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Phil Wang
2022-04-14 10:20:37 -07:00
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@@ -319,7 +319,7 @@ Offer training wrappers
- [x] finish off gaussian diffusion class for latent embedding - allow for prediction of epsilon
- [x] add what was proposed in the paper, where DDPM objective for image latent embedding predicts x0 directly (reread vq-diffusion paper and get caught up on that line of work)
- [x] make sure it works end to end to produce an output tensor, taking a single gradient step
- [ ] augment unet so that it can also be conditioned on text encodings (although in paper they hinted this didn't make much a difference)
- [x] augment unet so that it can also be conditioned on text encodings (although in paper they hinted this didn't make much a difference)
- [ ] look into Jonathan Ho's cascading DDPM for the decoder, as that seems to be what they are using. get caught up on DDPM literature
- [ ] figure out all the current bag of tricks needed to make DDPMs great (starting with the blur trick mentioned in paper)
- [ ] train on a toy task, offer in colab