aljaz/DALLE2-pytorch
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Phil Wang
2022-04-19 13:07:26 -07:00
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@@ -410,6 +410,7 @@ Offer training wrappers
- [x] figure out all the current bag of tricks needed to make DDPMs great (starting with the blur trick mentioned in paper) - [x] figure out all the current bag of tricks needed to make DDPMs great (starting with the blur trick mentioned in paper)
- [x] build the cascading ddpm by having Decoder class manage multiple unets at different resolutions - [x] build the cascading ddpm by having Decoder class manage multiple unets at different resolutions
- [x] add efficient attention in unet - [x] add efficient attention in unet
- [ ] be able to finely customize what to condition on (text, image embed) for specific unet in the cascade (super resolution ddpms near the end may not need too much conditioning)
- [ ] offload unets not being trained on to CPU for memory efficiency (for training each resolution unets separately) - [ ] offload unets not being trained on to CPU for memory efficiency (for training each resolution unets separately)
- [ ] build out latent diffusion architecture in separate file, as it is not faithful to dalle-2 (but offer it as as setting) - [ ] build out latent diffusion architecture in separate file, as it is not faithful to dalle-2 (but offer it as as setting)
- [ ] become an expert with unets, cleanup unet code, make it fully configurable, port all learnings over to https://github.com/lucidrains/x-unet - [ ] become an expert with unets, cleanup unet code, make it fully configurable, port all learnings over to https://github.com/lucidrains/x-unet