[-1])
return step_output

2
invokeai/backend/stable_diffusion/diffusion/cross_attention_control.py
View File

@ -11,7 +11,7 @@ from compel.cross_attention_control import Arguments
from diffusers.models.attention_processor import Attention, SlicedAttnProcessor
from diffusers.models.unets.unet_2d_condition import UNet2DConditionModel
from ...util import torch_dtype
from invokeai.backend.util.devices import torch_dtype
class CrossAttentionType(enum.Enum):

5
invokeai/backend/training/__init__.py
View File

@ -1,5 +0,0 @@
"""
Initialization file for invokeai.backend.training
"""
from .textual_inversion_training import do_textual_inversion_training, parse_args # noqa: F401

924
invokeai/backend/training/textual_inversion_training.py
View File

@ -1,924 +0,0 @@
# This code was copied from
# https://github.com/huggingface/diffusers/blob/main/examples/textual_inversion/textual_inversion.py
# on January 2, 2023
# and modified slightly by Lincoln Stein (@lstein) to work with InvokeAI
"""
This is the backend to "textual_inversion.py"
"""
import logging
import math
import os
import random
from argparse import Namespace
from pathlib import Path
from typing import Optional
import datasets
import diffusers
import numpy as np
import PIL
import torch
import torch.nn.functional as F
import torch.utils.checkpoint
import transformers
from accelerate import Accelerator
from accelerate.logging import get_logger
from accelerate.utils import ProjectConfiguration, set_seed
from diffusers import AutoencoderKL, DDPMScheduler, StableDiffusionPipeline, UNet2DConditionModel
from diffusers.optimization import get_scheduler
from diffusers.utils import check_min_version
from diffusers.utils.import_utils import is_xformers_available
from huggingface_hub import HfFolder, Repository, whoami
from packaging import version
from PIL import Image
from torch.utils.data import Dataset
from torchvision import transforms
from tqdm.auto import tqdm
from transformers import CLIPTextModel, CLIPTokenizer
# invokeai stuff
from invokeai.app.services.config import InvokeAIAppConfig, PagingArgumentParser
from invokeai.app.services.config.config_default import get_config
from invokeai.backend.install.install_helper import initialize_record_store
from invokeai.backend.model_manager import BaseModelType, ModelType
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
PIL_INTERPOLATION = {
"linear": PIL.Image.Resampling.BILINEAR,
"bilinear": PIL.Image.Resampling.BILINEAR,
"bicubic": PIL.Image.Resampling.BICUBIC,
"lanczos": PIL.Image.Resampling.LANCZOS,
"nearest": PIL.Image.Resampling.NEAREST,
}
else:
PIL_INTERPOLATION = {
"linear": PIL.Image.LINEAR,
"bilinear": PIL.Image.BILINEAR,
"bicubic": PIL.Image.BICUBIC,
"lanczos": PIL.Image.LANCZOS,
"nearest": PIL.Image.NEAREST,
}
# ------------------------------------------------------------------------------
# Will error if the minimal version of diffusers is not installed. Remove at your own risks.
check_min_version("0.10.0.dev0")
logger = get_logger(__name__)
def save_progress(text_encoder, placeholder_token_id, accelerator, placeholder_token, save_path):
logger.info("Saving embeddings")
learned_embeds = accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[placeholder_token_id]
learned_embeds_dict = {placeholder_token: learned_embeds.detach().cpu()}
torch.save(learned_embeds_dict, save_path)
def parse_args() -> Namespace:
config = get_config()
parser = PagingArgumentParser(description="Textual inversion training")
general_group = parser.add_argument_group("General")
model_group = parser.add_argument_group("Models and Paths")
image_group = parser.add_argument_group("Training Image Location and Options")
trigger_group = parser.add_argument_group("Trigger Token")
training_group = parser.add_argument_group("Training Parameters")
checkpointing_group = parser.add_argument_group("Checkpointing and Resume")
integration_group = parser.add_argument_group("Integration")
general_group.add_argument(
"--front_end",
"--gui",
dest="front_end",
action="store_true",
default=False,
help="Activate the text-based graphical front end for collecting parameters. Aside from --root_dir, other parameters will be ignored.",
)
general_group.add_argument(
"--root_dir",
"--root",
type=Path,
default=config.root_path,
help="Path to the invokeai runtime directory",
)
general_group.add_argument(
"--logging_dir",
type=Path,
default="logs",
help=(
"[TensorBoard](https://www.tensorflow.org/tensorboard) log directory. Will default to"
" *output_dir/runs/**CURRENT_DATETIME_HOSTNAME***."
),
)
general_group.add_argument(
"--output_dir",
type=Path,
default=f"{config.root_path}/text-inversion-model",
help="The output directory where the model predictions and checkpoints will be written.",
)
model_group.add_argument(
"--model",
type=str,
default="sd-1/main/stable-diffusion-v1-5",
help="Name of the diffusers model to train against.",
)
model_group.add_argument(
"--revision",
type=str,
default=None,
required=False,
help="Revision of pretrained model identifier from huggingface.co/models.",
)
model_group.add_argument(
"--tokenizer_name",
type=str,
default=None,
help="Pretrained tokenizer name or path if not the same as model_name",
)
image_group.add_argument(
"--train_data_dir",
type=Path,
default=None,
help="A folder containing the training data.",
)
image_group.add_argument(
"--resolution",
type=int,
default=512,
help=(
"The resolution for input images, all the images in the train/validation dataset will be resized to this"
" resolution"
),
)
image_group.add_argument(
"--center_crop",
action="store_true",
help="Whether to center crop images before resizing to resolution",
)
trigger_group.add_argument(
"--placeholder_token",
"--trigger_term",
dest="placeholder_token",
type=str,
default=None,
help='A token to use as a placeholder for the concept. This token will trigger the concept when included in the prompt as "<trigger>".',
)
trigger_group.add_argument(
"--learnable_property",
type=str,
choices=["object", "style"],
default="object",
help="Choose between 'object' and 'style'",
)
trigger_group.add_argument(
"--initializer_token",
type=str,
default="*",
help="A symbol to use as the initializer word.",
)
checkpointing_group.add_argument(
"--checkpointing_steps",
type=int,
default=500,
help=(
"Save a checkpoint of the training state every X updates. These checkpoints are only suitable for resuming"
" training using `--resume_from_checkpoint`."
),
)
checkpointing_group.add_argument(
"--resume_from_checkpoint",
type=Path,
default=None,
help=(
"Whether training should be resumed from a previous checkpoint. Use a path saved by"
' `--checkpointing_steps`, or `"latest"` to automatically select the last available checkpoint.'
),
)
checkpointing_group.add_argument(
"--save_steps",
type=int,
default=500,
help="Save learned_embeds.bin every X updates steps.",
)
training_group.add_argument(
"--repeats",
type=int,
default=100,
help="How many times to repeat the training data.",
)
training_group.add_argument("--seed", type=int, default=None, help="A seed for reproducible training.")
training_group.add_argument(
"--train_batch_size",
type=int,
default=16,
help="Batch size (per device) for the training dataloader.",
)
training_group.add_argument("--num_train_epochs", type=int, default=100)
training_group.add_argument(
"--max_train_steps",
type=int,
default=5000,
help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
)
training_group.add_argument(
"--gradient_accumulation_steps",
type=int,
default=1,
help="Number of updates steps to accumulate before performing a backward/update pass.",
)
training_group.add_argument(
"--gradient_checkpointing",
action="store_true",
help="Whether or not to use gradient checkpointing to save memory at the expense of slower backward pass.",
)
training_group.add_argument(
"--learning_rate",
type=float,
default=1e-4,
help="Initial learning rate (after the potential warmup period) to use.",
)
training_group.add_argument(
"--scale_lr",
action="store_true",
default=True,
help="Scale the learning rate by the number of GPUs, gradient accumulation steps, and batch size.",
)
training_group.add_argument(
"--lr_scheduler",
type=str,
default="constant",
help=(
'The scheduler type to use. Choose between ["linear", "cosine", "cosine_with_restarts", "polynomial",'
' "constant", "constant_with_warmup"]'
),
)
training_group.add_argument(
"--lr_warmup_steps",
type=int,
default=500,
help="Number of steps for the warmup in the lr scheduler.",
)
training_group.add_argument(
"--adam_beta1",
type=float,
default=0.9,
help="The beta1 parameter for the Adam optimizer.",
)
training_group.add_argument(
"--adam_beta2",
type=float,
default=0.999,
help="The beta2 parameter for the Adam optimizer.",
)
training_group.add_argument("--adam_weight_decay", type=float, default=1e-2, help="Weight decay to use.")
training_group.add_argument(
"--adam_epsilon",
type=float,
default=1e-08,
help="Epsilon value for the Adam optimizer",
)
training_group.add_argument(
"--mixed_precision",
type=str,
default="no",
choices=["no", "fp16", "bf16"],
help=(
"Whether to use mixed precision. Choose"
"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
"and an Nvidia Ampere GPU."
),
)
training_group.add_argument(
"--allow_tf32",
action="store_true",
help=(
"Whether or not to allow TF32 on Ampere GPUs. Can be used to speed up training. For more information, see"
" https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices"
),
)
training_group.add_argument(
"--local_rank",
type=int,
default=-1,
help="For distributed training: local_rank",
)
parser.add_argument(
"--enable_xformers_memory_efficient_attention",
action="store_true",
help="Whether or not to use xformers.",
)
integration_group.add_argument(
"--only_save_embeds",
action="store_true",
default=False,
help="Save only the embeddings for the new concept.",
)
integration_group.add_argument(
"--hub_model_id",
type=str,
default=None,
help="The name of the repository to keep in sync with the local `output_dir`.",
)
integration_group.add_argument(
"--report_to",
type=str,
default="tensorboard",
help=(
'The integration to report the results and logs to. Supported platforms are `"tensorboard"`'
' (default), `"wandb"` and `"comet_ml"`. Use `"all"` to report to all integrations.'
),
)
integration_group.add_argument(
"--push_to_hub",
action="store_true",
help="Whether or not to push the model to the Hub.",
)
integration_group.add_argument(
"--hub_token",
type=str,
default=None,
help="The token to use to push to the Model Hub.",
)
args = parser.parse_args()
return args
imagenet_templates_small = [
"a photo of a {}",
"a rendering of a {}",
"a cropped photo of the {}",
"the photo of a {}",
"a photo of a clean {}",
"a photo of a dirty {}",
"a dark photo of the {}",
"a photo of my {}",
"a photo of the cool {}",
"a close-up photo of a {}",
"a bright photo of the {}",
"a cropped photo of a {}",
"a photo of the {}",
"a good photo of the {}",
"a photo of one {}",
"a close-up photo of the {}",
"a rendition of the {}",
"a photo of the clean {}",
"a rendition of a {}",
"a photo of a nice {}",
"a good photo of a {}",
"a photo of the nice {}",
"a photo of the small {}",
"a photo of the weird {}",
"a photo of the large {}",
"a photo of a cool {}",
"a photo of a small {}",
]
imagenet_style_templates_small = [
"a painting in the style of {}",
"a rendering in the style of {}",
"a cropped painting in the style of {}",
"the painting in the style of {}",
"a clean painting in the style of {}",
"a dirty painting in the style of {}",
"a dark painting in the style of {}",
"a picture in the style of {}",
"a cool painting in the style of {}",
"a close-up painting in the style of {}",
"a bright painting in the style of {}",
"a cropped painting in the style of {}",
"a good painting in the style of {}",
"a close-up painting in the style of {}",
"a rendition in the style of {}",
"a nice painting in the style of {}",
"a small painting in the style of {}",
"a weird painting in the style of {}",
"a large painting in the style of {}",
]
class TextualInversionDataset(Dataset):
def __init__(
self,
data_root,
tokenizer,
learnable_property="object", # [object, style]
size=512,
repeats=100,
interpolation="bicubic",
flip_p=0.5,
set="train",
placeholder_token="*",
center_crop=False,
):
self.data_root = Path(data_root)
self.tokenizer = tokenizer
self.learnable_property = learnable_property
self.size = size
self.placeholder_token = placeholder_token
self.center_crop = center_crop
self.flip_p = flip_p
self.image_paths = [
self.data_root / file_path
for file_path in self.data_root.iterdir()
if file_path.is_file()
and file_path.name.endswith((".png", ".PNG", ".jpg", ".JPG", ".jpeg", ".JPEG", ".gif", ".GIF"))
]
self.num_images = len(self.image_paths)
self._length = self.num_images
if set == "train":
self._length = self.num_images * repeats
self.interpolation = {
"linear": PIL_INTERPOLATION["linear"],
"bilinear": PIL_INTERPOLATION["bilinear"],
"bicubic": PIL_INTERPOLATION["bicubic"],
"lanczos": PIL_INTERPOLATION["lanczos"],
}[interpolation]
self.templates = imagenet_style_templates_small if learnable_property == "style" else imagenet_templates_small
self.flip_transform = transforms.RandomHorizontalFlip(p=self.flip_p)
def __len__(self) -> int:
return self._length
def __getitem__(self, i):
example = {}
image = Image.open(self.image_paths[i % self.num_images])
if not image.mode == "RGB":
image = image.convert("RGB")
placeholder_string = self.placeholder_token
text = random.choice(self.templates).format(placeholder_string)
example["input_ids"] = self.tokenizer(
text,
padding="max_length",
truncation=True,
max_length=self.tokenizer.model_max_length,
return_tensors="pt",
).input_ids[0]
# default to score-sde preprocessing
img = np.array(image).astype(np.uint8)
if self.center_crop:
crop = min(img.shape[0], img.shape[1])
(
h,
w,
) = (
img.shape[0],
img.shape[1],
)
img = img[(h - crop) // 2 : (h + crop) // 2, (w - crop) // 2 : (w + crop) // 2]
image = Image.fromarray(img)
image = image.resize((self.size, self.size), resample=self.interpolation)
image = self.flip_transform(image)
image = np.array(image).astype(np.uint8)
image = (image / 127.5 - 1.0).astype(np.float32)
example["pixel_values"] = torch.from_numpy(image).permute(2, 0, 1)
return example
def get_full_repo_name(model_id: str, organization: Optional[str] = None, token: Optional[str] = None):
if token is None:
token = HfFolder.get_token()
if organization is None:
username = whoami(token)["name"]
return f"{username}/{model_id}"
else:
return f"{organization}/{model_id}"
def do_textual_inversion_training(
config: InvokeAIAppConfig,
model: str,
train_data_dir: Path,
output_dir: Path,
placeholder_token: str,
initializer_token: str,
save_steps: int = 500,
only_save_embeds: bool = False,
tokenizer_name: Optional[str] = None,
learnable_property: str = "object",
repeats: int = 100,
seed: Optional[int] = None,
resolution: int = 512,
center_crop: bool = False,
train_batch_size: int = 16,
num_train_epochs: int = 100,
max_train_steps: int = 5000,
gradient_accumulation_steps: int = 1,
gradient_checkpointing: bool = False,
learning_rate: float = 1e-4,
scale_lr: bool = True,
lr_scheduler: str = "constant",
lr_warmup_steps: int = 500,
adam_beta1: float = 0.9,
adam_beta2: float = 0.999,
adam_weight_decay: float = 1e-02,
adam_epsilon: float = 1e-08,
push_to_hub: bool = False,
hub_token: Optional[str] = None,
logging_dir: Path = Path("logs"),
mixed_precision: str = "fp16",
allow_tf32: bool = False,
report_to: str = "tensorboard",
local_rank: int = -1,
checkpointing_steps: int = 500,
resume_from_checkpoint: Optional[Path] = None,
enable_xformers_memory_efficient_attention: bool = False,
hub_model_id: Optional[str] = None,
**kwargs,
) -> None:
assert model, "Please specify a base model with --model"
assert train_data_dir, "Please specify a directory containing the training images using --train_data_dir"
assert placeholder_token, "Please specify a trigger term using --placeholder_token"
env_local_rank = int(os.environ.get("LOCAL_RANK", -1))
if env_local_rank != -1 and env_local_rank != local_rank:
local_rank = env_local_rank
# setting up things the way invokeai expects them
if not os.path.isabs(output_dir):
output_dir = config.root_path / output_dir
logging_dir = output_dir / logging_dir
accelerator_config = ProjectConfiguration()
accelerator_config.logging_dir = logging_dir
accelerator = Accelerator(
gradient_accumulation_steps=gradient_accumulation_steps,
mixed_precision=mixed_precision,
log_with=report_to,
project_config=accelerator_config,
)
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
logger.info(accelerator.state, main_process_only=False)
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_warning()
diffusers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
diffusers.utils.logging.set_verbosity_error()
# If passed along, set the training seed now.
if seed is not None:
set_seed(seed)
# Handle the repository creation
if accelerator.is_main_process:
if push_to_hub:
if hub_model_id is None:
repo_name = get_full_repo_name(Path(output_dir).name, token=hub_token)
else:
repo_name = hub_model_id
repo = Repository(output_dir, clone_from=repo_name)
with open(os.path.join(output_dir, ".gitignore"), "w+") as gitignore:
if "step_*" not in gitignore:
gitignore.write("step_*\n")
if "epoch_*" not in gitignore:
gitignore.write("epoch_*\n")
elif output_dir is not None:
os.makedirs(output_dir, exist_ok=True)
model_records = initialize_record_store(config)
base, type, name = model.split("/") # note frontend still returns old-style keys
try:
model_config = model_records.search_by_attr(
model_name=name, model_type=ModelType(type), base_model=BaseModelType(base)
)[0]
except IndexError:
raise Exception(f"Unknown model {model}")
model_path = config.models_path / model_config.path
pipeline_args = {"local_files_only": True}
if tokenizer_name:
tokenizer = CLIPTokenizer.from_pretrained(tokenizer_name, **pipeline_args)
else:
tokenizer = CLIPTokenizer.from_pretrained(model_path, subfolder="tokenizer", **pipeline_args)
# Load scheduler and models
noise_scheduler = DDPMScheduler.from_pretrained(model_path, subfolder="scheduler", **pipeline_args)
text_encoder = CLIPTextModel.from_pretrained(
model_path,
subfolder="text_encoder",
**pipeline_args,
)
vae = AutoencoderKL.from_pretrained(
model_path,
subfolder="vae",
**pipeline_args,
)
unet = UNet2DConditionModel.from_pretrained(
model_path,
subfolder="unet",
**pipeline_args,
)
# Add the placeholder token in tokenizer
num_added_tokens = tokenizer.add_tokens(placeholder_token)
if num_added_tokens == 0:
raise ValueError(
f"The tokenizer already contains the token {placeholder_token}. Please pass a different"
" `placeholder_token` that is not already in the tokenizer."
)
# Convert the initializer_token, placeholder_token to ids
token_ids = tokenizer.encode(initializer_token, add_special_tokens=False)
# Check if initializer_token is a single token or a sequence of tokens
if len(token_ids) > 1:
raise ValueError(
f"The initializer token must be a single token. Provided initializer={initializer_token}. Token ids={token_ids}"
)
initializer_token_id = token_ids[0]
placeholder_token_id = tokenizer.convert_tokens_to_ids(placeholder_token)
# Resize the token embeddings as we are adding new special tokens to the tokenizer
text_encoder.resize_token_embeddings(len(tokenizer))
# Initialise the newly added placeholder token with the embeddings of the initializer token
token_embeds = text_encoder.get_input_embeddings().weight.data
token_embeds[placeholder_token_id] = token_embeds[initializer_token_id]
# Freeze vae and unet
vae.requires_grad_(False)
unet.requires_grad_(False)
# Freeze all parameters except for the token embeddings in text encoder
text_encoder.text_model.encoder.requires_grad_(False)
text_encoder.text_model.final_layer_norm.requires_grad_(False)
text_encoder.text_model.embeddings.position_embedding.requires_grad_(False)
if gradient_checkpointing:
# Keep unet in train mode if we are using gradient checkpointing to save memory.
# The dropout cannot be != 0 so it doesn't matter if we are in eval or train mode.
unet.train()
text_encoder.gradient_checkpointing_enable()
unet.enable_gradient_checkpointing()
if enable_xformers_memory_efficient_attention:
if is_xformers_available():
unet.enable_xformers_memory_efficient_attention()
else:
raise ValueError("xformers is not available. Make sure it is installed correctly")
# Enable TF32 for faster training on Ampere GPUs,
# cf https://pytorch.org/docs/stable/notes/cuda.html#tensorfloat-32-tf32-on-ampere-devices
if allow_tf32:
torch.backends.cuda.matmul.allow_tf32 = True
if scale_lr:
learning_rate = learning_rate * gradient_accumulation_steps * train_batch_size * accelerator.num_processes
# Initialize the optimizer
optimizer = torch.optim.AdamW(
text_encoder.get_input_embeddings().parameters(), # only optimize the embeddings
lr=learning_rate,
betas=(adam_beta1, adam_beta2),
weight_decay=adam_weight_decay,
eps=adam_epsilon,
)
# Dataset and DataLoaders creation:
train_dataset = TextualInversionDataset(
data_root=train_data_dir,
tokenizer=tokenizer,
size=resolution,
placeholder_token=placeholder_token,
repeats=repeats,
learnable_property=learnable_property,
center_crop=center_crop,
set="train",
)
train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=train_batch_size, shuffle=True)
# Scheduler and math around the number of training steps.
overrode_max_train_steps = False
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / gradient_accumulation_steps)
if max_train_steps is None:
max_train_steps = num_train_epochs * num_update_steps_per_epoch
overrode_max_train_steps = True
scheduler = get_scheduler(
lr_scheduler,
optimizer=optimizer,
num_warmup_steps=lr_warmup_steps * gradient_accumulation_steps,
num_training_steps=max_train_steps * gradient_accumulation_steps,
)
# Prepare everything with our `accelerator`.
text_encoder, optimizer, train_dataloader, lr_scheduler = accelerator.prepare(
text_encoder, optimizer, train_dataloader, scheduler
)
# For mixed precision training we cast the unet and vae weights to half-precision
# as these models are only used for inference, keeping weights in full precision is not required.
weight_dtype = torch.float32
if accelerator.mixed_precision == "fp16":
weight_dtype = torch.float16
elif accelerator.mixed_precision == "bf16":
weight_dtype = torch.bfloat16
# Move vae and unet to device and cast to weight_dtype
unet.to(accelerator.device, dtype=weight_dtype)
vae.to(accelerator.device, dtype=weight_dtype)
# We need to recalculate our total training steps as the size of the training dataloader may have changed.
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / gradient_accumulation_steps)
if overrode_max_train_steps:
max_train_steps = num_train_epochs * num_update_steps_per_epoch
# Afterwards we recalculate our number of training epochs
num_train_epochs = math.ceil(max_train_steps / num_update_steps_per_epoch)
# We need to initialize the trackers we use, and also store our configuration.
# The trackers initializes automatically on the main process.
if accelerator.is_main_process:
params = locals()
for k in params: # init_trackers() doesn't like objects
params[k] = str(params[k]) if isinstance(params[k], object) else params[k]
accelerator.init_trackers("textual_inversion", config=params)
# Train!
total_batch_size = train_batch_size * accelerator.num_processes * gradient_accumulation_steps
logger.info("***** Running training *****")
logger.info(f" Num examples = {len(train_dataset)}")
logger.info(f" Num Epochs = {num_train_epochs}")
logger.info(f" Instantaneous batch size per device = {train_batch_size}")
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
logger.info(f" Gradient Accumulation steps = {gradient_accumulation_steps}")
logger.info(f" Total optimization steps = {max_train_steps}")
global_step = 0
first_epoch = 0
resume_step = None
# Potentially load in the weights and states from a previous save
if resume_from_checkpoint:
if resume_from_checkpoint != "latest":
path = os.path.basename(resume_from_checkpoint)
else:
# Get the most recent checkpoint
dirs = os.listdir(output_dir)
dirs = [d for d in dirs if d.startswith("checkpoint")]
dirs = sorted(dirs, key=lambda x: int(x.split("-")[1]))
path = dirs[-1] if len(dirs) > 0 else None
if path is None:
accelerator.print(f"Checkpoint '{resume_from_checkpoint}' does not exist. Starting a new training run.")
resume_from_checkpoint = None
else:
accelerator.print(f"Resuming from checkpoint {path}")
accelerator.load_state(os.path.join(output_dir, path))
global_step = int(path.split("-")[1])
resume_global_step = global_step * gradient_accumulation_steps
first_epoch = global_step // num_update_steps_per_epoch
resume_step = resume_global_step % (num_update_steps_per_epoch * gradient_accumulation_steps)
# Only show the progress bar once on each machine.
progress_bar = tqdm(
range(global_step, max_train_steps),
disable=not accelerator.is_local_main_process,
)
progress_bar.set_description("Steps")
# keep original embeddings as reference
orig_embeds_params = accelerator.unwrap_model(text_encoder).get_input_embeddings().weight.data.clone()
for epoch in range(first_epoch, num_train_epochs):
text_encoder.train()
for step, batch in enumerate(train_dataloader):
# Skip steps until we reach the resumed step
if resume_step and resume_from_checkpoint and epoch == first_epoch and step < resume_step:
if step % gradient_accumulation_steps == 0:
progress_bar.update(1)
continue
with accelerator.accumulate(text_encoder):
# Convert images to latent space
latents = vae.encode(batch["pixel_values"].to(dtype=weight_dtype)).latent_dist.sample().detach()
latents = latents * 0.18215
# Sample noise that we'll add to the latents
noise = torch.randn_like(latents)
bsz = latents.shape[0]
# Sample a random timestep for each image
timesteps = torch.randint(
0,
noise_scheduler.config.num_train_timesteps,
(bsz,),
device=latents.device,
)
timesteps = timesteps.long()
# Add noise to the latents according to the noise magnitude at each timestep
# (this is the forward diffusion process)
noisy_latents = noise_scheduler.add_noise(latents, noise, timesteps)
# Get the text embedding for conditioning
encoder_hidden_states = text_encoder(batch["input_ids"])[0].to(dtype=weight_dtype)
# Predict the noise residual
model_pred = unet(noisy_latents, timesteps, encoder_hidden_states).sample
# Get the target for loss depending on the prediction type
if noise_scheduler.config.prediction_type == "epsilon":
target = noise
elif noise_scheduler.config.prediction_type == "v_prediction":
target = noise_scheduler.get_velocity(latents, noise, timesteps)
else:
raise ValueError(f"Unknown prediction type {noise_scheduler.config.prediction_type}")
loss = F.mse_loss(model_pred.float(), target.float(), reduction="mean")
accelerator.backward(loss)
optimizer.step()
scheduler.step()
optimizer.zero_grad()
# Let's make sure we don't update any embedding weights besides the newly added token
index_no_updates = torch.arange(len(tokenizer)) != placeholder_token_id
with torch.no_grad():
accelerator.unwrap_model(text_encoder).get_input_embeddings().weight[index_no_updates] = (
orig_embeds_params[index_no_updates]
)
# Checks if the accelerator has performed an optimization step behind the scenes
if accelerator.sync_gradients:
progress_bar.update(1)
global_step += 1
if global_step % save_steps == 0:
save_path = os.path.join(output_dir, f"learned_embeds-steps-{global_step}.bin")
save_progress(
text_encoder,
placeholder_token_id,
accelerator,
placeholder_token,
save_path,
)
if global_step % checkpointing_steps == 0:
if accelerator.is_main_process:
save_path = os.path.join(output_dir, f"checkpoint-{global_step}")
accelerator.save_state(save_path)
logger.info(f"Saved state to {save_path}")
logs = {"loss": loss.detach().item(), "lr": scheduler.get_last_lr()[0]}
progress_bar.set_postfix(**logs)
accelerator.log(logs, step=global_step)
if global_step >= max_train_steps:
break
# Create the pipeline using using the trained modules and save it.
accelerator.wait_for_everyone()
if accelerator.is_main_process:
if push_to_hub and only_save_embeds:
logger.warn("Enabling full model saving because --push_to_hub=True was specified.")
save_full_model = True
else:
save_full_model = not only_save_embeds
if save_full_model:
pipeline = StableDiffusionPipeline.from_pretrained(
model_path,
text_encoder=accelerator.unwrap_model(text_encoder),
vae=vae,
unet=unet,
tokenizer=tokenizer,
**pipeline_args,
)
pipeline.save_pretrained(output_dir)
# Save the newly trained embeddings
save_path = os.path.join(output_dir, "learned_embeds.bin")
save_progress(
text_encoder,
placeholder_token_id,
accelerator,
placeholder_token,
save_path,
)
if push_to_hub:
repo.push_to_hub(commit_message="End of training", blocking=False, auto_lfs_prune=True)
accelerator.end_training()

22
invokeai/backend/util/__init__.py
View File

@ -2,32 +2,14 @@
Initialization file for invokeai.backend.util
"""
from .attention import auto_detect_slice_size # noqa: F401
from .devices import ( # noqa: F401
CPU_DEVICE,
CUDA_DEVICE,
MPS_DEVICE,
choose_precision,
choose_torch_device,
normalize_device,
torch_dtype,
)
from .devices import choose_precision, choose_torch_device
from .logging import InvokeAILogger
from .util import ( # TO DO: Clean this up; remove the unused symbols
GIG,
Chdir,
ask_user, # noqa
directory_size,
download_with_resume,
instantiate_from_config, # noqa
url_attachment_name, # noqa
)
from .util import GIG, Chdir, directory_size
__all__ = [
"GIG",
"directory_size",
"Chdir",
"download_with_resume",
"InvokeAILogger",
"choose_precision",
"choose_torch_device",

67
invokeai/backend/util/log.py
View File

@ -1,67 +0,0 @@
"""
Functions for better format logging
write_log -- logs the name of the output image, prompt, and prompt args to the terminal and different types of file
1 write_log_message -- Writes a message to the console
2 write_log_files -- Writes a message to files
2.1 write_log_default -- File in plain text
2.2 write_log_txt -- File in txt format
2.3 write_log_markdown -- File in markdown format
"""
import os
def write_log(results, log_path, file_types, output_cntr):
"""
logs the name of the output image, prompt, and prompt args to the terminal and files
"""
output_cntr = write_log_message(results, output_cntr)
write_log_files(results, log_path, file_types)
return output_cntr
def write_log_message(results, output_cntr):
"""logs to the terminal"""
if len(results) == 0:
return output_cntr
log_lines = [f"{path}: {prompt}\n" for path, prompt in results]
if len(log_lines) > 1:
subcntr = 1
for ll in log_lines:
print(f"[{output_cntr}.{subcntr}] {ll}", end="")
subcntr += 1
else:
print(f"[{output_cntr}] {log_lines[0]}", end="")
return output_cntr + 1
def write_log_files(results, log_path, file_types):
for file_type in file_types:
if file_type == "txt":
write_log_txt(log_path, results)
elif file_type == "md" or file_type == "markdown":
write_log_markdown(log_path, results)
else:
print(f"'{file_type}' format is not supported, so write in plain text")
write_log_default(log_path, results, file_type)
def write_log_default(log_path, results, file_type):
plain_txt_lines = [f"{path}: {prompt}\n" for path, prompt in results]
with open(log_path + "." + file_type, "a", encoding="utf-8") as file:
file.writelines(plain_txt_lines)
def write_log_txt(log_path, results):
txt_lines = [f"{path}: {prompt}\n" for path, prompt in results]
with open(log_path + ".txt", "a", encoding="utf-8") as file:
file.writelines(txt_lines)
def write_log_markdown(log_path, results):
md_lines = []
for path, prompt in results:
file_name = os.path.basename(path)
md_lines.append(f"## {file_name}\n![]({file_name})\n\n{prompt}\n")
with open(log_path + ".md", "a", encoding="utf-8") as file:
file.writelines(md_lines)

352
invokeai/backend/util/util.py
View File

@ -1,29 +1,13 @@
import base64
import importlib
import io
import math
import multiprocessing as mp
import os
import re
import warnings
from collections import abc
from inspect import isfunction
from pathlib import Path
from queue import Queue
from threading import Thread
import numpy as np
import requests
import torch
from diffusers import logging as diffusers_logging
from PIL import Image, ImageDraw, ImageFont
from tqdm import tqdm
from PIL import Image
from transformers import logging as transformers_logging
import invokeai.backend.util.logging as logger
from .devices import torch_dtype
# actual size of a gig
GIG = 1073741824
@ -41,340 +25,6 @@ def directory_size(directory: Path) -> int:
return sum
def log_txt_as_img(wh, xc, size=10):
# wh a tuple of (width, height)
# xc a list of captions to plot
b = len(xc)
txts = []
for bi in range(b):
txt = Image.new("RGB", wh, color="white")
draw = ImageDraw.Draw(txt)
font = ImageFont.load_default()
nc = int(40 * (wh[0] / 256))
lines = "\n".join(xc[bi][start : start + nc] for start in range(0, len(xc[bi]), nc))
try:
draw.text((0, 0), lines, fill="black", font=font)
except UnicodeEncodeError:
logger.warning("Cant encode string for logging. Skipping.")
txt = np.array(txt).transpose(2, 0, 1) / 127.5 - 1.0
txts.append(txt)
txts = np.stack(txts)
txts = torch.tensor(txts)
return txts
def ismap(x):
if not isinstance(x, torch.Tensor):
return False
return (len(x.shape) == 4) and (x.shape[1] > 3)
def isimage(x):
if not isinstance(x, torch.Tensor):
return False
return (len(x.shape) == 4) and (x.shape[1] == 3 or x.shape[1] == 1)
def exists(x):
return x is not None
def default(val, d):
if exists(val):
return val
return d() if isfunction(d) else d
def mean_flat(tensor):
"""
https://github.com/openai/guided-diffusion/blob/27c20a8fab9cb472df5d6bdd6c8d11c8f430b924/guided_diffusion/nn.py#L86
Take the mean over all non-batch dimensions.
"""
return tensor.mean(dim=list(range(1, len(tensor.shape))))
def count_params(model, verbose=False):
total_params = sum(p.numel() for p in model.parameters())
if verbose:
logger.debug(f"{model.__class__.__name__} has {total_params * 1.e-6:.2f} M params.")
return total_params
def instantiate_from_config(config, **kwargs):
if "target" not in config:
if config == "__is_first_stage__":
return None
elif config == "__is_unconditional__":
return None
raise KeyError("Expected key `target` to instantiate.")
return get_obj_from_str(config["target"])(**config.get("params", {}), **kwargs)
def get_obj_from_str(string, reload=False):
module, cls = string.rsplit(".", 1)
if reload:
module_imp = importlib.import_module(module)
importlib.reload(module_imp)
return getattr(importlib.import_module(module, package=None), cls)
def _do_parallel_data_prefetch(func, Q, data, idx, idx_to_fn=False):
# create dummy dataset instance
# run prefetching
if idx_to_fn:
res = func(data, worker_id=idx)
else:
res = func(data)
Q.put([idx, res])
Q.put("Done")
def parallel_data_prefetch(
func: callable,
data,
n_proc,
target_data_type="ndarray",
cpu_intensive=True,
use_worker_id=False,
):
# if target_data_type not in ["ndarray", "list"]:
# raise ValueError(
# "Data, which is passed to parallel_data_prefetch has to be either of type list or ndarray."
# )
if isinstance(data, np.ndarray) and target_data_type == "list":
raise ValueError("list expected but function got ndarray.")
elif isinstance(data, abc.Iterable):
if isinstance(data, dict):
logger.warning(
'"data" argument passed to parallel_data_prefetch is a dict: Using only its values and disregarding keys.'
)
data = list(data.values())
if target_data_type == "ndarray":
data = np.asarray(data)
else:
data = list(data)
else:
raise TypeError(
f"The data, that shall be processed parallel has to be either an np.ndarray or an Iterable, but is actually {type(data)}."
)
if cpu_intensive:
Q = mp.Queue(1000)
proc = mp.Process
else:
Q = Queue(1000)
proc = Thread
# spawn processes
if target_data_type == "ndarray":
arguments = [[func, Q, part, i, use_worker_id] for i, part in enumerate(np.array_split(data, n_proc))]
else:
step = int(len(data) / n_proc + 1) if len(data) % n_proc != 0 else int(len(data) / n_proc)
arguments = [
[func, Q, part, i, use_worker_id]
for i, part in enumerate([data[i : i + step] for i in range(0, len(data), step)])
]
processes = []
for i in range(n_proc):
p = proc(target=_do_parallel_data_prefetch, args=arguments[i])
processes += [p]
# start processes
logger.info("Start prefetching...")
import time
start = time.time()
gather_res = [[] for _ in range(n_proc)]
try:
for p in processes:
p.start()
k = 0
while k < n_proc:
# get result
res = Q.get()
if res == "Done":
k += 1
else:
gather_res[res[0]] = res[1]
except Exception as e:
logger.error("Exception: ", e)
for p in processes:
p.terminate()
raise e
finally:
for p in processes:
p.join()
logger.info(f"Prefetching complete. [{time.time() - start} sec.]")
if target_data_type == "ndarray":
if not isinstance(gather_res[0], np.ndarray):
return np.concatenate([np.asarray(r) for r in gather_res], axis=0)
# order outputs
return np.concatenate(gather_res, axis=0)
elif target_data_type == "list":
out = []
for r in gather_res:
out.extend(r)
return out
else:
return gather_res
def rand_perlin_2d(shape, res, device, fade=lambda t: 6 * t**5 - 15 * t**4 + 10 * t**3):
delta = (res[0] / shape[0], res[1] / shape[1])
d = (shape[0] // res[0], shape[1] // res[1])
grid = (
torch.stack(
torch.meshgrid(
torch.arange(0, res[0], delta[0]),
torch.arange(0, res[1], delta[1]),
indexing="ij",
),
dim=-1,
).to(device)
% 1
)
rand_val = torch.rand(res[0] + 1, res[1] + 1)
angles = 2 * math.pi * rand_val
gradients = torch.stack((torch.cos(angles), torch.sin(angles)), dim=-1).to(device)
def tile_grads(slice1, slice2):
return (
gradients[slice1[0] : slice1[1], slice2[0] : slice2[1]]
.repeat_interleave(d[0], 0)
.repeat_interleave(d[1], 1)
)
def dot(grad, shift):
return (
torch.stack(
(
grid[: shape[0], : shape[1], 0] + shift[0],
grid[: shape[0], : shape[1], 1] + shift[1],
),
dim=-1,
)
* grad[: shape[0], : shape[1]]
).sum(dim=-1)
n00 = dot(tile_grads([0, -1], [0, -1]), [0, 0]).to(device)
n10 = dot(tile_grads([1, None], [0, -1]), [-1, 0]).to(device)
n01 = dot(tile_grads([0, -1], [1, None]), [0, -1]).to(device)
n11 = dot(tile_grads([1, None], [1, None]), [-1, -1]).to(device)
t = fade(grid[: shape[0], : shape[1]])
noise = math.sqrt(2) * torch.lerp(torch.lerp(n00, n10, t[..., 0]), torch.lerp(n01, n11, t[..., 0]), t[..., 1]).to(
device
)
return noise.to(dtype=torch_dtype(device))
def ask_user(question: str, answers: list):
from itertools import chain, repeat
user_prompt = f"\n>> {question} {answers}: "
invalid_answer_msg = "Invalid answer. Please try again."
pose_question = chain([user_prompt], repeat("\n".join([invalid_answer_msg, user_prompt])))
user_answers = map(input, pose_question)
valid_response = next(filter(answers.__contains__, user_answers))
return valid_response
# -------------------------------------
def download_with_resume(url: str, dest: Path, access_token: str = None) -> Path:
"""
Download a model file.
:param url: https, http or ftp URL
:param dest: A Path object. If path exists and is a directory, then we try to derive the filename
from the URL's Content-Disposition header and copy the URL contents into
dest/filename
:param access_token: Access token to access this resource
"""
header = {"Authorization": f"Bearer {access_token}"} if access_token else {}
open_mode = "wb"
exist_size = 0
resp = requests.get(url, headers=header, stream=True, allow_redirects=True)
content_length = int(resp.headers.get("content-length", 0))
if dest.is_dir():
try:
file_name = re.search('filename="(.+)"', resp.headers.get("Content-Disposition")).group(1)
except AttributeError:
file_name = os.path.basename(url)
dest = dest / file_name
else:
dest.parent.mkdir(parents=True, exist_ok=True)
if dest.exists():
exist_size = dest.stat().st_size
header["Range"] = f"bytes={exist_size}-"
open_mode = "ab"
resp = requests.get(url, headers=header, stream=True) # new request with range
if exist_size > content_length:
logger.warning("corrupt existing file found. re-downloading")
os.remove(dest)
exist_size = 0
if resp.status_code == 416 or (content_length > 0 and exist_size == content_length):
logger.warning(f"{dest}: complete file found. Skipping.")
return dest
elif resp.status_code == 206 or exist_size > 0:
logger.warning(f"{dest}: partial file found. Resuming...")
elif resp.status_code != 200:
logger.error(f"An error occurred during downloading {dest}: {resp.reason}")
else:
logger.info(f"{dest}: Downloading...")
try:
if content_length < 2000:
logger.error(f"ERROR DOWNLOADING {url}: {resp.text}")
return None
with (
open(dest, open_mode) as file,
tqdm(
desc=str(dest),
initial=exist_size,
total=content_length,
unit="iB",
unit_scale=True,
unit_divisor=1000,
) as bar,
):
for data in resp.iter_content(chunk_size=1024):
size = file.write(data)
bar.update(size)
except Exception as e:
logger.error(f"An error occurred while downloading {dest}: {str(e)}")
return None
return dest
def url_attachment_name(url: str) -> dict:
try:
resp = requests.get(url, stream=True)
match = re.search('filename="(.+)"', resp.headers.get("Content-Disposition"))
return match.group(1)
except Exception:
return None
def download_with_progress_bar(url: str, dest: Path) -> bool:
result = download_with_resume(url, dest, access_token=None)
return result is not None
def image_to_dataURL(image: Image.Image, image_format: str = "PNG") -> str:
"""
Converts an image into a base64 image dataURL.

189
invokeai/configs/INITIAL_MODELS.yaml
View File

@ -1,189 +0,0 @@
# This file predefines a few models that the user may want to install.
sd-1/main/stable-diffusion-v1-5:
description: Stable Diffusion version 1.5 diffusers model (4.27 GB)
source: runwayml/stable-diffusion-v1-5
recommended: True
default: True
sd-1/main/stable-diffusion-v1-5-inpainting:
description: RunwayML SD 1.5 model optimized for inpainting, diffusers version (4.27 GB)
source: runwayml/stable-diffusion-inpainting
recommended: True
sd-2/main/stable-diffusion-2-1:
description: Stable Diffusion version 2.1 diffusers model, trained on 768 pixel images (5.21 GB)
source: stabilityai/stable-diffusion-2-1
recommended: False
sd-2/main/stable-diffusion-2-inpainting:
description: Stable Diffusion version 2.0 inpainting model (5.21 GB)
source: stabilityai/stable-diffusion-2-inpainting
recommended: False
sdxl/main/stable-diffusion-xl-base-1-0:
description: Stable Diffusion XL base model (12 GB)
source: stabilityai/stable-diffusion-xl-base-1.0
recommended: True
sdxl-refiner/main/stable-diffusion-xl-refiner-1-0:
description: Stable Diffusion XL refiner model (12 GB)
source: stabilityai/stable-diffusion-xl-refiner-1.0
recommended: False
sdxl/vae/sdxl-vae-fp16-fix:
description: Version of the SDXL-1.0 VAE that works in half precision mode
source: madebyollin/sdxl-vae-fp16-fix
recommended: True
sd-1/main/Analog-Diffusion:
description: An SD-1.5 model trained on diverse analog photographs (2.13 GB)
source: wavymulder/Analog-Diffusion
recommended: False
sd-1/main/Deliberate:
description: Versatile model that produces detailed images up to 768px (4.27 GB)
source: https://huggingface.co/XpucT/Deliberate/resolve/main/Deliberate_v5.safetensors?download=true
recommended: False
sd-1/main/Dungeons-and-Diffusion:
description: Dungeons & Dragons characters (2.13 GB)
source: 0xJustin/Dungeons-and-Diffusion
recommended: False
sd-1/main/dreamlike-photoreal-2:
description: A photorealistic model trained on 768 pixel images based on SD 1.5 (2.13 GB)
source: dreamlike-art/dreamlike-photoreal-2.0
recommended: False
sd-1/main/Inkpunk-Diffusion:
description: Stylized illustrations inspired by Gorillaz, FLCL and Shinkawa; prompt with "nvinkpunk" (4.27 GB)
source: Envvi/Inkpunk-Diffusion
recommended: False
sd-1/main/openjourney:
description: An SD 1.5 model fine tuned on Midjourney; prompt with "mdjrny-v4 style" (2.13 GB)
source: prompthero/openjourney
recommended: False
sd-1/main/seek.art_MEGA:
source: coreco/seek.art_MEGA
description: A general use SD-1.5 "anything" model that supports multiple styles (2.1 GB)
recommended: False
sd-1/main/trinart_stable_diffusion_v2:
description: An SD-1.5 model finetuned with ~40K assorted high resolution manga/anime-style images (2.13 GB)
source: naclbit/trinart_stable_diffusion_v2
recommended: False
sd-1/controlnet/qrcode_monster:
source: monster-labs/control_v1p_sd15_qrcode_monster
description: Controlnet model that generates scannable creative QR codes
subfolder: v2
sd-1/controlnet/canny:
description: Controlnet weights trained on sd-1.5 with canny conditioning.
source: lllyasviel/control_v11p_sd15_canny
recommended: True
sd-1/controlnet/inpaint:
source: lllyasviel/control_v11p_sd15_inpaint
description: Controlnet weights trained on sd-1.5 with canny conditioning, inpaint version
sd-1/controlnet/mlsd:
description: Controlnet weights trained on sd-1.5 with canny conditioning, MLSD version
source: lllyasviel/control_v11p_sd15_mlsd
sd-1/controlnet/depth:
description: Controlnet weights trained on sd-1.5 with depth conditioning
source: lllyasviel/control_v11f1p_sd15_depth
recommended: True
sd-1/controlnet/normal_bae:
description: Controlnet weights trained on sd-1.5 with normalbae image conditioning
source: lllyasviel/control_v11p_sd15_normalbae
sd-1/controlnet/seg:
description: Controlnet weights trained on sd-1.5 with seg image conditioning
source: lllyasviel/control_v11p_sd15_seg
sd-1/controlnet/lineart:
description: Controlnet weights trained on sd-1.5 with lineart image conditioning
source: lllyasviel/control_v11p_sd15_lineart
recommended: True
sd-1/controlnet/lineart_anime:
description: Controlnet weights trained on sd-1.5 with anime image conditioning
source: lllyasviel/control_v11p_sd15s2_lineart_anime
sd-1/controlnet/openpose:
description: Controlnet weights trained on sd-1.5 with openpose image conditioning
source: lllyasviel/control_v11p_sd15_openpose
recommended: True
sd-1/controlnet/scribble:
source: lllyasviel/control_v11p_sd15_scribble
description: Controlnet weights trained on sd-1.5 with scribble image conditioning
recommended: False
sd-1/controlnet/softedge:
source: lllyasviel/control_v11p_sd15_softedge
description: Controlnet weights trained on sd-1.5 with soft edge conditioning
sd-1/controlnet/shuffle:
source: lllyasviel/control_v11e_sd15_shuffle
description: Controlnet weights trained on sd-1.5 with shuffle image conditioning
sd-1/controlnet/tile:
source: lllyasviel/control_v11f1e_sd15_tile
description: Controlnet weights trained on sd-1.5 with tiled image conditioning
sd-1/controlnet/ip2p:
source: lllyasviel/control_v11e_sd15_ip2p
description: Controlnet weights trained on sd-1.5 with ip2p conditioning.
sdxl/controlnet/canny-sdxl:
description: Controlnet weights trained on sdxl-1.0 with canny conditioning.
source: diffusers/controlnet-canny-sdxl-1.0
recommended: True
sdxl/controlnet/depth-sdxl:
description: Controlnet weights trained on sdxl-1.0 with depth conditioning.
source: diffusers/controlnet-depth-sdxl-1.0
recommended: True
sdxl/controlnet/softedge-dexined-sdxl:
description: Controlnet weights trained on sdxl-1.0 with dexined soft edge preprocessing.
source: SargeZT/controlnet-sd-xl-1.0-softedge-dexined
sdxl/controlnet/depth-16bit-zoe-sdxl:
description: Controlnet weights trained on sdxl-1.0 with Zoe's preprocessor (16 bits).
source: SargeZT/controlnet-sd-xl-1.0-depth-16bit-zoe
sdxl/controlnet/depth-zoe-sdxl:
description: Controlnet weights trained on sdxl-1.0 with Zoe's preprocessor (32 bits).
source: diffusers/controlnet-zoe-depth-sdxl-1.0
sd-1/t2i_adapter/canny-sd15:
source: TencentARC/t2iadapter_canny_sd15v2
sd-1/t2i_adapter/sketch-sd15:
source: TencentARC/t2iadapter_sketch_sd15v2
sd-1/t2i_adapter/depth-sd15:
source: TencentARC/t2iadapter_depth_sd15v2
sd-1/t2i_adapter/zoedepth-sd15:
source: TencentARC/t2iadapter_zoedepth_sd15v1
sdxl/t2i_adapter/canny-sdxl:
source: TencentARC/t2i-adapter-canny-sdxl-1.0
sdxl/t2i_adapter/zoedepth-sdxl:
source: TencentARC/t2i-adapter-depth-zoe-sdxl-1.0
sdxl/t2i_adapter/lineart-sdxl:
source: TencentARC/t2i-adapter-lineart-sdxl-1.0
sdxl/t2i_adapter/sketch-sdxl:
source: TencentARC/t2i-adapter-sketch-sdxl-1.0
sd-1/embedding/EasyNegative:
source: https://huggingface.co/embed/EasyNegative/resolve/main/EasyNegative.safetensors
recommended: True
description: A textual inversion to use in the negative prompt to reduce bad anatomy
sd-1/lora/FlatColor:
source: https://civitai.com/models/6433/loraflatcolor
recommended: True
description: A LoRA that generates scenery using solid blocks of color
sd-1/lora/Ink scenery:
source: https://civitai.com/api/download/models/83390
description: Generate india ink-like landscapes
sd-1/ip_adapter/ip_adapter_sd15:
source: InvokeAI/ip_adapter_sd15
recommended: True
requires:
- InvokeAI/ip_adapter_sd_image_encoder
description: IP-Adapter for SD 1.5 models
sd-1/ip_adapter/ip_adapter_plus_sd15:
source: InvokeAI/ip_adapter_plus_sd15
recommended: False
requires:
- InvokeAI/ip_adapter_sd_image_encoder
description: Refined IP-Adapter for SD 1.5 models
sd-1/ip_adapter/ip_adapter_plus_face_sd15:
source: InvokeAI/ip_adapter_plus_face_sd15
recommended: False
requires:
- InvokeAI/ip_adapter_sd_image_encoder
description: Refined IP-Adapter for SD 1.5 models, adapted for faces
sdxl/ip_adapter/ip_adapter_sdxl:
source: InvokeAI/ip_adapter_sdxl
recommended: False
requires:
- InvokeAI/ip_adapter_sdxl_image_encoder
description: IP-Adapter for SDXL models
any/clip_vision/ip_adapter_sd_image_encoder:
source: InvokeAI/ip_adapter_sd_image_encoder
recommended: False
description: Required model for using IP-Adapters with SD-1/2 models
any/clip_vision/ip_adapter_sdxl_image_encoder:
source: InvokeAI/ip_adapter_sdxl_image_encoder
recommended: False
description: Required model for using IP-Adapters with SDXL models

11
invokeai/frontend/cli/arg_parser.py
View File

@ -3,19 +3,16 @@ from typing import Optional
from invokeai.version import __version__
_root_help = r"""Sets a root directory for the app.
If omitted, the app will search for the root directory in the following order:
_root_help = r"""Path to the runtime root directory. If omitted, the app will search for the root directory in the following order:
- The `$INVOKEAI_ROOT` environment variable
- The currently active virtual environment's parent directory
- `$HOME/invokeai`"""
_ignore_missing_core_models_help = r"""If set, the app will ignore missing core diffusers conversion models.
These are required to use checkpoint/safetensors models.
If you only use diffusers models, you can safely enable this."""
_config_file_help = r"""Path to the invokeai.yaml configuration file. If omitted, the app will search for the file in the root directory."""
_parser = ArgumentParser(description="Invoke Studio", formatter_class=RawTextHelpFormatter)
_parser.add_argument("--root", type=str, help=_root_help)
_parser.add_argument("--ignore_missing_core_models", action="store_true", help=_ignore_missing_core_models_help)
_parser.add_argument("--config", dest="config_file", type=str, help=_config_file_help)
_parser.add_argument("--version", action="version", version=__version__, help="Displays the version and exits.")
@ -39,9 +36,11 @@ class InvokeAIArgs:
"""
args: Optional[Namespace] = None
did_parse: bool = False
@staticmethod
def parse_args() -> Optional[Namespace]:
"""Parse CLI args and store the result."""
InvokeAIArgs.args = _parser.parse_args()
InvokeAIArgs.did_parse = True
return InvokeAIArgs.args

60
invokeai/frontend/install/invokeai_configure.py
View File

@ -1,60 +0,0 @@
"""
Wrapper for invokeai.backend.configure.invokeai_configure
"""
import argparse
def run_configure() -> None:
# Before doing _anything_, parse CLI args!
from invokeai.frontend.cli.arg_parser import InvokeAIArgs
parser = argparse.ArgumentParser(description="InvokeAI model downloader")
parser.add_argument(
"--skip-sd-weights",
dest="skip_sd_weights",
action=argparse.BooleanOptionalAction,
default=False,
help="skip downloading the large Stable Diffusion weight files",
)
parser.add_argument(
"--skip-support-models",
dest="skip_support_models",
action=argparse.BooleanOptionalAction,
default=False,
help="skip downloading the support models",
)
parser.add_argument(
"--full-precision",
dest="full_precision",
action=argparse.BooleanOptionalAction,
type=bool,
default=False,
help="use 32-bit weights instead of faster 16-bit weights",
)
parser.add_argument(
"--yes",
"-y",
dest="yes_to_all",
action="store_true",
help='answer "yes" to all prompts',
)
parser.add_argument(
"--default_only",
action="store_true",
help="when --yes specified, only install the default model",
)
parser.add_argument(
"--root_dir",
dest="root",
type=str,
default=None,
help="path to root of install directory",
)
opt = parser.parse_args()
InvokeAIArgs.args = opt
from invokeai.backend.install.invokeai_configure import main as invokeai_configure
invokeai_configure(opt)

652
invokeai/frontend/install/model_install.py
View File

@ -1,652 +0,0 @@
#!/usr/bin/env python
# Copyright (c) 2022 Lincoln D. Stein (https://github.com/lstein)
# Before running stable-diffusion on an internet-isolated machine,
# run this script from one with internet connectivity. The
# two machines must share a common .cache directory.
"""
This is the npyscreen frontend to the model installation application.
It is currently named model_install2.py, but will ultimately replace model_install.py.
"""
import argparse
import curses
import pathlib
import sys
import traceback
import warnings
from argparse import Namespace
from shutil import get_terminal_size
from typing import Any, Dict, List, Optional, Set
import npyscreen
import torch
from npyscreen import widget
from invokeai.app.services.config.config_default import get_config
from invokeai.app.services.model_install import ModelInstallServiceBase
from invokeai.backend.install.check_directories import validate_directories
from invokeai.backend.install.install_helper import InstallHelper, InstallSelections, UnifiedModelInfo
from invokeai.backend.model_manager import ModelType
from invokeai.backend.util import choose_precision, choose_torch_device
from invokeai.backend.util.logging import InvokeAILogger
from invokeai.frontend.install.widgets import (
MIN_COLS,
MIN_LINES,
CenteredTitleText,
CyclingForm,
MultiSelectColumns,
SingleSelectColumns,
TextBox,
WindowTooSmallException,
set_min_terminal_size,
)
warnings.filterwarnings("ignore", category=UserWarning) # noqa: E402
config = get_config()
logger = InvokeAILogger.get_logger("ModelInstallService", config=config)
# logger.setLevel("WARNING")
# logger.setLevel('DEBUG')
# build a table mapping all non-printable characters to None
# for stripping control characters
# from https://stackoverflow.com/questions/92438/stripping-non-printable-characters-from-a-string-in-python
NOPRINT_TRANS_TABLE = {i: None for i in range(0, sys.maxunicode + 1) if not chr(i).isprintable()}
# maximum number of installed models we can display before overflowing vertically
MAX_OTHER_MODELS = 72
def make_printable(s: str) -> str:
"""Replace non-printable characters in a string."""
return s.translate(NOPRINT_TRANS_TABLE)
class addModelsForm(CyclingForm, npyscreen.FormMultiPage):
"""Main form for interactive TUI."""
# for responsive resizing set to False, but this seems to cause a crash!
FIX_MINIMUM_SIZE_WHEN_CREATED = True
# for persistence
current_tab = 0
def __init__(self, parentApp: npyscreen.NPSAppManaged, name: str, multipage: bool = False, **keywords: Any):
self.multipage = multipage
self.subprocess = None
super().__init__(parentApp=parentApp, name=name, **keywords)
def create(self) -> None:
self.installer = self.parentApp.install_helper.installer
self.model_labels = self._get_model_labels()
self.keypress_timeout = 10
self.counter = 0
self.subprocess_connection = None
window_width, window_height = get_terminal_size()
# npyscreen has no typing hints
self.nextrely -= 1 # type: ignore
self.add_widget_intelligent(
npyscreen.FixedText,
value="Use ctrl-N and ctrl-P to move to the <N>ext and <P>revious fields. Cursor keys navigate, and <space> selects.",
editable=False,
color="CAUTION",
)
self.nextrely += 1 # type: ignore
self.tabs = self.add_widget_intelligent(
SingleSelectColumns,
values=[
"STARTERS",
"MAINS",
"CONTROLNETS",
"T2I-ADAPTERS",
"IP-ADAPTERS",
"LORAS",
"TI EMBEDDINGS",
],
value=[self.current_tab],
columns=7,
max_height=2,
relx=8,
scroll_exit=True,
)
self.tabs.on_changed = self._toggle_tables
top_of_table = self.nextrely # type: ignore
self.starter_pipelines = self.add_starter_pipelines()
bottom_of_table = self.nextrely # type: ignore
self.nextrely = top_of_table
self.pipeline_models = self.add_pipeline_widgets(
model_type=ModelType.Main, window_width=window_width, exclude=self.starter_models
)
# self.pipeline_models['autoload_pending'] = True
bottom_of_table = max(bottom_of_table, self.nextrely)
self.nextrely = top_of_table
self.controlnet_models = self.add_model_widgets(
model_type=ModelType.ControlNet,
window_width=window_width,
)
bottom_of_table = max(bottom_of_table, self.nextrely)
self.nextrely = top_of_table
self.t2i_models = self.add_model_widgets(
model_type=ModelType.T2IAdapter,
window_width=window_width,
)
bottom_of_table = max(bottom_of_table, self.nextrely)
self.nextrely = top_of_table
self.ipadapter_models = self.add_model_widgets(
model_type=ModelType.IPAdapter,
window_width=window_width,
)
bottom_of_table = max(bottom_of_table, self.nextrely)
self.nextrely = top_of_table
self.lora_models = self.add_model_widgets(
model_type=ModelType.LoRA,
window_width=window_width,
)
bottom_of_table = max(bottom_of_table, self.nextrely)
self.nextrely = top_of_table
self.ti_models = self.add_model_widgets(
model_type=ModelType.TextualInversion,
window_width=window_width,
)
bottom_of_table = max(bottom_of_table, self.nextrely)
self.nextrely = bottom_of_table + 1
self.nextrely += 1
back_label = "BACK"
cancel_label = "CANCEL"
current_position = self.nextrely
if self.multipage:
self.back_button = self.add_widget_intelligent(
npyscreen.ButtonPress,
name=back_label,
when_pressed_function=self.on_back,
)
else:
self.nextrely = current_position
self.cancel_button = self.add_widget_intelligent(
npyscreen.ButtonPress, name=cancel_label, when_pressed_function=self.on_cancel
)
self.nextrely = current_position
label = "APPLY CHANGES"
self.nextrely = current_position
self.done = self.add_widget_intelligent(
npyscreen.ButtonPress,
name=label,
relx=window_width - len(label) - 15,
when_pressed_function=self.on_done,
)
# This restores the selected page on return from an installation
for _i in range(1, self.current_tab + 1):
self.tabs.h_cursor_line_down(1)
self._toggle_tables([self.current_tab])
############# diffusers tab ##########
def add_starter_pipelines(self) -> dict[str, npyscreen.widget]:
"""Add widgets responsible for selecting diffusers models"""
widgets: Dict[str, npyscreen.widget] = {}
all_models = self.all_models # master dict of all models, indexed by key
model_list = [x for x in self.starter_models if all_models[x].type in ["main", "vae"]]
model_labels = [self.model_labels[x] for x in model_list]
widgets.update(
label1=self.add_widget_intelligent(
CenteredTitleText,
name="Select from a starter set of Stable Diffusion models from HuggingFace and Civitae.",
editable=False,
labelColor="CAUTION",
)
)
self.nextrely -= 1
# if user has already installed some initial models, then don't patronize them
# by showing more recommendations
show_recommended = len(self.installed_models) == 0
checked = [
model_list.index(x)
for x in model_list
if (show_recommended and all_models[x].recommended) or all_models[x].installed
]
widgets.update(
models_selected=self.add_widget_intelligent(
MultiSelectColumns,
columns=1,
name="Install Starter Models",
values=model_labels,
value=checked,
max_height=len(model_list) + 1,
relx=4,
scroll_exit=True,
),
models=model_list,
)
self.nextrely += 1
return widgets
############# Add a set of model install widgets ########
def add_model_widgets(
self,
model_type: ModelType,
window_width: int = 120,
install_prompt: Optional[str] = None,
exclude: Optional[Set[str]] = None,
) -> dict[str, npyscreen.widget]:
"""Generic code to create model selection widgets"""
if exclude is None:
exclude = set()
widgets: Dict[str, npyscreen.widget] = {}
all_models = self.all_models
model_list = sorted(
[x for x in all_models if all_models[x].type == model_type and x not in exclude],
key=lambda x: all_models[x].name or "",
)
model_labels = [self.model_labels[x] for x in model_list]
show_recommended = len(self.installed_models) == 0
truncated = False
if len(model_list) > 0:
max_width = max([len(x) for x in model_labels])
columns = window_width // (max_width + 8) # 8 characters for "[x] " and padding
columns = min(len(model_list), columns) or 1
prompt = (
install_prompt
or f"Select the desired {model_type.value.title()} models to install. Unchecked models will be purged from disk."
)
widgets.update(
label1=self.add_widget_intelligent(
CenteredTitleText,
name=prompt,
editable=False,
labelColor="CAUTION",
)
)
if len(model_labels) > MAX_OTHER_MODELS:
model_labels = model_labels[0:MAX_OTHER_MODELS]
truncated = True
widgets.update(
models_selected=self.add_widget_intelligent(
MultiSelectColumns,
columns=columns,
name=f"Install {model_type} Models",
values=model_labels,
value=[
model_list.index(x)
for x in model_list
if (show_recommended and all_models[x].recommended) or all_models[x].installed
],
max_height=len(model_list) // columns + 1,
relx=4,
scroll_exit=True,
),
models=model_list,
)
if truncated:
widgets.update(
warning_message=self.add_widget_intelligent(
npyscreen.FixedText,
value=f"Too many models to display (max={MAX_OTHER_MODELS}). Some are not displayed.",
editable=False,
color="CAUTION",
)
)
self.nextrely += 1
widgets.update(
download_ids=self.add_widget_intelligent(
TextBox,
name="Additional URLs, or HuggingFace repo_ids to install (Space separated. Use shift-control-V to paste):",
max_height=6,
scroll_exit=True,
editable=True,
)
)
return widgets
### Tab for arbitrary diffusers widgets ###
def add_pipeline_widgets(
self,
model_type: ModelType = ModelType.Main,
window_width: int = 120,
**kwargs,
) -> dict[str, npyscreen.widget]:
"""Similar to add_model_widgets() but adds some additional widgets at the bottom
to support the autoload directory"""
widgets = self.add_model_widgets(
model_type=model_type,
window_width=window_width,
install_prompt=f"Installed {model_type.value.title()} models. Unchecked models in the InvokeAI root directory will be deleted. Enter URLs, paths or repo_ids to import.",
**kwargs,
)
return widgets
def resize(self) -> None:
super().resize()
if s := self.starter_pipelines.get("models_selected"):
if model_list := self.starter_pipelines.get("models"):
s.values = [self.model_labels[x] for x in model_list]
def _toggle_tables(self, value: List[int]) -> None:
selected_tab = value[0]
widgets = [
self.starter_pipelines,
self.pipeline_models,
self.controlnet_models,
self.t2i_models,
self.ipadapter_models,
self.lora_models,
self.ti_models,
]
for group in widgets:
for _k, v in group.items():
try:
v.hidden = True
v.editable = False
except Exception:
pass
for _k, v in widgets[selected_tab].items():
try:
v.hidden = False
if not isinstance(v, (npyscreen.FixedText, npyscreen.TitleFixedText, CenteredTitleText)):
v.editable = True
except Exception:
pass
self.__class__.current_tab = selected_tab # for persistence
self.display()
def _get_model_labels(self) -> dict[str, str]:
"""Return a list of trimmed labels for all models."""
window_width, window_height = get_terminal_size()
checkbox_width = 4
spacing_width = 2
result = {}
models = self.all_models
label_width = max([len(models[x].name or "") for x in self.starter_models])
description_width = window_width - label_width - checkbox_width - spacing_width
for key in self.all_models:
description = models[key].description
description = (
description[0 : description_width - 3] + "..."
if description and len(description) > description_width
else description
if description
else ""
)
result[key] = f"%-{label_width}s %s" % (models[key].name, description)
return result
def _get_columns(self) -> int:
window_width, window_height = get_terminal_size()
cols = 4 if window_width > 240 else 3 if window_width > 160 else 2 if window_width > 80 else 1
return min(cols, len(self.installed_models))
def confirm_deletions(self, selections: InstallSelections) -> bool:
remove_models = selections.remove_models
if remove_models:
model_names = [self.all_models[x].name or "" for x in remove_models]
mods = "\n".join(model_names)
is_ok = npyscreen.notify_ok_cancel(
f"These unchecked models will be deleted from disk. Continue?\n---------\n{mods}"
)
assert isinstance(is_ok, bool) # npyscreen doesn't have return type annotations
return is_ok
else:
return True
@property
def all_models(self) -> Dict[str, UnifiedModelInfo]:
# npyscreen doesn't having typing hints
return self.parentApp.install_helper.all_models # type: ignore
@property
def starter_models(self) -> List[str]:
return self.parentApp.install_helper._starter_models # type: ignore
@property
def installed_models(self) -> List[str]:
return self.parentApp.install_helper._installed_models # type: ignore
def on_back(self) -> None:
self.parentApp.switchFormPrevious()
self.editing = False
def on_cancel(self) -> None:
self.parentApp.setNextForm(None)
self.parentApp.user_cancelled = True
self.editing = False
def on_done(self) -> None:
self.marshall_arguments()
if not self.confirm_deletions(self.parentApp.install_selections):
return
self.parentApp.setNextForm(None)
self.parentApp.user_cancelled = False
self.editing = False
def marshall_arguments(self) -> None:
"""
Assemble arguments and store as attributes of the application:
.starter_models: dict of model names to install from INITIAL_CONFIGURE.yaml
True => Install
False => Remove
.scan_directory: Path to a directory of models to scan and import
.autoscan_on_startup: True if invokeai should scan and import at startup time
.import_model_paths: list of URLs, repo_ids and file paths to import
"""
selections = self.parentApp.install_selections
all_models = self.all_models
# Defined models (in INITIAL_CONFIG.yaml or invokeai.db) to add/remove
ui_sections = [
self.starter_pipelines,
self.pipeline_models,
self.controlnet_models,
self.t2i_models,
self.ipadapter_models,
self.lora_models,
self.ti_models,
]
for section in ui_sections:
if "models_selected" not in section:
continue
selected = {section["models"][x] for x in section["models_selected"].value}
models_to_install = [x for x in selected if not self.all_models[x].installed]
models_to_remove = [x for x in section["models"] if x not in selected and self.all_models[x].installed]
selections.remove_models.extend(models_to_remove)
selections.install_models.extend([all_models[x] for x in models_to_install])
# models located in the 'download_ids" section
for section in ui_sections:
if downloads := section.get("download_ids"):
models = [UnifiedModelInfo(source=x) for x in downloads.value.split()]
selections.install_models.extend(models)
class AddModelApplication(npyscreen.NPSAppManaged): # type: ignore
def __init__(self, opt: Namespace, install_helper: InstallHelper):
super().__init__()
self.program_opts = opt
self.user_cancelled = False
self.install_selections = InstallSelections()
self.install_helper = install_helper
def onStart(self) -> None:
npyscreen.setTheme(npyscreen.Themes.DefaultTheme)
self.main_form = self.addForm(
"MAIN",
addModelsForm,
name="Install Stable Diffusion Models",
cycle_widgets=False,
)
def list_models(installer: ModelInstallServiceBase, model_type: ModelType):
"""Print out all models of type model_type."""
models = installer.record_store.search_by_attr(model_type=model_type)
print(f"Installed models of type `{model_type}`:")
for model in models:
path = (config.models_path / model.path).resolve()
print(f"{model.name:40}{model.base.value:5}{model.type.value:8}{model.format.value:12}{path}")
# --------------------------------------------------------
def select_and_download_models(opt: Namespace) -> None:
"""Prompt user for install/delete selections and execute."""
precision = "float32" if opt.full_precision else choose_precision(torch.device(choose_torch_device()))
config.precision = precision
install_helper = InstallHelper(config, logger)
installer = install_helper.installer
if opt.list_models:
list_models(installer, opt.list_models)
elif opt.add or opt.delete:
selections = InstallSelections(
install_models=[UnifiedModelInfo(source=x) for x in (opt.add or [])], remove_models=opt.delete or []
)
install_helper.add_or_delete(selections)
elif opt.default_only:
default_model = install_helper.default_model()
assert default_model is not None
selections = InstallSelections(install_models=[default_model])
install_helper.add_or_delete(selections)
elif opt.yes_to_all:
selections = InstallSelections(install_models=install_helper.recommended_models())
install_helper.add_or_delete(selections)
# this is where the TUI is called
else:
if not set_min_terminal_size(MIN_COLS, MIN_LINES):
raise WindowTooSmallException(
"Could not increase terminal size. Try running again with a larger window or smaller font size."
)
installApp = AddModelApplication(opt, install_helper)
try:
installApp.run()
except KeyboardInterrupt:
print("Aborted...")
sys.exit(-1)
install_helper.add_or_delete(installApp.install_selections)
# -------------------------------------
def main() -> None:
parser = argparse.ArgumentParser(description="InvokeAI model downloader")
parser.add_argument(
"--add",
nargs="*",
help="List of URLs, local paths or repo_ids of models to install",
)
parser.add_argument(
"--delete",
nargs="*",
help="List of names of models to delete. Use type:name to disambiguate, as in `controlnet:my_model`",
)
parser.add_argument(
"--full-precision",
dest="full_precision",
action=argparse.BooleanOptionalAction,
type=bool,
default=False,
help="use 32-bit weights instead of faster 16-bit weights",
)
parser.add_argument(
"--yes",
"-y",
dest="yes_to_all",
action="store_true",
help='answer "yes" to all prompts',
)
parser.add_argument(
"--default_only",
action="store_true",
help="Only install the default model",
)
parser.add_argument(
"--list-models",
choices=[x.value for x in ModelType],
help="list installed models",
)
parser.add_argument(
"--root_dir",
dest="root",
type=pathlib.Path,
default=None,
help="path to root of install directory",
)
opt = parser.parse_args()
invoke_args: dict[str, Any] = {}
if opt.full_precision:
invoke_args["precision"] = "float32"
config.update_config(invoke_args)
if opt.root:
config.set_root(opt.root)
logger = InvokeAILogger().get_logger(config=config)
try:
validate_directories(config)
except AssertionError:
logger.info("Your InvokeAI root directory is not set up. Calling invokeai-configure.")
sys.argv = ["invokeai_configure", "--yes", "--skip-sd-weights"]
from invokeai.frontend.install.invokeai_configure import invokeai_configure
invokeai_configure()
sys.exit(0)
try:
select_and_download_models(opt)
except AssertionError as e:
logger.error(e)
sys.exit(-1)
except KeyboardInterrupt:
curses.nocbreak()
curses.echo()
curses.endwin()
logger.info("Goodbye! Come back soon.")
except WindowTooSmallException as e:
logger.error(str(e))
except widget.NotEnoughSpaceForWidget as e:
if str(e).startswith("Height of 1 allocated"):
logger.error("Insufficient vertical space for the interface. Please make your window taller and try again")
input("Press any key to continue...")
except Exception as e:
if str(e).startswith("addwstr"):
logger.error(
"Insufficient horizontal space for the interface. Please make your window wider and try again."
)
else:
print(f"An exception has occurred: {str(e)} Details:")
print(traceback.format_exc(), file=sys.stderr)
input("Press any key to continue...")
# -------------------------------------
if __name__ == "__main__":
main()

441
invokeai/frontend/install/widgets.py
View File

@ -1,441 +0,0 @@
"""
Widget class definitions used by model_select.py, merge_diffusers.py and textual_inversion.py
"""
import curses
import math
import os
import platform
import struct
import subprocess
import sys
import textwrap
from curses import BUTTON2_CLICKED, BUTTON3_CLICKED
from shutil import get_terminal_size
from typing import Optional
import npyscreen
import npyscreen.wgmultiline as wgmultiline
import pyperclip
from npyscreen import fmPopup
# minimum size for UIs
MIN_COLS = 150
MIN_LINES = 40
class WindowTooSmallException(Exception):
pass
# -------------------------------------
def set_terminal_size(columns: int, lines: int) -> bool:
OS = platform.uname().system
screen_ok = False
while not screen_ok:
ts = get_terminal_size()
width = max(columns, ts.columns)
height = max(lines, ts.lines)
if OS == "Windows":
pass
# not working reliably - ask user to adjust the window
# _set_terminal_size_powershell(width,height)
elif OS in ["Darwin", "Linux"]:
_set_terminal_size_unix(width, height)
# check whether it worked....
ts = get_terminal_size()
if ts.columns < columns or ts.lines < lines:
print(
f"\033[1mThis window is too small for the interface. InvokeAI requires {columns}x{lines} (w x h) characters, but window is {ts.columns}x{ts.lines}\033[0m"
)
resp = input(
"Maximize the window and/or decrease the font size then press any key to continue. Type [Q] to give up.."
)
if resp.upper().startswith("Q"):
break
else:
screen_ok = True
return screen_ok
def _set_terminal_size_powershell(width: int, height: int):
script = f"""
$pshost = get-host
$pswindow = $pshost.ui.rawui
$newsize = $pswindow.buffersize
$newsize.height = 3000
$newsize.width = {width}
$pswindow.buffersize = $newsize
$newsize = $pswindow.windowsize
$newsize.height = {height}
$newsize.width = {width}
$pswindow.windowsize = $newsize
"""
subprocess.run(["powershell", "-Command", "-"], input=script, text=True)
def _set_terminal_size_unix(width: int, height: int):
import fcntl
import termios
# These terminals accept the size command and report that the
# size changed, but they lie!!!
for bad_terminal in ["TERMINATOR_UUID", "ALACRITTY_WINDOW_ID"]:
if os.environ.get(bad_terminal):
return
winsize = struct.pack("HHHH", height, width, 0, 0)
fcntl.ioctl(sys.stdout.fileno(), termios.TIOCSWINSZ, winsize)
sys.stdout.write("\x1b[8;{height};{width}t".format(height=height, width=width))
sys.stdout.flush()
def set_min_terminal_size(min_cols: int, min_lines: int) -> bool:
# make sure there's enough room for the ui
term_cols, term_lines = get_terminal_size()
if term_cols >= min_cols and term_lines >= min_lines:
return True
cols = max(term_cols, min_cols)
lines = max(term_lines, min_lines)
return set_terminal_size(cols, lines)
class IntSlider(npyscreen.Slider):
def translate_value(self):
stri = "%2d / %2d" % (self.value, self.out_of)
length = (len(str(self.out_of))) * 2 + 4
stri = stri.rjust(length)
return stri
# -------------------------------------
# fix npyscreen form so that cursor wraps both forward and backward
class CyclingForm(object):
def find_previous_editable(self, *args):
done = False
n = self.editw - 1
while not done:
if self._widgets__[n].editable and not self._widgets__[n].hidden:
self.editw = n
done = True
n -= 1
if n < 0:
if self.cycle_widgets:
n = len(self._widgets__) - 1
else:
done = True
# -------------------------------------
class CenteredTitleText(npyscreen.TitleText):
def __init__(self, *args, **keywords):
super().__init__(*args, **keywords)
self.resize()
def resize(self):
super().resize()
maxy, maxx = self.parent.curses_pad.getmaxyx()
label = self.name
self.relx = (maxx - len(label)) // 2
# -------------------------------------
class CenteredButtonPress(npyscreen.ButtonPress):
def resize(self):
super().resize()
maxy, maxx = self.parent.curses_pad.getmaxyx()
label = self.name
self.relx = (maxx - len(label)) // 2
# -------------------------------------
class OffsetButtonPress(npyscreen.ButtonPress):
def __init__(self, screen, offset=0, *args, **keywords):
super().__init__(screen, *args, **keywords)
self.offset = offset
def resize(self):
maxy, maxx = self.parent.curses_pad.getmaxyx()
width = len(self.name)
self.relx = self.offset + (maxx - width) // 2
class IntTitleSlider(npyscreen.TitleText):
_entry_type = IntSlider
class FloatSlider(npyscreen.Slider):
# this is supposed to adjust display precision, but doesn't
def translate_value(self):
stri = "%3.2f / %3.2f" % (self.value, self.out_of)
length = (len(str(self.out_of))) * 2 + 4
stri = stri.rjust(length)
return stri
class FloatTitleSlider(npyscreen.TitleText):
_entry_type = npyscreen.Slider
class SelectColumnBase:
"""Base class for selection widget arranged in columns."""
def make_contained_widgets(self):
self._my_widgets = []
column_width = self.width // self.columns
for h in range(self.value_cnt):
self._my_widgets.append(
self._contained_widgets(
self.parent,
rely=self.rely + (h % self.rows) * self._contained_widget_height,
relx=self.relx + (h // self.rows) * column_width,
max_width=column_width,
max_height=self.__class__._contained_widget_height,
)
)
def set_up_handlers(self):
super().set_up_handlers()
self.handlers.update(
{
curses.KEY_UP: self.h_cursor_line_left,
curses.KEY_DOWN: self.h_cursor_line_right,
}
)
def h_cursor_line_down(self, ch):
self.cursor_line += self.rows
if self.cursor_line >= len(self.values):
if self.scroll_exit:
self.cursor_line = len(self.values) - self.rows
self.h_exit_down(ch)
return True
else:
self.cursor_line -= self.rows
return True
def h_cursor_line_up(self, ch):
self.cursor_line -= self.rows
if self.cursor_line < 0:
if self.scroll_exit:
self.cursor_line = 0
self.h_exit_up(ch)
else:
self.cursor_line = 0
def h_cursor_line_left(self, ch):
super().h_cursor_line_up(ch)
def h_cursor_line_right(self, ch):
super().h_cursor_line_down(ch)
def handle_mouse_event(self, mouse_event):
mouse_id, rel_x, rel_y, z, bstate = self.interpret_mouse_event(mouse_event)
column_width = self.width // self.columns
column_height = math.ceil(self.value_cnt / self.columns)
column_no = rel_x // column_width
row_no = rel_y // self._contained_widget_height
self.cursor_line = column_no * column_height + row_no
if bstate & curses.BUTTON1_DOUBLE_CLICKED:
if hasattr(self, "on_mouse_double_click"):
self.on_mouse_double_click(self.cursor_line)
self.display()
class MultiSelectColumns(SelectColumnBase, npyscreen.MultiSelect):
def __init__(self, screen, columns: int = 1, values: Optional[list] = None, **keywords):
if values is None:
values = []
self.columns = columns
self.value_cnt = len(values)
self.rows = math.ceil(self.value_cnt / self.columns)
super().__init__(screen, values=values, **keywords)
def on_mouse_double_click(self, cursor_line):
self.h_select_toggle(cursor_line)
class SingleSelectWithChanged(npyscreen.SelectOne):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.on_changed = None
def h_select(self, ch):
super().h_select(ch)
if self.on_changed:
self.on_changed(self.value)
class CheckboxWithChanged(npyscreen.Checkbox):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.on_changed = None
def whenToggled(self):
super().whenToggled()
if self.on_changed:
self.on_changed(self.value)
class SingleSelectColumnsSimple(SelectColumnBase, SingleSelectWithChanged):
"""Row of radio buttons. Spacebar to select."""
def __init__(self, screen, columns: int = 1, values: list = None, **keywords):
if values is None:
values = []
self.columns = columns
self.value_cnt = len(values)
self.rows = math.ceil(self.value_cnt / self.columns)
self.on_changed = None
super().__init__(screen, values=values, **keywords)
def h_cursor_line_right(self, ch):
self.h_exit_down("bye bye")
def h_cursor_line_left(self, ch):
self.h_exit_up("bye bye")
class SingleSelectColumns(SingleSelectColumnsSimple):
"""Row of radio buttons. When tabbing over a selection, it is auto selected."""
def when_cursor_moved(self):
self.h_select(self.cursor_line)
class TextBoxInner(npyscreen.MultiLineEdit):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.yank = None
self.handlers.update(
{
"^A": self.h_cursor_to_start,
"^E": self.h_cursor_to_end,
"^K": self.h_kill,
"^F": self.h_cursor_right,
"^B": self.h_cursor_left,
"^Y": self.h_yank,
"^V": self.h_paste,
}
)
def h_cursor_to_start(self, input):
self.cursor_position = 0
def h_cursor_to_end(self, input):
self.cursor_position = len(self.value)
def h_kill(self, input):
self.yank = self.value[self.cursor_position :]
self.value = self.value[: self.cursor_position]
def h_yank(self, input):
if self.yank:
self.paste(self.yank)
def paste(self, text: str):
self.value = self.value[: self.cursor_position] + text + self.value[self.cursor_position :]
self.cursor_position += len(text)
def h_paste(self, input: int = 0):
try:
text = pyperclip.paste()
except ModuleNotFoundError:
text = "To paste with the mouse on Linux, please install the 'xclip' program."
self.paste(text)
def handle_mouse_event(self, mouse_event):
mouse_id, rel_x, rel_y, z, bstate = self.interpret_mouse_event(mouse_event)
if bstate & (BUTTON2_CLICKED | BUTTON3_CLICKED):
self.h_paste()
class TextBox(npyscreen.BoxTitle):
_contained_widget = TextBoxInner
class BufferBox(npyscreen.BoxTitle):
_contained_widget = npyscreen.BufferPager
class ConfirmCancelPopup(fmPopup.ActionPopup):
DEFAULT_COLUMNS = 100
def on_ok(self):
self.value = True
def on_cancel(self):
self.value = False
class FileBox(npyscreen.BoxTitle):
_contained_widget = npyscreen.Filename
class PrettyTextBox(npyscreen.BoxTitle):
_contained_widget = TextBox
def _wrap_message_lines(message, line_length):
lines = []
for line in message.split("\n"):
lines.extend(textwrap.wrap(line.rstrip(), line_length))
return lines
def _prepare_message(message):
if isinstance(message, list) or isinstance(message, tuple):
return "\n".join([s.rstrip() for s in message])
# return "\n".join(message)
else:
return message
def select_stable_diffusion_config_file(
form_color: str = "DANGER",
wrap: bool = True,
model_name: str = "Unknown",
):
message = f"Please select the correct prediction type for the checkpoint named '{model_name}'. Press <CANCEL> to skip installation."
title = "CONFIG FILE SELECTION"
options = [
"'epsilon' - most v1.5 models and v2 models trained on 512 pixel images",
"'vprediction' - v2 models trained on 768 pixel images and a few v1.5 models)",
"Accept the best guess; you can fix it in the Web UI later",
]
F = ConfirmCancelPopup(
name=title,
color=form_color,
cycle_widgets=True,
lines=16,
)
F.preserve_selected_widget = True
mlw = F.add(
wgmultiline.Pager,
max_height=4,
editable=False,
)
mlw_width = mlw.width - 1
if wrap:
message = _wrap_message_lines(message, mlw_width)
mlw.values = message
choice = F.add(
npyscreen.SelectOne,
values=options,
value=[2],
max_height=len(options) + 1,
scroll_exit=True,
)
F.editw = 1
F.edit()
if not F.value:
return None
assert choice.value[0] in range(0, 3), "invalid choice"
choices = ["epsilon", "v", "guess"]
return choices[choice.value[0]]

22
invokeai/frontend/legacy_launch_invokeai.py
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@ -1,22 +0,0 @@
import argparse
import sys
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--web", action="store_true")
opts, _ = parser.parse_known_args()
if opts.web:
sys.argv.pop(sys.argv.index("--web"))
from invokeai.app.api_app import invoke_api
invoke_api()
else:
from invokeai.app.cli_app import invoke_cli
invoke_cli()
if __name__ == "__main__":
main()

5
invokeai/frontend/merge/__init__.py
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@ -1,5 +0,0 @@
"""
Initialization file for invokeai.frontend.merge
"""
from .merge_diffusers import main as invokeai_merge_diffusers # noqa: F401

448
invokeai/frontend/merge/merge_diffusers.py
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@ -1,448 +0,0 @@
"""
invokeai.frontend.merge exports a single function call merge_diffusion_models()
used to merge 2-3 models together and create a new InvokeAI-registered diffusion model.
Copyright (c) 2023 Lincoln Stein and the InvokeAI Development Team
"""
import argparse
import curses
import re
import sys
from argparse import Namespace
from pathlib import Path
from typing import List, Optional, Tuple
import npyscreen
from npyscreen import widget
from invokeai.app.services.config.config_default import get_config
from invokeai.app.services.download import DownloadQueueService
from invokeai.app.services.image_files.image_files_disk import DiskImageFileStorage
from invokeai.app.services.model_install import ModelInstallService
from invokeai.app.services.model_records import ModelRecordServiceBase, ModelRecordServiceSQL
from invokeai.app.services.shared.sqlite.sqlite_util import init_db
from invokeai.backend.model_manager import (
BaseModelType,
ModelFormat,
ModelType,
ModelVariantType,
)
from invokeai.backend.model_manager.merge import ModelMerger
from invokeai.backend.util.logging import InvokeAILogger
from invokeai.frontend.install.widgets import FloatTitleSlider, SingleSelectColumns, TextBox
config = get_config()
logger = InvokeAILogger.get_logger()
BASE_TYPES = [
(BaseModelType.StableDiffusion1, "Models Built on SD-1.x"),
(BaseModelType.StableDiffusion2, "Models Built on SD-2.x"),
(BaseModelType.StableDiffusionXL, "Models Built on SDXL"),
]
def _parse_args() -> Namespace:
parser = argparse.ArgumentParser(description="InvokeAI model merging")
parser.add_argument(
"--root_dir",
type=Path,
default=config.root_path,
help="Path to the invokeai runtime directory",
)
parser.add_argument(
"--front_end",
"--gui",
dest="front_end",
action="store_true",
default=False,
help="Activate the text-based graphical front end for collecting parameters. Aside from --root_dir, other parameters will be ignored.",
)
parser.add_argument(
"--models",
dest="model_names",
type=str,
nargs="+",
help="Two to three model names to be merged",
)
parser.add_argument(
"--base_model",
type=str,
choices=[x[0].value for x in BASE_TYPES],
help="The base model shared by the models to be merged",
)
parser.add_argument(
"--merged_model_name",
"--destination",
dest="merged_model_name",
type=str,
help="Name of the output model. If not specified, will be the concatenation of the input model names.",
)
parser.add_argument(
"--alpha",
type=float,
default=0.5,
help="The interpolation parameter, ranging from 0 to 1. It affects the ratio in which the checkpoints are merged. Higher values give more weight to the 2d and 3d models",
)
parser.add_argument(
"--interpolation",
dest="interp",
type=str,
choices=["weighted_sum", "sigmoid", "inv_sigmoid", "add_difference"],
default="weighted_sum",
help='Interpolation method to use. If three models are present, only "add_difference" will work.',
)
parser.add_argument(
"--force",
action="store_true",
help="Try to merge models even if they are incompatible with each other",
)
parser.add_argument(
"--clobber",
"--overwrite",
dest="clobber",
action="store_true",
help="Overwrite the merged model if --merged_model_name already exists",
)
return parser.parse_args()
# ------------------------- GUI HERE -------------------------
class mergeModelsForm(npyscreen.FormMultiPageAction):
interpolations = ["weighted_sum", "sigmoid", "inv_sigmoid"]
def __init__(self, parentApp, name):
self.parentApp = parentApp
self.ALLOW_RESIZE = True
self.FIX_MINIMUM_SIZE_WHEN_CREATED = False
super().__init__(parentApp, name)
@property
def record_store(self):
return self.parentApp.record_store
def afterEditing(self):
self.parentApp.setNextForm(None)
def create(self):
window_height, window_width = curses.initscr().getmaxyx()
self.current_base = 0
self.models = self.get_models(BASE_TYPES[self.current_base][0])
self.model_names = [x[1] for x in self.models]
max_width = max([len(x) for x in self.model_names])
max_width += 6
horizontal_layout = max_width * 3 < window_width
self.add_widget_intelligent(
npyscreen.FixedText,
color="CONTROL",
value="Select two models to merge and optionally a third.",
editable=False,
)
self.add_widget_intelligent(
npyscreen.FixedText,
color="CONTROL",
value="Use up and down arrows to move, <space> to select an item, <tab> and <shift-tab> to move from one field to the next.",
editable=False,
)
self.nextrely += 1
self.base_select = self.add_widget_intelligent(
SingleSelectColumns,
values=[x[1] for x in BASE_TYPES],
value=[self.current_base],
columns=4,
max_height=2,
relx=8,
scroll_exit=True,
)
self.base_select.on_changed = self._populate_models
self.add_widget_intelligent(
npyscreen.FixedText,
value="MODEL 1",
color="GOOD",
editable=False,
rely=6 if horizontal_layout else None,
)
self.model1 = self.add_widget_intelligent(
npyscreen.SelectOne,
values=self.model_names,
value=0,
max_height=len(self.model_names),
max_width=max_width,
scroll_exit=True,
rely=7,
)
self.add_widget_intelligent(
npyscreen.FixedText,
value="MODEL 2",
color="GOOD",
editable=False,
relx=max_width + 3 if horizontal_layout else None,
rely=6 if horizontal_layout else None,
)
self.model2 = self.add_widget_intelligent(
npyscreen.SelectOne,
name="(2)",
values=self.model_names,
value=1,
max_height=len(self.model_names),
max_width=max_width,
relx=max_width + 3 if horizontal_layout else None,
rely=7 if horizontal_layout else None,
scroll_exit=True,
)
self.add_widget_intelligent(
npyscreen.FixedText,
value="MODEL 3",
color="GOOD",
editable=False,
relx=max_width * 2 + 3 if horizontal_layout else None,
rely=6 if horizontal_layout else None,
)
models_plus_none = self.model_names.copy()
models_plus_none.insert(0, "None")
self.model3 = self.add_widget_intelligent(
npyscreen.SelectOne,
name="(3)",
values=models_plus_none,
value=0,
max_height=len(self.model_names) + 1,
max_width=max_width,
scroll_exit=True,
relx=max_width * 2 + 3 if horizontal_layout else None,
rely=7 if horizontal_layout else None,
)
for m in [self.model1, self.model2, self.model3]:
m.when_value_edited = self.models_changed
self.merged_model_name = self.add_widget_intelligent(
TextBox,
name="Name for merged model:",
labelColor="CONTROL",
max_height=3,
value="",
scroll_exit=True,
)
self.force = self.add_widget_intelligent(
npyscreen.Checkbox,
name="Force merge of models created by different diffusers library versions",
labelColor="CONTROL",
value=True,
scroll_exit=True,
)
self.nextrely += 1
self.merge_method = self.add_widget_intelligent(
npyscreen.TitleSelectOne,
name="Merge Method:",
values=self.interpolations,
value=0,
labelColor="CONTROL",
max_height=len(self.interpolations) + 1,
scroll_exit=True,
)
self.alpha = self.add_widget_intelligent(
FloatTitleSlider,
name="Weight (alpha) to assign to second and third models:",
out_of=1.0,
step=0.01,
lowest=0,
value=0.5,
labelColor="CONTROL",
scroll_exit=True,
)
self.model1.editing = True
def models_changed(self):
models = self.model1.values
selected_model1 = self.model1.value[0]
selected_model2 = self.model2.value[0]
selected_model3 = self.model3.value[0]
merged_model_name = f"{models[selected_model1]}+{models[selected_model2]}"
self.merged_model_name.value = merged_model_name
if selected_model3 > 0:
self.merge_method.values = ["add_difference ( A+(B-C) )"]
self.merged_model_name.value += f"+{models[selected_model3 -1]}" # In model3 there is one more element in the list (None). So we have to subtract one.
else:
self.merge_method.values = self.interpolations
self.merge_method.value = 0
def on_ok(self):
if self.validate_field_values() and self.check_for_overwrite():
self.parentApp.setNextForm(None)
self.editing = False
self.parentApp.merge_arguments = self.marshall_arguments()
npyscreen.notify("Starting the merge...")
else:
self.editing = True
def on_cancel(self):
sys.exit(0)
def marshall_arguments(self) -> dict:
model_keys = [x[0] for x in self.models]
models = [
model_keys[self.model1.value[0]],
model_keys[self.model2.value[0]],
]
if self.model3.value[0] > 0:
models.append(model_keys[self.model3.value[0] - 1])
interp = "add_difference"
else:
interp = self.interpolations[self.merge_method.value[0]]
args = {
"model_keys": models,
"base_model": tuple(BaseModelType)[self.base_select.value[0]],
"alpha": self.alpha.value,
"interp": interp,
"force": self.force.value,
"merged_model_name": self.merged_model_name.value,
}
return args
def check_for_overwrite(self) -> bool:
model_out = self.merged_model_name.value
if model_out not in self.model_names:
return True
else:
return npyscreen.notify_yes_no(
f"The chosen merged model destination, {model_out}, is already in use. Overwrite?"
)
def validate_field_values(self) -> bool:
bad_fields = []
model_names = self.model_names
selected_models = {model_names[self.model1.value[0]], model_names[self.model2.value[0]]}
if self.model3.value[0] > 0:
selected_models.add(model_names[self.model3.value[0] - 1])
if len(selected_models) < 2:
bad_fields.append(f"Please select two or three DIFFERENT models to compare. You selected {selected_models}")
if len(bad_fields) > 0:
message = "The following problems were detected and must be corrected:"
for problem in bad_fields:
message += f"\n* {problem}"
npyscreen.notify_confirm(message)
return False
else:
return True
def get_models(self, base_model: Optional[BaseModelType] = None) -> List[Tuple[str, str]]: # key to name
models = [
(x.key, x.name)
for x in self.record_store.search_by_attr(model_type=ModelType.Main, base_model=base_model)
if x.format == ModelFormat("diffusers") and x.variant == ModelVariantType("normal")
]
return sorted(models, key=lambda x: x[1])
def _populate_models(self, value: List[int]):
base_model = BASE_TYPES[value[0]][0]
self.models = self.get_models(base_model)
self.model_names = [x[1] for x in self.models]
models_plus_none = self.model_names.copy()
models_plus_none.insert(0, "None")
self.model1.values = self.model_names
self.model2.values = self.model_names
self.model3.values = models_plus_none
self.display()
class Mergeapp(npyscreen.NPSAppManaged):
def __init__(self, record_store: ModelRecordServiceBase):
super().__init__()
self.record_store = record_store
def onStart(self):
npyscreen.setTheme(npyscreen.Themes.ElegantTheme)
self.main = self.addForm("MAIN", mergeModelsForm, name="Merge Models Settings")
def run_gui(args: Namespace) -> None:
record_store: ModelRecordServiceBase = get_config_store()
mergeapp = Mergeapp(record_store)
mergeapp.run()
args = mergeapp.merge_arguments
merger = get_model_merger(record_store)
merger.merge_diffusion_models_and_save(**args)
merged_model_name = args["merged_model_name"]
logger.info(f'Models merged into new model: "{merged_model_name}".')
def run_cli(args: Namespace):
assert args.alpha >= 0 and args.alpha <= 1.0, "alpha must be between 0 and 1"
assert (
args.model_names and len(args.model_names) >= 1 and len(args.model_names) <= 3
), "Please provide the --models argument to list 2 to 3 models to merge. Use --help for full usage."
if not args.merged_model_name:
args.merged_model_name = "+".join(args.model_names)
logger.info(f'No --merged_model_name provided. Defaulting to "{args.merged_model_name}"')
record_store: ModelRecordServiceBase = get_config_store()
assert (
len(record_store.search_by_attr(args.merged_model_name, args.base_model, ModelType.Main)) == 0 or args.clobber
), f'A model named "{args.merged_model_name}" already exists. Use --clobber to overwrite.'
merger = get_model_merger(record_store)
model_keys = []
for name in args.model_names:
if len(name) == 32 and re.match(r"^[0-9a-f]$", name):
model_keys.append(name)
else:
models = record_store.search_by_attr(
model_name=name, model_type=ModelType.Main, base_model=BaseModelType(args.base_model)
)
assert len(models) > 0, f"{name}: Unknown model"
assert len(models) < 2, f"{name}: More than one model by this name. Please specify the model key instead."
model_keys.append(models[0].key)
merger.merge_diffusion_models_and_save(
alpha=args.alpha,
model_keys=model_keys,
merged_model_name=args.merged_model_name,
interp=args.interp,
force=args.force,
)
logger.info(f'Models merged into new model: "{args.merged_model_name}".')
def get_config_store() -> ModelRecordServiceSQL:
output_path = config.outputs_path
assert output_path is not None
image_files = DiskImageFileStorage(output_path / "images")
db = init_db(config=config, logger=InvokeAILogger.get_logger(), image_files=image_files)
return ModelRecordServiceSQL(db)
def get_model_merger(record_store: ModelRecordServiceBase) -> ModelMerger:
installer = ModelInstallService(app_config=config, record_store=record_store, download_queue=DownloadQueueService())
installer.start()
return ModelMerger(installer)
def main():
args = _parse_args()
if args.root_dir:
config.set_root(Path(args.root_dir))
try:
if args.front_end:
run_gui(args)
else:
run_cli(args)
except widget.NotEnoughSpaceForWidget as e:
if str(e).startswith("Height of 1 allocated"):
logger.error("You need to have at least two diffusers models in order to merge")
else:
logger.error("Not enough room for the user interface. Try making this window larger.")
sys.exit(-1)
except Exception as e:
logger.error(e)
sys.exit(-1)
except KeyboardInterrupt:
sys.exit(-1)
if __name__ == "__main__":
main()

5
invokeai/frontend/training/__init__.py
View File

@ -1,5 +0,0 @@
"""
Initialization file for invokeai.frontend.training
"""
from .textual_inversion import main as invokeai_textual_inversion # noqa: F401

452
invokeai/frontend/training/textual_inversion.py
View File

@ -1,452 +0,0 @@
#!/usr/bin/env python
"""
This is the frontend to "textual_inversion_training.py".
Copyright (c) 2023-24 Lincoln Stein and the InvokeAI Development Team
"""
import os
import re
import shutil
import sys
import traceback
from argparse import Namespace
from pathlib import Path
from typing import Dict, List, Optional, Tuple
import npyscreen
from npyscreen import widget
from omegaconf import OmegaConf
import invokeai.backend.util.logging as logger
from invokeai.app.services.config.config_default import get_config
from invokeai.backend.install.install_helper import initialize_installer
from invokeai.backend.model_manager import ModelType
from invokeai.backend.training import do_textual_inversion_training, parse_args
TRAINING_DATA = "text-inversion-training-data"
TRAINING_DIR = "text-inversion-output"
CONF_FILE = "preferences.conf"
config = None
class textualInversionForm(npyscreen.FormMultiPageAction):
resolutions = [512, 768, 1024]
lr_schedulers = [
"linear",
"cosine",
"cosine_with_restarts",
"polynomial",
"constant",
"constant_with_warmup",
]
precisions = ["no", "fp16", "bf16"]
learnable_properties = ["object", "style"]
def __init__(self, parentApp: npyscreen.NPSAppManaged, name: str, saved_args: Optional[Dict[str, str]] = None):
self.saved_args = saved_args or {}
super().__init__(parentApp, name)
def afterEditing(self) -> None:
self.parentApp.setNextForm(None)
def create(self) -> None:
self.model_names, default = self.get_model_names()
default_initializer_token = "★"
default_placeholder_token = ""
saved_args = self.saved_args
assert config is not None
try:
default = self.model_names.index(saved_args["model"])
except Exception:
pass
self.add_widget_intelligent(
npyscreen.FixedText,
value="Use ctrl-N and ctrl-P to move to the <N>ext and <P>revious fields, cursor arrows to make a selection, and space to toggle checkboxes.",
editable=False,
)
self.model = self.add_widget_intelligent(
npyscreen.TitleSelectOne,
name="Model Name:",
values=sorted(self.model_names),
value=default,
max_height=len(self.model_names) + 1,
scroll_exit=True,
)
self.placeholder_token = self.add_widget_intelligent(
npyscreen.TitleText,
name="Trigger Term:",
value="", # saved_args.get('placeholder_token',''), # to restore previous term
scroll_exit=True,
)
self.placeholder_token.when_value_edited = self.initializer_changed
self.nextrely -= 1
self.nextrelx += 30
self.prompt_token = self.add_widget_intelligent(
npyscreen.FixedText,
name="Trigger term for use in prompt",
value="",
editable=False,
scroll_exit=True,
)
self.nextrelx -= 30
self.initializer_token = self.add_widget_intelligent(
npyscreen.TitleText,
name="Initializer:",
value=saved_args.get("initializer_token", default_initializer_token),
scroll_exit=True,
)
self.resume_from_checkpoint = self.add_widget_intelligent(
npyscreen.Checkbox,
name="Resume from last saved checkpoint",
value=False,
scroll_exit=True,
)
self.learnable_property = self.add_widget_intelligent(
npyscreen.TitleSelectOne,
name="Learnable property:",
values=self.learnable_properties,
value=self.learnable_properties.index(saved_args.get("learnable_property", "object")),
max_height=4,
scroll_exit=True,
)
self.train_data_dir = self.add_widget_intelligent(
npyscreen.TitleFilename,
name="Data Training Directory:",
select_dir=True,
must_exist=False,
value=str(
saved_args.get(
"train_data_dir",
config.root_path / TRAINING_DATA / default_placeholder_token,
)
),
scroll_exit=True,
)
self.output_dir = self.add_widget_intelligent(
npyscreen.TitleFilename,
name="Output Destination Directory:",
select_dir=True,
must_exist=False,
value=str(
saved_args.get(
"output_dir",
config.root_path / TRAINING_DIR / default_placeholder_token,
)
),
scroll_exit=True,
)
self.resolution = self.add_widget_intelligent(
npyscreen.TitleSelectOne,
name="Image resolution (pixels):",
values=self.resolutions,
value=self.resolutions.index(saved_args.get("resolution", 512)),
max_height=4,
scroll_exit=True,
)
self.center_crop = self.add_widget_intelligent(
npyscreen.Checkbox,
name="Center crop images before resizing to resolution",
value=saved_args.get("center_crop", False),
scroll_exit=True,
)
self.mixed_precision = self.add_widget_intelligent(
npyscreen.TitleSelectOne,
name="Mixed Precision:",
values=self.precisions,
value=self.precisions.index(saved_args.get("mixed_precision", "fp16")),
max_height=4,
scroll_exit=True,
)
self.num_train_epochs = self.add_widget_intelligent(
npyscreen.TitleSlider,
name="Number of training epochs:",
out_of=1000,
step=50,
lowest=1,
value=saved_args.get("num_train_epochs", 100),
scroll_exit=True,
)
self.max_train_steps = self.add_widget_intelligent(
npyscreen.TitleSlider,
name="Max Training Steps:",
out_of=10000,
step=500,
lowest=1,
value=saved_args.get("max_train_steps", 3000),
scroll_exit=True,
)
self.train_batch_size = self.add_widget_intelligent(
npyscreen.TitleSlider,
name="Batch Size (reduce if you run out of memory):",
out_of=50,
step=1,
lowest=1,
value=saved_args.get("train_batch_size", 8),
scroll_exit=True,
)
self.gradient_accumulation_steps = self.add_widget_intelligent(
npyscreen.TitleSlider,
name="Gradient Accumulation Steps (may need to decrease this to resume from a checkpoint):",
out_of=10,
step=1,
lowest=1,
value=saved_args.get("gradient_accumulation_steps", 4),
scroll_exit=True,
)
self.lr_warmup_steps = self.add_widget_intelligent(
npyscreen.TitleSlider,
name="Warmup Steps:",
out_of=100,
step=1,
lowest=0,
value=saved_args.get("lr_warmup_steps", 0),
scroll_exit=True,
)
self.learning_rate = self.add_widget_intelligent(
npyscreen.TitleText,
name="Learning Rate:",
value=str(
saved_args.get("learning_rate", "5.0e-04"),
),
scroll_exit=True,
)
self.scale_lr = self.add_widget_intelligent(
npyscreen.Checkbox,
name="Scale learning rate by number GPUs, steps and batch size",
value=saved_args.get("scale_lr", True),
scroll_exit=True,
)
self.enable_xformers_memory_efficient_attention = self.add_widget_intelligent(
npyscreen.Checkbox,
name="Use xformers acceleration",
value=saved_args.get("enable_xformers_memory_efficient_attention", False),
scroll_exit=True,
)
self.lr_scheduler = self.add_widget_intelligent(
npyscreen.TitleSelectOne,
name="Learning rate scheduler:",
values=self.lr_schedulers,
max_height=7,
value=self.lr_schedulers.index(saved_args.get("lr_scheduler", "constant")),
scroll_exit=True,
)
self.model.editing = True
def initializer_changed(self) -> None:
placeholder = self.placeholder_token.value
self.prompt_token.value = f"(Trigger by using <{placeholder}> in your prompts)"
self.train_data_dir.value = str(config.root_path / TRAINING_DATA / placeholder)
self.output_dir.value = str(config.root_path / TRAINING_DIR / placeholder)
self.resume_from_checkpoint.value = Path(self.output_dir.value).exists()
def on_ok(self):
if self.validate_field_values():
self.parentApp.setNextForm(None)
self.editing = False
self.parentApp.ti_arguments = self.marshall_arguments()
npyscreen.notify("Launching textual inversion training. This will take a while...")
else:
self.editing = True
def ok_cancel(self):
sys.exit(0)
def validate_field_values(self) -> bool:
bad_fields = []
if self.model.value is None:
bad_fields.append("Model Name must correspond to a known model in invokeai.db")
if not re.match("^[a-zA-Z0-9.-]+$", self.placeholder_token.value):
bad_fields.append("Trigger term must only contain alphanumeric characters, the dot and hyphen")
if self.train_data_dir.value is None:
bad_fields.append("Data Training Directory cannot be empty")
if self.output_dir.value is None:
bad_fields.append("The Output Destination Directory cannot be empty")
if len(bad_fields) > 0:
message = "The following problems were detected and must be corrected:"
for problem in bad_fields:
message += f"\n* {problem}"
npyscreen.notify_confirm(message)
return False
else:
return True
def get_model_names(self) -> Tuple[List[str], int]:
global config
assert config is not None
installer = initialize_installer(config)
store = installer.record_store
main_models = store.search_by_attr(model_type=ModelType.Main)
model_names = [f"{x.base.value}/{x.type.value}/{x.name}" for x in main_models if x.format == "diffusers"]
default = 0
return (model_names, default)
def marshall_arguments(self) -> dict:
args = {}
# the choices
args.update(
model=self.model_names[self.model.value[0]],
resolution=self.resolutions[self.resolution.value[0]],
lr_scheduler=self.lr_schedulers[self.lr_scheduler.value[0]],
mixed_precision=self.precisions[self.mixed_precision.value[0]],
learnable_property=self.learnable_properties[self.learnable_property.value[0]],
)
# all the strings and booleans
for attr in (
"initializer_token",
"placeholder_token",
"train_data_dir",
"output_dir",
"scale_lr",
"center_crop",
"enable_xformers_memory_efficient_attention",
):
args[attr] = getattr(self, attr).value
# all the integers
for attr in (
"train_batch_size",
"gradient_accumulation_steps",
"num_train_epochs",
"max_train_steps",
"lr_warmup_steps",
):
args[attr] = int(getattr(self, attr).value)
# the floats (just one)
args.update(learning_rate=float(self.learning_rate.value))
# a special case
if self.resume_from_checkpoint.value and Path(self.output_dir.value).exists():
args["resume_from_checkpoint"] = "latest"
return args
class MyApplication(npyscreen.NPSAppManaged):
def __init__(self, saved_args: Optional[Dict[str, str]] = None):
super().__init__()
self.ti_arguments = None
self.saved_args = saved_args
def onStart(self):
npyscreen.setTheme(npyscreen.Themes.DefaultTheme)
self.main = self.addForm(
"MAIN",
textualInversionForm,
name="Textual Inversion Settings",
saved_args=self.saved_args,
)
def copy_to_embeddings_folder(args: Dict[str, str]) -> None:
"""
Copy learned_embeds.bin into the embeddings folder, and offer to
delete the full model and checkpoints.
"""
assert config is not None
source = Path(args["output_dir"], "learned_embeds.bin")
dest_dir_name = args["placeholder_token"].strip("<>")
destination = config.root_path / "embeddings" / dest_dir_name
os.makedirs(destination, exist_ok=True)
logger.info(f"Training completed. Copying learned_embeds.bin into {str(destination)}")
shutil.copy(source, destination)
if (input("Delete training logs and intermediate checkpoints? [y] ") or "y").startswith(("y", "Y")):
shutil.rmtree(Path(args["output_dir"]))
else:
logger.info(f'Keeping {args["output_dir"]}')
def save_args(args: dict) -> None:
"""
Save the current argument values to an omegaconf file
"""
assert config is not None
dest_dir = config.root_path / TRAINING_DIR
os.makedirs(dest_dir, exist_ok=True)
conf_file = dest_dir / CONF_FILE
conf = OmegaConf.create(args)
OmegaConf.save(config=conf, f=conf_file)
def previous_args() -> dict:
"""
Get the previous arguments used.
"""
assert config is not None
conf_file = config.root_path / TRAINING_DIR / CONF_FILE
try:
conf = OmegaConf.load(conf_file)
conf["placeholder_token"] = conf["placeholder_token"].strip("<>")
except Exception:
conf = None
return conf
def do_front_end() -> None:
global config
saved_args = previous_args()
myapplication = MyApplication(saved_args=saved_args)
myapplication.run()
if my_args := myapplication.ti_arguments:
os.makedirs(my_args["output_dir"], exist_ok=True)
# Automatically add angle brackets around the trigger
if not re.match("^<.+>$", my_args["placeholder_token"]):
my_args["placeholder_token"] = f"<{my_args['placeholder_token']}>"
my_args["only_save_embeds"] = True
save_args(my_args)
try:
print(my_args)
do_textual_inversion_training(config, **my_args)
copy_to_embeddings_folder(my_args)
except Exception as e:
logger.error("An exception occurred during training. The exception was:")
logger.error(str(e))
logger.error("DETAILS:")
logger.error(traceback.format_exc())
def main() -> None:
global config
args: Namespace = parse_args()
config = get_config()
# change root if needed
if args.root_dir:
config.set_root(args.root_dir)
try:
if args.front_end:
do_front_end()
else:
do_textual_inversion_training(config, **vars(args))
except AssertionError as e:
logger.error(e)
sys.exit(-1)
except KeyboardInterrupt:
pass
except (widget.NotEnoughSpaceForWidget, Exception) as e:
if str(e).startswith("Height of 1 allocated"):
logger.error("You need to have at least one diffusers models defined in invokeai.db in order to train")
elif str(e).startswith("addwstr"):
logger.error("Not enough window space for the interface. Please make your window larger and try again.")
else:
logger.error(e)
sys.exit(-1)
if __name__ == "__main__":
main()

26
invokeai/frontend/web/public/locales/en.json
View File

@ -638,6 +638,14 @@
"huggingFacePlaceholder": "owner/model-name",
"huggingFaceRepoID": "HuggingFace Repo ID",
"huggingFaceHelper": "If multiple models are found in this repo, you will be prompted to select one to install.",
"hfToken": "HuggingFace Token",
"hfTokenHelperText": "A HF token is required to use checkpoint models. Click here to create or get your token.",
"hfTokenInvalid": "Invalid or Missing HF Token",
"hfTokenInvalidErrorMessage": "Invalid or missing HuggingFace token.",
"hfTokenInvalidErrorMessage2": "Update it in the ",
"hfTokenUnableToVerify": "Unable to Verify HF Token",
"hfTokenUnableToVerifyErrorMessage": "Unable to verify HuggingFace token. This is likely due to a network error. Please try again later.",
"hfTokenSaved": "HF Token Saved",
"imageEncoderModelId": "Image Encoder Model ID",
"installQueue": "Install Queue",
"inplaceInstall": "In-place install",
@ -648,6 +656,8 @@
"load": "Load",
"localOnly": "local only",
"manual": "Manual",
"loraModels": "LoRAs",
"main": "Main",
"metadata": "Metadata",
"model": "Model",
"modelConversionFailed": "Model Conversion Failed",
@ -667,6 +677,8 @@
"modelUpdated": "Model Updated",
"modelUpdateFailed": "Model Update Failed",
"name": "Name",
"noModelsInstalled": "No Models Installed",
"noModelsInstalledDesc1": "Install models with the",
"noModelSelected": "No Model Selected",
"none": "none",
"path": "Path",
@ -686,7 +698,9 @@
"settings": "Settings",
"simpleModelPlaceholder": "URL or path to a local file or diffusers folder",
"source": "Source",
"starterModels": "Starter Models",
"syncModels": "Sync Models",
"textualInversions": "Textual Inversions",
"triggerPhrases": "Trigger Phrases",
"loraTriggerPhrases": "LoRA Trigger Phrases",
"mainModelTriggerPhrases": "Main Model Trigger Phrases",
@ -957,6 +971,7 @@
"refiner": "Refiner",
"refinermodel": "Refiner Model",
"refinerStart": "Refiner Start",
"refinerSteps": "Refiner Steps",
"scheduler": "Scheduler",
"steps": "Steps"
},
@ -1423,9 +1438,17 @@
"undo": "Undo"
},
"workflows": {
"ascending": "Ascending",
"created": "Created",
"descending": "Descending",
"workflows": "Workflows",
"workflowLibrary": "Library",
"userWorkflows": "My Workflows",
"defaultWorkflows": "Default Workflows",
"projectWorkflows": "Project Workflows",
"opened": "Opened",
"openWorkflow": "Open Workflow",
"updated": "Updated",
"uploadWorkflow": "Load from File",
"deleteWorkflow": "Delete Workflow",
"unnamedWorkflow": "Unnamed Workflow",
@ -1436,6 +1459,9 @@
"savingWorkflow": "Saving Workflow...",
"problemSavingWorkflow": "Problem Saving Workflow",
"workflowSaved": "Workflow Saved",
"name": "Name",
"noRecentWorkflows": "No Recent Workflows",
"noUserWorkflows": "No User Workflows",
"noWorkflows": "No Workflows",
"problemLoading": "Problem Loading Workflows",
"loading": "Loading Workflows",

45
invokeai/frontend/web/public/locales/it.json
View File

@ -24,7 +24,7 @@
"areYouSure": "Sei sicuro?",
"dontAskMeAgain": "Non chiedermelo più",
"batch": "Gestione Lotto",
"modelManager": "Gestore Modelli",
"modelManager": "Gestione Modelli",
"communityLabel": "Comunità",
"nodeEditor": "Editor dei nodi",
"advanced": "Avanzate",
@ -36,7 +36,7 @@
"auto": "Automatico",
"simple": "Semplice",
"details": "Dettagli",
"format": "formato",
"format": "Formato",
"unknown": "Sconosciuto",
"folder": "Cartella",
"error": "Errore",
@ -336,8 +336,8 @@
"modelManager": {
"modelManager": "Gestione Modelli",
"model": "Modello",
"allModels": "Tutti i Modelli",
"modelUpdated": "Modello Aggiornato",
"allModels": "Tutti i modelli",
"modelUpdated": "Modello aggiornato",
"manual": "Manuale",
"name": "Nome",
"description": "Descrizione",
@ -364,7 +364,7 @@
"convertToDiffusersHelpText6": "Vuoi convertire questo modello?",
"modelConverted": "Modello convertito",
"alpha": "Alpha",
"convertToDiffusersHelpText1": "Questo modello verrà convertito nel formato 🧨 Diffusore.",
"convertToDiffusersHelpText1": "Questo modello verrà convertito nel formato 🧨 Diffusori.",
"convertToDiffusersHelpText3": "Il file Checkpoint su disco verrà eliminato se si trova nella cartella principale di InvokeAI. Se si trova invece in una posizione personalizzata, NON verrà eliminato.",
"v2_base": "v2 (512px)",
"v2_768": "v2 (768px)",
@ -381,7 +381,7 @@
"modelsSynced": "Modelli sincronizzati",
"modelSyncFailed": "Sincronizzazione modello non riuscita",
"settings": "Impostazioni",
"syncModels": "Sincronizza Modelli",
"syncModels": "Sincronizza modelli",
"predictionType": "Tipo di previsione",
"advanced": "Avanzate",
"modelType": "Tipo di modello",
@ -421,7 +421,7 @@
"inplaceInstallDesc": "Installa i modelli senza copiare i file. Quando si utilizza il modello, verrà caricato da questa posizione. Se disabilitato, i file del modello verranno copiati nella directory dei modelli gestiti da Invoke durante l'installazione.",
"installQueue": "Coda di installazione",
"install": "Installa",
"installRepo": "Installa Repo",
"installRepo": "Installa Repository",
"huggingFacePlaceholder": "proprietario/nome-modello",
"huggingFaceHelper": "Se in questo repository vengono trovati più modelli, ti verrà richiesto di selezionarne uno da installare.",
"installAll": "Installa tutto",
@ -429,7 +429,20 @@
"scanPlaceholder": "Percorso di una cartella locale",
"simpleModelPlaceholder": "URL o percorso di un file locale o di una cartella diffusori",
"urlOrLocalPath": "URL o percorso locale",
"urlOrLocalPathHelper": "Gli URL dovrebbero puntare a un singolo file. I percorsi locali possono puntare a un singolo file o cartella per un singolo modello di diffusore."
"urlOrLocalPathHelper": "Gli URL dovrebbero puntare a un singolo file. I percorsi locali possono puntare a un singolo file o cartella per un singolo modello di diffusore.",
"hfTokenHelperText": "Per utilizzare i modelli checkpoint è necessario un token HF. Clicca qui per creare o ottenere il tuo token.",
"hfTokenInvalid": "Token HF non valido o mancante",
"hfTokenInvalidErrorMessage": "Token HuggingFace non valido o mancante.",
"hfTokenUnableToVerify": "Impossibile verificare il token HF",
"hfTokenUnableToVerifyErrorMessage": "Impossibile verificare il token HuggingFace. Ciò è probabilmente dovuto a un errore di rete. Per favore riprova più tardi.",
"hfTokenSaved": "Token HF salvato",
"loraModels": "LoRA",
"starterModels": "Modelli iniziali",
"textualInversions": "Inversioni Testuali",
"noModelsInstalled": "Nessun modello installato",
"hfTokenInvalidErrorMessage2": "Aggiornalo in ",
"main": "Principali",
"noModelsInstalledDesc1": "Installa i modelli con"
},
"parameters": {
"images": "Immagini",
@ -1381,7 +1394,8 @@
"refinermodel": "Modello Affinatore",
"posAestheticScore": "Punteggio estetico positivo",
"posStylePrompt": "Prompt Stile positivo",
"freePromptStyle": "Prompt di stile manuale"
"freePromptStyle": "Prompt di stile manuale",
"refinerSteps": "Passi Affinamento"
},
"metadata": {
"initImage": "Immagine iniziale",
@ -1447,7 +1461,18 @@
"uploadWorkflow": "Carica da file",
"noWorkflows": "Nessun flusso di lavoro",
"workflowCleared": "Flusso di lavoro cancellato",
"saveWorkflowToProject": "Salva flusso di lavoro nel progetto"
"saveWorkflowToProject": "Salva flusso di lavoro nel progetto",
"noUserWorkflows": "Nessun flusso di lavoro utente",
"defaultWorkflows": "Flussi di lavoro predefiniti",
"userWorkflows": "I miei flussi di lavoro",
"descending": "Discendente",
"created": "Creato",
"ascending": "Ascendente",
"noRecentWorkflows": "Nessun flusso di lavoro recente",
"name": "Nome",
"updated": "Aggiornato",
"projectWorkflows": "Flussi di lavoro del progetto",
"opened": "Aperto"
},
"app": {
"storeNotInitialized": "Il negozio non è inizializzato"

32
invokeai/frontend/web/public/locales/ru.json
View File

@ -433,7 +433,21 @@
"scanPlaceholder": "Путь к локальной папке",
"simpleModelPlaceholder": "URL или путь к локальному файлу или папке diffusers",
"urlOrLocalPath": "URL или локальный путь",
"urlOrLocalPathHelper": "URL-адреса должны указывать на один файл. Локальные пути могут указывать на один файл или папку для одной модели диффузоров."
"urlOrLocalPathHelper": "URL-адреса должны указывать на один файл. Локальные пути могут указывать на один файл или папку для одной модели диффузоров.",
"hfToken": "Токен HuggingFace",
"hfTokenInvalid": "Недействительный или отсутствующий HF-токен",
"hfTokenInvalidErrorMessage2": "Обновите его в . ",
"hfTokenUnableToVerify": "Невозможно проверить HF-токен",
"hfTokenSaved": "HF-токен сохранен",
"starterModels": "Стартовые модели",
"textualInversions": "Текстовые инверсии",
"hfTokenHelperText": "Для использования моделей контрольных точек требуется токен HF. Нажмите здесь, чтобы создать или получить свой токен.",
"hfTokenInvalidErrorMessage": "Недействительный или отсутствующий HuggingFace токен.",
"hfTokenUnableToVerifyErrorMessage": "Невозможно проверить токен HuggingFace. Вероятно, это связано с сетевой ошибкой. Пожалуйста, повторите попытку позже.",
"loraModels": "LoRAs",
"main": "Основные",
"noModelsInstalled": "Нет установленных моделей",
"noModelsInstalledDesc1": "Установите модели с помощью"
},
"parameters": {
"images": "Изображения",
@ -1395,7 +1409,8 @@
"loading": "Загрузка...",
"steps": "Шаги",
"posStylePrompt": "Запрос стиля",
"freePromptStyle": "Ручной запрос стиля"
"freePromptStyle": "Ручной запрос стиля",
"refinerSteps": "Шаги доработчика"
},
"invocationCache": {
"useCache": "Использовать кэш",
@ -1437,7 +1452,18 @@
"newWorkflowCreated": "Создан новый рабочий процесс",
"saveWorkflowToProject": "Сохранить рабочий процесс в проект",
"workflowCleared": "Рабочий процесс очищен",
"noWorkflows": "Нет рабочих процессов"
"noWorkflows": "Нет рабочих процессов",
"opened": "Открыто",
"updated": "Обновлено",
"noUserWorkflows": "Нет рабочих процессов пользователя",
"ascending": "Восходящий",
"created": "Создано",
"descending": "Спуск",
"userWorkflows": "Мои рабочие процессы",
"projectWorkflows": "Рабочие процессы проекта",
"defaultWorkflows": "Стандартные рабочие процессы",
"name": "Имя",
"noRecentWorkflows": "Нет последних рабочих процессов"
},
"hrf": {
"enableHrf": "Включить исправление высокого разрешения",

5
invokeai/frontend/web/src/app/components/App.tsx
View File

@ -11,6 +11,8 @@ import { useGlobalHotkeys } from 'common/hooks/useGlobalHotkeys';
import ChangeBoardModal from 'features/changeBoardModal/components/ChangeBoardModal';
import DeleteImageModal from 'features/deleteImageModal/components/DeleteImageModal';
import { DynamicPromptsModal } from 'features/dynamicPrompts/components/DynamicPromptsPreviewModal';
import { useHFLoginToast } from 'features/modelManagerV2/hooks/useHFLoginToast';
import { useStarterModelsToast } from 'features/modelManagerV2/hooks/useStarterModelsToast';
import { configChanged } from 'features/system/store/configSlice';
import { languageSelector } from 'features/system/store/systemSelectors';
import InvokeTabs from 'features/ui/components/InvokeTabs';
@ -68,6 +70,9 @@ const App = ({ config = DEFAULT_CONFIG, selectedImage }: Props) => {
dispatch(appStarted());
}, [dispatch]);
useStarterModelsToast();
useHFLoginToast();
return (
<ErrorBoundary onReset={handleReset} FallbackComponent={AppErrorBoundaryFallback}>
<Box

8
invokeai/frontend/web/src/app/store/middleware/listenerMiddleware/listeners/setDefaultSettings.ts
View File

@ -1,14 +1,14 @@
import type { AppStartListening } from 'app/store/middleware/listenerMiddleware';
import { setDefaultSettings } from 'features/parameters/store/actions';
import {
heightChanged,
heightRecalled,
setCfgRescaleMultiplier,
setCfgScale,
setScheduler,
setSteps,
vaePrecisionChanged,
vaeSelected,
widthChanged,
widthRecalled,
} from 'features/parameters/store/generationSlice';
import {
isParameterCFGRescaleMultiplier,
@ -100,13 +100,13 @@ export const addSetDefaultSettingsListener = (startAppListening: AppStartListeni
if (width) {
if (isParameterWidth(width)) {
dispatch(widthChanged(width));
dispatch(widthRecalled(width));
}
}
if (height) {
if (isParameterHeight(height)) {
dispatch(heightChanged(height));
dispatch(heightRecalled(height));
}
}

5
invokeai/frontend/web/src/app/types/invokeai.ts
View File

@ -24,7 +24,10 @@ export type AppFeature =
| 'resumeQueue'
| 'prependQueue'
| 'invocationCache'
| 'bulkDownload';
| 'bulkDownload'
| 'starterModels'
| 'hfToken';
/**
* A disable-able Stable Diffusion feature
*/

49
invokeai/frontend/web/src/features/controlAdapters/components/parameters/ParamControlAdapterModel.tsx
View File

@ -1,24 +1,36 @@
import { CustomSelect, FormControl } from '@invoke-ai/ui-library';
import { Combobox, FormControl, Tooltip } from '@invoke-ai/ui-library';
import { createMemoizedSelector } from 'app/store/createMemoizedSelector';
import { useAppDispatch, useAppSelector } from 'app/store/storeHooks';
import { useModelCustomSelect } from 'common/hooks/useModelCustomSelect';
import { useGroupedModelCombobox } from 'common/hooks/useGroupedModelCombobox';
import { useControlAdapterIsEnabled } from 'features/controlAdapters/hooks/useControlAdapterIsEnabled';
import { useControlAdapterModel } from 'features/controlAdapters/hooks/useControlAdapterModel';
import { useControlAdapterModels } from 'features/controlAdapters/hooks/useControlAdapterModels';
import { useControlAdapterType } from 'features/controlAdapters/hooks/useControlAdapterType';
import { controlAdapterModelChanged } from 'features/controlAdapters/store/controlAdaptersSlice';
import { selectGenerationSlice } from 'features/parameters/store/generationSlice';
import { memo, useCallback, useMemo } from 'react';
import type { ControlNetModelConfig, IPAdapterModelConfig, T2IAdapterModelConfig } from 'services/api/types';
import { useTranslation } from 'react-i18next';
import type {
AnyModelConfig,
ControlNetModelConfig,
IPAdapterModelConfig,
T2IAdapterModelConfig,
} from 'services/api/types';
type ParamControlAdapterModelProps = {
id: string;
};
const selectMainModel = createMemoizedSelector(selectGenerationSlice, (generation) => generation.model);
const ParamControlAdapterModel = ({ id }: ParamControlAdapterModelProps) => {
const isEnabled = useControlAdapterIsEnabled(id);
const controlAdapterType = useControlAdapterType(id);
const { modelConfig } = useControlAdapterModel(id);
const dispatch = useAppDispatch();
const currentBaseModel = useAppSelector((s) => s.generation.model?.base);
const mainModel = useAppSelector(selectMainModel);
const { t } = useTranslation();
const [modelConfigs, { isLoading }] = useControlAdapterModels(controlAdapterType);
@ -42,24 +54,35 @@ const ParamControlAdapterModel = ({ id }: ParamControlAdapterModelProps) => {
[controlAdapterType, modelConfig]
);
const { items, selectedItem, onChange, placeholder } = useModelCustomSelect({
const getIsDisabled = useCallback(
(model: AnyModelConfig): boolean => {
const isCompatible = currentBaseModel === model.base;
const hasMainModel = Boolean(currentBaseModel);
return !hasMainModel || !isCompatible;
},
[currentBaseModel]
);
const { options, value, onChange, noOptionsMessage } = useGroupedModelCombobox({
modelConfigs,
isLoading,
selectedModel,
onChange: _onChange,
modelFilter: (model) => model.base === currentBaseModel,
selectedModel,
getIsDisabled,
isLoading,
});
return (
<FormControl isDisabled={!items.length || !isEnabled} isInvalid={!selectedItem || !items.length}>
<CustomSelect
key={items.length}
selectedItem={selectedItem}
placeholder={placeholder}
items={items}
<Tooltip label={value?.description}>
<FormControl isDisabled={!isEnabled} isInvalid={!value || mainModel?.base !== modelConfig?.base}>
<Combobox
options={options}
placeholder={t('controlnet.selectModel')}
value={value}
onChange={onChange}
noOptionsMessage={noOptionsMessage}
/>
</FormControl>
</Tooltip>
);
};

27
invokeai/frontend/web/src/features/controlAdapters/components/processors/CannyProcessor.tsx
View File

@ -15,7 +15,7 @@ type CannyProcessorProps = {
const CannyProcessor = (props: CannyProcessorProps) => {
const { controlNetId, processorNode, isEnabled } = props;
const { low_threshold, high_threshold, image_resolution } = processorNode;
const { low_threshold, high_threshold, image_resolution, detect_resolution } = processorNode;
const processorChanged = useProcessorNodeChanged();
const { t } = useTranslation();
const defaults = useGetDefaultForControlnetProcessor(
@ -43,6 +43,13 @@ const CannyProcessor = (props: CannyProcessorProps) => {
[controlNetId, processorChanged]
);
const handleDetectResolutionChanged = useCallback(
(v: number) => {
processorChanged(controlNetId, { detect_resolution: v });
},
[controlNetId, processorChanged]
);
return (
<ProcessorWrapper>
<FormControl isDisabled={!isEnabled}>
@ -97,6 +104,24 @@ const CannyProcessor = (props: CannyProcessorProps) => {
max={4096}
/>
</FormControl>
<FormControl isDisabled={!isEnabled}>
<FormLabel>{t('controlnet.detectResolution')}</FormLabel>
<CompositeSlider
value={detect_resolution}
onChange={handleDetectResolutionChanged}
defaultValue={defaults.detect_resolution}
min={0}
max={4096}
marks
/>
<CompositeNumberInput
value={detect_resolution}
onChange={handleDetectResolutionChanged}
defaultValue={defaults.detect_resolution}
min={0}
max={4096}
/>
</FormControl>
</ProcessorWrapper>
);
};

27
invokeai/frontend/web/src/features/controlAdapters/components/processors/MediapipeFaceProcessor.tsx
View File

@ -15,7 +15,7 @@ type Props = {
const MediapipeFaceProcessor = (props: Props) => {
const { controlNetId, processorNode, isEnabled } = props;
const { max_faces, min_confidence, image_resolution } = processorNode;
const { max_faces, min_confidence, image_resolution, detect_resolution } = processorNode;
const processorChanged = useProcessorNodeChanged();
const { t } = useTranslation();
@ -44,6 +44,13 @@ const MediapipeFaceProcessor = (props: Props) => {
[controlNetId, processorChanged]
);
const handleDetectResolutionChanged = useCallback(
(v: number) => {
processorChanged(controlNetId, { detect_resolution: v });
},
[controlNetId, processorChanged]
);
return (
<ProcessorWrapper>
<FormControl isDisabled={!isEnabled}>
@ -102,6 +109,24 @@ const MediapipeFaceProcessor = (props: Props) => {
max={4096}
/>
</FormControl>
<FormControl isDisabled={!isEnabled}>
<FormLabel>{t('controlnet.detectResolution')}</FormLabel>
<CompositeSlider
value={detect_resolution}
onChange={handleDetectResolutionChanged}
defaultValue={defaults.detect_resolution}
min={0}
max={4096}
marks
/>
<CompositeNumberInput
value={detect_resolution}
onChange={handleDetectResolutionChanged}
defaultValue={defaults.detect_resolution}
min={0}
max={4096}
/>
</FormControl>
</ProcessorWrapper>
);
};

27
invokeai/frontend/web/src/features/controlAdapters/components/processors/MidasDepthProcessor.tsx
View File

@ -15,7 +15,7 @@ type Props = {
const MidasDepthProcessor = (props: Props) => {
const { controlNetId, processorNode, isEnabled } = props;
const { a_mult, bg_th, image_resolution } = processorNode;
const { a_mult, bg_th, image_resolution, detect_resolution } = processorNode;
const processorChanged = useProcessorNodeChanged();
const { t } = useTranslation();
@ -44,6 +44,13 @@ const MidasDepthProcessor = (props: Props) => {
[controlNetId, processorChanged]
);
const handleDetectResolutionChanged = useCallback(
(v: number) => {
processorChanged(controlNetId, { detect_resolution: v });
},
[controlNetId, processorChanged]
);
return (
<ProcessorWrapper>
<FormControl isDisabled={!isEnabled}>
@ -104,6 +111,24 @@ const MidasDepthProcessor = (props: Props) => {
max={4096}
/>
</FormControl>
<FormControl isDisabled={!isEnabled}>
<FormLabel>{t('controlnet.detectResolution')}</FormLabel>
<CompositeSlider
value={detect_resolution}
onChange={handleDetectResolutionChanged}
defaultValue={defaults.detect_resolution}
min={0}
max={4096}
marks
/>
<CompositeNumberInput
value={detect_resolution}
onChange={handleDetectResolutionChanged}
defaultValue={defaults.detect_resolution}
min={0}
max={4096}
/>
</FormControl>
</ProcessorWrapper>
);
};

3
invokeai/frontend/web/src/features/controlAdapters/store/constants.ts
View File

@ -48,6 +48,7 @@ export const CONTROLNET_PROCESSORS: ControlNetProcessorsDict = {
low_threshold: 100,
high_threshold: 200,
image_resolution: baseModel === 'sdxl' ? 1024 : 512,
detect_resolution: baseModel === 'sdxl' ? 1024 : 512,
}),
},
color_map_image_processor: {
@ -158,6 +159,7 @@ export const CONTROLNET_PROCESSORS: ControlNetProcessorsDict = {
max_faces: 1,
min_confidence: 0.5,
image_resolution: baseModel === 'sdxl' ? 1024 : 512,
detect_resolution: baseModel === 'sdxl' ? 1024 : 512,
}),
},
midas_depth_image_processor: {
@ -174,6 +176,7 @@ export const CONTROLNET_PROCESSORS: ControlNetProcessorsDict = {
a_mult: 2,
bg_th: 0.1,
image_resolution: baseModel === 'sdxl' ? 1024 : 512,
detect_resolution: baseModel === 'sdxl' ? 1024 : 512,
}),
},
mlsd_image_processor: {

7
invokeai/frontend/web/src/features/controlAdapters/store/controlAdaptersSlice.ts
View File

@ -37,10 +37,10 @@ export const {
} = caAdapterSelectors;
const initialControlAdaptersState: ControlAdaptersState = caAdapter.getInitialState<{
_version: 1;
_version: 2;
pendingControlImages: string[];
}>({
_version: 1,
_version: 2,
pendingControlImages: [],
});
@ -405,6 +405,9 @@ const migrateControlAdaptersState = (state: any): any => {
if (!('_version' in state)) {
state._version = 1;
}
if (state._version === 1) {
state = cloneDeep(initialControlAdaptersState);
}
return state;
};

6
invokeai/frontend/web/src/features/controlAdapters/store/types.ts
View File

@ -72,7 +72,7 @@ export const isControlAdapterProcessorType = (v: unknown): v is ControlAdapterPr
*/
export type RequiredCannyImageProcessorInvocation = O.Required<
CannyImageProcessorInvocation,
'type' | 'low_threshold' | 'high_threshold' | 'image_resolution'
'type' | 'low_threshold' | 'high_threshold' | 'image_resolution' | 'detect_resolution'
>;
/**
@ -133,7 +133,7 @@ export type RequiredLineartImageProcessorInvocation = O.Required<
*/
export type RequiredMediapipeFaceProcessorInvocation = O.Required<
MediapipeFaceProcessorInvocation,
'type' | 'max_faces' | 'min_confidence' | 'image_resolution'
'type' | 'max_faces' | 'min_confidence' | 'image_resolution' | 'detect_resolution'
>;
/**
@ -141,7 +141,7 @@ export type RequiredMediapipeFaceProcessorInvocation = O.Required<
*/
export type RequiredMidasDepthImageProcessorInvocation = O.Required<
MidasDepthImageProcessorInvocation,
'type' | 'a_mult' | 'bg_th' | 'image_resolution'
'type' | 'a_mult' | 'bg_th' | 'image_resolution' | 'detect_resolution'
>;
/**

8
invokeai/frontend/web/src/features/lora/store/loraSlice.ts
View File

@ -18,12 +18,12 @@ export const defaultLoRAConfig: Pick<LoRA, 'weight' | 'isEnabled'> = {
};
type LoraState = {
_version: 1;
_version: 2;
loras: Record<string, LoRA>;
};
const initialLoraState: LoraState = {
_version: 1,
_version: 2,
loras: {},
};
@ -72,6 +72,10 @@ const migrateLoRAState = (state: any): any => {
if (!('_version' in state)) {
state._version = 1;
}
if (state._version === 1) {
// Model type has changed, so we need to reset the state - too risky to migrate
state = cloneDeep(initialLoraState);
}
return state;
};

7
invokeai/frontend/web/src/features/metadata/util/handlers.ts
View File

@ -247,10 +247,11 @@ export const handlers = {
recaller: recallers.refinerCFGScale,
}),
refinerModel: buildHandlers({
getLabel: () => t('sdxl.refinerModel'),
getLabel: () => t('sdxl.refinermodel'),
parser: parsers.refinerModel,
recaller: recallers.refinerModel,
validator: validators.refinerModel,
renderValue: renderModelConfigValue,
}),
refinerNegativeAestheticScore: buildHandlers({
getLabel: () => t('sdxl.posAestheticScore'),
@ -268,12 +269,12 @@ export const handlers = {
recaller: recallers.refinerScheduler,
}),
refinerStart: buildHandlers({
getLabel: () => t('sdxl.refiner_start'),
getLabel: () => t('sdxl.refinerStart'),
parser: parsers.refinerStart,
recaller: recallers.refinerStart,
}),
refinerSteps: buildHandlers({
getLabel: () => t('sdxl.refiner_steps'),
getLabel: () => t('sdxl.refinerSteps'),
parser: parsers.refinerSteps,
recaller: recallers.refinerSteps,
}),

78
invokeai/frontend/web/src/features/modelManagerV2/components/HFToken.tsx Normal file
View File

@ -0,0 +1,78 @@
import {
Button,
ExternalLink,
Flex,
FormControl,
FormErrorMessage,
FormHelperText,
FormLabel,
Input,
useToast,
} from '@invoke-ai/ui-library';
import { skipToken } from '@reduxjs/toolkit/query';
import { useFeatureStatus } from 'features/system/hooks/useFeatureStatus';
import type { ChangeEvent } from 'react';
import { useCallback, useMemo, useState } from 'react';
import { useTranslation } from 'react-i18next';
import { useGetHFTokenStatusQuery, useSetHFTokenMutation } from 'services/api/endpoints/models';
export const HFToken = () => {
const { t } = useTranslation();
const isEnabled = useFeatureStatus('hfToken').isFeatureEnabled;
const [token, setToken] = useState('');
const { currentData } = useGetHFTokenStatusQuery(isEnabled ? undefined : skipToken);
const [trigger, { isLoading }] = useSetHFTokenMutation();
const toast = useToast();
const onChange = useCallback((e: ChangeEvent<HTMLInputElement>) => {
setToken(e.target.value);
}, []);
const onClick = useCallback(() => {
trigger({ token })
.unwrap()
.then((res) => {
if (res === 'valid') {
setToken('');
toast({
title: t('modelManager.hfTokenSaved'),
status: 'success',
duration: 3000,
});
}
});
}, [t, toast, token, trigger]);
const error = useMemo(() => {
if (!currentData || isLoading) {
return null;
}
if (currentData === 'invalid') {
return t('modelManager.hfTokenInvalidErrorMessage');
}
if (currentData === 'unknown') {
return t('modelManager.hfTokenUnableToVerifyErrorMessage');
}
return null;
}, [currentData, isLoading, t]);
if (!currentData || currentData === 'valid') {
return null;
}
return (
<Flex borderRadius="base" w="full">
<FormControl isInvalid={Boolean(error)} orientation="vertical">
<FormLabel>{t('modelManager.hfToken')}</FormLabel>
<Flex gap={3} alignItems="center" w="full">
<Input type="password" value={token} onChange={onChange} />
<Button onClick={onClick} size="sm" isDisabled={token.trim().length === 0} isLoading={isLoading}>
{t('common.save')}
</Button>
</Flex>
<FormHelperText>
<ExternalLink label={t('modelManager.hfTokenHelperText')} href="https://huggingface.co/settings/tokens" />
</FormHelperText>
<FormErrorMessage>{error}</FormErrorMessage>
</FormControl>
</Flex>
);
};

88
invokeai/frontend/web/src/features/modelManagerV2/hooks/useHFLoginToast.tsx Normal file
View File

@ -0,0 +1,88 @@
import { Button, Text, useToast } from '@invoke-ai/ui-library';
import { skipToken } from '@reduxjs/toolkit/query';
import { useAppDispatch } from 'app/store/storeHooks';
import { useFeatureStatus } from 'features/system/hooks/useFeatureStatus';
import { setActiveTab } from 'features/ui/store/uiSlice';
import { t } from 'i18next';
import { useCallback, useEffect, useState } from 'react';
import { useTranslation } from 'react-i18next';
import { useGetHFTokenStatusQuery } from 'services/api/endpoints/models';
import type { S } from 'services/api/types';
const FEATURE_ID = 'hfToken';
const getTitle = (token_status: S['HFTokenStatus']) => {
switch (token_status) {
case 'invalid':
return t('modelManager.hfTokenInvalid');
case 'unknown':
return t('modelManager.hfTokenUnableToVerify');
}
};
export const useHFLoginToast = () => {
const { t } = useTranslation();
const isEnabled = useFeatureStatus(FEATURE_ID).isFeatureEnabled;
const [didToast, setDidToast] = useState(false);
const { data } = useGetHFTokenStatusQuery(isEnabled ? undefined : skipToken);
const toast = useToast();
useEffect(() => {
if (toast.isActive(FEATURE_ID)) {
if (data === 'valid') {
setDidToast(true);
toast.close(FEATURE_ID);
}
return;
}
if (data && data !== 'valid' && !didToast && isEnabled) {
const title = getTitle(data);
toast({
id: FEATURE_ID,
title,
description: <ToastDescription token_status={data} />,
status: 'info',
isClosable: true,
duration: null,
onCloseComplete: () => setDidToast(true),
});
}
}, [data, didToast, isEnabled, t, toast]);
};
type Props = {
token_status: S['HFTokenStatus'];
};
const ToastDescription = ({ token_status }: Props) => {
const { t } = useTranslation();
const dispatch = useAppDispatch();
const toast = useToast();
const onClick = useCallback(() => {
dispatch(setActiveTab('modelManager'));
toast.close(FEATURE_ID);
}, [dispatch, toast]);
if (token_status === 'invalid') {
return (
<Text fontSize="md">
{t('modelManager.hfTokenInvalidErrorMessage')} {t('modelManager.hfTokenInvalidErrorMessage2')}
<Button onClick={onClick} variant="link" color="base.50" flexGrow={0}>
{t('modelManager.modelManager')}.
</Button>
</Text>
);
}
if (token_status === 'unknown') {
return (
<Text fontSize="md">
{t('modelManager.hfTokenUnableToErrorMessage')}{' '}
<Button onClick={onClick} variant="link" color="base.50" flexGrow={0}>
{t('modelManager.modelManager')}.
</Button>
</Text>
);
}
};

54
invokeai/frontend/web/src/features/modelManagerV2/hooks/useStarterModelsToast.tsx Normal file
View File

@ -0,0 +1,54 @@
import { Button, Text, useToast } from '@invoke-ai/ui-library';
import { useAppDispatch } from 'app/store/storeHooks';
import { useFeatureStatus } from 'features/system/hooks/useFeatureStatus';
import { setActiveTab } from 'features/ui/store/uiSlice';
import { useCallback, useEffect, useState } from 'react';
import { useTranslation } from 'react-i18next';
import { useMainModels } from 'services/api/hooks/modelsByType';
const TOAST_ID = 'starterModels';
export const useStarterModelsToast = () => {
const { t } = useTranslation();
const isEnabled = useFeatureStatus('starterModels').isFeatureEnabled;
const [didToast, setDidToast] = useState(false);
const [mainModels, { data }] = useMainModels();
const toast = useToast();
useEffect(() => {
if (toast.isActive(TOAST_ID)) {
return;
}
if (data && mainModels.length === 0 && !didToast && isEnabled) {
toast({
id: TOAST_ID,
title: t('modelManager.noModelsInstalled'),
description: <ToastDescription />,
status: 'info',
isClosable: true,
duration: null,
onCloseComplete: () => setDidToast(true),
});
}
}, [data, didToast, isEnabled, mainModels.length, t, toast]);
};
const ToastDescription = () => {
const { t } = useTranslation();
const dispatch = useAppDispatch();
const toast = useToast();
const onClick = useCallback(() => {
dispatch(setActiveTab('modelManager'));
toast.close(TOAST_ID);
}, [dispatch, toast]);
return (
<Text fontSize="md">
{t('modelManager.noModelsInstalledDesc1')}{' '}
<Button onClick={onClick} variant="link" color="base.50" flexGrow={0}>
{t('modelManager.modelManager')}.
</Button>
</Text>
);
};

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