finished paper

Author: lucidrains

README.md

@@ -4,7 +4,7 @@
 
 ## 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. 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 latents.
+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 latents. The last ingredient seems to be a new noise function based around the sigmoid, which the author claims is better than cosine scheduler for larger images.
 
 The big surprise is that the generations can reach this level of fidelity. Will need to verify this on my own machine