We were storing all types of models by their model ID, which is a format like `sd-1/main/deliberate`.
This meant we had to do a lot of extra parsing, because nodes actually wants something like `{base_model: 'sd-1', model_name: 'deliberate'}`.
Some of this parsing was done with zod's error-throwing `parse()` method, and in other places it was done with brittle string parsing.
This commit refactors the state to use the object form of models.
There is still a bit of string parsing done in the to construct the ID from the object form, but it's far less complicated.
Also, the zod parsing is now done using `safeParse()`, which does not throw. This requires a few more conditional checks, but should prevent further crashes.
* feat(ui): salvaged gallery UI enhancements
* restore boardimage functionality, load boardimages and remove some cachine optimizations in the name of data integrity
* fix assets, fix load more params
* jk NOW fix assets, fix load more params
---------
Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
Co-authored-by: Mary Hipp Rogers <maryhipp@gmail.com>
- available infill methods is server state - remove it from client state, use the query to populate the dropdown
- add listener to ensure the selected infill method is an available one
As it said in comment to this branch we want to use conditioning run:
```python
if cfg_injection: # only applying ControlNet to conditional instead of in unconditioned
```
But in code used unconditioning
embeddings(`conditioning_data.unconditioned_embeddings`).
Later in code confirms that we want to run conditioning generation by
comment and tensor concatenation order(as all code expect to get [uc, c]
tensor):
```python
if cfg_injection:
# Inferred ControlNet only for the conditional batch.
# To apply the output of ControlNet to both the unconditional and conditional batches,
# add 0 to the unconditional batch to keep it unchanged.
down_samples = [torch.cat([torch.zeros_like(d), d]) for d in down_samples]
mid_sample = torch.cat([torch.zeros_like(mid_sample), mid_sample])
```
Adds a Clear Nodes Button with Confirmation Dialog, I think I Did it
right 😃
I am sure there is a way to make the Confirmation look better and have
Yes/No instead of OK/Cancel
- Restore recall functionality to `CurrentImageButtons` and `ImageContextMenu`.
- Debounce metadata requests for `ImageMetadataViewer` and `CurrentImageButtons` by 500ms. It's possible to scroll through these really fast, so we want to debounce the network requests.
- `ImageContextMenu` is lazy-mounted so it does not need to be debounced; it makes the metadata request as soon as you click it.
- Move next/prev image selection logic into hook and add the hotkeys for this to `CurrentImageButtons`. The hotkeys now work when metadata viewer is open.
I will follow up with improved loading state during the debounced calls in the future
- Update for new routes
- Update model storage in state to be `MainModelField` type instead of `string`, simplifies a lot of model handling
- Update model-related stuff for model `name` --> `model_name`
- Update linear graphs to use `MetadataAccumulator`
- Update `ImageMetadataViewer` UI
- Ensure all `recall` functions work (well, the ones that are active anyways)
Metadata for the Linear UI is now sneakily provided via a `MetadataAccumulator` node, which the client populates / hooks up while building the graph.
Additionally, we provide the unexpanded graph with the metadata API response.
Both of these are embedded into the PNGs.
- Remove `metadata` from `ImageDTO`
- Split up the `images/` routes to accomodate this; metadata is only retrieved per-image
- `images/{image_name}` now gets the DTO
- `images/{image_name}/metadata` gets the new metadata
- `images/{image_name}/full` gets the full-sized image file
- Remove old metadata service
- Add `MetadataAccumulator` node, `CoreMetadataField`, hook up to `LatentsToImage` node
- Add `get_raw()` method to `ItemStorage`, retrieves the row from DB as a string, no pydantic parsing
- Update `images`related services to handle storing and retrieving the new metadata
- Add `get_metadata_graph_from_raw_session` which extracts the `graph` from `session` without needing to hydrate the session in pydantic, in preparation for providing it as metadata; also removes all references to the `MetadataAccumulator` node
Our model fields use `model_name`, but the API response uses `name`. Some places use `model_type` but the API response used `type`.
Changed the API response to provide `model_name` and `model_type`, which simplifies how we manage models on the client substantially.
