## Summary
- Integrate Git LFS to our automated Python tests in CI
- Add stripped model files with git-lfs
- `README.md` instructions to install and configure git-lfs
- Unrelated change (skip hashing to make unit test run faster)
## Related Issues / Discussions
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## Checklist
- [ ] _The PR has a short but descriptive title, suitable for a
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- [ ] _Tests added / updated (if applicable)_
- [ ] _Documentation added / updated (if applicable)_
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## Summary
**Problem**
We want to have automated tests for model classification/probing, but
model files are too large to include in the source.
**Proposed Solution**
Classification/probing only requires metadata (key names, tensor
shapes), not weights.
This PR introduces "stripped" models - lightweight versions that retains
only essential metadata.
- Added script to strip models
- Added stripped models to automated tests
**Model size before and after "stripping":**
```
LLaVA Onevision Qwen2 0.5b-ov-hf before: 1.8 GB, after: 11.6 MB
text_encoder before: 246.1 MB, after: 35.6 kB
llava-onevision-qwen2-7b-si-hf before: 16.1 GB, after: 11.7 MB
RealESRGAN_x2plus.pth before: 67.1 MB, after: 143.0 kB
IP Adapter SD1 before: 2.5 GB, after: 94.9 kB
Hard Edge Detection (canny) before: 722.6 MB, after: 63.6 kB
Lineart before: 722.6 MB, after: 63.6 kB
Segmentation Map before: 722.6 MB, after: 63.6 kB
EasyNegative before: 24.7 kB, after: 151 Bytes
Face Reference (IP Adapter Plus Face) before: 98.2 MB, after: 13.7 kB
Standard Reference (IP Adapter) before: 44.6 MB, after: 6.0 kB
shinkai_makoto_offset before: 151.1 MB, after: 160.0 kB
thickline_fp16 before: 151.1 MB, after: 160.0 kB
Alien Style before: 228.5 MB, after: 582.6 kB
Noodles Style before: 228.5 MB, after: 582.6 kB
Juggernaut XL v9 before: 6.9 GB, after: 3.7 MB
dreamshaper-8 before: 168.9 MB, after: 1.6 MB
```
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## Checklist
- [ ] _The PR has a short but descriptive title, suitable for a
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- [ ] _Tests added / updated (if applicable)_
- [ ] _Documentation added / updated (if applicable)_
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## Summary
The _goal_ of this PR is to make it easier to add an new config type.
This _scope_ of this PR is to integrate the API and does not include
adding new configs (outside tests) or porting existing ones.
One of the glaring issues of the existing *legacy probe* is that the
logic for each type is spread across multiple classes and intertwined
with the other configs. This means that adding a new config type (or
modifying an existing one) is complex and error prone.
This PR attempts to remedy this by providing a new API for adding
configs that:
- Is backwards compatible with the existing probe.
- Encapsulates fields and logic in a single class, keeping things
self-contained and easy to modify safely.
Below is a minimal toy example illustrating the proposed new structure:
```python
class MinimalConfigExample(ModelConfigBase):
type: ModelType = ModelType.Main
format: ModelFormat = ModelFormat.Checkpoint
fun_quote: str
@classmethod
def matches(cls, mod: ModelOnDisk) -> bool:
return mod.path.suffix == ".json"
@classmethod
def parse(cls, mod: ModelOnDisk) -> dict[str, Any]:
with open(mod.path, "r") as f:
contents = json.load(f)
return {
"fun_quote": contents["quote"],
"base": BaseModelType.Any,
}
```
To create a new config type, one needs to inherit from `ModelConfigBase`
and implement its interface.
The code falls back to the legacy model probe for existing models using
the old API.
This allows us to incrementally port the configs one by one.
## Related Issues / Discussions
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discord. If this PR closes an issue, please use the "Closes #1234"
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## Merge Plan
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DB schemas, may need some care when merging. For example, a careful
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## 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)_
- [ ] _Updated `What's New` copy (if doing a release after this PR)_
In #7688 we optimized queuing preparation logic. This inadvertently broke retrying queue items.
Previously, a `NamedTuple` was used to store the values to insert in the DB when enqueuing. This handy class provides an API similar to a dataclass, where you can instantiate it with kwargs in any order. The resultant tuple re-orders the kwargs to match the order in the class definition.
For example, consider this `NamedTuple`:
```py
class SessionQueueValueToInsert(NamedTuple):
foo: str
bar: str
```
When instantiating it, no matter the order of the kwargs, if you make a normal tuple out of it, the tuple values are in the same order as in the class definition:
```
t1 = SessionQueueValueToInsert(foo="foo", bar="bar")
print(tuple(t1)) # -> ('foo', 'bar')
t2 = SessionQueueValueToInsert(bar="bar", foo="foo")
print(tuple(t2)) # -> ('foo', 'bar')
```
So, in the old code, when we used the `NamedTuple`, it implicitly normalized the order of the values we insert into the DB.
In the retry logic, the values of the tuple were not ordered correctly, but the use of `NamedTuple` had secretly fixed the order for us.
In the linked PR, `NamedTuple` was dropped for a normal tuple, after profiling showed `NamedTuple` to be meaningfully slower than a normal tuple.
The implicit order normalization behaviour wasn't understood, and the order wasn't fixed when changin the retry logic to use a normal tuple instead of `NamedTuple`. This results in a bug where we incorrectly create queue items in the DB. For example, we stored the `destination` in the `field_values` column.
When such an incorrectly-created queue item is dequeued, it fails pydantic validation and causes what appears to be an endless loop of errors.
The only user-facing solution is to add this line to `invokeai.yaml` and restart the app:
```yaml
clear_queue_on_startup: true
```
On next startup, the queue is forcibly cleared before the error loop is triggered. Then the user should remove this line so their queue is persisted across app launches per usual.
The solution is simple - fix the ordering of the tuple. I also added a type annotation and comment to the tuple type alias definition.
Note: The endless error loop, as a general problem, will take some thinking to fix. The queue service methods to cancel and fail a queue item still retrieve it and parse it. And the list queue items methods parse the queue items. Bit of a catch 22, maybe the solution is to simply delete totally borked queue items and log an error.
