There are unresolved platform-specific issues with this component, and its utility is debatable.
Should be easy to just revert this commit to add it back in the future if desired.
When recalling metadata and/or using control image dimensions, it was possible to set a width or height that was not a multiple of 8, resulting in generation failures.
Added a `clamp` option to the w/h actions to fix this. The option is used for all untrusted sources - everything except for the w/h number inputs, which clamp the values themselves.
These changes were left over from the previous attempt to handle control adapters in control layers with the same logic. Control Layers are now handled totally separately, so these changes may be reverted.
- Revise control adapter config types
- Recreate all control adapter mutations in control layers slice
- Bit of renaming along the way - typing 'RegionalGuidanceLayer' over and over again was getting tedious
Handful of intertwined fixes.
- Create and use helper function to reset staging area.
- Clear staging area when queue items are canceled, failed, cleared, etc. Fixes a bug where the bbox ends up offset and images are put into the wrong spot.
- Fix a number of similar bugs where canvas would "forget" it had pending generations, but they continued to generate. Canvas needs to track batches that should be displayed in it using `state.canvas.batchIds`, and this was getting cleared without actually canceling those batches.
- Disable the `discard current image` button on canvas if there is only one image. Prevents accidentally canceling all canvas batches if you spam the button.
- 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.
