- Remove OmegaConf. It functioned as an intermediary data format, between YAML/argparse and pydantic. It's not necessary - we can parse YAML or CLI args directly with pydantic.
- Remove dynamic CLI args. Only `root` is explicitly supported. This greatly simplifies config handling. Configuration is done by editing the YAML file. Frequently-used args can be added if there is a demand.
- A separate arg parser is created to handle the slimmed-down CLI args. It's run immediately in the `invokeai-web` script to handle `--version` and `--help`. It is also used inside the singleton config getter (see below).
- Remove categories from the config. Our settings model is mostly flat. Handling categories adds complexity for both us and users - we have to handle transforming a flat config to categorized config (and vice-versa), while users have to be careful with indentation in their YAML file.
- Add a `meta` key to the config file. Currently, this holds the config schema version only. It is not a part of the config object itself.
- Remove legacy settings that are no longer referenced, or were effectively no-op settings when referenced in code.
- Implement simple migration logic to for v3 configs. If migration is successful, the v3 config file is backed up to `invokeai.yaml.bak` and the new config written to `invokeai.yaml`.
- Previously, the singleton config was accessed by calling `InvokeAIAppConfig.get_config()`. This returned an instance of `InvokeAIAppConfig`, which _also_ has the `get_config` function. This created to a confusing situation where you weren't sure if you needed to call `get_config` or just use the config object. This method is replaced by a standalone `get_config` function which returns a singleton config object.
- Wrap CLI arg parsing (for `root`) and loading/migrating `invokeai.yaml` into the new `get_config()` function.
- Move `generate_config_docstrings` into standalone utility function.
- Make `root` a private attr (`_root`). This reduces the temptation to directly modify and or use this sensitive field and ensures it is neither serialized nor read from input data. Use `root_path` to access the resolved root path, or `set_root` to set the root to something.
* allow removal of models with legacy relative path addressing
* added five controlnet models for sdxl to INITIAL_MODELS
---------
Co-authored-by: Lincoln Stein <lstein@gmail.com>
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [ ] Bug Fix
- [X] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Have you discussed this change with the InvokeAI team?
- [X] Yes
- [ ] No, because:
## Have you updated all relevant documentation?
- [X] Yes
- [ ] No
## Description
We've been using a forked copy of the diffusers safetensors->diffusers
model conversion code, which was hacked to read CLIP and the other
models needed for conversion from the local invokeai root models
directory. This was getting unsustainable as the code bases diverged,
and also required the installation and maintenance of the "core/convert"
directory.
This PR gets rid of the hacked conversion code and reverts to using the
native diffusers methods. Core convert models are no longer installed at
root configure time. Instead we rely on the HuggingFace hub system to
download the conversion models if and when they are needed. They are
relatively small and the initial delay seems minor.
Conversion of SD-1, SD-2 (both epsilon and v-prediction), SDXL, VAE and
ControlNet SD-1/2 models has been tested. ControlNet SDXL models are
still a WIP due to the need for some work on the prober.
The main implication of this change is that InvokeAI is no longer
internet-independent and will need an internet connection at least the
first time a safetensors file needs to be converted. However, there are
several other places where the "no internet" rule is already violated,
and I suggest that we abandon this principle.
## Related Tickets & Documents
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- Related Issue #
- Closes#5964
## QA Instructions, Screenshots, Recordings
1. Remove or move `$INVOKEAI_ROOT/models/.cache`
2. Move `$INVOKEAI/models/core/convert`
3. Try generating with an unconverted .safetensors model.
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## Merge Plan
Merge when approved.
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## 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?
- No longer install core conversion models. Use the HuggingFace cache to load
them if and when needed.
- Call directly into the diffusers library to perform conversions with only shallow
wrappers around them to massage arguments, etc.
- At root configuration time, do not create all the possible model subdirectories,
but let them be created and populated at model install time.
- Remove checks for missing core conversion files, since they are no
longer installed.
In the client, a controlnet or t2i adapter has two images:
- The source control image: the image the user selected (required)
- The processed control image: the user's image after we've processed it (optional)
The processed image is optional because a user may provide a pre-processed image.
We only actually use one of these images when building the graph, and until this change, we only stored one of the in image metadata. This created a situation where only a processed image was stored in metadata - say, a canny edge map - and the user-selected image wasn't provided.
By adding the processed image to metadata, we can recall both the control image and optional processed image.
This commit is followed by a UI-facing changes to support the change.
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [z] Feature
- [ ] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Description
Single query, with simple wrapper hooks (type-safe). Updated everywhere
in frontend.
## QA Instructions, Screenshots, Recordings
Things that use models should work. All of this code is strictly
typechecked, so we can be confident in this change.
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## Merge Plan
This PR can be merged when approved
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merged"
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the
database in any way.
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We were passing a PIL image when we needed to pass the np image.
Closes#5956
## What type of PR is this? (check all applicable)
- [ ] Refactor
- [ ] Feature
- [x] Bug Fix
- [ ] Optimization
- [ ] Documentation Update
- [ ] Community Node Submission
## Description
We were passing a PIL image when we needed to pass the np image.
Closes#5956
## Related Tickets & Documents
<!--
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below.
For example having the text: "closes #1234" would connect the current
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- Related Issue #
- Closes#5956
## QA Instructions, Screenshots, Recordings
Depth anything processor should work.
<!--
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software specifications as well as any other pertinent information.
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## Merge Plan
This PR can be merged when approved
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- "This must be squash-merged when approved"
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- This adds additional logic to the safetensors->diffusers conversion script
to check for and install missing core conversion models at runtime.
- Fixes#5934
BLAKE3 has poor performance on spinning disks when parallelized. See https://github.com/BLAKE3-team/BLAKE3/issues/31
- Replace `skip_model_hash` setting with `hashing_algorithm`. Any algorithm we support is accepted.
- Add `random` algorithm: hashes a UUID with BLAKE3 to create a random "hash". Equivalent to the previous skip functionality.
- Add `blake3_single` algorithm: hashes on a single thread using BLAKE3, fixes the aforementioned performance issue
- Update model probe to accept the algorithm to hash with as an optional arg, defaulting to `blake3`
- Update all calls of the probe to use the app's configured hashing algorithm
- Update an external script that probes models
- Update tests
- Move ModelHash into its own module to avoid circuclar import issues
This script removes unused translations from the `en.json` source translation file:
- Parse `en.json` to build a list of all keys, e.g. `controlnet.depthAnything`
- Check every frontend file for every key
- If the key is not found, it is removed from the translation file
- Exact matches (e.g. `controlnet.depthAnything`) and stem matches (e.g. `depthAnything`) are ignored
