In order for delete by match to work, we need the whole invocation output to be stringified.
For some reason, the serialization of the output was set to only include the `type` field. It should instead include the whole output.
I don't understand how this ever worked unless pydantic had different serialization behaviour in v1 (though it appears to have been the same).
Closes#5805
* move defaultModel logic to modelsLoaded and update to work for key instead of name/base/type string
* lint fix
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Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
- Update all queries
- Remove Advanced Add
- Removed un-editable, internal-only model attributes from model edit UI (e.g. format, repo variant, model type)
- Update model tags so the list refreshes when a model installs
- Rename some queries, components, variables, types to match backend
- Fix divide-by-zero in install queue
Rename MM routes to be consistent:
- "import" -> "install"
- "model_record" -> "model"
Comment several unused routes while I work (may end up removing them?):
- list model summary (we use the search route instead)
- add model record
- convert model
- merge models
There is a breaking change in python 3.11 related to how enums with `str` as a mixin are formatted. This appears to have not caused any grief for us until now.
Re-jigger the discriminator setup to use `.value` so everything works on both python 3.10 and 3.11.
- Metadata is merged with the config. We can simplify the MM substantially and remove the handling for metadata.
- Per discussion, we don't have an ETA for frontend implementation of tags, and with the realization that the tags from CivitAI are largely useless, there's no reason to keep tags in the MM right now. When we are ready to implement tags on the frontend, we can refer back to the implementation here and use it if it supports the design.
- Fix all tests.
Sometimes, diffusers model components (tokenizer, unet, etc.) have multiple weights files in the same directory.
In this situation, we assume the files are different versions of the same weights. For example, we may have multiple
formats (`.bin`, `.safetensors`) with different precisions. When downloading model files, we want to select only
the best of these files for the requested format and precision/variant.
The previous logic assumed that each model weights file would have the same base filename, but this assumption was
not always true. The logic is revised score each file and choose the best scoring file, resulting in only a single
file being downloaded for each submodel/subdirectory.
