vall-e/vall_e
2024-11-20 16:10:47 -06:00
..
emb fixes 2024-11-10 20:37:50 -06:00
engines I cannot believe it's not actually called Wand DB (added wandb logging support since I think it would have been a much better way to look at my metrics) 2024-11-20 16:10:47 -06:00
ext maybe final tweaks, I really needed to unify my json read/write and orjson is proven to be fast enough for me to try and rely on it more 2024-09-17 22:57:04 -05:00
models 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
utils default set cfg strength to 3.0 since the reference model is updated 2024-11-17 10:23:40 -06:00
__init__.py
__main__.py 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
config.py I cannot believe it's not actually called Wand DB (added wandb logging support since I think it would have been a much better way to look at my metrics) 2024-11-20 16:10:47 -06:00
data.py oops 2024-11-18 14:12:26 -06:00
demo.py 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
export.py two weeks of agony concludes 2024-11-18 21:29:28 -06:00
inference.py 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
plot.py very, very naive layerskip speculative sampling (it just checks if the current layer's state is good enough) 2024-11-02 11:49:05 -05:00
samplers.py 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
train.py default set cfg strength to 3.0 since the reference model is updated 2024-11-17 10:23:40 -06:00
webui.py 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