41 Commits

Author	SHA1	Message	Date
Kohaku-Blueleaf	c4afdb7895	For no constraint	2024-02-22 00:43:32 +08:00
Kohaku-Blueleaf	64179c3221	Update network_oft.py	2024-02-21 22:50:43 +08:00
Kohaku-Blueleaf	591470d86d	linting	2024-02-20 17:21:34 +08:00
Kohaku-Blueleaf	a5436a3ac0	Update network_oft.py	2024-02-20 17:20:14 +08:00
Kohaku-Blueleaf	4eb949625c	prevent undefined variable	2024-02-19 14:43:07 +08:00
Kohaku-Blueleaf	5a8dd0c549	Fix rescale	2024-02-18 14:58:41 +08:00
Kohaku-Blueleaf	90441294db	Add rescale mechanism ... LyCORIS will support save oft_blocks instead of oft_diag in the near future (for both OFT and BOFT) But this means we need to store the rescale if user enable it.	2024-02-12 14:25:09 +08:00
v0xie	613b0d9548	doc: add boft comment	2024-02-08 21:58:59 -08:00
v0xie	325eaeb584	fix: get boft params from weight shape	2024-02-08 11:55:05 -08:00
v0xie	2f1073dc6e	style: fix lint	2024-02-07 04:55:11 -08:00
v0xie	a4668a16b6	fix: calculate butterfly factor	2024-02-07 04:51:22 -08:00
v0xie	9588721197	feat: support LyCORIS BOFT	2024-02-07 04:49:17 -08:00
v0xie	fd383140cf	fix: wrong devices for eye and constraint	2024-01-22 02:52:34 -08:00
Kohaku-Blueleaf	f8f38c7c28	Fix dtype casting for OFT module	2024-01-05 16:31:48 +08:00
Kohaku-Blueleaf	3772a82a70	better naming and correct order for device.	2023-12-14 01:47:13 +08:00
Kohaku-Blueleaf	8fc67f3851	remove debug print	2023-12-14 01:44:49 +08:00
Kohaku-Blueleaf	265bc26c21	Use self.scale instead of custom finalize	2023-12-14 01:43:24 +08:00
Kohaku-Blueleaf	735c9e8059	Fix network_oft	2023-12-14 01:38:32 +08:00
v0xie	eb667e715a	feat: LyCORIS/kohya OFT network support	2023-11-15 18:28:48 -08:00
v0xie	d6d0b22e66	fix: ignore calc_scale() for COFT which has very small alpha	2023-11-15 03:08:50 -08:00
v0xie	bbf00a96af	refactor: remove unused function	2023-11-04 14:56:47 -07:00
v0xie	329c8bacce	refactor: use same updown for both kohya OFT and LyCORIS diag-oft	2023-11-04 14:54:36 -07:00
v0xie	f6c8201e56	refactor: move factorization to lyco_helpers, separate calc_updown for kohya and kb	2023-11-03 19:35:15 -07:00
v0xie	fe1967a4c4	skip multihead attn for now	2023-11-03 17:52:55 -07:00
v0xie	d727ddfccd	no idea what i'm doing, trying to support both type of OFT, kblueleaf diag_oft has MultiheadAttn which kohya's doesn't?, attempt create new module based off network_lora.py, errors about tensor dim mismatch	2023-11-02 00:13:11 -07:00
v0xie	a2fad6ee05	test implementation based on kohaku diag-oft implementation	2023-11-01 22:34:27 -07:00
v0xie	6523edb8a4	style: conform style	2023-10-22 09:31:15 -07:00
v0xie	3b8515d2c9	fix: multiplier applied twice in finalize_updown	2023-10-22 09:27:48 -07:00
v0xie	4a50c9638c	refactor: remove used OFT functions	2023-10-22 08:54:24 -07:00
v0xie	de8ee92ed8	fix: use merge_weight to cache value	2023-10-21 17:37:17 -07:00
v0xie	76f5abdbdb	style: cleanup oft	2023-10-21 16:07:45 -07:00
v0xie	fce86ab7d7	fix: support multiplier, no forward pass hook	2023-10-21 16:03:54 -07:00
v0xie	7683547728	fix: return orig weights during updown, merge weights before forward	2023-10-21 14:42:24 -07:00
v0xie	2d8c894b27	refactor: use forward hook instead of custom forward	2023-10-21 13:43:31 -07:00
v0xie	0550659ce6	style: fix ambiguous variable name	2023-10-19 13:13:02 -07:00
v0xie	d10c4db57e	style: formatting	2023-10-19 12:52:14 -07:00
v0xie	321680ccd0	refactor: fix constraint, re-use get_weight	2023-10-19 12:41:17 -07:00
v0xie	eb01d7f0e0	faster by calculating R in updown and using cached R in forward	2023-10-18 04:56:53 -07:00
v0xie	853e21d98e	faster by using cached R in forward	2023-10-18 04:27:44 -07:00
v0xie	1c6efdbba7	inference working but SLOW	2023-10-18 04:16:01 -07:00
v0xie	ec718f76b5	wip incorrect OFT implementation	2023-10-17 23:35:50 -07:00