InvokeAI/invokeai
psychedelicious f0b1bb0327 feat(nodes): redo tile infill
The previous algorithm errored if the image wasn't divisible by the tile size. I've reimplemented it from scratch to mitigate this issue.

The new algorithm is simpler. We create a pool of tiles, then use them to create an image composed completely of tiles. If there is any awkwardly sized space on the edge of the image, the tiles are cropped to fit.

Finally, paste the original image over the tile image.

I've added a jupyter notebook to do a smoke test of infilling methods, and 10 test images.

The other infill algorithms can be easily tested with the notebook on the same images, though I didn't set that up yet.

Tested and confirmed this gives results just as good as the earlier infill, though of course they aren't the same due to the change in the algorithm.
2024-04-05 08:49:13 +11:00
..
app feat(nodes): redo tile infill 2024-04-05 08:49:13 +11:00
assets feat(api): chore: pydantic & fastapi upgrade 2023-10-17 14:59:25 +11:00
backend feat(nodes): redo tile infill 2024-04-05 08:49:13 +11:00
configs fix(mm): add missing v2-midas-inference.yaml 2024-03-27 07:48:54 -04:00
frontend ui: Color Infill UI 2024-04-05 08:49:13 +11:00
invocation_api feat(nodes): "ModelField" -> "ModelIdentifierField", add hash/name/base/type 2024-03-10 11:03:38 +11:00
version chore: v4.0.2 2024-04-04 15:46:51 +11:00
__init__.py Various fixes 2023-01-30 18:42:17 -05:00