Updated to "Release of AVID" (#109)

Co-authored-by: Marc Rigter <t-marcrigter@microsoft.com>

Author: marc-rigter

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "causica"
-version = "0.4.2"
+version = "0.4.3"
 description = ""
 readme = "README.md"
 authors = ["Microsoft Research - Causica"]
@@ -69,8 +69,15 @@ junit_family = "xunit1"
 
 [tool.mypy]
 ignore_missing_imports = true
+exclude = [
+  "research_experiments/avid"
+]
 
 [tool.pylint.main]
+ignore-paths = [
+  "research_experiments/avid/latent_diffusion/libs",
+]
+
 # Specify a score threshold to be exceeded before program exits with error.
 fail-under = 9.94
 # Use multiple processes to speed up Pylint. Specifying 0 will auto-detect the

research_experiments/avid/README.md

@@ -0,0 +1,30 @@
+# Adapting Video Diffusion Models to World Models
+### Marc Rigter, Tarun Gupta, Agrin Hilmkil and Chao Ma
+
+[![Project Page](https://img.shields.io/badge/Project%20Page-green)](https://sites.google.com/view/avid-world-model-adapters/home)
+
+<p float="left">
+  <img src="images/rt1_1.gif" width="142" />
+  <img src="images/rt1_2.gif" width="142" />
+  <img src="images/rt1_3.gif" width="142" />
+  <img src="images/rt1_4.gif" width="142" />
+  <img src="images/rt1_5.gif" width="142" />
+</p>
+<p float="left">
+  <img src="images/coinrun1.gif" width="100" />
+  <img src="images/coinrun2.gif" width="100" />
+  <img src="images/coinrun3.gif" width="100" />
+  <img src="images/coinrun4.gif" width="100" />
+  <img src="images/coinrun5.gif" width="100" />
+  <img src="images/coinrun6.gif" width="100" />
+  <img src="images/coinrun7.gif" width="100" />
+</p>
+
+Official code to reproduce the experiments for ["Adapting Video Diffusion Models to World Models"](https://sites.google.com/view/avid-world-model-adapters/home) (AVID), which proposes to adapt pretrained video diffusion models to action-conditioned world models. 
+
+AVID is implemented using both pixel-space diffusion and latent space diffusion. For instructions on how to use each of the codebases, please see [pixel_diffusion/README.md](pixel_diffusion/README.md) and [latent_diffusion/README.md](latent_diffusion/README.md). Results are logged to Weights and Biases.
+
+
+### Acknowledgements
+
+This project utilises code from [video-diffusion-pytorch](https://github.com/lucidrains/video-diffusion-pytorch), [DynamiCrafter](https://github.com/Doubiiu/DynamiCrafter), and [octo](https://github.com/octo-models/octo).