aljaz/DALLE2-pytorch
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Phil Wang
2022-04-22 11:39:58 -07:00
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@@ -412,7 +412,7 @@ Offer training wrappers
- [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] offload unets not being trained on to CPU for memory efficiency (for training each resolution unets separately)
- [ ] build out latent diffusion architecture, with the vq-reg variant (vqgan-vae), make it completely optional
- [ ] build out latent diffusion architecture, with the vq-reg variant (vqgan-vae), make it completely optional and compatible with cascading ddpms
- [ ] become an expert with unets, cleanup unet code, make it fully configurable, port all learnings over to https://github.com/lucidrains/x-unet
- [ ] copy the cascading ddpm code to a separate repo (perhaps https://github.com/lucidrains/denoising-diffusion-pytorch) as the main contribution of dalle2 really is just the prior network
- [ ] train on a toy task, offer in colab