* [MM2] replace untyped config dict passed to install_model with typed ModelRecordChanges
- adjusted frontend to work with new schema
- used this facility to assign "starter model" names and descriptions to the installed
models.
* documentation fix
* [MM2] replace untyped config dict passed to install_model with typed ModelRecordChanges
- adjusted frontend to work with new schema
- used this facility to assign "starter model" names and descriptions to the installed
models.
* documentation fix
* remove v9 pnpm lockfile
* [MM2] replace untyped config dict passed to install_model with typed ModelRecordChanges
- adjusted frontend to work with new schema
- used this facility to assign "starter model" names and descriptions to the installed
models.
* [MM2] replace untyped config dict passed to install_model with typed ModelRecordChanges
- adjusted frontend to work with new schema
- used this facility to assign "starter model" names and descriptions to the installed
models.
* remove v9 pnpm lockfile
* regenerate schema.ts
* prettified
---------
Co-authored-by: Lincoln Stein <lstein@gmail.com>
The previous super-minimal implementation had a major issue - the saved workflow didn't take into account batched field values. When generating with multiple iterations or dynamic prompts, the same workflow with the first prompt, seed, etc was stored in each image.
As a result, when the batch results in multiple queue items, only one of the images has the correct workflow - the others are mismatched.
To work around this, we can store the _graph_ in the image metadata (alongside the workflow, if generated via workflow editor). When loading a workflow from an image, we can choose to load the workflow or the graph, preferring the workflow.
Internally, we need to update images router image-saving services. The changes are minimal.
To avoid pydantic errors deserializing the graph, when we extract it from the image, we will leave it as stringified JSON and let the frontend's more sophisticated and flexible parsing handle it. The worklow is also changed to just return stringified JSON, so the API is consistent.
- Display a toast on UI launch if the HF token is invalid
- Show form in MM if token is invalid or unable to be verified, let user set the token via this form
When consolidating all the model queries I messed up the query tags. Fixed now, so that when a model is installed, removed, or changed, the list refreshes.
- 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
* UI in MM to create trigger phrases
* add scheduler and vaePrecision to config
* UI for configuring default settings for models'
* hook MM default model settings up to API
* add button to set default settings in parameters
* pull out trigger phrases
* back-end for default settings
* lint
* remove log;
gi
* ruff
* ruff format
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Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
