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library_name: diffusers |
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## EQ-VAE: Equivariance Regularized Latent Space for Improved Generative Image Modeling |
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Arxiv: https://arxiv.org/abs/2502.09509 <br> |
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**EQ-VAE** regularizes the latent space of pretrained autoencoders by enforcing equivariance under scaling and rotation transformations. |
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#### Model Description |
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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. |
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## Model Usage |
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2. **Loading the Model** |
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You can load the model from the Hugging Face Hub: |
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```python |
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from transformers import AutoencoderKL |
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model = AutoencoderKL.from_pretrained("zelaki/eq-vae-ema") |
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#### Metrics |
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Reconstruction performance of eq-vae-ema on Imagenet Validation Set. |
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| **Metric** | **Score** | |
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|------------|-----------| |
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| **FID** | 0.552 | |
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| **PSNR** | 26.158 | |
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| **LPIPS** | 0.133 | |
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| **SSIM** | 0.725 | |
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