add autoencoder training details, arxiv link and figures

Author: rromb

README.md

@@ -1,4 +1,23 @@
 # Latent Diffusion Models
+[arXiv](https://arxiv.org/abs/2112.10752) | [BibTeX](#bibtex)
+
+<p align="center">
+<img src=assets/results.gif />
+</p>
+
+
+
+[**High-Resolution Image Synthesis with Latent Diffusion Models**](https://arxiv.org/abs/2112.10752)<br/>
+[Robin Rombach](https://github.com/rromb)\*,
+[Andreas Blattmann](https://github.com/ablattmann)\*,
+[Dominik Lorenz](https://github.com/qp-qp)\,
+[Patrick Esser](https://github.com/pesser),
+[Björn Ommer](https://hci.iwr.uni-heidelberg.de/Staff/bommer)<br/>
+\* equal contribution
+
+<p align="center">
+<img src=assets/modelfigure.png />
+</p>
 
 ## Requirements
 A suitable [conda](https://conda.io/) environment named `ldm` can be created
@@ -31,12 +50,24 @@ conda activate ldm
 ### Get the models
 
 Running the following script downloads und extracts all available pretrained autoencoding models.   
-
 ```shell script
 bash scripts/download_first_stages.sh
 ```
 
 The first stage models can then be found in `models/first_stage_models/<model_spec>`
+### Training autoencoder models
+
+Configs for training a KL-regularized autoencoder on ImageNet are provided at `configs/autoencoder`.
+Training can be started by running
+```
+CUDA_VISIBLE_DEVICES=<GPU_ID> python main.py --base configs/autoencoder/<config_spec> -t --gpus 0,    
+```
+where `config_spec` is one of {`autoencoder_kl_8x8x64.yaml`(f=32, d=64), `autoencoder_kl_16x16x16.yaml`(f=16, d=16), 
+`autoencoder_kl_32x32x4`(f=8, d=4), `autoencoder_kl_64x64x3`(f=4, d=3)}.
+
+For training VQ-regularized models, see the [taming-transformers](https://github.com/CompVis/taming-transformers) 
+repository.
+
 
 ## Pretrained LDMs
 | Datset                          |   Task    | Model        | FID           | IS              | Prec | Recall | Link                                                                                                                                                                                   | Comments                                        
@@ -102,4 +133,17 @@ Thanks for open-sourcing!
 - The implementation of the transformer encoder is from [x-transformers](https://github.com/lucidrains/x-transformers) by [lucidrains](https://github.com/lucidrains?tab=repositories). 
 
 
+## BibTeX
+
+```
+@misc{rombach2021highresolution,
+      title={High-Resolution Image Synthesis with Latent Diffusion Models}, 
+      author={Robin Rombach and Andreas Blattmann and Dominik Lorenz and Patrick Esser and Björn Ommer},
+      year={2021},
+      eprint={2112.10752},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+```
+