This allows comboboxes for models to have more granular groupings. For example, Control Adapter models can be grouped by base model & model type.
Before:
- `SD-1`
- `SDXL`
After:
- `SD-1 / ControlNet`
- `SD-1 / T2I Adapter`
- `SDXL / ControlNet`
- `SDXL / T2I Adapter`
- Viewer only exists on Generation tab
- Viewer defaults to open
- When clicking the Control Layers tab on the left panel, close the viewer (i.e. open the CL editor)
- Do not switch to editor when adding layers (this is handled by clicking the Control Layers tab)
- Do not open viewer when single-clicking images in gallery
- _Do_ open viewer when _double_-clicking images in gallery
- Do not change viewer state when switching between app tabs (this no longer makes sense; the viewer only exists on generation tab)
- Change the button to a drop down menu that states what you are currently doing, e.g. Viewing vs Editing
Trying a lot of different things as I iterated, so this is smooshed into one big commit... too hard to split it now.
- Iterated on IP adapter handling and UI. Unfortunately there is an bug related to undo/redo. The IP adapter state is split across the `controlAdapters` slice and the `regionalPrompts` slice, but only the `regionalPrompts` slice supports undo/redo. If you delete the IP adapter and then undo/redo to a history state where it existed, you'll get an error. The fix is likely to merge the slices... Maybe there's a workaround.
- Iterated on UI. I think the layers are OK now.
- Removed ability to disable RP globally for now. It's enabled if you have enabled RP layers.
- Many minor tweaks and fixes.
- Add and use more performant `deepClone` method for deep copying throughout the UI.
Benchmarks indicate the Really Fast Deep Clone library (`rfdc`) is the best all-around way to deep-clone large objects.
This is particularly relevant in canvas. When drawing or otherwise manipulating canvas objects, we need to do a lot of deep cloning of the canvas layer state objects.
Previously, we were using lodash's `cloneDeep`.
I did some fairly realistic benchmarks with a handful of deep-cloning algorithms/libraries (including the native `structuredClone`). I used a snapshot of the canvas state as the data to be copied:
On Chromium, `rfdc` is by far the fastest, over an order of magnitude faster than `cloneDeep`.
On FF, `fastest-json-copy` and `recursiveDeepCopy` are even faster, but are rather limited in data types. `rfdc`, while only half as fast as the former 2, is still nearly an order of magnitude faster than `cloneDeep`.
On Safari, `structuredClone` is the fastest, about 2x as fast as `cloneDeep`. `rfdc` is only 30% faster than `cloneDeep`.
`rfdc`'s peak memory usage is about 10% more than `cloneDeep` on Chrome. I couldn't get memory measurements from FF and Safari, but let's just assume the memory usage is similar relative to the other algos.
Overall, `rfdc` is the best choice for a single algo for all browsers. It's definitely the best for Chromium, by far the most popular desktop browser and thus our primary target.
A future enhancement might be to detect the browser and use that to determine which algorithm to use.
Add concepts for metadata handlers. Handlers include parsers, recallers and validators for different metadata types:
- Parsers parse a raw metadata object of any shape to a structured object.
- Recallers load the parsed metadata into state. Recallers are optional, as some metadata types don't need to be loaded into state.
- Validators provide an additional layer of validation before recalling the metadata. This is needed because a metadata object may be valid, but not able to be recalled due to some other requirement, like base model compatibility. Validators are optional.
Sometimes metadata is not a single object but a list of items - like LoRAs. Metadata handlers may implement an optional set of "item" handlers which operate on individual items in the list.
Parsers and validators are async to allow fetching additional data, like a model config. Recallers are synchronous.
The these handlers are composed into a public API, exported as a `handlers` object. Besides the handlers functions, a metadata handler set includes:
- A function to get the label of the metadata type.
- An optional function to render the value of the metadata type.
- An optional function to render the _item_ value of the metadata type.
