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.
Adds an additional negative conditioning using the inverted mask of the positive conditioning and the positive prompt. May be useful for mutually exclusive regions.
This is intended for debug usage, so it's hidden away in the workflow library `...` menu. Hold shift to see the button for it.
- Paste a graph (from a network request, for example) and then click the convert button to convert it to a workflow.
- Disable auto layout to stack the nodes with an offset (try it out). If you change this, you must re-convert to get the changes.
- Edit the workflow JSON if you need to tweak something before loading it.
- 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.
Loading default workflows sometimes requires we mutate the workflow object in order to change the category or ID of the workflow.
This happens in `invokeai/frontend/web/src/features/nodes/util/workflow/validateWorkflow.ts`
The data we get back from the query hooks is frozen and sealed by redux, because they are part of redux state. We need to clone the workflow before operating on it.
It's not clear how this ever worked in the past, because redux state has always been frozen and sealed.
The graph builders used awaited functions within `Array.prototype.forEach` loops. This doesn't do what you'd think. This caused graphs to be enqueued before they were fully constructed.
Changed to `for..of` loops to fix this.
There wasn't enough validation of control adapters during graph building. It would be possible for a graph to be built with empty collect node, causing an error. Addressed with an extra check.
This should never happen in practice, because the invoke button should be disabled if an invalid CA is active.
