* add support for probing and loading SDXL VAE checkpoint files
* broaden regexp probe for SDXL VAEs
---------
Co-authored-by: Lincoln Stein <lstein@gmail.com>
## Summary
No functional changes, just cleaning some things up as I touch the code.
This PR cleans up the `SilenceWarnings` context manager:
- Fix type errors
- Enable SilenceWarnings to be used as both a context manager and a
decorator
- Remove duplicate implementation
- Check the initial verbosity on `__enter__()` rather than `__init__()`
- Save an indentation level in DenoiseLatents
## QA Instructions
I generated an image to confirm that warnings are still muted.
## Merge Plan
- [x] ⚠️ Merge https://github.com/invoke-ai/InvokeAI/pull/6492 first,
then change the target branch to `main`.
## Checklist
- [x] _The PR has a short but descriptive title, suitable for a
changelog_
- [x] _Tests added / updated (if applicable)_
- [x] _Documentation added / updated (if applicable)_
- Fix type errors
- Enable SilenceWarnings to be used as both a context manager and a decorator
- Remove duplicate implementation
- Check the initial verbosity on __enter__() rather than __init__()
## Summary
added route to install huggingface models from model marketplace
<!--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
<!--WHEN APPLICABLE: List any related issues or discussions on github or
discord. If this PR closes an issue, please use the "Closes #1234"
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merges.-->
## QA Instructions
test by going to
http://localhost:5173/api/v2/models/install/huggingface?source=${hfRepo}
<!--WHEN APPLICABLE: Describe how we can test the changes in this PR.-->
## Merge Plan
<!--WHEN APPLICABLE: Large PRs, or PRs that touch sensitive things like
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## Checklist
- [ ] _The PR has a short but descriptive title, suitable for a
changelog_
- [ ] _Tests added / updated (if applicable)_
- [ ] _Documentation added / updated (if applicable)_
When a model install is initiated from outside the client, we now trigger the model manager tab's model install list to update.
- Handle new `model_install_download_started` event
- Handle `model_install_download_complete` event (this event is not new but was never handled)
- Update optimistic updates/cache invalidation logic to efficiently update the model install list
Previously, we used `model_install_download_progress` for both download starting and progressing. When handling this event, we don't know which actual thing it represents.
Add `model_install_download_started` event to explicitly represent a model download started event.
* allow model patcher to optimize away the unpatching step when feasible
* remove lazy_offloading functionality
* allow model patcher to optimize away the unpatching step when feasible
* remove lazy_offloading functionality
* do not save original weights if there is a CPU copy of state dict
* Update invokeai/backend/model_manager/load/load_base.py
Co-authored-by: Ryan Dick <ryanjdick3@gmail.com>
* documentation fixes requested during penultimate review
* add non-blocking=True parameters to several torch.nn.Module.to() calls, for slight performance increases
* fix ruff errors
* prevent crash on non-cuda-enabled systems
---------
Co-authored-by: Lincoln Stein <lstein@gmail.com>
Co-authored-by: Kent Keirsey <31807370+hipsterusername@users.noreply.github.com>
Co-authored-by: Ryan Dick <ryanjdick3@gmail.com>
## Summary
This three two model manager-related methods to the InvocationContext
uniform API. They are accessible via `context.models.*`:
1. **`load_local_model(model_path: Path, loader:
Optional[Callable[[Path], AnyModel]] = None) ->
LoadedModelWithoutConfig`**
*Load the model located at the indicated path.*
This will load a local model (.safetensors, .ckpt or diffusers
directory) into the model manager RAM cache and return its
`LoadedModelWithoutConfig`. If the optional loader argument is provided,
the loader will be invoked to load the model into memory. Otherwise the
method will call `safetensors.torch.load_file()` `torch.load()` (with a
pickle scan), or `from_pretrained()` as appropriate to the path type.
Be aware that the `LoadedModelWithoutConfig` object differs from
`LoadedModel` by having no `config` attribute.
Here is an example of usage:
```
def invoke(self, context: InvocatinContext) -> ImageOutput:
model_path = Path('/opt/models/RealESRGAN_x4plus.pth')
loadnet = context.models.load_local_model(model_path)
with loadnet as loadnet_model:
upscaler = RealESRGAN(loadnet=loadnet_model,...)
```
---
2. **`load_remote_model(source: str | AnyHttpUrl, loader:
Optional[Callable[[Path], AnyModel]] = None) ->
LoadedModelWithoutConfig`**
*Load the model located at the indicated URL or repo_id.*
This is similar to `load_local_model()` but it accepts either a
HugginFace repo_id (as a string), or a URL. The model's file(s) will be
downloaded to `models/.download_cache` and then loaded, returning a
```
def invoke(self, context: InvocatinContext) -> ImageOutput:
model_url = 'https://github.com/xinntao/Real-ESRGAN/releases/download/v0.1.0/RealESRGAN_x4plus.pth'
loadnet = context.models.load_remote_model(model_url)
with loadnet as loadnet_model:
upscaler = RealESRGAN(loadnet=loadnet_model,...)
```
---
3. **`download_and_cache_model( source: str | AnyHttpUrl, access_token:
Optional[str] = None, timeout: Optional[int] = 0) -> Path`**
Download the model file located at source to the models cache and return
its Path. This will check `models/.download_cache` for the desired model
file and download it from the indicated source if not already present.
The local Path to the downloaded file is then returned.
---
## Other Changes
This PR performs a migration, in which it renames `models/.cache` to
`models/.convert_cache`, and migrates previously-downloaded ESRGAN,
openpose, DepthAnything and Lama inpaint models from the `models/core`
directory into `models/.download_cache`.
There are a number of legacy model files in `models/core`, such as
GFPGAN, which are no longer used. This PR deletes them and tidies up the
`models/core` directory.
## Related Issues / Discussions
I have systematically replaced all the calls to
`download_with_progress_bar()`. This function is no longer used
elsewhere and has been removed.
<!--WHEN APPLICABLE: List any related issues or discussions on github or
discord. If this PR closes an issue, please use the "Closes #1234"
format, so that the issue will be automatically closed when the PR
merges.-->
## QA Instructions
I have added unit tests for the three new calls. You may test that the
`load_and_cache_model()` call is working by running the upscaler within
the web app. On first try, you will see the model file being downloaded
into the models `.cache` directory. On subsequent tries, the model will
either load from RAM (if it hasn't been displaced) or will be loaded
from the filesystem.
<!--WHEN APPLICABLE: Describe how we can test the changes in this PR.-->
## Merge Plan
Squash merge when 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.-->
## Checklist
- [X] _The PR has a short but descriptive title, suitable for a
changelog_
- [X] _Tests added / updated (if applicable)_
- [X] _Documentation added / updated (if applicable)_
## Summary
I've started working towards a better tiled upscaling implementation. It
is going to require some refactoring of `DenoiseLatentsInvocation`. As a
first step, this PR splits up all of the invocations in latent.py into
their own files. That file had become a bit of a dumping ground - it
should be a bit more manageable to work with now.
This PR just re-organizes the code. There should be no functional
changes.
## QA Instructions
I've done some light smoke testing. I'll do some more before merging.
The main risk is that I missed a broken import, or some other copy-paste
error.
## Checklist
- [x] _The PR has a short but descriptive title, suitable for a
changelog_
- [x] _Tests added / updated (if applicable)_: N/A
- [x] _Documentation added / updated (if applicable)_: N/A
