The invocation cache provides simple node memoization functionality. Nodes that use the cache are memoized and not re-executed if their inputs haven't changed. Instead, the stored output is returned.
## Results
This feature provides anywhere some significant to massive performance improvement.
The improvement is most marked on large batches of generations where you only change a couple things (e.g. different seed or prompt for each iteration) and low-VRAM systems, where skipping an extraneous model load is a big deal.
## Overview
A new `invocation_cache` service is added to handle the caching. There's not much to it.
All nodes now inherit a boolean `use_cache` field from `BaseInvocation`. This is a node field and not a class attribute, because specific instances of nodes may want to opt in or out of caching.
The recently-added `invoke_internal()` method on `BaseInvocation` is used as an entrypoint for the cache logic.
To create a cache key, the invocation is first serialized using pydantic's provided `json()` method, skipping the unique `id` field. Then python's very fast builtin `hash()` is used to create an integer key. All implementations of `InvocationCacheBase` must provide a class method `create_key()` which accepts an invocation and outputs a string or integer key.
## In-Memory Implementation
An in-memory implementation is provided. In this implementation, the node outputs are stored in memory as python classes. The in-memory cache does not persist application restarts.
Max node cache size is added as `node_cache_size` under the `Generation` config category.
It defaults to 512 - this number is up for discussion, but given that these are relatively lightweight pydantic models, I think it's safe to up this even higher.
Note that the cache isn't storing the big stuff - tensors and images are store on disk, and outputs include only references to them.
## Node Definition
The default for all nodes is to use the cache. The `@invocation` decorator now accepts an optional `use_cache: bool` argument to override the default of `True`.
Non-deterministic nodes, however, should set this to `False`. Currently, all random-stuff nodes, including `dynamic_prompt`, are set to `False`.
The field name `use_cache` is now effectively a reserved field name and possibly a breaking change if any community nodes use this as a field name. In hindsight, all our reserved field names should have been prefixed with underscores or something.
## One Gotcha
Leaf nodes probably want to opt out of the cache, because if they are not cached, their outputs are not saved again.
If you run the same graph multiple times, you only end up with a single image output, because the image storage side-effects are in the `invoke()` method, which is bypassed if we have a cache hit.
## Linear UI
The linear graphs _almost_ just work, but due to the gotcha, we need to be careful about the final image-outputting node. To resolve this, a `SaveImageInvocation` node is added and used in the linear graphs.
This node is similar to `ImagePrimitive`, except it saves a copy of its input image, and has `use_cache` set to `False` by default.
This is now the leaf node in all linear graphs, and is the only node in those graphs with `use_cache == False` _and_ the only node with `is_intermedate == False`.
## Workflow Editor
All nodes now have a footer with a new `Use Cache [ ]` checkbox. It defaults to the value set by the invocation in its python definition, but can be changed by the user.
The workflow/node validation logic has been updated to migrate old workflows to use the new default values for `use_cache`. Users may still want to review the settings that have been chosen. In the event of catastrophic failure when running this migration, the default value of `True` is applied, as this is correct for most nodes.
Users should consider saving their workflows after loading them in and having them updated.
## Future Enhancements - Callback
A future enhancement would be to provide a callback to the `use_cache` flag that would be run as the node is executed to determine, based on its own internal state, if the cache should be used or not.
This would be useful for `DynamicPromptInvocation`, where the deterministic behaviour is determined by the `combinatorial: bool` field.
## Future Enhancements - Persisted Cache
Similar to how the latents storage is backed by disk, the invocation cache could be persisted to the database or disk. We'd need to be very careful about deserializing outputs, but it's perhaps worth exploring in the future.
The immutable and serializable checks for redux can cause substantial performance issues. The immutable check in particular is pretty heavy. It's only run in dev mode, but this and really slow down the already-slower performance of dev mode.
The most important one for us is serializable, which has far less of a performance impact.
The immutable check is largely redundant because we use immer-backed RTK for everything and immer gives us confidence there.
Disable the immutable check, leaving serializable in.
A few weeks back, we changed how the canvas scales in response to changes in window/panel size.
This introduced a bug where if we the user hadn't already clicked the canvas tab once to initialize the stage elements, the stage's dimensions were zero, then the calculation of the stage's scale ends up zero, then something is divided by that zero and Konva dies.
This is only a problem on Chromium browsers - somehow Firefox handles it gracefully.
Now, when calculating the stage scale, never return a 0 - if it's a zero, return 1 instead. This is enough to fix the crash, but the image ends up centered on the top-left corner of the stage (the origin of the canvas).
Because the canvas elements are not initialized at this point (we haven't switched tabs yet), the stage dimensions fall back to (0,0). This means the center of the stage is also (0,0) - so the image is centered on (0,0), the top-left corner of the stage.
To fix this, we need to ensure we:
- Change to the canvas tab before actually setting the image, so the stage elements are able to initialize
- Use `flushSync` to flush DOM updates for this tab change so we actually have DOM elements to work with
- Update the stage dimensions once on first load of it (so in the effect that sets up the resize observer, we update the stage dimensions)
The result now is the expected behaviour - images sent to canvas do not crash and end up in the center of the canvas.
JSX is not serializable, so it cannot be in redux. Non-serializable global state may be put into `nanostores`.
- Use `nanostores` for `customStarUI`
- Use `nanostores` for `headerComponent`
- Re-enable the serializable & immutable check redux middlewares
This maps values to labels for multiple-choice fields.
This allows "enum" fields (i.e. `Literal["val1", "val2", ...]` fields) to use code-friendly string values for choices, but present this to the UI as human-friendly labels.
This simply hides nodes from the workflow editor. The nodes will still work if an API request is made with them. For example, you could hide `iterate` nodes from the workflow editor, but if the Linear UI makes use of those nodes, they will still function.
- Update `AppConfig` with optional property `nodesDenylist: string[]`
- If provided, nodes are filtered out by `type` in the workflow editor
* Consolidated saturation/luminosity adjust.
Now allows increasing and inverting.
Accepts any color PIL format and channel designation.
* Updated docs/nodes/defaultNodes.md
* shortened tags list to channel types only
* fix typo in mode list
* split features into offset and multiply nodes
* Updated documentation
* Change invert to discrete boolean.
Previous math was unclear and had issues with 0 values.
* chore: black
* chore(ui): typegen
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Co-authored-by: psychedelicious <4822129+psychedelicious@users.noreply.github.com>
