Enable ^C during app startup when hashing models

Currently translated at 99.5% (1117 of 1122 strings)

Co-authored-by: Васянатор <ilabulanov339@gmail.com>
Translate-URL: https://hosted.weblate.org/projects/invokeai/web-ui/ru/
Translation: InvokeAI/Web UI

.github/pull_request_template.md

@@ -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.-->
 
-      
-## Have you updated all relevant documentation?
-- [ ] Yes
-- [ ] No
+## QA Instructions
 
-
-## 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)_

docker/.env.sample

@@ -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

docker/Dockerfile

@@ -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"]

docker/docker-compose.yml

@@ -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:

docker/docker-entrypoint.sh

@@ -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).

docs/features/CONFIGURATION.md

@@ -6,205 +6,136 @@ title: Configuration
 
 ## Intro
 
-InvokeAI has numerous runtime settings which can be used to adjust
-many aspects of its operations, including the location of files and
-directories, memory usage, and performance. These settings can be
-viewed and customized in several ways:
+Runtime settings, including the location of files and
+directories, memory usage, and performance, are managed via the
+`invokeai.yaml` config file or environment variables. A subset
+of settings may be set via commandline arguments.
 
-1. By editing settings in the `invokeai.yaml` file.
-2. By setting environment variables.
-3. On the command-line, when InvokeAI is launched.
+Settings sources are used in this order:
 
-In addition, the most commonly changed settings are accessible
-graphically via the `invokeai-configure` script.
+- CLI args
+- Environment variables
+- `invokeai.yaml` settings
+- Fallback: defaults
 
-### How the Configuration System Works
+### InvokeAI Root Directory
 
-When InvokeAI is launched, the very first thing it needs to do is to
-find its "root" directory, which contains its configuration files,
-installed models, its database of images, and the folder(s) of
-generated images themselves. In this document, the root directory will
-be referred to as ROOT.
+On startup, InvokeAI searches for its "root" directory. This is the directory
+that contains models, images, the database, and so on. It also contains
+a configuration file called `invokeai.yaml`.
 
-#### Finding the Root Directory
+InvokeAI searches for the root directory in this order:
 
-To find its root directory, InvokeAI uses the following recipe:
+1. The `--root <path>` CLI arg.
+2. The environment variable INVOKEAI_ROOT.
+3. The directory containing the currently active virtual environment.
+4. Fallback: a directory in the current user's home directory named `invokeai`.
 
-1. It first looks for the argument `--root <path>` on the command line
-   it was launched from, and uses the indicated path if present.
+### InvokeAI Configuration File
 
-2. Next it looks for the environment variable INVOKEAI_ROOT, and uses
-   the directory path found there if present.
+Inside the root directory, we read settings from the `invokeai.yaml` file.
 
-3. If neither of these are present, then InvokeAI looks for the
-   folder containing the `.venv` Python virtual environment directory for
-   the currently active environment. This directory is checked for files
-   expected inside the InvokeAI root before it is used.
+It has two sections - one for internal use and one for user settings:
 
-4. Finally, InvokeAI looks for a directory in the current user's home
-   directory named `invokeai`.
+```yaml
+# Internal metadata - do not edit:
+schema_version: 4
 
-#### Reading the InvokeAI Configuration File
-
-Once the root directory has been located, InvokeAI looks for a file
-named `ROOT/invokeai.yaml`, and if present reads configuration values
-from it. The top of this file looks like this:
-
-```
-InvokeAI:
-  Web Server:
-    host: localhost
-    port: 9090
-    allow_origins: []
-    allow_credentials: true
-    allow_methods:
-    - '*'
-    allow_headers:
-    - '*'
-  Features:
-    esrgan: true
-    internet_available: true
-    log_tokenization: false
-    patchmatch: true
-    restore: true
-...
+# 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
 ```
 
-This lines in this file are used to establish default values for
-Invoke's settings. In the above fragment, the Web Server's listening
-port is set to 9090 by the `port` setting.
+The settings in this file will override the defaults. You only need
+to change this file if the default for a particular setting doesn't
+work for you.
 
-You can edit this file with a text editor such as "Notepad" (do not
-use Word or any other word processor). When editing, be careful to
-maintain the indentation, and do not add extraneous text, as syntax
-errors will prevent InvokeAI from launching. A basic guide to the
-format of YAML files can be found
-[here](https://circleci.com/blog/what-is-yaml-a-beginner-s-guide/).
+Some settings, like [Model Marketplace API Keys], require the YAML
+to be formatted correctly. Here is a [basic guide to YAML files].
 
 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".
 
-#### Reading Environment Variables
+#### Custom Config File Location
 
-Next InvokeAI looks for defined environment variables in the format
-`INVOKEAI_<setting_name>`, for example `INVOKEAI_port`. Environment
-variable values take precedence over configuration file variables. On
-a Macintosh system, for example, you could change the port that the
-web server listens on by setting the environment variable this way:
+You can use any config file with the `--config` CLI arg. Pass in the path to the `invokeai.yaml` file you want to use.
 
-```
-export INVOKEAI_port=8000
-invokeai-web
+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_`
+to the variable name. For example, `INVOKEAI_HOST` would set the `host`
+setting.
+
+For non-primitive values, pass a JSON-encoded string:
+
+```sh
+export INVOKEAI_REMOTE_API_TOKENS='[{"url_regex":"modelmarketplace", "token": "12345"}]'
 ```
 
-Please check out these
-[Macintosh](https://phoenixnap.com/kb/set-environment-variable-mac)
-and
-[Windows](https://phoenixnap.com/kb/windows-set-environment-variable)
-guides for setting temporary and permanent environment variables.
+We suggest using `invokeai.yaml`, as it is more user-friendly.
 
-#### Reading the Command Line
+### CLI Args
 
-Lastly, InvokeAI takes settings from the command line, which override
-everything else. The command-line settings have the same name as the
-corresponding configuration file settings, preceded by a `--`, for
-example `--port 8000`.
+A subset of settings may be specified using CLI args:
 
-If you are using the launcher (`invoke.sh` or `invoke.bat`) to launch
-InvokeAI, then just pass the command-line arguments to the launcher:
+- `--root`: specify the root directory
+- `--config`: override the default `invokeai.yaml` file location
 
-```
-invoke.bat --port 8000 --host 0.0.0.0
-```
-
-The arguments will be applied when you select the web server option
-(and the other options as well).
-
-If, on the other hand, you prefer to launch InvokeAI directly from the
-command line, you would first activate the virtual environment (known
-as the "developer's console" in the launcher), and run `invokeai-web`:
-
-```
-> C:\Users\Fred\invokeai\.venv\scripts\activate
-(.venv) > invokeai-web --port 8000 --host 0.0.0.0
-```
-
-You can get a listing and brief instructions for each of the
-command-line options by giving the `--help` argument:
-
-```
-(.venv) > invokeai-web --help
-usage: InvokeAI [-h] [--host HOST] [--port PORT] [--allow_origins [ALLOW_ORIGINS ...]] [--allow_credentials | --no-allow_credentials] [--allow_methods [ALLOW_METHODS ...]]
-                [--allow_headers [ALLOW_HEADERS ...]] [--esrgan | --no-esrgan] [--internet_available | --no-internet_available] [--log_tokenization | --no-log_tokenization]
-                [--patchmatch | --no-patchmatch] [--restore | --no-restore]
-                [--always_use_cpu | --no-always_use_cpu] [--free_gpu_mem | --no-free_gpu_mem] [--max_loaded_models MAX_LOADED_MODELS] [--max_cache_size MAX_CACHE_SIZE]
-                [--max_vram_cache_size MAX_VRAM_CACHE_SIZE] [--gpu_mem_reserved GPU_MEM_RESERVED] [--precision {auto,float16,float32,autocast}]
-                [--sequential_guidance | --no-sequential_guidance] [--xformers_enabled | --no-xformers_enabled] [--tiled_decode | --no-tiled_decode] [--root ROOT]
-                [--autoimport_dir AUTOIMPORT_DIR] [--lora_dir LORA_DIR] [--embedding_dir EMBEDDING_DIR] [--controlnet_dir CONTROLNET_DIR] [--conf_path CONF_PATH]
-                [--models_dir MODELS_DIR] [--legacy_conf_dir LEGACY_CONF_DIR] [--db_dir DB_DIR] [--outdir OUTDIR] [--from_file FROM_FILE]
-                [--use_memory_db | --no-use_memory_db] [--model MODEL] [--log_handlers [LOG_HANDLERS ...]] [--log_format {plain,color,syslog,legacy}]
-                [--log_level {debug,info,warning,error,critical}] [--version | --no-version]
-```
-
-## The Configuration Settings
-
-The config is managed by the `InvokeAIAppConfig` class, which is a pydantic model. The below docs are autogenerated from the class.
-
-When editing your `invokeai.yaml` file, you'll need to put settings under their appropriate group. The group for each setting is denoted in the table below.
+### All Settings
 
 Following the table are additional explanations for certain settings.
 
 <!-- prettier-ignore-start -->
 ::: invokeai.app.services.config.config_default.InvokeAIAppConfig
     options:
-        heading_level: 3
+        heading_level: 4
         members: false
+        show_docstring_description: false
+        group_by_category: true
+        show_category_heading: false
 <!-- prettier-ignore-end -->
 
-### Model Marketplace API Keys
+#### Model Marketplace API Keys
 
 Some model marketplaces require an API key to download models. You can provide a URL pattern and appropriate token in your `invokeai.yaml` file to provide that API key.
 
 The pattern can be any valid regex (you may need to surround the pattern with quotes):
 
 ```yaml
-InvokeAI:
-  Model Install:
-    remote_api_tokens:
-      # Any URL containing `models.com` will automatically use `your_models_com_token`
-      - url_regex: models.com
-        token: your_models_com_token
-      # Any URL matching this contrived regex will use `some_other_token`
-      - url_regex: '^[a-z]{3}whatever.*\.com$'
-        token: some_other_token
+remote_api_tokens:
+  # Any URL containing `models.com` will automatically use `your_models_com_token`
+  - url_regex: models.com
+    token: your_models_com_token
+  # Any URL matching this contrived regex will use `some_other_token`
+  - url_regex: '^[a-z]{3}whatever.*\.com$'
+    token: some_other_token
 ```
 
 The provided token will be added as a `Bearer` token to the network requests to download the model files. As far as we know, this works for all model marketplaces that require authorization.
 
-### Model Hashing
+#### 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
-InvokeAI:
-  Model Install:
-    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
-InvokeAI:
-  Model Install:
-    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.
 
-### Paths
+Most common algorithms are supported, like `md5`, `sha256`, and `sha512`. These are typically much, much slower than either of the BLAKE3 variants.
+
+#### Path Settings
 
 These options set the paths of various directories and files used by
 InvokeAI. Relative paths are interpreted relative to the root directory, so
@@ -216,15 +147,15 @@ Note that the autoimport directory will be searched recursively,
 allowing you to organize the models into folders and subfolders in any
 way you wish.
 
-### Logging
+#### Logging
 
 Several different log handler destinations are available, and multiple destinations are supported by providing a list:
 
-```
-  log_handlers:
-     - console
-     - syslog=localhost
-     - file=/var/log/invokeai.log
+```yaml
+log_handlers:
+  - console
+  - syslog=localhost
+  - file=/var/log/invokeai.log
 ```
 
 - `console` is the default. It prints log messages to the command-line window from which InvokeAI was launched.
@@ -258,3 +189,6 @@ The `log_format` option provides several alternative formats:
 - `plain` - same as above, but monochrome text only
 - `syslog` - the log level and error message only, allowing the syslog system to attach the time and date
 - `legacy` - a format similar to the one used by the legacy 2.3 InvokeAI releases.
+
+[basic guide to yaml files]: https://circleci.com/blog/what-is-yaml-a-beginner-s-guide/
+[Model Marketplace API Keys]: #model-marketplace-api-keys

docs/installation/010_INSTALL_AUTOMATED.md

@@ -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.

docs/installation/INSTALLATION.md

@@ -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).
 
 

installer/lib/installer.py

@@ -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

installer/lib/messages.py

@@ -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(

installer/templates/invoke.bat.in

@@ -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

installer/templates/invoke.sh.in

@@ -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'}

invokeai/README

@@ -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
-	

invokeai/app/api/dependencies.py

@@ -64,9 +64,8 @@ class ApiDependencies:
     def initialize(config: InvokeAIAppConfig, event_handler_id: int, logger: Logger = logger) -> None:
         logger.info(f"InvokeAI version {__version__}")
         logger.info(f"Root directory = {str(config.root_path)}")
-        logger.debug(f"Internet connectivity is {config.internet_available}")
 
-        output_folder = config.output_path
+        output_folder = config.outputs_path
         if output_folder is None:
             raise ValueError("Output folder is not set")
 

invokeai/app/api/routers/app_info.py

@@ -12,7 +12,6 @@ from pydantic import BaseModel, Field
 
 from invokeai.app.invocations.upscale import ESRGAN_MODELS
 from invokeai.app.services.invocation_cache.invocation_cache_common import InvocationCacheStatus
-from invokeai.backend.image_util.invisible_watermark import InvisibleWatermark
 from invokeai.backend.image_util.patchmatch import PatchMatch
 from invokeai.backend.image_util.safety_checker import SafetyChecker
 from invokeai.backend.util.logging import logging
@@ -114,9 +113,7 @@ async def get_config() -> AppConfig:
     if SafetyChecker.safety_checker_available():
         nsfw_methods.append("nsfw_checker")
 
-    watermarking_methods = []
-    if InvisibleWatermark.invisible_watermark_available():
-        watermarking_methods.append("invisible_watermark")
+    watermarking_methods = ["invisible_watermark"]
 
     return AppConfig(
         infill_methods=infill_methods,

invokeai/app/api/routers/model_manager.py

@@ -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

invokeai/app/api_app.py

@@ -1,73 +1,62 @@
-# parse_args() must be called before any other imports. if it is not called first, consumers of the config
-# which are imported/used before parse_args() is called will get the default config values instead of the
-# values from the command line or config file.
+import asyncio
+import mimetypes
+import os
+import signal
+import socket
 import sys
+from contextlib import asynccontextmanager
+from inspect import signature
+from pathlib import Path
+from typing import Any
 
+import uvicorn
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.middleware.gzip import GZipMiddleware
+from fastapi.openapi.docs import get_redoc_html, get_swagger_ui_html
+from fastapi.openapi.utils import get_openapi
+from fastapi.responses import HTMLResponse
+from fastapi_events.handlers.local import local_handler
+from fastapi_events.middleware import EventHandlerASGIMiddleware
+from pydantic.json_schema import models_json_schema
+from torch.backends.mps import is_available as is_mps_available
+
+# for PyCharm:
+# noinspection PyUnresolvedReferences
+import invokeai.backend.util.hotfixes  # noqa: F401 (monkeypatching on import)
+import invokeai.frontend.web as web_dir
+from invokeai.app.api.no_cache_staticfiles import NoCacheStaticFiles
 from invokeai.app.invocations.model import ModelIdentifierField
+from invokeai.app.services.config.config_default import get_config
 from invokeai.app.services.session_processor.session_processor_common import ProgressImage
-from invokeai.version.invokeai_version import __version__
 
-from .services.config import InvokeAIAppConfig
+from ..backend.util.logging import InvokeAILogger
+from .api.dependencies import ApiDependencies
+from .api.routers import (
+    app_info,
+    board_images,
+    boards,
+    download_queue,
+    images,
+    model_manager,
+    session_queue,
+    utilities,
+    workflows,
+)
+from .api.sockets import SocketIO
+from .invocations.baseinvocation import (
+    BaseInvocation,
+    UIConfigBase,
+)
+from .invocations.fields import InputFieldJSONSchemaExtra, OutputFieldJSONSchemaExtra
 
-app_config = InvokeAIAppConfig.get_config()
-app_config.parse_args()
-if app_config.version:
-    print(f"InvokeAI version {__version__}")
-    sys.exit(0)
-
-if True:  # hack to make flake8 happy with imports coming after setting up the config
-    import asyncio
-    import mimetypes
-    import socket
-    from contextlib import asynccontextmanager
-    from inspect import signature
-    from pathlib import Path
-    from typing import Any
-
-    import uvicorn
-    from fastapi import FastAPI
-    from fastapi.middleware.cors import CORSMiddleware
-    from fastapi.middleware.gzip import GZipMiddleware
-    from fastapi.openapi.docs import get_redoc_html, get_swagger_ui_html
-    from fastapi.openapi.utils import get_openapi
-    from fastapi.responses import HTMLResponse
-    from fastapi_events.handlers.local import local_handler
-    from fastapi_events.middleware import EventHandlerASGIMiddleware
-    from pydantic.json_schema import models_json_schema
-    from torch.backends.mps import is_available as is_mps_available
-
-    # for PyCharm:
-    # noinspection PyUnresolvedReferences
-    import invokeai.backend.util.hotfixes  # noqa: F401 (monkeypatching on import)
-    import invokeai.frontend.web as web_dir
-    from invokeai.app.api.no_cache_staticfiles import NoCacheStaticFiles
-
-    from ..backend.util.logging import InvokeAILogger
-    from .api.dependencies import ApiDependencies
-    from .api.routers import (
-        app_info,
-        board_images,
-        boards,
-        download_queue,
-        images,
-        model_manager,
-        session_queue,
-        utilities,
-        workflows,
-    )
-    from .api.sockets import SocketIO
-    from .invocations.baseinvocation import (
-        BaseInvocation,
-        UIConfigBase,
-    )
-    from .invocations.fields import InputFieldJSONSchemaExtra, OutputFieldJSONSchemaExtra
-
-    if is_mps_available():
-        import invokeai.backend.util.mps_fixes  # noqa: F401 (monkeypatching on import)
+app_config = get_config()
+
+
+if is_mps_available():
+    import invokeai.backend.util.mps_fixes  # noqa: F401 (monkeypatching on import)
 
 
-app_config = InvokeAIAppConfig.get_config()
-app_config.parse_args()
 logger = InvokeAILogger.get_logger(config=app_config)
 # fix for windows mimetypes registry entries being borked
 # see https://github.com/invoke-ai/InvokeAI/discussions/3684#discussioncomment-6391352
@@ -237,6 +226,27 @@ app.mount(
 
 
 def invoke_api() -> None:
+    class InterruptWatcher:
+        def __init__(self):
+            self.child = os.fork()
+            if self.child == 0:
+                return
+            else:
+                self.watch()
+
+        def watch(self) -> None:
+            try:
+                os.wait()
+            except KeyboardInterrupt:
+                self.kill()
+            sys.exit()
+
+        def kill(self) -> None:
+            try:
+                os.kill(self.child, signal.SIGKILL)
+            except OSError:
+                pass
+
     def find_port(port: int) -> int:
         """Find a port not in use starting at given port"""
         # Taken from https://waylonwalker.com/python-find-available-port/, thanks Waylon!
@@ -247,9 +257,7 @@ def invoke_api() -> None:
             else:
                 return port
 
-    from invokeai.backend.install.check_root import check_invokeai_root
-
-    check_invokeai_root(app_config)  # note, may exit with an exception if root not set up
+    InterruptWatcher()
 
     if app_config.dev_reload:
         try:

invokeai/app/invocations/__init__.py

@@ -3,9 +3,9 @@ import sys
 from importlib.util import module_from_spec, spec_from_file_location
 from pathlib import Path
 
-from invokeai.app.services.config.config_default import InvokeAIAppConfig
+from invokeai.app.services.config.config_default import get_config
 
-custom_nodes_path = Path(InvokeAIAppConfig.get_config().custom_nodes_path.resolve())
+custom_nodes_path = Path(get_config().custom_nodes_path)
 custom_nodes_path.mkdir(parents=True, exist_ok=True)
 
 custom_nodes_init_path = str(custom_nodes_path / "__init__.py")

invokeai/app/invocations/baseinvocation.py

@@ -33,7 +33,7 @@ from invokeai.app.invocations.fields import (
     FieldKind,
     Input,
 )
-from invokeai.app.services.config.config_default import InvokeAIAppConfig
+from invokeai.app.services.config.config_default import get_config
 from invokeai.app.services.shared.invocation_context import InvocationContext
 from invokeai.app.util.metaenum import MetaEnum
 from invokeai.app.util.misc import uuid_string
@@ -191,7 +191,7 @@ class BaseInvocation(ABC, BaseModel):
     @classmethod
     def get_invocations(cls) -> Iterable[BaseInvocation]:
         """Gets all invocations, respecting the allowlist and denylist."""
-        app_config = InvokeAIAppConfig.get_config()
+        app_config = get_config()
         allowed_invocations: set[BaseInvocation] = set()
         for sc in cls._invocation_classes:
             invocation_type = sc.get_type()

invokeai/app/invocations/compel.py

@@ -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."""

invokeai/app/invocations/controlnet_image_processors.py

@@ -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 +171,13 @@ 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)"
     )
@@ -187,9 +189,14 @@ 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(image, self.low_threshold, self.high_threshold)
+    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
 
 
@@ -198,7 +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"""
@@ -209,9 +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,
@@ -227,7 +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"""
@@ -236,9 +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
@@ -249,7 +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"""
@@ -257,9 +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,
@@ -272,13 +279,15 @@ 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")
 
@@ -288,6 +297,8 @@ class MidasDepthImageProcessorInvocation(ImageProcessorInvocation):
             image,
             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,
         )
@@ -299,7 +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"""
@@ -316,7 +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"""
@@ -339,7 +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"""
@@ -366,7 +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"""
@@ -396,7 +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"""
@@ -412,17 +423,25 @@ 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)
+        processed_image = mediapipe_face_processor(
+            image,
+            max_faces=self.max_faces,
+            min_confidence=self.min_confidence,
+            image_resolution=self.image_resolution,
+            detect_resolution=self.detect_resolution,
+        )
         return processed_image
 
 
@@ -431,7 +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"""
@@ -460,7 +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"""
@@ -500,18 +519,23 @@ 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):
         # segment_anything_processor = SamDetector.from_pretrained("ybelkada/segment-anything", subfolder="checkpoints")
         segment_anything_processor = SamDetectorReproducibleColors.from_pretrained(
             "ybelkada/segment-anything", subfolder="checkpoints"
         )
         np_img = np.array(image, dtype=np.uint8)
-        processed_image = segment_anything_processor(np_img)
+        processed_image = segment_anything_processor(
+            np_img, image_resolution=self.image_resolution, detect_resolution=self.detect_resolution
+        )
         return processed_image
 
 
@@ -542,7 +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"""
@@ -574,7 +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"""
@@ -597,7 +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"""

invokeai/app/invocations/cv.py

@@ -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."""
 

invokeai/app/invocations/facetools.py

@@ -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."""

invokeai/app/invocations/image.py

@@ -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):

invokeai/app/invocations/infill.py

@@ -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"""
 

invokeai/app/invocations/ip_adapter.py

@@ -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."""
 

invokeai/app/invocations/latent.py

@@ -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."""
@@ -866,7 +866,7 @@ class LatentsToImageInvocation(BaseInvocation, WithMetadata, WithBoard):
                 vae.to(dtype=torch.float16)
                 latents = latents.half()
 
-            if self.tiled or context.config.get().tiled_decode:
+            if self.tiled or context.config.get().force_tiled_decode:
                 vae.enable_tiling()
             else:
                 vae.disable_tiling()
@@ -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"""

invokeai/app/invocations/math.py

@@ -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."""

invokeai/app/invocations/metadata.py

@@ -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"""
 

invokeai/app/invocations/model.py

@@ -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):

invokeai/app/invocations/noise.py

@@ -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."""

invokeai/app/invocations/param_easing.py

@@ -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"""

invokeai/app/invocations/primitives.py

@@ -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"""

invokeai/app/invocations/prompt.py

@@ -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"""

invokeai/app/invocations/sdxl.py

@@ -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."""

invokeai/app/invocations/strings.py

@@ -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"""

invokeai/app/invocations/t2i_adapter.py

@@ -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."""

invokeai/app/invocations/tiles.py

@@ -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):

invokeai/app/invocations/upscale.py

@@ -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,

invokeai/app/run_app.py

@@ -0,0 +1,12 @@
+"""This is a wrapper around the main app entrypoint, to allow for CLI args to be parsed before running the app."""
+
+
+def run_app() -> None:
+    # Before doing _anything_, parse CLI args!
+    from invokeai.frontend.cli.arg_parser import InvokeAIArgs
+
+    InvokeAIArgs.parse_args()
+
+    from invokeai.app.api_app import invoke_api
+
+    invoke_api()

invokeai/app/services/config/__init__.py

@@ -2,6 +2,6 @@
 
 from invokeai.app.services.config.config_common import PagingArgumentParser
 
-from .config_default import InvokeAIAppConfig, get_invokeai_config
+from .config_default import InvokeAIAppConfig, get_config
 
-__all__ = ["InvokeAIAppConfig", "get_invokeai_config", "PagingArgumentParser"]
+__all__ = ["InvokeAIAppConfig", "get_config", "PagingArgumentParser"]

invokeai/app/services/config/config_base.py

@@ -1,241 +0,0 @@
-# Copyright (c) 2023 Lincoln Stein (https://github.com/lstein) and the InvokeAI Development Team
-
-"""
-Base class for the InvokeAI configuration system.
-It defines a type of pydantic BaseSettings object that
-is able to read and write from an omegaconf-based config file,
-with overriding of settings from environment variables and/or
-the command line.
-"""
-
-from __future__ import annotations
-
-import argparse
-import os
-import sys
-from argparse import ArgumentParser
-from pathlib import Path
-from typing import Any, ClassVar, Dict, List, Literal, Optional, Union, get_args, get_origin, get_type_hints
-
-from omegaconf import DictConfig, DictKeyType, ListConfig, OmegaConf
-from pydantic import BaseModel
-from pydantic_settings import BaseSettings, SettingsConfigDict
-
-from invokeai.app.services.config.config_common import PagingArgumentParser, int_or_float_or_str
-
-
-class InvokeAISettings(BaseSettings):
-    """Runtime configuration settings in which default values are read from an omegaconf .yaml file."""
-
-    initconf: ClassVar[Optional[DictConfig]] = None
-    argparse_groups: ClassVar[Dict[str, Any]] = {}
-
-    model_config = SettingsConfigDict(env_file_encoding="utf-8", arbitrary_types_allowed=True, case_sensitive=True)
-
-    def parse_args(self, argv: Optional[List[str]] = sys.argv[1:]) -> None:
-        """Call to parse command-line arguments."""
-        parser = self.get_parser()
-        opt, unknown_opts = parser.parse_known_args(argv)
-        if len(unknown_opts) > 0:
-            print("Unknown args:", unknown_opts)
-        for name in self.model_fields:
-            if name not in self._excluded():
-                value = getattr(opt, name)
-                if isinstance(value, ListConfig):
-                    value = list(value)
-                elif isinstance(value, DictConfig):
-                    value = dict(value)
-                setattr(self, name, value)
-
-    def to_yaml(self) -> str:
-        """Return a YAML string representing our settings. This can be used as the contents of `invokeai.yaml` to restore settings later."""
-        cls = self.__class__
-        type = get_args(get_type_hints(cls)["type"])[0]
-        field_dict: Dict[str, Dict[str, Any]] = {type: {}}
-        for name, field in self.model_fields.items():
-            if name in cls._excluded_from_yaml():
-                continue
-            assert isinstance(field.json_schema_extra, dict)
-            category = (
-                field.json_schema_extra.get("category", "Uncategorized") if field.json_schema_extra else "Uncategorized"
-            )
-            value = getattr(self, name)
-            assert isinstance(category, str)
-            if category not in field_dict[type]:
-                field_dict[type][category] = {}
-            if isinstance(value, BaseModel):
-                dump = value.model_dump(exclude_defaults=True, exclude_unset=True, exclude_none=True)
-                field_dict[type][category][name] = dump
-                continue
-            if isinstance(value, list):
-                if not value or len(value) == 0:
-                    continue
-                primitive = isinstance(value[0], get_args(DictKeyType))
-                if not primitive:
-                    val_list: List[Dict[str, Any]] = []
-                    for list_val in value:
-                        if isinstance(list_val, BaseModel):
-                            dump = list_val.model_dump(exclude_defaults=True, exclude_unset=True, exclude_none=True)
-                            val_list.append(dump)
-                    field_dict[type][category][name] = val_list
-                    continue
-            # keep paths as strings to make it easier to read
-            field_dict[type][category][name] = str(value) if isinstance(value, Path) else value
-        conf = OmegaConf.create(field_dict)
-        return OmegaConf.to_yaml(conf)
-
-    @classmethod
-    def add_parser_arguments(cls, parser: ArgumentParser) -> None:
-        """Dynamically create arguments for a settings parser."""
-        if "type" in get_type_hints(cls):
-            settings_stanza = get_args(get_type_hints(cls)["type"])[0]
-        else:
-            settings_stanza = "Uncategorized"
-
-        env_prefix = getattr(cls.model_config, "env_prefix", None)
-        env_prefix = env_prefix if env_prefix is not None else settings_stanza.upper()
-
-        initconf = (
-            cls.initconf.get(settings_stanza)
-            if cls.initconf and settings_stanza in cls.initconf
-            else OmegaConf.create()
-        )
-
-        # create an upcase version of the environment in
-        # order to achieve case-insensitive environment
-        # variables (the way Windows does)
-        upcase_environ = {}
-        for key, value in os.environ.items():
-            upcase_environ[key.upper()] = value
-
-        fields = cls.model_fields
-        cls.argparse_groups = {}
-
-        for name, field in fields.items():
-            if name not in cls._excluded():
-                current_default = field.default
-
-                category = (
-                    field.json_schema_extra.get("category", "Uncategorized")
-                    if field.json_schema_extra
-                    else "Uncategorized"
-                )
-                env_name = env_prefix + "_" + name
-                if category in initconf and name in initconf.get(category):
-                    field.default = initconf.get(category).get(name)
-                if env_name.upper() in upcase_environ:
-                    field.default = upcase_environ[env_name.upper()]
-                cls.add_field_argument(parser, name, field)
-
-                field.default = current_default
-
-    @classmethod
-    def cmd_name(cls, command_field: str = "type") -> str:
-        """Return the category of a setting."""
-        hints = get_type_hints(cls)
-        if command_field in hints:
-            result: str = get_args(hints[command_field])[0]
-            return result
-        else:
-            return "Uncategorized"
-
-    @classmethod
-    def get_parser(cls) -> ArgumentParser:
-        """Get the command-line parser for a setting."""
-        parser = PagingArgumentParser(
-            prog=cls.cmd_name(),
-            description=cls.__doc__,
-        )
-        cls.add_parser_arguments(parser)
-        return parser
-
-    @classmethod
-    def _excluded(cls) -> List[str]:
-        # internal fields that shouldn't be exposed as command line options
-        return ["type", "initconf"]
-
-    @classmethod
-    def _excluded_from_yaml(cls) -> List[str]:
-        # combination of deprecated parameters and internal ones that shouldn't be exposed as invokeai.yaml options
-        return [
-            "type",
-            "initconf",
-            "version",
-            "from_file",
-            "model",
-            "root",
-            "max_cache_size",
-            "max_vram_cache_size",
-            "always_use_cpu",
-            "free_gpu_mem",
-            "xformers_enabled",
-            "tiled_decode",
-            "lora_dir",
-            "embedding_dir",
-            "controlnet_dir",
-            "conf_path",
-        ]
-
-    @classmethod
-    def add_field_argument(cls, command_parser, name: str, field, default_override=None) -> None:
-        """Add the argparse arguments for a setting parser."""
-        field_type = get_type_hints(cls).get(name)
-        default = (
-            default_override
-            if default_override is not None
-            else field.default
-            if field.default_factory is None
-            else field.default_factory()
-        )
-        if category := (field.json_schema_extra.get("category", None) if field.json_schema_extra else None):
-            if category not in cls.argparse_groups:
-                cls.argparse_groups[category] = command_parser.add_argument_group(category)
-            argparse_group = cls.argparse_groups[category]
-        else:
-            argparse_group = command_parser
-
-        if get_origin(field_type) == Literal:
-            allowed_values = get_args(field.annotation)
-            allowed_types = set()
-            for val in allowed_values:
-                allowed_types.add(type(val))
-            allowed_types_list = list(allowed_types)
-            field_type = allowed_types_list[0] if len(allowed_types) == 1 else int_or_float_or_str
-
-            argparse_group.add_argument(
-                f"--{name}",
-                dest=name,
-                type=field_type,
-                default=default,
-                choices=allowed_values,
-                help=field.description,
-            )
-
-        elif get_origin(field_type) == Union:
-            argparse_group.add_argument(
-                f"--{name}",
-                dest=name,
-                type=int_or_float_or_str,
-                default=default,
-                help=field.description,
-            )
-
-        elif get_origin(field_type) == list:
-            argparse_group.add_argument(
-                f"--{name}",
-                dest=name,
-                nargs="*",
-                type=field.annotation,
-                default=default,
-                action=argparse.BooleanOptionalAction if field.annotation == bool else "store",
-                help=field.description,
-            )
-        else:
-            argparse_group.add_argument(
-                f"--{name}",
-                dest=name,
-                type=field.annotation,
-                default=default,
-                action=argparse.BooleanOptionalAction if field.annotation == bool else "store",
-                help=field.description,
-            )

invokeai/app/services/config/config_common.py

@@ -12,7 +12,6 @@ from __future__ import annotations
 
 import argparse
 import pydoc
-from typing import Union
 
 
 class PagingArgumentParser(argparse.ArgumentParser):
@@ -24,18 +23,3 @@ class PagingArgumentParser(argparse.ArgumentParser):
     def print_help(self, file=None) -> None:
         text = self.format_help()
         pydoc.pager(text)
-
-
-def int_or_float_or_str(value: str) -> Union[int, float, str]:
-    """
-    Workaround for argparse type checking.
-    """
-    try:
-        return int(value)
-    except Exception as e:  # noqa F841
-        pass
-    try:
-        return float(value)
-    except Exception as e:  # noqa F841
-        pass
-    return str(value)

invokeai/app/services/config/config_default.py

@@ -1,187 +1,23 @@
-# Copyright (c) 2023 Lincoln Stein (https://github.com/lstein) and the InvokeAI Development Team
-
-"""Invokeai configuration system.
-
-Arguments and fields are taken from the pydantic definition of the
-model.  Defaults can be set by creating a yaml configuration file that
-has a top-level key of "InvokeAI" and subheadings for each of the
-categories returned by `invokeai --help`. The file looks like this:
-
-[file: invokeai.yaml]
-
-InvokeAI:
-  Web Server:
-    host: 127.0.0.1
-    port: 9090
-    allow_origins: []
-    allow_credentials: true
-    allow_methods:
-    - '*'
-    allow_headers:
-    - '*'
-  Features:
-    esrgan: true
-    internet_available: true
-    log_tokenization: false
-    patchmatch: true
-    ignore_missing_core_models: false
-  Paths:
-    autoimport_dir: autoimport
-    lora_dir: null
-    embedding_dir: null
-    controlnet_dir: null
-    models_dir: models
-    legacy_conf_dir: configs/stable-diffusion
-    db_dir: databases
-    outdir: /home/lstein/invokeai-main/outputs
-    use_memory_db: false
-  Logging:
-    log_handlers:
-    - console
-    log_format: plain
-    log_level: info
-  Model Cache:
-    ram: 13.5
-    vram: 0.25
-    lazy_offload: true
-    log_memory_usage: false
-  Device:
-    device: auto
-    precision: auto
-  Generation:
-    sequential_guidance: false
-    attention_type: xformers
-    attention_slice_size: auto
-    force_tiled_decode: false
-
-The default name of the configuration file is `invokeai.yaml`, located
-in INVOKEAI_ROOT. You can replace supersede this by providing any
-OmegaConf dictionary object initialization time:
-
- omegaconf = OmegaConf.load('/tmp/init.yaml')
- conf = InvokeAIAppConfig()
- conf.parse_args(conf=omegaconf)
-
-InvokeAIAppConfig.parse_args() will parse the contents of `sys.argv`
-at initialization time. You may pass a list of strings in the optional
-`argv` argument to use instead of the system argv:
-
- conf.parse_args(argv=['--log_tokenization'])
-
-It is also possible to set a value at initialization time. However, if
-you call parse_args() it may be overwritten.
-
- conf = InvokeAIAppConfig(log_tokenization=True)
- conf.parse_args(argv=['--no-log_tokenization'])
- conf.log_tokenization
- # False
-
-To avoid this, use `get_config()` to retrieve the application-wide
-configuration object. This will retain any properties set at object
-creation time:
-
- conf = InvokeAIAppConfig.get_config(log_tokenization=True)
- conf.parse_args(argv=['--no-log_tokenization'])
- conf.log_tokenization
- # True
-
-Any setting can be overwritten by setting an environment variable of
-form: "INVOKEAI_<setting>", as in:
-
-  export INVOKEAI_port=8080
-
-Order of precedence (from highest):
-   1) initialization options
-   2) command line options
-   3) environment variable options
-   4) config file options
-   5) pydantic defaults
-
-Typical usage at the top level file:
-
- from invokeai.app.services.config import InvokeAIAppConfig
-
- # get global configuration and print its cache size
- conf = InvokeAIAppConfig.get_config()
- conf.parse_args()
- print(conf.ram_cache_size)
-
-Typical usage in a backend module:
-
- from invokeai.app.services.config import InvokeAIAppConfig
-
- # get global configuration and print its cache size value
- conf = InvokeAIAppConfig.get_config()
- print(conf.ram_cache_size)
-
-Computed properties:
-
-The InvokeAIAppConfig object has a series of properties that
-resolve paths relative to the runtime root directory. They each return
-a Path object:
-
- root_path          - path to InvokeAI root
- output_path        - path to default outputs directory
- conf               - alias for the above
- embedding_path     - path to the embeddings directory
- lora_path          - path to the LoRA directory
-
-In most cases, you will want to create a single InvokeAIAppConfig
-object for the entire application. The InvokeAIAppConfig.get_config() function
-does this:
-
-  config = InvokeAIAppConfig.get_config()
-  config.parse_args()   # read values from the command line/config file
-  print(config.root)
-
-# Subclassing
-
-If you wish to create a similar class, please subclass the
-`InvokeAISettings` class and define a Literal field named "type",
-which is set to the desired top-level name.  For example, to create a
-"InvokeBatch" configuration, define like this:
-
-  class InvokeBatch(InvokeAISettings):
-     type: Literal["InvokeBatch"] = "InvokeBatch"
-     node_count : int = Field(default=1, description="Number of nodes to run on", json_schema_extra=dict(category='Resources'))
-     cpu_count  : int = Field(default=8, description="Number of GPUs to run on per node", json_schema_extra=dict(category='Resources'))
-
-This will now read and write from the "InvokeBatch" section of the
-config file, look for environment variables named INVOKEBATCH_*, and
-accept the command-line arguments `--node_count` and `--cpu_count`. The
-two configs are kept in separate sections of the config file:
-
-  # invokeai.yaml
-
-  InvokeBatch:
-     Resources:
-        node_count: 1
-        cpu_count: 8
-
-  InvokeAI:
-     Paths:
-        root: /home/lstein/invokeai-main
-        legacy_conf_dir: configs/stable-diffusion
-        outdir: outputs
-     ...
-
-"""
+# TODO(psyche): pydantic-settings supports YAML settings sources. If we can figure out a way to integrate the YAML
+# migration logic, we could use that for simpler config loading.
 
 from __future__ import annotations
 
 import os
 import re
+import shutil
+from functools import lru_cache
 from pathlib import Path
-from typing import Any, ClassVar, Dict, List, Literal, Optional
+from typing import Any, Literal, Optional
 
-from omegaconf import DictConfig, OmegaConf
-from pydantic import BaseModel, Field, field_validator
-from pydantic.config import JsonDict
-from pydantic_settings import SettingsConfigDict
+import psutil
+import yaml
+from pydantic import BaseModel, Field, PrivateAttr, field_validator
+from pydantic_settings import BaseSettings, PydanticBaseSettingsSource, SettingsConfigDict
 
+import invokeai.configs as model_configs
 from invokeai.backend.model_hash.model_hash import HASHING_ALGORITHMS
-
-from .config_base import InvokeAISettings
+from invokeai.frontend.cli.arg_parser import InvokeAIArgs
 
 INIT_FILE = Path("invokeai.yaml")
 DB_FILE = Path("invokeai.db")
@@ -189,28 +25,34 @@ LEGACY_INIT_FILE = Path("invokeai.init")
 DEFAULT_RAM_CACHE = 10.0
 DEFAULT_VRAM_CACHE = 0.25
 DEFAULT_CONVERT_CACHE = 20.0
+DEVICE = Literal["auto", "cpu", "cuda", "cuda:1", "mps"]
+PRECISION = Literal["auto", "float16", "bfloat16", "float32", "autocast"]
+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 = "4.0.0"
 
 
-class Categories(object):
-    """Category headers for configuration variable groups."""
+def get_default_ram_cache_size() -> float:
+    """Run a heuristic for the default RAM cache based on installed RAM."""
 
-    WebServer: JsonDict = {"category": "Web Server"}
-    Features: JsonDict = {"category": "Features"}
-    Paths: JsonDict = {"category": "Paths"}
-    Logging: JsonDict = {"category": "Logging"}
-    Development: JsonDict = {"category": "Development"}
-    CLIArgs: JsonDict = {"category": "CLIArgs"}
-    ModelInstall: JsonDict = {"category": "Model Install"}
-    ModelCache: JsonDict = {"category": "Model Cache"}
-    Device: JsonDict = {"category": "Device"}
-    Generation: JsonDict = {"category": "Generation"}
-    Queue: JsonDict = {"category": "Queue"}
-    Nodes: JsonDict = {"category": "Nodes"}
-    MemoryPerformance: JsonDict = {"category": "Memory/Performance"}
-    Deprecated: JsonDict = {"category": "Deprecated"}
+    # 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 URLRegexToken(BaseModel):
+class URLRegexTokenPair(BaseModel):
     url_regex: str = Field(description="Regular expression to match against the URL")
     token: str = Field(description="Token to use when the URL matches the regex")
 
@@ -225,397 +67,421 @@ class URLRegexToken(BaseModel):
         return v
 
 
-class InvokeAIAppConfig(InvokeAISettings):
-    """Invoke App Configuration
+class InvokeAIAppConfig(BaseSettings):
+    """Invoke's global app configuration.
+
+    Typically, you won't need to interact with this class directly. Instead, use the `get_config` function from `invokeai.app.services.config` to get a singleton config object.
 
     Attributes:
-        host: **Web Server**: IP address to bind to. Use `0.0.0.0` to serve to your local network.
-        port: **Web Server**: Port to bind to.
-        allow_origins: **Web Server**: Allowed CORS origins.
-        allow_credentials: **Web Server**: Allow CORS credentials.
-        allow_methods: **Web Server**: Methods allowed for CORS.
-        allow_headers: **Web Server**: Headers allowed for CORS.
-        ssl_certfile: **Web Server**: SSL certificate file for HTTPS.
-        ssl_keyfile: **Web Server**: SSL key file for HTTPS.
-        esrgan: **Features**: Enables or disables the upscaling code.
-        internet_available: **Features**: If true, attempt to download models on the fly; otherwise only use local models.
-        log_tokenization: **Features**: Enable logging of parsed prompt tokens.
-        patchmatch: **Features**: Enable patchmatch inpaint code.
-        ignore_missing_core_models: **Features**: Ignore missing core models on startup. If `True`, the app will attempt to download missing models on startup.
-        root: **Paths**: The InvokeAI runtime root directory.
-        autoimport_dir: **Paths**: Path to a directory of models files to be imported on startup.
-        models_dir: **Paths**: Path to the models directory.
-        convert_cache_dir: **Paths**: 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: **Paths**: Path to directory of legacy checkpoint config files.
-        db_dir: **Paths**: Path to InvokeAI databases directory.
-        outdir: **Paths**: Path to directory for outputs.
-        custom_nodes_dir: **Paths**: Path to directory for custom nodes.
-        from_file: **Paths**: Take command input from the indicated file (command-line client only).
-        log_handlers: **Logging**: Log handler. Valid options are "console", "file=<path>", "syslog=path|address:host:port", "http=<url>".
-        log_format: **Logging**: Log format. Use "plain" for text-only, "color" for colorized output, "legacy" for 2.3-style logging and "syslog" for syslog-style.
-        log_level: **Logging**: Emit logging messages at this level or higher.
-        log_sql: **Logging**: Log SQL queries. `log_level` must be `debug` for this to do anything. Extremely verbose.
-        use_memory_db: **Development**: Use in-memory database. Useful for development.
-        dev_reload: **Development**: Automatically reload when Python sources are changed. Does not reload node definitions.
-        profile_graphs: **Development**: Enable graph profiling using `cProfile`.
-        profile_prefix: **Development**: An optional prefix for profile output files.
-        profiles_dir: **Development**: Path to profiles output directory.
-        version: **CLIArgs**: CLI arg - show InvokeAI version and exit.
-        hashing_algorithm: **Model Install**: 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.
-        remote_api_tokens: **Model Install**: 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.
-        ram: **Model Cache**: Maximum memory amount used by memory model cache for rapid switching (GB).
-        vram: **Model Cache**: Amount of VRAM reserved for model storage (GB)
-        convert_cache: **Model Cache**: Maximum size of on-disk converted models cache (GB)
-        lazy_offload: **Model Cache**: Keep models in VRAM until their space is needed.
-        log_memory_usage: **Model Cache**: If True, a memory snapshot will be captured before and after every model cache operation, and the result will be logged (at debug level). There is a time cost to capturing the memory snapshots, so it is recommended to only enable this feature if you are actively inspecting the model cache's behaviour.
-        device: **Device**: Preferred execution device. `auto` will choose the device depending on the hardware platform and the installed torch capabilities.
-        precision: **Device**: Floating point precision. `float16` will consume half the memory of `float32` but produce slightly lower-quality images. The `auto` setting will guess the proper precision based on your video card and operating system.
-        sequential_guidance: **Generation**: Whether to calculate guidance in serial instead of in parallel, lowering memory requirements.
-        attention_type: **Generation**: Attention type.
-        attention_slice_size: **Generation**: Slice size, valid when attention_type=="sliced".
-        force_tiled_decode: **Generation**: Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty).
-        png_compress_level: **Generation**: The compress_level setting of PIL.Image.save(), used for PNG encoding. All settings are lossless. 0 = no compression, 1 = fastest with slightly larger filesize, 9 = slowest with smallest filesize. 1 is typically the best setting.
-        max_queue_size: **Queue**: Maximum number of items in the session queue.
-        allow_nodes: **Nodes**: List of nodes to allow. Omit to allow all.
-        deny_nodes: **Nodes**: List of nodes to deny. Omit to deny none.
-        node_cache_size: **Nodes**: How many cached nodes to keep in memory.
+        host: IP address to bind to. Use `0.0.0.0` to serve to your local network.
+        port: Port to bind to.
+        allow_origins: Allowed CORS origins.
+        allow_credentials: Allow CORS credentials.
+        allow_methods: Methods allowed for CORS.
+        allow_headers: Headers allowed for CORS.
+        ssl_certfile: SSL certificate file for HTTPS. See https://www.uvicorn.org/settings/#https.
+        ssl_keyfile: SSL key file for HTTPS. See https://www.uvicorn.org/settings/#https.
+        log_tokenization: Enable logging of parsed prompt tokens.
+        patchmatch: Enable patchmatch inpaint code.
+        autoimport_dir: Path to a directory of models files to be imported on startup.
+        models_dir: Path to the models directory.
+        convert_cache_dir: Path to the converted models cache directory. When loading a non-diffusers model, it will be converted and store on disk at this location.
+        legacy_conf_dir: Path to directory of legacy checkpoint config files.
+        db_dir: Path to InvokeAI databases directory.
+        outputs_dir: Path to directory for outputs.
+        custom_nodes_dir: Path to directory for custom nodes.
+        log_handlers: Log handler. Valid options are "console", "file=<path>", "syslog=path|address:host:port", "http=<url>".
+        log_format: Log format. Use "plain" for text-only, "color" for colorized output, "legacy" for 2.3-style logging and "syslog" for syslog-style.<br>Valid values: `plain`, `color`, `syslog`, `legacy`
+        log_level: Emit logging messages at this level or higher.<br>Valid values: `debug`, `info`, `warning`, `error`, `critical`
+        log_sql: Log SQL queries. `log_level` must be `debug` for this to do anything. Extremely verbose.
+        use_memory_db: Use in-memory database. Useful for development.
+        dev_reload: Automatically reload when Python sources are changed. Does not reload node definitions.
+        profile_graphs: Enable graph profiling using `cProfile`.
+        profile_prefix: An optional prefix for profile output files.
+        profiles_dir: Path to profiles output directory.
+        ram: Maximum memory amount used by memory model cache for rapid switching (GB).
+        vram: Amount of VRAM reserved for model storage (GB).
+        convert_cache: Maximum size of on-disk converted models cache (GB).
+        lazy_offload: Keep models in VRAM until their space is needed.
+        log_memory_usage: If True, a memory snapshot will be captured before and after every model cache operation, and the result will be logged (at debug level). There is a time cost to capturing the memory snapshots, so it is recommended to only enable this feature if you are actively inspecting the model cache's behaviour.
+        device: Preferred execution device. `auto` will choose the device depending on the hardware platform and the installed torch capabilities.<br>Valid values: `auto`, `cpu`, `cuda`, `cuda:1`, `mps`
+        precision: Floating point precision. `float16` will consume half the memory of `float32` but produce slightly lower-quality images. The `auto` setting will guess the proper precision based on your video card and operating system.<br>Valid values: `auto`, `float16`, `bfloat16`, `float32`, `autocast`
+        sequential_guidance: Whether to calculate guidance in serial instead of in parallel, lowering memory requirements.
+        attention_type: Attention type.<br>Valid values: `auto`, `normal`, `xformers`, `sliced`, `torch-sdp`
+        attention_slice_size: Slice size, valid when attention_type=="sliced".<br>Valid values: `auto`, `balanced`, `max`, `1`, `2`, `3`, `4`, `5`, `6`, `7`, `8`
+        force_tiled_decode: Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty).
+        pil_compress_level: The compress_level setting of PIL.Image.save(), used for PNG encoding. All settings are lossless. 0 = no compression, 1 = fastest with slightly larger filesize, 9 = slowest with smallest filesize. 1 is typically the best setting.
+        max_queue_size: Maximum number of items in the session queue.
+        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_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.
     """
 
-    singleton_config: ClassVar[Optional[InvokeAIAppConfig]] = None
-    singleton_init: ClassVar[Optional[Dict[str, Any]]] = None
+    _root: Optional[Path] = PrivateAttr(default=None)
+    _config_file: Optional[Path] = PrivateAttr(default=None)
 
     # fmt: off
-    type: Literal["InvokeAI"] = "InvokeAI"
+
+    # INTERNAL
+    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
-    host                : str = Field(default="127.0.0.1", description="IP address to bind to. Use `0.0.0.0` to serve to your local network.", json_schema_extra=Categories.WebServer)
-    port                : int = Field(default=9090, description="Port to bind to.", json_schema_extra=Categories.WebServer)
-    allow_origins       : List[str] = Field(default=[], description="Allowed CORS origins.", json_schema_extra=Categories.WebServer)
-    allow_credentials   : bool = Field(default=True, description="Allow CORS credentials.", json_schema_extra=Categories.WebServer)
-    allow_methods       : List[str] = Field(default=["*"], description="Methods allowed for CORS.", json_schema_extra=Categories.WebServer)
-    allow_headers       : List[str] = Field(default=["*"], description="Headers allowed for CORS.", json_schema_extra=Categories.WebServer)
-    # SSL options correspond to https://www.uvicorn.org/settings/#https
-    ssl_certfile        : Optional[Path] = Field(default=None, description="SSL certificate file for HTTPS.", json_schema_extra=Categories.WebServer)
-    ssl_keyfile         : Optional[Path] = Field(default=None, description="SSL key file for HTTPS.", json_schema_extra=Categories.WebServer)
+    host:                           str = Field(default="127.0.0.1",        description="IP address to bind to. Use `0.0.0.0` to serve to your local network.")
+    port:                           int = Field(default=9090,               description="Port to bind to.")
+    allow_origins:            list[str] = Field(default=[],                 description="Allowed CORS origins.")
+    allow_credentials:             bool = Field(default=True,               description="Allow CORS credentials.")
+    allow_methods:            list[str] = Field(default=["*"],              description="Methods allowed for CORS.")
+    allow_headers:            list[str] = Field(default=["*"],              description="Headers allowed for CORS.")
+    ssl_certfile:        Optional[Path] = Field(default=None,               description="SSL certificate file for HTTPS. See https://www.uvicorn.org/settings/#https.")
+    ssl_keyfile:         Optional[Path] = Field(default=None,               description="SSL key file for HTTPS. See https://www.uvicorn.org/settings/#https.")
 
-    # FEATURES
-    esrgan              : bool = Field(default=True, description="Enables or disables the upscaling code.", json_schema_extra=Categories.Features)
-    # TODO(psyche): This is not used anywhere.
-    internet_available  : bool = Field(default=True, description="If true, attempt to download models on the fly; otherwise only use local models.", json_schema_extra=Categories.Features)
-    log_tokenization    : bool = Field(default=False, description="Enable logging of parsed prompt tokens.", json_schema_extra=Categories.Features)
-    patchmatch          : bool = Field(default=True, description="Enable patchmatch inpaint code.", json_schema_extra=Categories.Features)
-    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.', json_schema_extra=Categories.Features)
+    # 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.")
 
     # PATHS
-    root                : Optional[Path] = Field(default=None, description='The InvokeAI runtime root directory.', json_schema_extra=Categories.Paths)
-    autoimport_dir      : Path = Field(default=Path('autoimport'), description='Path to a directory of models files to be imported on startup.', json_schema_extra=Categories.Paths)
-    models_dir          : Path = Field(default=Path('models'), description='Path to the models directory.', json_schema_extra=Categories.Paths)
-    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.', json_schema_extra=Categories.Paths)
-    legacy_conf_dir     : Path = Field(default=Path('configs/stable-diffusion'), description='Path to directory of legacy checkpoint config files.', json_schema_extra=Categories.Paths)
-    db_dir              : Path = Field(default=Path('databases'), description='Path to InvokeAI databases directory.', json_schema_extra=Categories.Paths)
-    outdir              : Path = Field(default=Path('outputs'), description='Path to directory for outputs.', json_schema_extra=Categories.Paths)
-    custom_nodes_dir    : Path = Field(default=Path('nodes'), description='Path to directory for custom nodes.', json_schema_extra=Categories.Paths)
-    # TODO(psyche): This is not used anywhere.
-    from_file           : Optional[Path] = Field(default=None, description='Take command input from the indicated file (command-line client only).', json_schema_extra=Categories.Paths)
+    autoimport_dir:                Path = Field(default=Path("autoimport"), description="Path to a directory of models files to be imported on startup.")
+    models_dir:                    Path = Field(default=Path("models"),     description="Path to the models directory.")
+    convert_cache_dir:             Path = Field(default=Path("models/.cache"), description="Path to the converted models cache directory. When loading a non-diffusers model, it will be converted and store on disk at this location.")
+    legacy_conf_dir:               Path = Field(default=Path("configs"), description="Path to directory of legacy checkpoint config files.")
+    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.")
 
     # LOGGING
-    log_handlers        : List[str] = Field(default=["console"], description='Log handler. Valid options are "console", "file=<path>", "syslog=path|address:host:port", "http=<url>".', json_schema_extra=Categories.Logging)
+    log_handlers:             list[str] = Field(default=["console"],        description='Log handler. Valid options are "console", "file=<path>", "syslog=path|address:host:port", "http=<url>".')
     # note - would be better to read the log_format values from logging.py, but this creates circular dependencies issues
-    log_format          : Literal['plain', 'color', 'syslog', 'legacy'] = Field(default="color", description='Log format. Use "plain" for text-only, "color" for colorized output, "legacy" for 2.3-style logging and "syslog" for syslog-style.', json_schema_extra=Categories.Logging)
-    log_level           : Literal["debug", "info", "warning", "error", "critical"] = Field(default="info", description="Emit logging messages at this level or higher.", json_schema_extra=Categories.Logging)
-    log_sql             : bool = Field(default=False, description="Log SQL queries. `log_level` must be `debug` for this to do anything. Extremely verbose.", json_schema_extra=Categories.Logging)
+    log_format:              LOG_FORMAT = Field(default="color",            description='Log format. Use "plain" for text-only, "color" for colorized output, "legacy" for 2.3-style logging and "syslog" for syslog-style.')
+    log_level:                LOG_LEVEL = Field(default="info",             description="Emit logging messages at this level or higher.")
+    log_sql:                       bool = Field(default=False,              description="Log SQL queries. `log_level` must be `debug` for this to do anything. Extremely verbose.")
 
     # Development
-    use_memory_db       : bool = Field(default=False, description='Use in-memory database. Useful for development.', json_schema_extra=Categories.Development)
-    dev_reload          : bool = Field(default=False, description="Automatically reload when Python sources are changed. Does not reload node definitions.", json_schema_extra=Categories.Development)
-    profile_graphs      : bool = Field(default=False, description="Enable graph profiling using `cProfile`.", json_schema_extra=Categories.Development)
-    profile_prefix      : Optional[str] = Field(default=None, description="An optional prefix for profile output files.", json_schema_extra=Categories.Development)
-    profiles_dir        : Path = Field(default=Path('profiles'), description="Path to profiles output directory.", json_schema_extra=Categories.Development)
-
-    version             : bool = Field(default=False, description="CLI arg - show InvokeAI version and exit.", json_schema_extra=Categories.CLIArgs)
+    use_memory_db:                 bool = Field(default=False,              description="Use in-memory database. Useful for development.")
+    dev_reload:                    bool = Field(default=False,              description="Automatically reload when Python sources are changed. Does not reload node definitions.")
+    profile_graphs:                bool = Field(default=False,              description="Enable graph profiling using `cProfile`.")
+    profile_prefix:       Optional[str] = Field(default=None,               description="An optional prefix for profile output files.")
+    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).", json_schema_extra=Categories.ModelCache, )
-    vram                : float = Field(default=DEFAULT_VRAM_CACHE, ge=0, description="Amount of VRAM reserved for model storage (GB)", json_schema_extra=Categories.ModelCache, )
-    convert_cache       : float = Field(default=DEFAULT_CONVERT_CACHE, ge=0, description="Maximum size of on-disk converted models cache (GB)", json_schema_extra=Categories.ModelCache)
-
-    lazy_offload        : bool = Field(default=True, description="Keep models in VRAM until their space is needed.", json_schema_extra=Categories.ModelCache, )
-    log_memory_usage    : bool = Field(default=False, description="If True, a memory snapshot will be captured before and after every model cache operation, and the result will be logged (at debug level). There is a time cost to capturing the memory snapshots, so it is recommended to only enable this feature if you are actively inspecting the model cache's behaviour.", json_schema_extra=Categories.ModelCache)
+    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.")
+    log_memory_usage:              bool = Field(default=False,              description="If True, a memory snapshot will be captured before and after every model cache operation, and the result will be logged (at debug level). There is a time cost to capturing the memory snapshots, so it is recommended to only enable this feature if you are actively inspecting the model cache's behaviour.")
 
     # DEVICE
-    device              : Literal["auto", "cpu", "cuda", "cuda:1", "mps"] = Field(default="auto", description="Preferred execution device. `auto` will choose the device depending on the hardware platform and the installed torch capabilities.", json_schema_extra=Categories.Device)
-    precision           : Literal["auto", "float16", "bfloat16", "float32", "autocast"] = Field(default="auto", description="Floating point precision. `float16` will consume half the memory of `float32` but produce slightly lower-quality images. The `auto` setting will guess the proper precision based on your video card and operating system.", json_schema_extra=Categories.Device)
+    device:                      DEVICE = Field(default="auto",             description="Preferred execution device. `auto` will choose the device depending on the hardware platform and the installed torch capabilities.")
+    precision:                PRECISION = Field(default="auto",             description="Floating point precision. `float16` will consume half the memory of `float32` but produce slightly lower-quality images. The `auto` setting will guess the proper precision based on your video card and operating system.")
 
     # GENERATION
-    sequential_guidance : bool = Field(default=False, description="Whether to calculate guidance in serial instead of in parallel, lowering memory requirements.", json_schema_extra=Categories.Generation)
-    attention_type      : Literal["auto", "normal", "xformers", "sliced", "torch-sdp"] = Field(default="auto", description="Attention type.", json_schema_extra=Categories.Generation)
-    attention_slice_size: Literal["auto", "balanced", "max", 1, 2, 3, 4, 5, 6, 7, 8] = Field(default="auto", description='Slice size, valid when attention_type=="sliced".', json_schema_extra=Categories.Generation)
-    force_tiled_decode  : bool = Field(default=False, description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty).", json_schema_extra=Categories.Generation)
-    png_compress_level  : int = Field(default=1, description="The compress_level setting of PIL.Image.save(), used for PNG encoding. All settings are lossless. 0 = no compression, 1 = fastest with slightly larger filesize, 9 = slowest with smallest filesize. 1 is typically the best setting.", json_schema_extra=Categories.Generation)
-
-    # QUEUE
-    max_queue_size      : int = Field(default=10000, gt=0, description="Maximum number of items in the session queue.", json_schema_extra=Categories.Queue)
+    sequential_guidance:           bool = Field(default=False,              description="Whether to calculate guidance in serial instead of in parallel, lowering memory requirements.")
+    attention_type:      ATTENTION_TYPE = Field(default="auto",             description="Attention type.")
+    attention_slice_size: ATTENTION_SLICE_SIZE = Field(default="auto",      description='Slice size, valid when attention_type=="sliced".')
+    force_tiled_decode:            bool = Field(default=False,              description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty).")
+    pil_compress_level:             int = Field(default=1,                  description="The compress_level setting of PIL.Image.save(), used for PNG encoding. All settings are lossless. 0 = no compression, 1 = fastest with slightly larger filesize, 9 = slowest with smallest filesize. 1 is typically the best setting.")
+    max_queue_size:                 int = Field(default=10000, gt=0,        description="Maximum number of items in the session queue.")
 
     # NODES
-    allow_nodes         : Optional[List[str]] = Field(default=None, description="List of nodes to allow. Omit to allow all.", json_schema_extra=Categories.Nodes)
-    deny_nodes          : Optional[List[str]] = Field(default=None, description="List of nodes to deny. Omit to deny none.", json_schema_extra=Categories.Nodes)
-    node_cache_size     : int = Field(default=512, description="How many cached nodes to keep in memory.", json_schema_extra=Categories.Nodes)
+    allow_nodes:    Optional[list[str]] = Field(default=None,               description="List of nodes to allow. Omit to allow all.")
+    deny_nodes:     Optional[list[str]] = Field(default=None,               description="List of nodes to deny. Omit to deny none.")
+    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.", json_schema_extra=Categories.ModelInstall)
-    remote_api_tokens   : Optional[list[URLRegexToken]] = 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.",
-        json_schema_extra=Categories.ModelInstall
-    )
+    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.")
 
-    # TODO(psyche): Can we just remove these then?
-    # DEPRECATED FIELDS - STILL HERE IN ORDER TO OBTAN VALUES FROM PRE-3.1 CONFIG FILES
-    always_use_cpu      : bool = Field(default=False, description="If true, use the CPU for rendering even if a GPU is available.", json_schema_extra=Categories.Deprecated)
-    max_cache_size      : Optional[float] = Field(default=None, gt=0, description="Maximum memory amount used by model cache for rapid switching", json_schema_extra=Categories.Deprecated)
-    max_vram_cache_size : Optional[float] = Field(default=None, ge=0, description="Amount of VRAM reserved for model storage", json_schema_extra=Categories.Deprecated)
-    xformers_enabled    : bool = Field(default=True, description="Enable/disable memory-efficient attention", json_schema_extra=Categories.Deprecated)
-    tiled_decode        : bool = Field(default=False, description="Whether to enable tiled VAE decode (reduces memory consumption with some performance penalty)", json_schema_extra=Categories.Deprecated)
-    lora_dir            : Optional[Path] = Field(default=None, description='Path to a directory of LoRA/LyCORIS models to be imported on startup.', json_schema_extra=Categories.Deprecated)
-    embedding_dir       : Optional[Path] = Field(default=None, description='Path to a directory of Textual Inversion embeddings to be imported on startup.', json_schema_extra=Categories.Deprecated)
-    controlnet_dir      : Optional[Path] = Field(default=None, description='Path to a directory of ControlNet embeddings to be imported on startup.', json_schema_extra=Categories.Deprecated)
-    conf_path           : Path = Field(default=Path('configs/models.yaml'), description='Path to models definition file', json_schema_extra=Categories.Deprecated)
-
-    # this is not referred to in the source code and can be removed entirely
-    #free_gpu_mem        : Optional[bool] = Field(default=None, description="If true, purge model from GPU after each generation.", json_schema_extra=Categories.MemoryPerformance)
-
-    # See InvokeAIAppConfig subclass below for CACHE and DEVICE categories
     # fmt: on
 
-    model_config = SettingsConfigDict(validate_assignment=True, env_prefix="INVOKEAI")
+    model_config = SettingsConfigDict(env_prefix="INVOKEAI_", env_ignore_empty=True)
 
-    def parse_args(
-        self,
-        argv: Optional[list[str]] = None,
-        conf: Optional[DictConfig] = None,
-        clobber: Optional[bool] = False,
-    ) -> None:
+    def update_config(self, config: dict[str, Any] | InvokeAIAppConfig, clobber: bool = True) -> None:
+        """Updates the config, overwriting existing values.
+
+        Args:
+            config: A dictionary of config settings, or instance of `InvokeAIAppConfig`. If an instance of \
+                `InvokeAIAppConfig`, only the explicitly set fields will be merged into the singleton config.
+            clobber: If `True`, overwrite existing values. If `False`, only update fields that are not already set.
         """
-        Update settings with contents of init file, environment, and command-line settings.
 
-        :param conf: alternate Omegaconf dictionary object
-        :param argv: aternate sys.argv list
-        :param clobber: ovewrite any initialization parameters passed during initialization
+        if isinstance(config, dict):
+            new_config = self.model_validate(config)
+        else:
+            new_config = config
+
+        for field_name in new_config.model_fields_set:
+            new_value = getattr(new_config, field_name)
+            current_value = getattr(self, field_name)
+
+            if field_name in self.model_fields_set and not clobber:
+                continue
+
+            if new_value != current_value:
+                setattr(self, field_name, new_value)
+
+    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.
+
+        Args:
+            dest_path: Path to write the config to.
         """
-        # Set the runtime root directory. We parse command-line switches here
-        # in order to pick up the --root_dir option.
-        super().parse_args(argv)
-        loaded_conf = None
-        if conf is None:
-            try:
-                loaded_conf = OmegaConf.load(self.root_dir / INIT_FILE)
-            except Exception:
-                pass
-        if isinstance(loaded_conf, DictConfig):
-            InvokeAISettings.initconf = loaded_conf
-        else:
-            InvokeAISettings.initconf = conf
+        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 - skip legacy_models_yaml_path
+            meta_dict = self.model_dump(mode="json", include={"schema_version"})
 
-        # parse args again in order to pick up settings in configuration file
-        super().parse_args(argv)
+            # User settings
+            config_dict = self.model_dump(
+                mode="json",
+                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 self.singleton_init and not clobber:
-            # When setting values in this way, set validate_assignment to true if you want to validate the value.
-            for k, v in self.singleton_init.items():
-                setattr(self, k, v)
-
-    @classmethod
-    def get_config(cls, **kwargs: Any) -> InvokeAIAppConfig:
-        """Return a singleton InvokeAIAppConfig configuration object."""
-        if (
-            cls.singleton_config is None
-            or type(cls.singleton_config) is not cls
-            or (kwargs and cls.singleton_init != kwargs)
-        ):
-            cls.singleton_config = cls(**kwargs)
-            cls.singleton_init = kwargs
-        return cls.singleton_config
-
-    @property
-    def root_path(self) -> Path:
-        """Path to the runtime root directory."""
-        if self.root:
-            root = Path(self.root).expanduser().absolute()
-        else:
-            root = self.find_root().expanduser().absolute()
-        self.root = root  # insulate ourselves from relative paths that may change
-        return root.resolve()
-
-    @property
-    def root_dir(self) -> Path:
-        """Alias for above."""
-        return self.root_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 - 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 _resolve(self, partial_path: Path) -> Path:
         return (self.root_path / partial_path).resolve()
 
     @property
-    def init_file_path(self) -> Path:
-        """Path to invokeai.yaml."""
-        resolved_path = self._resolve(INIT_FILE)
+    def root_path(self) -> Path:
+        """Path to the runtime root directory, resolved to an absolute path."""
+        if self._root:
+            root = Path(self._root).expanduser().absolute()
+        else:
+            root = self.find_root().expanduser().absolute()
+        self._root = root  # insulate ourselves from relative paths that may change
+        return root.resolve()
+
+    @property
+    def config_file_path(self) -> Path:
+        """Path to invokeai.yaml, resolved to an absolute path.."""
+        resolved_path = self._resolve(self._config_file or INIT_FILE)
         assert resolved_path is not None
         return resolved_path
 
     @property
-    def output_path(self) -> Optional[Path]:
-        """Path to defaults outputs directory."""
-        return self._resolve(self.outdir)
+    def autoimport_path(self) -> Path:
+        """Path to the autoimports directory, resolved to an absolute path.."""
+        return self._resolve(self.autoimport_dir)
+
+    @property
+    def outputs_path(self) -> Optional[Path]:
+        """Path to the outputs directory, resolved to an absolute path.."""
+        return self._resolve(self.outputs_dir)
 
     @property
     def db_path(self) -> Path:
-        """Path to the invokeai.db file."""
+        """Path to the invokeai.db file, resolved to an absolute path.."""
         db_dir = self._resolve(self.db_dir)
         assert db_dir is not None
         return db_dir / DB_FILE
 
-    @property
-    def model_conf_path(self) -> Path:
-        """Path to models configuration file."""
-        return self._resolve(self.conf_path)
-
     @property
     def legacy_conf_path(self) -> Path:
-        """Path to directory of legacy configuration files (e.g. v1-inference.yaml)."""
+        """Path to directory of legacy configuration files (e.g. v1-inference.yaml), resolved to an absolute path.."""
         return self._resolve(self.legacy_conf_dir)
 
     @property
     def models_path(self) -> Path:
-        """Path to the models directory."""
+        """Path to the models directory, resolved to an absolute path.."""
         return self._resolve(self.models_dir)
 
     @property
-    def models_convert_cache_path(self) -> Path:
-        """Path to the converted cache models directory."""
+    def convert_cache_path(self) -> Path:
+        """Path to the converted cache models directory, resolved to an absolute path.."""
         return self._resolve(self.convert_cache_dir)
 
     @property
     def custom_nodes_path(self) -> Path:
-        """Path to the custom nodes directory."""
+        """Path to the custom nodes directory, resolved to an absolute path.."""
         custom_nodes_path = self._resolve(self.custom_nodes_dir)
         assert custom_nodes_path is not None
         return custom_nodes_path
 
-    # the following methods support legacy calls leftover from the Globals era
-    @property
-    def full_precision(self) -> bool:
-        """Return true if precision set to float32."""
-        return self.precision == "float32"
-
-    @property
-    def try_patchmatch(self) -> bool:
-        """Return true if patchmatch true."""
-        return self.patchmatch
-
-    @property
-    def nsfw_checker(self) -> bool:
-        """Return value for NSFW checker. The NSFW node is always active and disabled from Web UI."""
-        return True
-
-    @property
-    def invisible_watermark(self) -> bool:
-        """Return value of invisible watermark. It is always active and disabled from Web UI."""
-        return True
-
-    @property
-    def ram_cache_size(self) -> float:
-        """Return the ram cache size using the legacy or modern setting (GB)."""
-        return self.max_cache_size or self.ram
-
-    @property
-    def vram_cache_size(self) -> float:
-        """Return the vram cache size using the legacy or modern setting (GB)."""
-        return self.max_vram_cache_size or self.vram
-
-    @property
-    def convert_cache_size(self) -> float:
-        """Return the convert cache size on disk (GB)."""
-        return self.convert_cache
-
-    @property
-    def use_cpu(self) -> bool:
-        """Return true if the device is set to CPU or the always_use_cpu flag is set."""
-        return self.always_use_cpu or self.device == "cpu"
-
-    @property
-    def disable_xformers(self) -> bool:
-        """Return true if enable_xformers is false (reversed logic) and attention type is not set to xformers."""
-        disabled_in_config = not self.xformers_enabled
-        return disabled_in_config and self.attention_type != "xformers"
-
     @property
     def profiles_path(self) -> Path:
-        """Path to the graph profiles directory."""
+        """Path to the graph profiles directory, resolved to an absolute path.."""
         return self._resolve(self.profiles_dir)
 
     @staticmethod
     def find_root() -> Path:
         """Choose the runtime root directory when not specified on command line or init file."""
-        return _find_root()
-
-    @staticmethod
-    def generate_docstrings() -> str:
-        """Helper function for mkdocs. Generates a docstring for the InvokeAIAppConfig class.
-
-        You shouldn't run this manually. Instead, run `scripts/update-config-docstring.py` to update the docstring.
-        A makefile target is also available: `make update-config-docstring`.
-
-        See that script for more information about why this is necessary.
-        """
-        docstring = '    """Invoke App Configuration\n\n'
-        docstring += "    Attributes:"
-
-        field_descriptions: dict[str, list[str]] = {}
-
-        for k, v in InvokeAIAppConfig.model_fields.items():
-            if not isinstance(v.json_schema_extra, dict):
-                # Should never happen
-                continue
-
-            category = v.json_schema_extra.get("category", None)
-            if not isinstance(category, str) or category == "Deprecated":
-                continue
-            if not field_descriptions.get(category):
-                field_descriptions[category] = []
-            field_descriptions[category].append(f"        {k}: **{category}**: {v.description}")
-
-        for c in [
-            "Web Server",
-            "Features",
-            "Paths",
-            "Logging",
-            "Development",
-            "CLIArgs",
-            "Model Install",
-            "Model Cache",
-            "Device",
-            "Generation",
-            "Queue",
-            "Nodes",
-        ]:
-            docstring += "\n"
-            docstring += "\n".join(field_descriptions[c])
-
-        docstring += '\n    """'
-
-        return docstring
+        venv = Path(os.environ.get("VIRTUAL_ENV") or ".")
+        if os.environ.get("INVOKEAI_ROOT"):
+            root = Path(os.environ["INVOKEAI_ROOT"])
+        elif any((venv.parent / x).exists() for x in [INIT_FILE, LEGACY_INIT_FILE]):
+            root = (venv.parent).resolve()
+        else:
+            root = Path("~/invokeai").expanduser().resolve()
+        return root
 
 
-def get_invokeai_config(**kwargs: Any) -> InvokeAIAppConfig:
-    """Legacy function which returns InvokeAIAppConfig.get_config()."""
-    return InvokeAIAppConfig.get_config(**kwargs)
+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 _find_root() -> Path:
-    venv = Path(os.environ.get("VIRTUAL_ENV") or ".")
-    if os.environ.get("INVOKEAI_ROOT"):
-        root = Path(os.environ["INVOKEAI_ROOT"])
-    elif any((venv.parent / x).exists() for x in [INIT_FILE, LEGACY_INIT_FILE]):
-        root = (venv.parent).resolve()
+def migrate_v3_config_dict(config_dict: dict[str, Any]) -> InvokeAIAppConfig:
+    """Migrate a v3 config dictionary to a current config object.
+
+    Args:
+        config_dict: A dictionary of settings from a v3 config file.
+
+    Returns:
+        An instance of `InvokeAIAppConfig` with the migrated settings.
+
+    """
+    parsed_config_dict: dict[str, Any] = {}
+    for _category_name, category_dict in config_dict["InvokeAI"].items():
+        for k, v in category_dict.items():
+            # `outdir` was renamed to `outputs_dir` in v4
+            if k == "outdir":
+                parsed_config_dict["outputs_dir"] = v
+            # `max_cache_size` was renamed to `ram` some time in v3, but both names were used
+            if k == "max_cache_size" and "ram" not in category_dict:
+                parsed_config_dict["ram"] = v
+            # `max_vram_cache_size` was renamed to `vram` some time in v3, but both names were used
+            if k == "max_vram_cache_size" and "vram" not in category_dict:
+                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
+    # When migrating the config file, we should not include currently-set environment variables.
+    config = DefaultInvokeAIAppConfig.model_validate(parsed_config_dict)
+
+    return config
+
+
+def load_and_migrate_config(config_path: Path) -> InvokeAIAppConfig:
+    """Load and migrate a config file to the latest version.
+
+    Args:
+        config_path: Path to the config file.
+
+    Returns:
+        An instance of `InvokeAIAppConfig` with the loaded and migrated settings.
+    """
+    assert config_path.suffix == ".yaml"
+    with open(config_path) as file:
+        loaded_config_dict = yaml.safe_load(file)
+
+    assert isinstance(loaded_config_dict, dict)
+
+    if "InvokeAI" in loaded_config_dict:
+        # This is a v3 config file, attempt to migrate it
+        shutil.copy(config_path, config_path.with_suffix(".yaml.bak"))
+        try:
+            # 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
+        migrated_config.write_file(config_path)
+        return migrated_config
     else:
-        root = Path("~/invokeai").expanduser().resolve()
-    return root
+        # Attempt to load as a v4 config file
+        try:
+            # Meta is not included in the model fields, so we need to validate it separately
+            config = InvokeAIAppConfig.model_validate(loaded_config_dict)
+            assert (
+                config.schema_version == CONFIG_SCHEMA_VERSION
+            ), f"Invalid schema version, expected {CONFIG_SCHEMA_VERSION}: {config.schema_version}"
+            return config
+        except Exception as e:
+            raise RuntimeError(f"Failed to load config file {config_path}: {e}") from e
+
+
+@lru_cache(maxsize=1)
+def get_config() -> InvokeAIAppConfig:
+    """Get the global singleton app config.
+
+    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
+
+    # 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._root = Path(root)
+    if config_file := getattr(args, "config_file", None):
+        config._config_file = Path(config_file)
+
+    # 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)
+
+    # 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

invokeai/app/services/download/download_default.py

@@ -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(

invokeai/app/services/events/events_base.py

@@ -386,6 +386,17 @@ class EventServiceBase:
             },
         )
 
+    def emit_model_install_downloads_done(self, source: str) -> None:
+        """
+        Emit once when all parts are downloaded, but before the probing and registration start.
+
+        :param source: Source of the model; local path, repo_id or url
+        """
+        self.__emit_model_event(
+            event_name="model_install_downloads_done",
+            payload={"source": source},
+        )
+
     def emit_model_install_running(self, source: str) -> None:
         """
         Emit once when an install job becomes active.

invokeai/app/services/image_files/image_files_disk.py

@@ -82,7 +82,7 @@ class DiskImageFileStorage(ImageFileStorageBase):
                 image_path,
                 "PNG",
                 pnginfo=pnginfo,
-                compress_level=self.__invoker.services.configuration.png_compress_level,
+                compress_level=self.__invoker.services.configuration.pil_compress_level,
             )
 
             thumbnail_name = get_thumbnail_name(image_name)

invokeai/app/services/model_install/model_install_base.py

@@ -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
-        base += f":{self.variant or ''}"
-        base += f":{self.subfolder}" if self.subfolder else ""
+        if self.variant:
+            base += f":{self.variant or ''}"
+        if self.subfolder:
+            base += f":{self.subfolder}"
         return base
 
 

invokeai/app/services/model_install/model_install_default.py

@@ -11,8 +11,8 @@ from shutil import copyfile, copytree, move, rmtree
 from tempfile import mkdtemp
 from typing import Any, Dict, List, Optional, Set, Union
 
+import yaml
 from huggingface_hub import HfFolder
-from omegaconf import DictConfig, OmegaConf
 from pydantic.networks import AnyHttpUrl
 from requests import Session
 
@@ -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
@@ -124,15 +126,30 @@ class ModelInstallService(ModelInstallServiceBase):
             if not self._running:
                 raise Exception("Attempt to stop the install service before it was started")
             self._stop_event.set()
-            with self._install_queue.mutex:
-                self._install_queue.queue.clear()  # get rid of pending jobs
-            active_jobs = [x for x in self.list_jobs() if x.running]
-            if active_jobs:
-                self._logger.warning("Waiting for active install job to complete")
-            self.wait_for_installs()
+            self._clear_pending_jobs()
             self._download_cache.clear()
+            assert self._install_thread is not None
+            self._install_thread.join()
             self._running = False
 
+    def _clear_pending_jobs(self) -> None:
+        for job in self.list_jobs():
+            if not job.in_terminal_state:
+                self._logger.warning("Cancelling job {job.id}")
+                self.cancel_job(job)
+        while True:
+            try:
+                job = self._install_queue.get(block=False)
+                self._install_queue.task_done()
+            except Empty:
+                break
+
+    def _put_in_queue(self, job: ModelInstallJob) -> None:
+        if self._stop_event.is_set():
+            self.cancel_job(job)
+        else:
+            self._install_queue.put(job)
+
     def register_path(
         self,
         model_path: Union[Path, str],
@@ -218,7 +235,7 @@ class ModelInstallService(ModelInstallServiceBase):
 
         if isinstance(source, LocalModelSource):
             install_job = self._import_local_model(source, config)
-            self._install_queue.put(install_job)  # synchronously install
+            self._put_in_queue(install_job)  # synchronously install
         elif isinstance(source, HFModelSource):
             install_job = self._import_from_hf(source, config)
         elif isinstance(source, URLModelSource):
@@ -253,7 +270,6 @@ class ModelInstallService(ModelInstallServiceBase):
                 raise TimeoutError("Timeout exceeded")
         return job
 
-    # TODO: Better name? Maybe wait_for_jobs()? Maybe too easily confused with above
     def wait_for_installs(self, timeout: int = 0) -> List[ModelInstallJob]:  # noqa D102
         """Block until all installation jobs are done."""
         start = time.time()
@@ -263,6 +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:
@@ -279,53 +296,62 @@ class ModelInstallService(ModelInstallServiceBase):
     def sync_to_config(self) -> None:
         """Synchronize models on disk to those in the config record store database."""
         self._scan_models_directory()
-        if autoimport := self._app_config.autoimport_dir:
+        if self._app_config.autoimport_path:
             self._logger.info("Scanning autoimport directory for new models")
-            installed = self.scan_directory(self._app_config.root_path / autoimport)
+            installed = self.scan_directory(self._app_config.autoimport_path)
             self._logger.info(f"{len(installed)} new models registered")
         self._logger.info("Model installer (re)initialized")
 
     def _migrate_yaml(self) -> None:
         db_models = self.record_store.all_models()
-        try:
-            yaml = self._get_yaml()
-        except OSError:
-            return
 
-        yaml_metadata = yaml.pop("__metadata__")
-        yaml_version = yaml_metadata.get("version")
-
-        if yaml_version != "3.0.0":
-            raise ValueError(
-                f"Attempted migration of unsupported `models.yaml` v{yaml_version}. Only v3.0.0 is supported. Exiting."
-            )
-
-        self._logger.info(
-            f"Starting one-time migration of {len(yaml.items())} models from `models.yaml` to database. This may take a few minutes."
+        legacy_models_yaml_path = (
+            self._app_config.legacy_models_yaml_path or self._app_config.root_path / "configs" / "models.yaml"
         )
 
-        if len(db_models) == 0 and len(yaml.items()) != 0:
-            for model_key, stanza in yaml.items():
-                _, _, model_name = str(model_key).split("/")
-                model_path = Path(stanza["path"])
-                if not model_path.is_absolute():
-                    model_path = self._app_config.models_path / model_path
-                model_path = model_path.resolve()
+        # 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)
 
-                config: dict[str, Any] = {}
-                config["name"] = model_name
-                config["description"] = stanza.get("description")
-                config["config_path"] = stanza.get("config")
+        if legacy_models_yaml_path.exists():
+            legacy_models_yaml = yaml.safe_load(legacy_models_yaml_path.read_text())
 
-                try:
-                    id = self.register_path(model_path=model_path, config=config)
-                    self._logger.info(f"Migrated {model_name} with id {id}")
-                except Exception as e:
-                    self._logger.warning(f"Model at {model_path} could not be migrated: {e}")
+            yaml_metadata = legacy_models_yaml.pop("__metadata__")
+            yaml_version = yaml_metadata.get("version")
 
-        # Rename `models.yaml` to `models.yaml.bak` to prevent re-migration
-        yaml_path = self._app_config.model_conf_path
-        yaml_path.rename(yaml_path.with_suffix(".yaml.bak"))
+            if yaml_version != "3.0.0":
+                raise ValueError(
+                    f"Attempted migration of unsupported `models.yaml` v{yaml_version}. Only v3.0.0 is supported. Exiting."
+                )
+
+            self._logger.info(
+                f"Starting one-time migration of {len(legacy_models_yaml.items())} models from {str(legacy_models_yaml_path)}. This may take a few minutes."
+            )
+
+            if len(db_models) == 0 and len(legacy_models_yaml.items()) != 0:
+                for model_key, stanza in legacy_models_yaml.items():
+                    _, _, model_name = str(model_key).split("/")
+                    model_path = Path(stanza["path"])
+                    if not model_path.is_absolute():
+                        model_path = self._app_config.models_path / model_path
+                    model_path = model_path.resolve()
+
+                    config: dict[str, Any] = {}
+                    config["name"] = model_name
+                    config["description"] = stanza.get("description")
+                    config["config_path"] = stanza.get("config")
+
+                    try:
+                        id = self.register_path(model_path=model_path, config=config)
+                        self._logger.info(f"Migrated {model_name} with id {id}")
+                    except Exception as e:
+                        self._logger.warning(f"Model at {model_path} could not be migrated: {e}")
+
+            # Rename `models.yaml` to `models.yaml.bak` to prevent re-migration
+            legacy_models_yaml_path.rename(legacy_models_yaml_path.with_suffix(".yaml.bak"))
+
+        # Unset the path - we are done with it either way
+        self._app_config.legacy_models_yaml_path = None
 
     def scan_directory(self, scan_dir: Path, install: bool = False) -> List[str]:  # noqa D102
         self._cached_model_paths = {Path(x.path).resolve() for x in self.record_store.all_models()}
@@ -365,7 +391,7 @@ class ModelInstallService(ModelInstallServiceBase):
     ) -> Path:
         """Download the model file located at source to the models cache and return its Path."""
         model_hash = sha256(str(source).encode("utf-8")).hexdigest()[0:32]
-        model_path = self._app_config.models_convert_cache_path / model_hash
+        model_path = self._app_config.convert_cache_path / model_hash
 
         # We expect the cache directory to contain one and only one downloaded file.
         # We don't know the file's name in advance, as it is set by the download
@@ -393,20 +419,20 @@ 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:
                 continue
-
             assert job.local_path is not None
             try:
                 if job.cancelled:
@@ -415,39 +441,14 @@ 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
-                    job.total_bytes = self._stat_size(job.local_path)
-                    job.bytes = job.total_bytes
-                    self._signal_job_running(job)
-                    job.config_in["source"] = str(job.source)
-                    job.config_in["source_type"] = MODEL_SOURCE_TO_TYPE_MAP[job.source.__class__]
-                    # enter the metadata, if there is any
-                    if isinstance(job.source_metadata, (HuggingFaceMetadata)):
-                        job.config_in["source_api_response"] = job.source_metadata.api_response
-
-                    if job.inplace:
-                        key = self.register_path(job.local_path, job.config_in)
-                    else:
-                        key = self.install_path(job.local_path, job.config_in)
-                    job.config_out = self.record_store.get_model(key)
-                    self._signal_job_completed(job)
+                elif job.waiting or job.downloads_done:
+                    self._register_or_install(job)
 
             except InvalidModelConfigException as excp:
-                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(
-                            f"At least one file in {job.local_path} is an HTML page, not a model. This can happen when an access token is required to download."
-                        )
-                    )
-                else:
-                    job.set_error(excp)
-                self._signal_job_errored(job)
+                self._set_error(job, excp)
 
             except (OSError, DuplicateModelException) as excp:
-                job.set_error(excp)
-                self._signal_job_errored(job)
+                self._set_error(job, excp)
 
             finally:
                 # if this is an install of a remote file, then clean up the temporary directory
@@ -455,8 +456,36 @@ class ModelInstallService(ModelInstallServiceBase):
                     rmtree(job._install_tmpdir)
                 self._install_completed_event.set()
                 self._install_queue.task_done()
+        self._logger.info(f"Installer thread {threading.get_ident()} exiting")
 
-        self._logger.info("Install thread 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)
+        job.config_in["source"] = str(job.source)
+        job.config_in["source_type"] = MODEL_SOURCE_TO_TYPE_MAP[job.source.__class__]
+        # enter the metadata, if there is any
+        if isinstance(job.source_metadata, (HuggingFaceMetadata)):
+            job.config_in["source_api_response"] = job.source_metadata.api_response
+
+        if job.inplace:
+            key = self.register_path(job.local_path, job.config_in)
+        else:
+            key = self.install_path(job.local_path, job.config_in)
+        job.config_out = self.record_store.get_model(key)
+        self._signal_job_completed(job)
+
+    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(
+                    f"At least one file in {job.local_path} is an HTML page, not a model. This can happen when an access token is required to download."
+                )
+            )
+        else:
+            job.set_error(excp)
+        self._signal_job_errored(job)
 
     # --------------------------------------------------------------------------------------------
     # Internal functions that manage the models directory
@@ -589,9 +618,9 @@ 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.root_dir / self.app_config.legacy_conf_dir / info.config_path).resolve()
+            legacy_conf = (self.app_config.legacy_conf_path / info.config_path).resolve()
             info.config_path = legacy_conf.as_posix()
         self.record_store.add_model(info)
         return info.key
@@ -602,16 +631,6 @@ class ModelInstallService(ModelInstallServiceBase):
             self._next_job_id += 1
         return id
 
-    # --------------------------------------------------------------------------------------------
-    # Internal functions that manage the old yaml config
-    # --------------------------------------------------------------------------------------------
-    def _get_yaml(self) -> DictConfig:
-        """Fetch the models.yaml DictConfig for this installation."""
-        yaml_path = self._app_config.model_conf_path
-        omegaconf = OmegaConf.load(yaml_path)
-        assert isinstance(omegaconf, DictConfig)
-        return omegaconf
-
     @staticmethod
     def _guess_variant() -> Optional[ModelRepoVariant]:
         """Guess the best HuggingFace variant type to download."""
@@ -713,12 +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
@@ -754,7 +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
@@ -780,17 +800,17 @@ 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]
-            self._download_cache.pop(download_job.source, None)
 
             # are there any more active jobs left in this task?
             if install_job.downloading and all(x.complete for x in install_job.download_parts):
-                install_job.status = InstallStatus.DOWNLOADS_DONE
-                self._install_queue.put(install_job)
+                self._signal_job_downloads_done(install_job)
+                self._put_in_queue(install_job)
 
             # Let other threads know that the number of downloads has changed
+            self._download_cache.pop(download_job.source, None)
             self._downloads_changed_event.set()
 
     def _download_error_callback(self, download_job: DownloadJob, excp: Optional[Exception] = None) -> None:
@@ -813,7 +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()
@@ -830,14 +850,14 @@ class ModelInstallService(ModelInstallServiceBase):
 
         if all(x.in_terminal_state for x in install_job.download_parts):
             # When all parts have reached their terminal state, we finalize the job to clean up the temporary directory and other resources
-            self._install_queue.put(install_job)
+            self._put_in_queue(install_job)
 
     # ------------------------------------------------------------------------------------------------
     # Internal methods that put events on the event bus
     # ------------------------------------------------------------------------------------------------
     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))
 
@@ -863,12 +883,17 @@ class ModelInstallService(ModelInstallServiceBase):
                 id=job.id,
             )
 
+    def _signal_job_downloads_done(self, job: ModelInstallJob) -> None:
+        job.status = InstallStatus.DOWNLOADS_DONE
+        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
@@ -876,7 +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
@@ -885,12 +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}'")

invokeai/app/services/model_manager/model_manager_default.py

@@ -78,14 +78,12 @@ class ModelManagerService(ModelManagerServiceBase):
         logger.setLevel(app_config.log_level.upper())
 
         ram_cache = ModelCache(
-            max_cache_size=app_config.ram_cache_size,
-            max_vram_cache_size=app_config.vram_cache_size,
+            max_cache_size=app_config.ram,
+            max_vram_cache_size=app_config.vram,
             logger=logger,
             execution_device=execution_device,
         )
-        convert_cache = ModelConvertCache(
-            cache_path=app_config.models_convert_cache_path, max_size=app_config.convert_cache_size
-        )
+        convert_cache = ModelConvertCache(cache_path=app_config.convert_cache_path, max_size=app_config.convert_cache)
         loader = ModelLoadService(
             app_config=app_config,
             ram_cache=ram_cache,

invokeai/app/services/session_queue/session_queue_sqlite.py

@@ -151,7 +151,7 @@ class SqliteSessionQueue(SessionQueueBase):
 
             # TODO: how does this work in a multi-user scenario?
             current_queue_size = self._get_current_queue_size(queue_id)
-            max_queue_size = self.__invoker.services.configuration.get_config().max_queue_size
+            max_queue_size = self.__invoker.services.configuration.max_queue_size
             max_new_queue_items = max_queue_size - current_queue_size
 
             priority = 0

invokeai/app/services/shared/invocation_context.py

@@ -423,7 +423,7 @@ class ConfigInterface(InvocationContextInterface):
             The app's config.
         """
 
-        return self._services.configuration.get_config()
+        return self._services.configuration
 
 
 class UtilInterface(InvocationContextInterface):

invokeai/app/util/download_with_progress.py

@@ -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

invokeai/app/util/suppress_output.py

@@ -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

invokeai/backend/embeddings/__init__.py

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

invokeai/backend/embeddings/embedding_base.py

@@ -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."""

invokeai/backend/image_util/__init__.py

@@ -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

invokeai/backend/image_util/canny.py

@@ -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)

invokeai/backend/image_util/depth_anything/__init__.py

@@ -9,14 +9,14 @@ from einops import repeat
 from PIL import Image
 from torchvision.transforms import Compose
 
-from invokeai.app.services.config.config_default import InvokeAIAppConfig
+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 = InvokeAIAppConfig.get_config()
+config = get_config()
 logger = InvokeAILogger.get_logger(config=config)
 
 DEPTH_ANYTHING_MODELS = {
@@ -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

invokeai/backend/image_util/dw_openpose/wholebody.py

@@ -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 InvokeAIAppConfig
+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 = InvokeAIAppConfig.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

invokeai/backend/image_util/hed.py

@@ -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)

invokeai/backend/image_util/invisible_watermark.py

@@ -10,9 +10,9 @@ from imwatermark import WatermarkEncoder
 from PIL import Image
 
 import invokeai.backend.util.logging as logger
-from invokeai.app.services.config import InvokeAIAppConfig
+from invokeai.app.services.config.config_default import get_config
 
-config = InvokeAIAppConfig.get_config()
+config = get_config()
 
 
 class InvisibleWatermark:
@@ -20,14 +20,8 @@ class InvisibleWatermark:
     Wrapper around InvisibleWatermark module.
     """
 
-    @classmethod
-    def invisible_watermark_available(cls) -> bool:
-        return config.invisible_watermark
-
     @classmethod
     def add_watermark(cls, image: Image.Image, watermark_text: str) -> Image.Image:
-        if not cls.invisible_watermark_available():
-            return image
         logger.debug(f'Applying invisible watermark "{watermark_text}"')
         bgr = cv2.cvtColor(np.array(image.convert("RGB")), cv2.COLOR_RGB2BGR)
         encoder = WatermarkEncoder()

invokeai/backend/image_util/invoke_metadata.py

@@ -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)

invokeai/backend/image_util/lama.py

@@ -6,7 +6,7 @@ import torch
 from PIL import Image
 
 import invokeai.backend.util.logging as logger
-from invokeai.app.services.config import get_invokeai_config
+from invokeai.app.services.config.config_default import get_config
 from invokeai.backend.util.devices import choose_torch_device
 
 
@@ -29,7 +29,7 @@ def load_jit_model(url_or_path, device):
 class LaMA:
     def __call__(self, input_image: Image.Image, *args: Any, **kwds: Any) -> Any:
         device = choose_torch_device()
-        model_location = get_invokeai_config().models_path / "core/misc/lama/lama.pt"
+        model_location = get_config().models_path / "core/misc/lama/lama.pt"
         model = load_jit_model(model_location, device)
 
         image = np.asarray(input_image.convert("RGB"))

invokeai/backend/image_util/lineart.py

@@ -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)

invokeai/backend/image_util/lineart_anime.py

@@ -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)

invokeai/backend/image_util/patchmatch.py

@@ -8,9 +8,7 @@ be suppressed or deferred
 import numpy as np
 
 import invokeai.backend.util.logging as logger
-from invokeai.app.services.config import InvokeAIAppConfig
-
-config = InvokeAIAppConfig.get_config()
+from invokeai.app.services.config.config_default import get_config
 
 
 class PatchMatch:
@@ -28,7 +26,7 @@ class PatchMatch:
     def _load_patch_match(self):
         if self.tried_load:
             return
-        if config.try_patchmatch:
+        if get_config().patchmatch:
             from patchmatch import patch_match as pm
 
             if pm.patchmatch_available:

invokeai/backend/image_util/safety_checker.py

@@ -4,16 +4,18 @@ 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
+from transformers import AutoFeatureExtractor
 
 import invokeai.backend.util.logging as logger
-from invokeai.app.services.config import InvokeAIAppConfig
+from invokeai.app.services.config.config_default import get_config
 from invokeai.backend.util.devices import choose_torch_device
 from invokeai.backend.util.silence_warnings import SilenceWarnings
 
-config = InvokeAIAppConfig.get_config()
-
 CHECKER_PATH = "core/convert/stable-diffusion-safety-checker"
 
 
@@ -31,30 +33,24 @@ class SafetyChecker:
         if cls.tried_load:
             return
 
-        if config.nsfw_checker:
-            try:
-                from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
-                from transformers import AutoFeatureExtractor
-
-                cls.safety_checker = StableDiffusionSafetyChecker.from_pretrained(config.models_path / CHECKER_PATH)
-                cls.feature_extractor = AutoFeatureExtractor.from_pretrained(config.models_path / CHECKER_PATH)
-                logger.info("NSFW checker initialized")
-            except Exception as e:
-                logger.warning(f"Could not load NSFW checker: {str(e)}")
-        else:
-            logger.info("NSFW checker loading disabled")
+        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)
+        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
-
         device = choose_torch_device()
         features = cls.feature_extractor([image], return_tensors="pt")
         features.to(device)

invokeai/backend/image_util/txt2mask.py

@@ -1,115 +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 import InvokeAIAppConfig
-
-CLIPSEG_MODEL = "CIDAS/clipseg-rd64-refined"
-CLIPSEG_SIZE = 352
-config = InvokeAIAppConfig.get_config()
-
-
-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=config.cache_dir)
-        self.model = CLIPSegForImageSegmentation.from_pretrained(CLIPSEG_MODEL, cache_dir=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

invokeai/backend/image_util/util.py

@@ -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

invokeai/backend/install/check_root.py

@@ -1,25 +0,0 @@
-"""
-Check that the invokeai_root is correctly configured and exit if not.
-"""
-
-import sys
-
-from invokeai.app.services.config import InvokeAIAppConfig
-
-
-def check_invokeai_root(config: InvokeAIAppConfig):
-    try:
-        assert config.db_path.parent.exists(), f"{config.db_path.parent} not found"
-        assert config.models_path.exists(), f"{config.models_path} not found"
-    except Exception as e:
-        print()
-        print(f"An exception has occurred: {str(e)}")
-        print("== STARTUP ABORTED ==")
-        print("** One or more necessary files is missing from your InvokeAI root directory **")
-        print("** Please rerun the configuration script to fix this problem. **")
-        print("** From the launcher, selection option [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)

invokeai/backend/install/install_helper.py

@@ -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.output_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.parse_obj(model.dict())
-            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.parse_obj(model.dict())
-            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()

invokeai/backend/install/invokeai_configure.py

@@ -1,955 +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 argparse
-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, Set, 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 omegaconf import DictConfig, OmegaConf
-from pydantic.error_wrappers import ValidationError
-from tqdm import tqdm
-from transformers import AutoFeatureExtractor
-
-import invokeai.configs as configs
-from invokeai.app.services.config import InvokeAIAppConfig
-from invokeai.backend.install.install_helper import InstallHelper, InstallSelections
-from invokeai.backend.install.legacy_arg_parsing import legacy_parser
-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 = InvokeAIAppConfig.get_config()
-
-Model_dir = "models"
-Default_config_file = config.model_conf_path
-SD_Configs = config.legacy_conf_path
-
-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, 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 = 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_cache_size, 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_cache_size, 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.root_path / config.autoimport_dir) if config.autoimport_dir else "",
-            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 default_ramcache() -> 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 default_startup_options(init_file: Path) -> InvokeAIAppConfig:
-    opts = InvokeAIAppConfig.get_config()
-    opts.ram = default_ramcache()
-    opts.precision = "float32" if FORCE_FULL_PRECISION else choose_precision(torch.device(choose_torch_device()))
-    return opts
-
-
-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(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, initfile: Path, install_helper: InstallHelper
-) -> Tuple[Optional[Namespace], Optional[InstallSelections]]:
-    first_time = not (config.root_path / "invokeai.yaml").exists()
-    invokeai_opts = default_startup_options(initfile) if first_time else config
-    invokeai_opts.root = 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, invokeai_opts, install_helper)
-    editApp.run()
-    if editApp.user_cancelled:
-        return (None, None)
-    else:
-        return (editApp.new_opts, editApp.install_selections)
-
-
-# -------------------------------------
-def write_opts(opts: InvokeAIAppConfig, init_file: Path) -> None:
-    """
-    Update the invokeai.yaml file with values from current settings.
-    """
-    # this will load current settings
-    new_config = InvokeAIAppConfig.get_config()
-    new_config.root = config.root
-
-    for key, value in vars(opts).items():
-        if hasattr(new_config, key):
-            setattr(new_config, key, value)
-
-    with open(init_file, "w", encoding="utf-8") as file:
-        file.write(new_config.to_yaml())
-
-    if hasattr(opts, "hf_token") and opts.hf_token:
-        HfLogin(opts.hf_token)
-
-
-# -------------------------------------
-def default_output_dir() -> Path:
-    return config.root_path / "outputs"
-
-
-# -------------------------------------
-def write_default_options(program_opts: Namespace, initfile: Path) -> None:
-    opt = default_startup_options(initfile)
-    write_opts(opt, initfile)
-
-
-# -------------------------------------
-# Here we bring in
-# the legacy Args object in order to parse
-# the old init file and write out the new
-# yaml format.
-def migrate_init_file(legacy_format: Path) -> None:
-    old = legacy_parser.parse_args([f"@{str(legacy_format)}"])
-    new = InvokeAIAppConfig.get_config()
-
-    for attr in InvokeAIAppConfig.model_fields.keys():
-        if hasattr(old, attr):
-            try:
-                setattr(new, attr, getattr(old, attr))
-            except ValidationError as e:
-                print(f"* Ignoring incompatible value for field {attr}:\n  {str(e)}")
-
-    # a few places where the field names have changed and we have to
-    # manually add in the new names/values
-    new.xformers_enabled = old.xformers
-    new.conf_path = old.conf
-    new.root = legacy_format.parent.resolve()
-
-    invokeai_yaml = legacy_format.parent / "invokeai.yaml"
-    with open(invokeai_yaml, "w", encoding="utf-8") as outfile:
-        outfile.write(new.to_yaml())
-
-    legacy_format.replace(legacy_format.parent / "invokeai.init.orig")
-
-
-# -------------------------------------
-def migrate_models(root: Path) -> None:
-    from invokeai.backend.install.migrate_to_3 import do_migrate
-
-    do_migrate(root, root)
-
-
-def migrate_if_needed(opt: Namespace, root: Path) -> bool:
-    # We check for to see if the runtime directory is correctly initialized.
-    old_init_file = root / "invokeai.init"
-    new_init_file = root / "invokeai.yaml"
-    old_hub = root / "models/hub"
-    migration_needed = (old_init_file.exists() and not new_init_file.exists()) and old_hub.exists()
-
-    if migration_needed:
-        if opt.yes_to_all or yes_or_no(
-            f"{str(config.root_path)} appears to be a 2.3 format root directory. Convert to version 3.0?"
-        ):
-            logger.info("** Migrating invokeai.init to invokeai.yaml")
-            migrate_init_file(old_init_file)
-            omegaconf = OmegaConf.load(new_init_file)
-            assert isinstance(omegaconf, DictConfig)
-            config.parse_args(argv=[], conf=omegaconf)
-
-            if old_hub.exists():
-                migrate_models(config.root_path)
-        else:
-            print("Cannot continue without conversion. Aborting.")
-
-    return migration_needed
-
-
-# -------------------------------------
-def main() -> None:
-    global FORCE_FULL_PRECISION  # FIXME
-    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(
-        "--config_file",
-        "-c",
-        dest="config_file",
-        type=str,
-        default=None,
-        help="path to configuration file to create",
-    )
-    parser.add_argument(
-        "--root_dir",
-        dest="root",
-        type=str,
-        default=None,
-        help="path to root of install directory",
-    )
-    opt = parser.parse_args()
-    invoke_args = []
-    if opt.root:
-        invoke_args.extend(["--root", opt.root])
-    if opt.full_precision:
-        invoke_args.extend(["--precision", "float32"])
-    config.parse_args(invoke_args)
-    logger = InvokeAILogger().get_logger(config=config)
-
-    errors = set()
-    FORCE_FULL_PRECISION = opt.full_precision  # FIXME global
-    new_init_file = config.root_path / "invokeai.yaml"
-    backup_init_file = new_init_file.with_suffix(".bak")
-    if new_init_file.exists():
-        copy(new_init_file, backup_init_file)
-
-    try:
-        # if we do a root migration/upgrade, then we are keeping previous
-        # configuration and we are done.
-        if migrate_if_needed(opt, config.root_path):
-            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:
-            write_default_options(opt, new_init_file)
-            init_options = Namespace(precision="float32" if opt.full_precision else "float16")
-
-        else:
-            init_options, models_to_download = run_console_ui(opt, new_init_file, install_helper)
-            if init_options:
-                write_opts(init_options, new_init_file)
-            else:
-                logger.info('\n** CANCELLED AT USER\'S REQUEST. USE THE "invoke.sh" LAUNCHER TO RUN LATER **\n')
-                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()

invokeai/backend/install/legacy_arg_parsing.py

@@ -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",
-)

invokeai/backend/model_hash/model_hash.py

@@ -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}:"

invokeai/backend/model_manager/config.py

@@ -331,7 +331,7 @@ class IPAdapterConfig(ModelConfigBase):
         return Tag(f"{ModelType.IPAdapter.value}.{ModelFormat.InvokeAI.value}")
 
 
-class CLIPVisionDiffusersConfig(ModelConfigBase):
+class CLIPVisionDiffusersConfig(DiffusersConfigBase):
     """Model config for CLIPVision."""
 
     type: Literal[ModelType.CLIPVision] = ModelType.CLIPVision
@@ -342,7 +342,7 @@ class CLIPVisionDiffusersConfig(ModelConfigBase):
         return Tag(f"{ModelType.CLIPVision.value}.{ModelFormat.Diffusers.value}")
 
 
-class T2IAdapterConfig(ModelConfigBase, ControlAdapterConfigBase):
+class T2IAdapterConfig(DiffusersConfigBase, ControlAdapterConfigBase):
     """Model config for T2I."""
 
     type: Literal[ModelType.T2IAdapter] = ModelType.T2IAdapter

invokeai/backend/model_manager/load/model_loaders/controlnet.py

@@ -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,

invokeai/backend/model_manager/load/model_loaders/generic_diffusers.py

@@ -36,7 +36,15 @@ class GenericDiffusersLoader(ModelLoader):
         if submodel_type is not None:
             raise Exception(f"There are no submodels in models of type {model_class}")
         variant = model_variant.value if model_variant else None
-        result: AnyModel = model_class.from_pretrained(model_path, torch_dtype=self._torch_dtype, variant=variant)  # type: ignore
+        try:
+            result: AnyModel = model_class.from_pretrained(model_path, torch_dtype=self._torch_dtype, variant=variant)
+        except OSError as e:
+            if variant and "no file named" in str(
+                e
+            ):  # try without the variant, just in case user's preferences changed
+                result = model_class.from_pretrained(model_path, torch_dtype=self._torch_dtype)
+            else:
+                raise e
         return result
 
     # TO DO: Add exception handling
@@ -63,7 +71,7 @@ class GenericDiffusersLoader(ModelLoader):
                     assert class_name is not None
                     result = self._hf_definition_to_type(module="transformers", class_name=class_name[0])
                 if not class_name:
-                    raise InvalidModelConfigException("Unable to decifer Load Class based on given config.json")
+                    raise InvalidModelConfigException("Unable to decipher Load Class based on given config.json")
             except KeyError as e:
                 raise InvalidModelConfigException("An expected config.json file is missing from this model.") from e
         assert result is not None

invokeai/backend/model_manager/load/model_loaders/stable_diffusion.py

@@ -44,11 +44,20 @@ class StableDiffusionDiffusersModel(GenericDiffusersLoader):
         load_class = self.get_hf_load_class(model_path, submodel_type)
         variant = model_variant.value if model_variant else None
         model_path = model_path / submodel_type.value
-        result: AnyModel = load_class.from_pretrained(
-            model_path,
-            torch_dtype=self._torch_dtype,
-            variant=variant,
-        )  # type: ignore
+        try:
+            result: AnyModel = load_class.from_pretrained(
+                model_path,
+                torch_dtype=self._torch_dtype,
+                variant=variant,
+            )
+        except OSError as e:
+            if variant and "no file named" in str(
+                e
+            ):  # try without the variant, just in case user's preferences changed
+                result = load_class.from_pretrained(model_path, torch_dtype=self._torch_dtype)
+            else:
+                raise e
+
         return result
 
     def _needs_conversion(self, config: AnyModelConfig, model_path: Path, dest_path: Path) -> bool:
@@ -67,7 +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 = (
@@ -83,7 +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,

invokeai/backend/model_manager/load/model_loaders/vae.py

@@ -44,7 +44,7 @@ class VAELoader(GenericDiffusersLoader):
             raise Exception(f"VAE conversion not supported for model type: {config.base}")
         else:
             assert isinstance(config, CheckpointConfigBase)
-            config_file = 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(

invokeai/backend/model_manager/merge.py

@@ -118,7 +118,7 @@ class ModelMerger(object):
         config = self._installer.app_config
         store = self._installer.record_store
         base_models: Set[BaseModelType] = set()
-        variant = None if self._installer.app_config.full_precision else "fp16"
+        variant = None if self._installer.app_config.precision == "float32" else "fp16"
 
         assert (
             len(model_keys) <= 2 or interp == MergeInterpolationMethod.AddDifference

invokeai/backend/model_manager/probe.py

@@ -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.
@@ -132,11 +132,12 @@ class ModelProbe(object):
 
         format_type = ModelFormat.Diffusers if model_path.is_dir() else ModelFormat.Checkpoint
         model_info = None
-        model_type = None
-        if format_type is ModelFormat.Diffusers:
-            model_type = cls.get_model_type_from_folder(model_path)
-        else:
-            model_type = cls.get_model_type_from_checkpoint(model_path)
+        model_type = ModelType(fields["type"]) if "type" in fields and fields["type"] else None
+        if not model_type:
+            if format_type is ModelFormat.Diffusers:
+                model_type = cls.get_model_type_from_folder(model_path)
+            else:
+                model_type = cls.get_model_type_from_checkpoint(model_path)
         format_type = ModelFormat.ONNX if model_type == ModelType.ONNX else format_type
 
         probe_class = cls.PROBES[format_type].get(model_type)
@@ -156,7 +157,7 @@ class ModelProbe(object):
         fields["image_encoder_model_id"] = fields.get("image_encoder_model_id") or probe.get_image_encoder_model_id()
         fields["name"] = fields.get("name") or cls.get_model_name(model_path)
         fields["description"] = (
-            fields.get("description") or f"{fields['base'].value} {fields['type'].value} model {fields['name']}"
+            fields.get("description") or f"{fields['base'].value} {model_type.value} model {fields['name']}"
         )
         fields["format"] = fields.get("format") or probe.get_format()
         fields["hash"] = fields.get("hash") or ModelHash(algorithm=hash_algo).hash(model_path)
@@ -177,13 +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 [
@@ -225,8 +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
@@ -297,23 +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
@@ -506,15 +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):

invokeai/backend/model_manager/starter_models.py

@@ -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"

invokeai/backend/stable_diffusion/diffusers_pipeline.py

@@ -15,19 +15,18 @@ 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
 
-from invokeai.app.services.config import InvokeAIAppConfig
+from invokeai.app.services.config.config_default import get_config
 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)
@@ -251,7 +232,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
         """
         if xformers is available, use it, otherwise use sliced attention.
         """
-        config = InvokeAIAppConfig.get_config()
+        config = get_config()
         if config.attention_type == "xformers":
             self.enable_xformers_memory_efficient_attention()
             return
@@ -275,7 +256,7 @@ class StableDiffusionGeneratorPipeline(StableDiffusionPipeline):
 
         # the remainder if this code is called when attention_type=='auto'
         if self.unet.device.type == "cuda":
-            if is_xformers_available() and not config.disable_xformers:
+            if is_xformers_available():
                 self.enable_xformers_memory_efficient_attention()
                 return
             elif hasattr(torch.nn.functional, "scaled_dot_product_attention"):
@@ -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
 

invokeai/backend/stable_diffusion/diffusion/cross_attention_control.py

@@ -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):

invokeai/backend/stable_diffusion/diffusion/shared_invokeai_diffusion.py

@@ -8,7 +8,7 @@ import torch
 from diffusers import UNet2DConditionModel
 from typing_extensions import TypeAlias
 
-from invokeai.app.services.config import InvokeAIAppConfig
+from invokeai.app.services.config.config_default import get_config
 from invokeai.backend.stable_diffusion.diffusion.conditioning_data import (
     ConditioningData,
     ExtraConditioningInfo,
@@ -54,7 +54,7 @@ class InvokeAIDiffuserComponent:
         :param model: the unet model to pass through to cross attention control
         :param model_forward_callback: a lambda with arguments (x, sigma, conditioning_to_apply). will be called repeatedly. most likely, this should simply call model.forward(x, sigma, conditioning)
         """
-        config = InvokeAIAppConfig.get_config()
+        config = get_config()
         self.conditioning = None
         self.model = model
         self.model_forward_callback = model_forward_callback

invokeai/backend/training/__init__.py

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

invokeai/backend/training/textual_inversion_training.py

@@ -1,923 +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.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 = InvokeAIAppConfig.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,
-        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}/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 = os.path.join(config.root, 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()

invokeai/backend/util/__init__.py

@@ -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",

invokeai/backend/util/devices.py

@@ -7,18 +7,17 @@ import torch
 from torch import autocast
 
 from invokeai.app.services.config import InvokeAIAppConfig
+from invokeai.app.services.config.config_default import get_config
 
 CPU_DEVICE = torch.device("cpu")
 CUDA_DEVICE = torch.device("cuda")
 MPS_DEVICE = torch.device("mps")
-config = InvokeAIAppConfig.get_config()
 
 
 def choose_torch_device() -> torch.device:
     """Convenience routine for guessing which GPU device to run model on"""
-    if config.use_cpu:  # legacy setting - force CPU
-        return CPU_DEVICE
-    elif config.device == "auto":
+    config = get_config()
+    if config.device == "auto":
         if torch.cuda.is_available():
             return torch.device("cuda")
         if hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
@@ -35,7 +34,7 @@ def choose_precision(
     device: torch.device, app_config: Optional[InvokeAIAppConfig] = None
 ) -> Literal["float32", "float16", "bfloat16"]:
     """Return an appropriate precision for the given torch device."""
-    app_config = app_config or config
+    app_config = app_config or get_config()
     if device.type == "cuda":
         device_name = torch.cuda.get_device_name(device)
         if not ("GeForce GTX 1660" in device_name or "GeForce GTX 1650" in device_name):

invokeai/backend/util/log.py

@@ -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)

invokeai/backend/util/logging.py

@@ -181,6 +181,7 @@ from pathlib import Path
 from typing import Any, Dict, Optional
 
 from invokeai.app.services.config import InvokeAIAppConfig
+from invokeai.app.services.config.config_default import get_config
 
 try:
     import syslog
@@ -338,9 +339,8 @@ class InvokeAILogger(object):  # noqa D102
     loggers: Dict[str, logging.Logger] = {}
 
     @classmethod
-    def get_logger(
-        cls, name: str = "InvokeAI", config: InvokeAIAppConfig = InvokeAIAppConfig.get_config()
-    ) -> logging.Logger:  # noqa D102
+    def get_logger(cls, name: str = "InvokeAI", config: Optional[InvokeAIAppConfig] = None) -> logging.Logger:  # noqa D102
+        config = config or get_config()
         if name in cls.loggers:
             return cls.loggers[name]
 

invokeai/backend/util/util.py

@@ -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.

invokeai/configs/INITIAL_MODELS.yaml

@@ -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

invokeai/frontend/CLI/__init__.py

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

invokeai/frontend/cli/arg_parser.py

@@ -0,0 +1,46 @@
+from argparse import ArgumentParser, Namespace, RawTextHelpFormatter
+from typing import Optional
+
+from invokeai.version import __version__
+
+_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`"""
+
+_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("--config", dest="config_file", type=str, help=_config_file_help)
+_parser.add_argument("--version", action="version", version=__version__, help="Displays the version and exits.")
+
+
+class InvokeAIArgs:
+    """Helper class for parsing CLI args.
+
+    Args should never be parsed within the application code, only in the CLI entrypoints. Parsing args within the
+    application creates conflicts when running tests or when using application modules directly.
+
+    If the args are needed within the application, the consumer should access them from this class.
+
+    Example:
+    ```
+    # In a CLI wrapper
+    from invokeai.frontend.cli.arg_parser import InvokeAIArgs
+    InvokeAIArgs.parse_args()
+
+    # In the application
+    from invokeai.frontend.cli.arg_parser import InvokeAIArgs
+    args = InvokeAIArgs.args
+    """
+
+    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

invokeai/frontend/install/import_images.py

@@ -25,10 +25,10 @@ from prompt_toolkit.completion import PathCompleter
 from prompt_toolkit.key_binding import KeyBindings
 from prompt_toolkit.shortcuts import message_dialog
 
-from invokeai.app.services.config import InvokeAIAppConfig
+from invokeai.app.services.config.config_default import get_config
 from invokeai.app.util.misc import uuid_string
 
-app_config = InvokeAIAppConfig.get_config()
+app_config = get_config()
 
 bindings = KeyBindings()
 

invokeai/frontend/install/invokeai_configure.py

@@ -1,7 +0,0 @@
-"""
-Wrapper for invokeai.backend.configure.invokeai_configure
-"""
-
-from ...backend.install.invokeai_configure import main as invokeai_configure  # noqa: F401
-
-__all__ = ["invokeai_configure"]

invokeai/frontend/install/model_install.py

@@ -1,648 +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 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 import InvokeAIAppConfig
-from invokeai.app.services.model_install import ModelInstallServiceBase
-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 = InvokeAIAppConfig.get_config()
-logger = InvokeAILogger.get_logger("ModelInstallService")
-# 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()))
-    # unsure how to avoid a typing complaint in the next line: config.precision is an enumerated Literal
-    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=str,
-        default=None,
-        help="path to root of install directory",
-    )
-    opt = parser.parse_args()
-
-    invoke_args = []
-    if opt.root:
-        invoke_args.extend(["--root", opt.root])
-    if opt.full_precision:
-        invoke_args.extend(["--precision", "float32"])
-    config.parse_args(invoke_args)
-    logger = InvokeAILogger().get_logger(config=config)
-
-    if not config.models_path.exists():
-        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()