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@@ -38,8 +38,9 @@ Todo
 
 ## Todo
 
-- [ ] finish off gaussian diffusion class for latent embedding - allow for both prediction of epsilon as well as directly predicting embedding
-- [ ] make sure it works end to end
+- [x] finish off gaussian diffusion class for latent embedding - allow for prediction of epsilon
+- [ ] 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)
+- [ ] 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)
 - [ ] 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)