project management

This commit is contained in:
Phil Wang
2022-07-17 17:27:44 -07:00
parent a2ee3fa3cc
commit c7fe4f2f44

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@@ -1048,11 +1048,10 @@ Once built, images will be saved to the same directory the command is invoked
- [x] bring in skip-layer excitations (from lightweight gan paper) to see if it helps for either decoder of unet or vqgan-vae training (doesnt work well)
- [x] test out grid attention in cascading ddpm locally, decide whether to keep or remove https://arxiv.org/abs/2204.01697 (keeping, seems to be fine)
- [x] allow for unet to be able to condition non-cross attention style as well
- [ ] become an expert with unets, cleanup unet code, make it fully configurable, port all learnings over to https://github.com/lucidrains/x-unet (test out unet² in ddpm repo) - consider https://github.com/lucidrains/uformer-pytorch attention-based unet
- [ ] speed up inference, read up on papers (ddim or diffusion-gan, etc)
- [ ] figure out if possible to augment with external memory, as described in https://arxiv.org/abs/2204.11824
- [ ] interface out the vqgan-vae so a pretrained one can be pulled off the shelf to validate latent diffusion + DALL-E2
- [x] speed up inference, read up on papers (ddim)
- [ ] add inpainting ability using resampler from repaint paper https://arxiv.org/abs/2201.09865
- [ ] become an expert with unets, cleanup unet code, make it fully configurable, port all learnings over to https://github.com/lucidrains/x-unet (test out unet² in ddpm repo) - consider https://github.com/lucidrains/uformer-pytorch attention-based unet
- [ ] interface out the vqgan-vae so a pretrained one can be pulled off the shelf to validate latent diffusion + DALL-E2
## Citations