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Phil Wang
2022-04-20 11:34:51 -07:00
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@@ -412,7 +412,7 @@ Offer training wrappers
- [x] add efficient attention in unet - [x] add efficient attention in unet
- [x] 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) - [x] 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)
- [x] offload unets not being trained on to CPU for memory efficiency (for training each resolution unets separately) - [x] offload unets not being trained on to CPU for memory efficiency (for training each resolution unets separately)
- [ ] build out latent diffusion architecture, make it completely optional (additional autoencoder + some regularizations [kl and vq regs]) - [ ] build out latent diffusion architecture, make it completely optional (additional autoencoder + some regularizations [kl and vq regs]) (figure out if latent diffusion + cascading ddpm can be used in conjunction)
- [ ] 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
- [ ] train on a toy task, offer in colab - [ ] train on a toy task, offer in colab