Improve model card: Add metadata, external links, and usage example

README.md

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+---
+pipeline_tag: image-to-image
+library_name: diffusers
+license: mit
+---
 
 ## 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. 
 
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 #### 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 44 epochs on Imagenet with EMA weights.
+This model (`eq-vae-ema`) is a regularized version of [SD-VAE](https://github.com/CompVis/latent-diffusion). We finetune it with EQ-VAE regularization for 44 epochs on Imagenet with EMA weights.
 
 
 ## 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). 
-If you are looking for the model to use with the 🧨 diffusers library, [come here](https://huggingface.co/zelaki/eq-vae-ema).
 
+You can also use this model with the 🧨 diffusers library:
+```python
+from diffusers import AutoencoderKL
+eqvae = AutoencoderKL.from_pretrained("zelaki/eq-vae-ema")
+```
 
 #### Metrics
 Reconstruction performance of eq-vae-ema on Imagenet Validation Set.
@@ -23,5 +34,4 @@ Reconstruction performance of eq-vae-ema on Imagenet Validation Set.
 | **PSNR**   | 26.158    |
 | **LPIPS**  | 0.133     |
 | **SSIM**   | 0.725     |
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