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library_name: diffusers
pipeline_tag: image-to-image
license: mit
---

## EQ-VAE: Equivariance Regularized Latent Space for Improved Generative Image Modeling

**EQ-VAE** regularizes the latent space of pretrained autoencoders by enforcing equivariance under scaling and rotation transformations. 

Project page: https://eq-vae.github.io/.

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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 5 epochs on OpenImages.

## Model Usage


2. **Loading the Model**  
   You can load the model from the Hugging Face Hub:
   ```python
   from transformers import AutoencoderKL
   model = AutoencoderKL.from_pretrained("zelaki/eq-vae")

#### Metrics
Reconstruction performance of eq-vae-ema on Imagenet Validation Set.

| **Metric** | **Score** |
|------------|-----------|
| **FID**    | 0.82      |
| **PSNR**   | 25.95     |
| **LPIPS**  | 0.141     |
| **SSIM**   | 0.72      |
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