* feat(ui): group nodes by category in add-node dialog
Add collapsible category grouping to the node picker command palette.
Categories are parsed from the backend schema and displayed as
expandable sections with caret icons. All categories auto-expand
when searching.
* feat(ui): add toggle for category grouping in add-node dialog and prioritize exact matches
Add a persistent "Group Nodes by Category" setting to workflow editor settings,
allowing users to switch between grouped and flat node list views. Also sort
exact title matches to the top when searching.
* fix: update test schema categories to match expected templates
* feat: add expand/collapse all buttons to node picker and fix node categories
Add "Expand All" and "Collapse All" link-buttons above the grouped
category list in the add-node dialog so users can quickly open or
close all categories at once. Buttons are hidden during search since
categories auto-expand while searching.
Fix two miscategorized nodes: Z-Image ControlNet was in "Control"
instead of "Controlnet", and Upscale (RealESRGAN) was in "Esrgan"
instead of "Upscale".
* refactor(nodes): clean up node category taxonomy
Reorganize all built-in invocation categories into a consistent set of
18 groups (model, prompt, conditioning, controlnet_preprocessors,
latents, image, mask, inpaint, tiles, upscale, segmentation, math,
strings, primitives, batch, metadata, multimodal, canvas).
- Move denoise/i2l/l2i nodes consistently into "latents"
- Move all mask creation/manipulation nodes into "mask"
- Split ControlNet preprocessors out of "controlnet" into their own group
- Fold "unet", "vllm", "string", "ip_adapter", "t2i_adapter" into larger
groups
- Move metadata_linked denoise wrappers from "latents" to "metadata"
- Add missing category to ideal_size
- Introduce dedicated "canvas" group for canvas/output/panel nodes
Also adds the now-required `category` field to invocation template
fixtures in validateConnection.test.ts.
* Chore Ruff Format
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Co-authored-by: dunkeroni <dunkeroni@gmail.com>
The polynomial fit isn't perfect and we end up with alpha values of 1 instead of 0 when applying the mask. This in turn causes issues on canvas where outputs aren't 100% transparent and individual layer bbox calculations are incorrect.
Previously we used erode/dilate and a Gaussian blur to expand and fade the edges of Canvas masks. The implementation a number of problems:
- Erode/dilate kernel sizes were not calculated correctly, and extra iterations were run to compensate. The result is the blur size, which should have been pixels, was very inaccurate and unreliable.
- What we want is to add a "soft bleed" - like a drop shadow with no offset - starting from the edge of the mask, extending out by however many pixels. But Gaussian blur does not do this. The blurred area starts _inside_ the mask and extends outside it. So it kinda blurs inwards and outwards. We compensated for this by expanding the mask.
- Using a Gaussian blur can cause banding artifacts. Gaussian blur doesn't have a "size" or "radius" parameter in the sense that you think it should. It's a convolution matrix and there are _no non-zero values in the result_. This means that, far away from the mask, once compositing completes, we have some values that are very close to zero but not quite zero. These values are quantized by HTML Canvas, resulting in banding artifacts where you'd expect the blur to have faded to 0% alpha. At least, that is my understanding of why the banding artifacts occur.
The new node uses a better strategy to expand the mask and add the fade out effect:
- Calculate the distance from each white pixel to the nearest black pixel.
- Normalize this distance by dividing by the fade size in px, then clip the values to 0 - 1. The result represents the distance of each white pixel to its nearest black pixel as a percentage of the fade size. At this point, it is a linear distribution.
- Create a polynomial to describe the fade's intensity so that we can have a smooth transition from the masked region (black) to unmasked (white). There are some magic numbers here, deterined experimentally.
- Evaluate the polynomial over the normalized distances, so we now have a matrix representing the fade intensity for every pixel
- Convert this matrix back to uint8 and apply it to the mask
This works soooo much better than the previous method. Not only does it fix the banding issues, but when we enable "output only generated regions", we get a much smaller image. Will add images to the PR to clarify.
