|
|
cf97560e70
|
minimum CFG of 3 for NAR-len because it seems the model will auto-default to NAR-len now
|
2024-12-03 19:40:05 -06:00 |
|
|
|
ca31da0a95
|
sageattn (forgot to bother with testing this the other day, seems ifne)
|
2024-12-03 15:14:57 -06:00 |
|
|
|
84a05acb6d
|
touch ups in docs
|
2024-12-02 19:10:42 -06:00 |
|
|
|
dcaf38b359
|
fixed training tqdm being stubborn
|
2024-11-23 09:45:23 -06:00 |
|
|
|
41d7c30ea5
|
added much cleaner non-causal mask generation
|
2024-11-22 19:43:32 -06:00 |
|
|
|
c99a74e834
|
actually generate a causal mask because it seems sometimes it does not actually generate one because it makes assumptions
|
2024-11-22 18:30:24 -06:00 |
|
|
|
ccee5fc11c
|
that was actually all pointless since sdpa always had an attention mask fed to it and does not need is_causal to implicitly generate one
|
2024-11-22 16:51:50 -06:00 |
|
|
|
4aa685e749
|
what has science done
|
2024-11-22 16:45:40 -06:00 |
|
|
|
147219a5e0
|
huge oversight in the attention masking......... (i realized I have not been providing a non-causal mask to non-causal tasks)
|
2024-11-22 13:44:43 -06:00 |
|
|
|
24d888c47c
|
temporarily dropping support for xformers because it's breaking when using an attention mask (which i dont remember commenting it out when being passed), default to not use wandb because it's being a pain when doing tests and not actual sessionsS)
|
2024-11-22 11:29:12 -06:00 |
|
|
|
8aafae91fd
|
dont use timeembedding
|
2024-11-21 23:14:52 -06:00 |
|
|
|
2cef97e43f
|
cleanup
|
2024-11-21 23:08:43 -06:00 |
|
|
|
67f7bad168
|
added mixed modality AR+NAR-len to generate a short prefix through the AR, then inference with said prefix through the NAR-len (need to experiment with it more to ensure that the masked off tokens are the only tokens getting updated)
|
2024-11-20 14:22:12 -06:00 |
|
|
|
b1369e7824
|
better modality selection (pick AR+NAR by default for the ar+nar model, pick NAR-len by default for the nar-len model), lowered default CFG because it makes the AR+NAR output sped up (but can't be too low since it's required for the NAR-len)
|
2024-11-19 18:51:17 -06:00 |
|
|
|
190a917b3e
|
I did it.
|
2024-11-19 12:24:33 -06:00 |
|
|
|
0e621354e7
|
cleaned up classifier-free guidance logit processing (in order to try and cope with a bad nar-len model)
|
2024-11-19 10:30:05 -06:00 |
|
|
|
5ba80686e1
|
two weeks of agony concludes
|
2024-11-18 21:29:28 -06:00 |
|
|
|
2b29790173
|
oops
|
2024-11-18 14:12:26 -06:00 |
|
|
|
6cfdf94bf9
|
swap priority to use nar-len if available, added notes
|
2024-11-18 09:40:04 -06:00 |
|
|
|
069b27570f
|
set option to set training masking ratio (I don't think for tts a fixed masking ratio is beneficial since the magic of the AR+NAR is being able to still reference the prior sequence of tokens for predicting things)
|
2024-11-17 17:04:07 -06:00 |
|
|
|
88d840218d
|
default set cfg strength to 3.0 since the reference model is updated
|
2024-11-17 10:23:40 -06:00 |
|
|
|
a3e1fa3518
|
ugh
|
2024-11-17 09:28:33 -06:00 |
|
|
|
23fdba0c98
|
tweaks and changes
|
2024-11-16 15:49:06 -06:00 |
|
|
|
2fbeacfe92
|
ugh
|
2024-11-14 22:18:33 -06:00 |
|
|
|
39096f8ff3
|
redid loss calculation to be cleaner, and position ID generation, and other things (I might need to train the NAR-len from scratch and not resume from an existing checkpoint.........)
|
2024-11-14 22:17:47 -06:00 |
|
|
|
e412e98125
|
ugh
|
2024-11-14 07:34:22 -06:00 |
|
|
|
c00fc18b62
|
actually use the right embedding for nar-len
|
2024-11-13 18:04:04 -06:00 |
|
|
|
3ea8a610d6
|
fix STT
|
2024-11-13 14:27:15 -06:00 |
|
|
|
910033343c
|
overhauled how the right resp level / classifier gets picked to avoid cringemath
|
2024-11-13 13:31:17 -06:00 |
|
|
|
269648605e
|
move NAR-len rvq level 0 to separate embedding
|
2024-11-13 11:38:58 -06:00 |
|
|
|
be83ddabaa
|
better causal-ness for split loss calc, and also do masking for NAR-len for it
|
2024-11-13 10:17:52 -06:00 |
|
|
|
6b76419123
|
ugh
|
2024-11-13 09:54:20 -06:00 |
|
|
|
ad7cfffc00
|
NAR-len RVQ-0 was being trained causally.............
|
2024-11-13 09:43:50 -06:00 |
|
|
|
8286aa54c8
|
do not pass timestep token/embedding since it doesn't seem to matter at all after all, fixed training masking rate to 80% because a paper said so
|
2024-11-13 09:07:10 -06:00 |
|
|
|
0f2584eba7
|
new meme sampler PogChamp new meme sampler PogChamp (it sort of helps?)
|
2024-11-12 22:30:09 -06:00 |
|
|
|
663f07038d
|
haha... (do not create a token dropout/noise mask when not training (this sadly didnt fix NAR-len output))
|
2024-11-12 16:41:58 -06:00 |
|
|
|
b09328069e
|
actually do CFG sampling for base AR+NAR tasks
|
2024-11-12 13:42:39 -06:00 |
|
|
|
2495a7ef67
|
Fixed STT in the web UI
|
2024-11-12 12:49:53 -06:00 |
|
|
|
8927bad7bc
|
actually fixed rep pen (for ar and nar, it seems to help with nar unmasking)
|
2024-11-11 21:40:19 -06:00 |
|
|
|
b1f4db39c8
|
threw in CFG sampling for normal model as well to experiment with
|
2024-11-11 20:27:38 -06:00 |
|
|
|
2f56696506
|
overhauled inference/sampler kwargs to stop being a bloated mess
|
2024-11-11 20:21:16 -06:00 |
|
|
|
a748e223ce
|
tweaks
|
2024-11-11 12:40:41 -06:00 |
|
|
|
48490757da
|
fixes
|
2024-11-10 20:37:50 -06:00 |
|
|
|
9def34cd66
|
lol
|
2024-11-10 12:48:41 -06:00 |
|
|
|
9cb0b6901b
|
unified nar.py into ar_nar.py
|
2024-11-10 12:19:48 -06:00 |
|
|
|
a9d2faf2d7
|
all I can do now until I wait for the model to (re)train for pure NAR
|
2024-11-09 22:57:34 -06:00 |
|
|
|
ad7e290a5e
|
ugh (ROCm seems to silently clamp any token value >= logits.shape[-1] for loss calculation, while cuda will throw an assert, making it hard to find this dumb fuckup)
|
2024-11-09 19:40:02 -06:00 |
|
|
|
943fe70c10
|
I don't know why this fixes an assert thrown but it does
|
2024-11-09 19:04:13 -06:00 |
|
|
|
f50d92ba6c
|
Almost made a mistake
|
2024-11-09 18:12:54 -06:00 |
|
|
|
c6a38693a2
|
This better work
|
2024-11-09 18:04:59 -06:00 |
|
