| 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/. | |
| --- | |
| #### 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 | | |
| --- |