From 09613a40f32f9f33565cdf6945db2f9947afad52 Mon Sep 17 00:00:00 2001
From: Phil Wang <lucidrains@gmail.com>
Date: Sat, 7 May 2022 05:47:21 -0700
Subject: [PATCH] cleanup

---
 README.md | 44 ++++++++++++++++++++++----------------------
 1 file changed, 22 insertions(+), 22 deletions(-)

diff --git a/README.md b/README.md
index b648146..6da93f0 100644
--- a/README.md
+++ b/README.md
@@ -4,7 +4,7 @@
 
 Implementation of <a href="https://arxiv.org/abs/2006.11239">Denoising Diffusion Probabilistic Model</a> in Pytorch. It is a new approach to generative modeling that may <a href="https://ajolicoeur.wordpress.com/the-new-contender-to-gans-score-matching-with-langevin-sampling/">have the potential</a> to rival GANs. It uses denoising score matching to estimate the gradient of the data distribution, followed by Langevin sampling to sample from the true distribution.
 
-This implementation was transcribed from the official Tensorflow version <a href="https://github.com/hojonathanho/diffusion">here</a> and then modified to use <a href="https://arxiv.org/abs/2201.03545">ConvNext</a> blocks instead of Resnets.
+This implementation was transcribed from the official Tensorflow version <a href="https://github.com/hojonathanho/diffusion">here</a>
 
 <img src="./sample.png" width="500px"><img>
 
@@ -80,31 +80,31 @@ Samples and model checkpoints will be logged to `./results` periodically
 
 ```bibtex
 @inproceedings{NEURIPS2020_4c5bcfec,
-    author = {Ho, Jonathan and Jain, Ajay and Abbeel, Pieter},
-    booktitle = {Advances in Neural Information Processing Systems},
-    editor = {H. Larochelle and M. Ranzato and R. Hadsell and M.F. Balcan and H. Lin},
-    pages = {6840--6851},
-    publisher = {Curran Associates, Inc.},
-    title = {Denoising Diffusion Probabilistic Models},
-    url = {https://proceedings.neurips.cc/paper/2020/file/4c5bcfec8584af0d967f1ab10179ca4b-Paper.pdf},
-    volume = {33},
-    year = {2020}
+    author      = {Ho, Jonathan and Jain, Ajay and Abbeel, Pieter},
+    booktitle   = {Advances in Neural Information Processing Systems},
+    editor      = {H. Larochelle and M. Ranzato and R. Hadsell and M.F. Balcan and H. Lin},
+    pages       = {6840--6851},
+    publisher   = {Curran Associates, Inc.},
+    title       = {Denoising Diffusion Probabilistic Models},
+    url         = {https://proceedings.neurips.cc/paper/2020/file/4c5bcfec8584af0d967f1ab10179ca4b-Paper.pdf},
+    volume      = {33},
+    year        = {2020}
 }
 ```
 
 ```bibtex
 @InProceedings{pmlr-v139-nichol21a,
-    title = 	 {Improved Denoising Diffusion Probabilistic Models},
-    author =       {Nichol, Alexander Quinn and Dhariwal, Prafulla},
-    booktitle = 	 {Proceedings of the 38th International Conference on Machine Learning},
-    pages = 	 {8162--8171},
-    year = 	 {2021},
-    editor = 	 {Meila, Marina and Zhang, Tong},
-    volume = 	 {139},
-    series = 	 {Proceedings of Machine Learning Research},
-    month = 	 {18--24 Jul},
-    publisher =    {PMLR},
-    pdf = 	 {http://proceedings.mlr.press/v139/nichol21a/nichol21a.pdf},
-    url = 	 {https://proceedings.mlr.press/v139/nichol21a.html},
+    title       = {Improved Denoising Diffusion Probabilistic Models},
+    author      = {Nichol, Alexander Quinn and Dhariwal, Prafulla},
+    booktitle   = {Proceedings of the 38th International Conference on Machine Learning},
+    pages       = {8162--8171},
+    year        = {2021},
+    editor      = {Meila, Marina and Zhang, Tong},
+    volume      = {139},
+    series      = {Proceedings of Machine Learning Research},
+    month       = {18--24 Jul},
+    publisher   = {PMLR},
+    pdf         = {http://proceedings.mlr.press/v139/nichol21a/nichol21a.pdf},
+    url         = {https://proceedings.mlr.press/v139/nichol21a.html},
 }
 ```