self conditioning being the other key ingredient

Author: lucidrains

README.md

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 ## Recurrent Interface Network (RIN) - Pytorch (wip)
 
-Implementation of <a href="https://arxiv.org/abs/2212.11972">Recurrent Interface Network (RIN)</a>, for highly efficient generation of images and video without cascading networks, in Pytorch. The author unawaredly reinvented the <a href="https://github.com/lucidrains/isab-pytorch">induced set-attention block</a> from the <a href="https://arxiv.org/abs/1810.00825">set transformers</a> paper. The big surprise is that the generations can reach this level of fidelity. Will need to verify this on my own machine
+Implementation of <a href="https://arxiv.org/abs/2212.11972">Recurrent Interface Network (RIN)</a>, for highly efficient generation of images and video without cascading networks, in Pytorch. The author unawaredly reinvented the <a href="https://github.com/lucidrains/isab-pytorch">induced set-attention block</a> from the <a href="https://arxiv.org/abs/1810.00825">set transformers</a> paper. They also combine this with the self-conditioning technique from the <a href="https://arxiv.org/abs/2208.04202">Bit Diffusion paper</a>, specifically for the latnets.
+
+The big surprise is that the generations can reach this level of fidelity. Will need to verify this on my own machine
 
 ## Citations