Phil Wang
|
124d8577c8
|
move the inverse normalization function called before image embeddings are derived from clip to within the diffusion prior and decoder classes
|
2022-05-14 00:37:52 -07:00 |
|
Phil Wang
|
2db0c9794c
|
comments
|
2022-05-12 14:25:20 -07:00 |
|
Phil Wang
|
2277b47ffd
|
make sure learned variance can work for any number of unets in the decoder, defaults to first unet, as suggested was used in the paper
|
2022-05-12 14:18:15 -07:00 |
|
Phil Wang
|
28b58e568c
|
cleanup in preparation of option for learned variance
|
2022-05-12 12:04:52 -07:00 |
|
Phil Wang
|
6021945fc8
|
default to l2 loss
|
2022-05-11 19:24:51 -07:00 |
|
Phil Wang
|
3dda2570ed
|
fix amp issue for https://github.com/lucidrains/DALLE2-pytorch/issues/82
|
2022-05-11 08:21:39 -07:00 |
|
Phil Wang
|
2f3c02dba8
|
numerical accuracy for noise schedule parameters
|
2022-05-10 15:28:46 -07:00 |
|
Phil Wang
|
908088cfea
|
wrap up cross embed layer feature
|
2022-05-10 12:19:34 -07:00 |
|
Phil Wang
|
35f89556ba
|
bring in the cross embed layer from Crossformer paper for initial convolution in unet
|
2022-05-10 11:50:38 -07:00 |
|
Phil Wang
|
fc8fce38fb
|
make sure cascading DDPM can be trained unconditionally, to ready for CLI one command training for the public
|
2022-05-10 10:48:10 -07:00 |
|
Phil Wang
|
b1e7b5f6bb
|
make sure resnet groups in unet is finely customizable
|
2022-05-10 10:12:50 -07:00 |
|
Phil Wang
|
9b322ea634
|
patch
|
2022-05-09 19:46:19 -07:00 |
|
Phil Wang
|
64f7be1926
|
some cleanup
|
2022-05-09 16:50:21 -07:00 |
|
Phil Wang
|
db805e73e1
|
fix a bug with numerical stability in attention, sorry! 🐛
|
2022-05-09 16:23:37 -07:00 |
|
Phil Wang
|
e46eaec817
|
deal the diffusion prior problem yet another blow
|
2022-05-09 11:08:52 -07:00 |
|
Kumar R
|
8647cb5e76
|
Val loss changes, with quite a few other changes. This is in place of the earlier PR(https://github.com/lucidrains/DALLE2-pytorch/pull/67) (#77)
* Val_loss changes - no rebased with lucidrains' master.
* Val Loss changes - now rebased with lucidrains' master
* train_diffusion_prior.py updates
* dalle2_pytorch.py updates
* __init__.py changes
* Update train_diffusion_prior.py
* Update dalle2_pytorch.py
* Update train_diffusion_prior.py
* Update train_diffusion_prior.py
* Update dalle2_pytorch.py
* Update train_diffusion_prior.py
* Update train_diffusion_prior.py
* Update train_diffusion_prior.py
* Update train_diffusion_prior.py
* Update README.md
* Update README.md
* Update README.md
* Update README.md
* Update README.md
* Update README.md
* Update README.md
* Update README.md
* Update README.md
|
2022-05-09 08:53:29 -07:00 |
|
Phil Wang
|
53c189e46a
|
give more surface area for attention in diffusion prior
|
2022-05-09 08:08:11 -07:00 |
|
Phil Wang
|
dde51fd362
|
revert restriction for classifier free guidance for diffusion prior, given @crowsonkb advice
|
2022-05-07 20:55:41 -07:00 |
|
Phil Wang
|
4010aec033
|
turn off classifier free guidance if predicting x_start for diffusion prior
|
2022-05-07 09:38:17 -07:00 |
|
Phil Wang
|
830afd3c15
|
sinusoidal embed time embeddings for diffusion prior as well, for continuous version
|
2022-05-07 08:32:43 -07:00 |
|
Phil Wang
|
8f93729d19
|
when in doubt, make it a hyperparameter
|
2022-05-07 07:52:17 -07:00 |
|
Phil Wang
|
85ed77d512
|
fix a potentially huge bug thanks to @CiaoHe https://github.com/lucidrains/DALLE2-pytorch/issues/71
|
2022-05-07 05:05:54 -07:00 |
|
Phil Wang
|
28e944f328
|
make sure openai clip adapter outputs l2normed embeddings
|
2022-05-06 10:12:03 -07:00 |
|
Phil Wang
|
14e63a3f67
|
also offer l2norm clamping in diffusion prior during training, if one were using predict x0 objective
|
2022-05-06 10:05:14 -07:00 |
|
Phil Wang
|
ad20a14a4d
|
bring in rotary embeddings for diffusion prior causal transformer (the most powerful relative positional encoding, used in PaLM) - 0.1.0 because of breaking change
|
2022-05-06 08:45:30 -07:00 |
|
Phil Wang
|
0be1e0d64c
|
support CoCa, which seems to be better than CLIP (has an autoregressive text encoder) https://arxiv.org/abs/2205.01917
|
2022-05-06 08:27:12 -07:00 |
|
Phil Wang
|
98df1ba51e
|
add diffusion prior trainer, which automatically takes care of the exponential moving average (training and sampling), as well as mixed precision, gradient clipping
|
2022-05-06 08:11:09 -07:00 |
|
Phil Wang
|
878b555ef7
|
fix training with clip
|
2022-05-06 07:37:57 -07:00 |
|
Phil Wang
|
c76a964fd6
|
allow for CLIP to be optional in Decoder, and allow DecoderTrainer to work off training pre-encoded image embeddings
|
2022-05-05 08:11:01 -07:00 |
|
Phil Wang
|
8518684ae9
|
does not make much sense, as researchers may want to try predicting noise with diffusionprior instead of predicting x0
|
2022-05-05 07:37:00 -07:00 |
|
Phil Wang
|
1d5dc08810
|
take @crowsonkb 's suggestion at https://github.com/lucidrains/DALLE2-pytorch/issues/60#issue-1226116132
|
2022-05-05 07:28:53 -07:00 |
|
Phil Wang
|
896f19786d
|
remove convnext blocks, they are illsuited for generative work, validated by early experimental results at https://github.com/lucidrains/video-diffusion-pytorch
|
2022-05-05 07:07:21 -07:00 |
|
Phil Wang
|
aec5575d09
|
take a bet on resize right, given Katherine is using it
|
2022-05-04 19:26:45 -07:00 |
|
Phil Wang
|
9773f10d6c
|
use inference mode whenever possible, cleanup
|
2022-05-04 15:25:05 -07:00 |
|
Phil Wang
|
86e692d24f
|
fix random crop probability
|
2022-05-04 11:52:24 -07:00 |
|
Phil Wang
|
97b751209f
|
allow for last unet in the cascade to be trained on crops, if it is convolution-only
|
2022-05-04 11:48:48 -07:00 |
|
Phil Wang
|
5b619c2fd5
|
make sure some hyperparameters for unet block is configurable
|
2022-05-04 11:18:32 -07:00 |
|
Phil Wang
|
9ff228188b
|
offer old resnet blocks, from the original DDPM paper, just in case convnexts are unsuitable for generative work
|
2022-05-04 10:52:58 -07:00 |
|
Phil Wang
|
70282de23b
|
add ability to turn on normformer settings, given @borisdayma reported good results and some personal anecdata
|
2022-05-02 11:33:15 -07:00 |
|
Phil Wang
|
11469dc0c6
|
makes more sense to keep this as True as default, for stability
|
2022-05-02 10:50:55 -07:00 |
|
Phil Wang
|
0fc6c9cdf3
|
provide option to l2norm the output of the diffusion prior
|
2022-05-02 09:41:03 -07:00 |
|
Phil Wang
|
ad87bfe28f
|
switch to using linear attention for the sparse attention layers within unet, given success in GAN projects
|
2022-05-01 17:59:03 -07:00 |
|
Phil Wang
|
b8cf1e5c20
|
more attention
|
2022-05-01 11:00:33 -07:00 |
|
Phil Wang
|
5e421bd5bb
|
let researchers do the hyperparameter search
|
2022-05-01 08:46:21 -07:00 |
|
Phil Wang
|
67fcab1122
|
add MLP based time conditioning to all convnexts, in addition to cross attention. also add an initial convolution, given convnext first depthwise conv
|
2022-05-01 08:41:02 -07:00 |
|
Phil Wang
|
d1a697ac23
|
allows one to shortcut sampling at a specific unet number, if one were to be training in stages
|
2022-04-30 16:05:13 -07:00 |
|
Phil Wang
|
a9421f49ec
|
simplify Decoder training for the public
|
2022-04-30 11:45:18 -07:00 |
|
Phil Wang
|
77fa34eae9
|
fix all clipping / clamping issues
|
2022-04-30 10:08:24 -07:00 |
|
Phil Wang
|
1c1e508369
|
fix all issues with text encodings conditioning in the decoder, using null padding tokens technique from dalle v1
|
2022-04-30 09:13:34 -07:00 |
|
Phil Wang
|
f19c99ecb0
|
fix decoder needing separate conditional dropping probabilities for image embeddings and text encodings, thanks to @xiankgx !
|
2022-04-30 08:48:05 -07:00 |
|