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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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