Update model card with metadata, links, and usage example

README.md

@@ -1,20 +1,32 @@
-
+---
+license: mit
+library_name: diffusers
+pipeline_tag: image-to-image
+---
 
 ## EQ-VAE: Equivariance Regularized Latent Space for Improved Generative Image Modeling
-Arxiv: https://arxiv.org/abs/2502.09509 <br>
-
+Arxiv: [https://arxiv.org/abs/2502.09509](https://arxiv.org/abs/2502.09509)
+Project Page: [https://eq-vae.github.io/](https://eq-vae.github.io/)
+Code: [https://github.com/zelaki/eqvae](https://github.com/zelaki/eqvae)
 
-**EQ-VAE** regularizes the latent space of pretrained autoencoders by enforcing equivariance under scaling and rotation transformations. 
+**EQ-VAE** regularizes the latent space of pretrained autoencoders by enforcing equivariance under scaling and rotation transformations.
 
 ---
-#### Model Description 
-This model is a regularized version of [SD-VAE](https://github.com/CompVis/latent-diffusion). We finetune it with EQ-VAE regularization  for 5 epochs on OpenImages.
-
+#### Model Description
+This model is a regularized version of [SD-VAE](https://github.com/CompVis/latent-diffusion). We finetune it with EQ-VAE regularization for 5 epochs on OpenImages.
 
 ## Model Usage
-These weights are intended to be used with the [EQ-VAE codebase](https://github.com/zelaki/eqvae) or the [CompVis Stable Diffusion codebase](https://github.com/CompVis/stable-diffusion). 
+These weights are intended to be used with the [EQ-VAE codebase](https://github.com/zelaki/eqvae) or the [CompVis Stable Diffusion codebase](https://github.com/CompVis/stable-diffusion).
 If you are looking for the model to use with the 🧨 diffusers library, [come here](https://huggingface.co/zelaki/eq-vae).
 
+### Quick Start with 🧨 Diffusers
+If you just want to use EQ-VAE to speed up 🚀 the training on your diffusion model, you can use our HuggingFace checkpoints 🤗. We provide two models: [eq-vae](https://huggingface.co/zelaki/eq-vae) and [eq-vae-ema](https://huggingface.co/zelaki/eq-vae-ema).
+
+```python
+from diffusers import AutoencoderKL
+eqvae = AutoencoderKL.from_pretrained("zelaki/eq-vae")
+```
+If you are looking for the weights in the original LDM format you can find them here: [eq-vae-ldm](https://huggingface.co/zelaki/eq-vae-ldm), [eq-vae-ema-ldm](https://huggingface.co/zelaki/eq-vae-ema-ldm)
 
 #### Metrics
 Reconstruction performance of eq-vae-ema on Imagenet Validation Set.
@@ -27,4 +39,17 @@ Reconstruction performance of eq-vae-ema on Imagenet Validation Set.
 | **SSIM**   | 0.72      |
 ---
 
-
+## Acknowledgement
+This code is mainly built upon [LDM](https://github.com/CompVis/latent-diffusion) and [fastDiT](https://github.com/chuanyangjin/fast-DiT).
+
+## Citation
+```bibtex
+@inproceedings{
+  kouzelis2025eqvae,
+  title={{EQ}-{VAE}: Equivariance Regularized Latent Space for Improved Generative Image Modeling},
+  author={Theodoros Kouzelis and Ioannis Kakogeorgiou and Spyros Gidaris and Nikos Komodakis},
+  booktitle={Forty-second International Conference on Machine Learning},
+  year={2025},
+  url={https://openreview.net/forum?id=UWhW5YYLo6}
+}
+```