kijai
84e536e226
make latent normalization optional for testing
2024-11-05 22:30:13 +02:00
kijai
fbd2252dc4
fix
2024-11-05 20:36:32 +02:00
kijai
70ad32621b
reorder
2024-11-05 20:21:47 +02:00
kijai
70bd024456
Update asymm_models_joint.py
2024-11-05 19:40:35 +02:00
kijai
cdf9fdbc2d
allow apex norm to work
2024-11-05 19:40:22 +02:00
kijai
7391389276
cleanup
2024-11-05 12:29:53 +02:00
kijai
ab3b18a153
Update mochi_fastercache_test_01.json
2024-11-05 09:41:18 +02:00
kijai
f6020f71e0
dynamic compile
2024-11-05 08:57:48 +02:00
kijai
04d15b64ae
Update asymm_models_joint.py
2024-11-05 08:51:59 +02:00
kijai
1811b7b6c5
Update asymm_models_joint.py
2024-11-05 08:50:36 +02:00
kijai
d29e95d707
Add example
2024-11-05 08:44:55 +02:00
kijai
24a7edfca6
update, works
2024-11-05 08:41:50 +02:00
kijai
3535a846a8
update from main
2024-11-05 07:08:14 +02:00
Jukka Seppänen
21374934d3
Merge pull request #66 from niw/workaround_mps_pad_problem
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Workaround pad problem on mps
2024-11-05 13:48:45 +09:00
kijai
fdecd4ee08
Update nodes.py
2024-11-05 06:18:49 +02:00
Yoshimasa Niwa
5ca4bbf319
Workaround pad problem on mps
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When using `torch.nn.functional.pad` with tensor that size is
larger than 2^16 (65526), the output tensor would be broken.
This patch moves tensor to CPU to workaround the problem.
It doesn't too much impacts in terms of speed of vea on mps.
2024-11-05 12:46:13 +09:00
kijai
78f9e7b896
Update nodes.py
2024-11-04 15:10:15 +02:00
kijai
f94cf43331
Update nodes.py
2024-11-04 15:06:20 +02:00
kijai
56b5dbbf82
Add different RMSNorm functions for testing
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Initial testing for me shows that the RMSNorm from flash_attn.ops.triton.layer_norm is ~8-10% faster, apex is untested as I don't currently have it installed.
2024-11-04 14:11:58 +02:00
kijai
fd4a02e6a6
Update nodes.py
2024-11-03 19:41:08 +02:00
kijai
4a7458ffd6
restore MochiEdit compatiblity
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temporary
2024-11-03 19:18:29 +02:00
kijai
0dc011d1b6
cleanup and align more to comfy code, switch to using cpu seed as well
2024-11-03 18:36:23 +02:00
kijai
3cf9289e08
cleanup code
2024-11-03 01:47:34 +02:00
kijai
a6e545531c
test
2024-11-03 00:32:51 +02:00
kijai
5ec01cbff4
fix vae download path
2024-11-02 01:14:14 +02:00
kijai
09f327326b
Add denoise
2024-11-01 23:05:34 +02:00
kijai
85c996d7b8
Add MochiSigmaSchedule node, better denoise formula
2024-11-01 19:36:40 +02:00
kijai
ec298a1d64
fix encoding precision
2024-11-01 18:21:34 +02:00
kijai
a5b06b02ad
tiled encoding
2024-11-01 18:03:47 +02:00
kijai
69ab797b8c
Add sampler preview
2024-11-01 06:18:41 +02:00
kijai
ac5de728ad
Add VAE encoder
2024-11-01 05:22:49 +02:00
kijai
ebd0f62d53
Update nodes.py
2024-10-30 22:08:22 +02:00
kijai
d971a19410
spatial VAE decoder fixes
2024-10-30 22:06:55 +02:00
kijai
f0f939b20b
cleanup, fix untiled spatial vae decode
2024-10-30 21:12:34 +02:00
kijai
3dce06b28b
make compatible with comfy cliptextencode
2024-10-28 12:23:14 +02:00
kijai
ce903c0384
Update mz_gguf_loader.py
2024-10-28 04:07:11 +02:00
kijai
db23e2ecc0
should sleep more
2024-10-27 20:15:44 +02:00
kijai
0b6812671c
oops
2024-10-27 20:11:07 +02:00
kijai
3395aa8ca0
cleanup, sampler output name fix
2024-10-27 20:02:44 +02:00
kijai
195da244df
make cfg 1.0 not do uncond, set steps by sigma schedule
2024-10-27 19:52:16 +02:00
kijai
4348d1ed20
fix Q4 cublas ops
2024-10-27 14:04:51 +02:00
kijai
d1155ad305
Update nodes.py
2024-10-27 12:41:43 +02:00
kijai
185f4e0bee
custom sigmas
2024-10-27 12:41:33 +02:00
kijai
3613700752
possible sdpa kernel fixes and add optional cfg scheduling
2024-10-27 12:23:01 +02:00
kijai
e20eb66f93
cleanup
2024-10-27 03:00:52 +02:00
kijai
e82e6ee3f7
Update mz_gguf_loader.py
2024-10-26 18:10:45 +03:00
kijai
c5c136cb11
fix
2024-10-26 17:57:02 +03:00
kijai
ddfb3a6bf2
backends
2024-10-26 17:49:15 +03:00
kijai
f29f739707
support cublas_ops with GGUF
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pretty big speed boost on 4090 at least, needs this installed:
https://github.com/aredden/torch-cublas-hgemm
2024-10-26 16:42:25 +03:00
kijai
0d15c0bd69
torch compile for vae loader
2024-10-26 03:24:25 +03:00
