6 Commits

Author	SHA1	Message	Date
psychedelicious	fcb071f30c	feat(backend): lift managed model loading out of lama class	2024-04-29 08:12:51 +10:00
Lincoln Stein	70903ef057	refactor load_ckpt_from_url()	2024-04-28 11:33:23 -04:00
Lincoln Stein	41b909cbe3	port dw_openpose, depth_anything, and lama processors to new model download scheme	2024-04-14 15:57:03 -04:00
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
blessedcoolant	3c195d74a5	fix: bypass edge pixels which cannot transform to tile size ... Still need to fix this somehow	2024-04-05 08:49:13 +11:00
blessedcoolant	32a6b758cd	wip: Initial Infill Methods Refactor	2024-04-05 08:49:13 +11:00