I'm not sure if this was just my local install, but even after a fresh
`yarn install` my upload network request was failing because no file was
passed in. I don't think the `bodySerializer` part is getting run
My PR to fix an issue with the handling of formdata in `openapi-fetch` is released. This means we no longer need to patch the package (no patches at all now!).
This PR bumps its version and adds a transformer to our typegen script to handle typing binary form fields correctly as `Blob`.
Also regens types.
* load images on gallery render
* wait for models to be loaded before you can invoke
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Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
* close modal when user clicks cancel
* close modal when delete image context cleared
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Co-authored-by: Mary Hipp <maryhipp@Marys-MacBook-Air.local>
- Accordions now may be opened or closed regardless of whether or not their contents are enabled or active
- Accordions have a short text indicator alerting the user if their contents are enabled, either a simple `Enabled` or, for accordions like LoRA or ControlNet, `X Active` if any are active
This caused a lot of re-rendering whenever the selection changed, which caused a huge performance hit. It also made changing the current image lag a bit.
Instead of providing an array of image names as a multi-select dnd payload, there is now no multi-select dnd payload at all - instead, the payload types are used by the `imageDropped` listener to pull the selection out of redux.
Now, the only big re-renders are when the selectionCount changes. In the future I'll figure out a good way to do image names as payload without incurring re-renders.
Every `GalleryImage` was rerendering any time the app rerendered bc the selector function itself was not memoized. This resulted in the memoization cache inside the selector constantly being reset.
Same for `BatchImage`.
Also updated memoization for a few other selectors.
Eg `useGetMainModelsQuery()`, `useGetLoRAModelsQuery()` instead of `useListModelsQuery({base_type})`.
Add specific adapters for each model type. Just more organised and easier to consume models now.
Also updated LoRA UI to use the model name.
This introduces the core functionality for batch operations on images and multiple selection in the gallery/batch manager.
A number of other substantial changes are included:
- `imagesSlice` is consolidated into `gallerySlice`, allowing for simpler selection of filtered images
- `batchSlice` is added to manage the batch
- The wonky context pattern for image deletion has been changed, much simpler now using a `imageDeletionSlice` and redux listeners; this needs to be implemented still for the other image modals
- Minimum gallery size in px implemented as a hook
- Many style fixes & several bug fixes
TODO:
- The UI and UX need to be figured out, especially for controlnet
- Batch processing is not hooked up; generation does not do anything with batch
- Routes to support batch image operations, specifically delete and add/remove to/from boards
- Set min size for floating gallery panel
- Correct the default pinned width (it cannot be less than the min width and this was sometimes happening during window resize)
Add `useMinimumPanelSize()` hook to provide minimum resizable panel sizes (in pixels).
The library we are using for the gallery panel uses percentages only. To provide a minimum size in pixels, we need to do some math to calculate the percentage of window size that corresponds to the desired min width in pixels.
The node polyfills needed to run the `swagger-parser` library (used to dereference the OpenAPI schema) cause the canvas tab to immediately crash when the package build is used in another react application.
I'm sure this is fixable but it's not clear what is causing the issue and troubleshooting is very time consuming.
Selectively rolling back the implementation of `swagger-parser`.
Themes are very fun but due to the differences in perceived saturation and lightness across the
the color spectrum, it's impossible to have have multiple themes that look great without hand-
crafting *every* shade for *every* theme. We've ended up with 4 OK themes (well, 3, because the
light theme was pretty bad).
I've removed the themes and added color mode support. There is now a single dark and light mode,
each with their own color palette and the classic grey / purple / yellow invoke colors that
@blessedcoolant first designed.
I've re-styled almost everything except the model manager and lightbox, which I keep forgetting
to work on.
One new concept is the Chakra `layerStyle`. This lets us define "layers" - think body, first layer,
second layer, etc - that can be applied on various components. By defining layers, we can be more
consistent about the z-axis and its relationship to color and lightness.
The TS Language Server slows down immensely with our translation JSON, which is used to provide kinda-type-safe translation keys. I say "kinda", because you don't get autocomplete - you only get red squigglies when the key is incorrect.
To improve the performance, we can opt out of this process entirely, at the cost of no red squigglies for translation keys. Hopefully we can resolve this in the future.
It's not clear why this became an issue only recently (like past couple weeks). We've tried rolling back the app dependencies, VSCode extensions, VSCode itself, and the TS version to before the time when the issue started, but nothing seems to improve the performance.
1. Disable `resolveJsonModule` in `tsconfig.json`
2. Ignore TS in `i18n.ts` when importing the JSON
3. Comment out the custom types in `i18.d.ts` entirely
It's possible that only `3` is needed to fix the issue.
I've tested building the app and running the build - it works fine, and translation works fine.
Everything seems to be working.
- Due to a change to `reactflow`, I regenerated `yarn.lock`
- New chakra CLI fixes issue I had made a patch for; removed the patch
- Change to fontsource changed how we import that font
- Change to fontawesome means we lost the txt2img tab icon, just chose a similar one
Only "real" conflicts were in:
invokeai/frontend/web/src/features/controlNet/components/ControlNet.tsx
invokeai/frontend/web/src/features/controlNet/store/controlNetSlice.ts
- Reset and Upload buttons along top of initial image
- Also had to mess around with the control net & DnD image stuff after changing the styles
- Abstract image upload logic into hook - does not handle native HTML drag and drop upload - only the button click upload
`openapi-fetch` does not handle non-JSON `body`s, always stringifying them, and sets the `content-type` to `application/json`.
The patch here does two things:
- Do not stringify `body` if it is one of the types that should not be stringified (https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API/Using_Fetch#body)
- Do not add `content-type: application/json` unless it really is stringified JSON.
Upstream issue: https://github.com/drwpow/openapi-typescript/issues/1123
I'm not a bit lost on fixing the types and adding tests, so not raising a PR upstream.
*migrate from `openapi-typescript-codegen` to `openapi-typescript` and `openapi-fetch`*
`openapi-typescript-codegen` is not very actively maintained - it's been over a year since the last update.
`openapi-typescript` and `openapi-fetch` are part of the actively maintained repo. key differences:
- provides a `fetch` client instead of `axios`, which means we need to be a bit more verbose with typing thunks
- fetch client is created at runtime and has a very nice typescript DX
- generates a single file with all types in it, from which we then extract individual types. i don't like how verbose this is, but i do like how it is more explicit.
- removed npm api generation scripts - now we have a single `typegen` script
overall i have more confidence in this new library.
*use nanostores for api base and token*
very simple reactive store for api base url and token. this was suggested in the `openapi-fetch` docs and i quite like the strategy.
*organise rtk-query api*
split out each endpoint (models, images, boards, boardImages) into their own api extensions. tidy!
Basically updated all slices to be more descriptive in their names. Did so in order to make sure theres good naming scheme available for secondary models.
