| --- |
| language: en |
| license: mit |
| tags: |
| - diffusion |
| - autoencoder |
| - feature-space |
| - svg |
| references: |
| - https://arxiv.org/abs/2510.15301 |
| --- |
| |
| # SVG: Latent Diffusion Model without Variational Autoencoder |
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| ## Model Description |
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| SVG is a latent diffusion model framework that replaces the traditional VAE latent space with semantically structured features from self-supervised vision models (e.g., DINOv3). This design improves generative capability and downstream transferability while maintaining efficiency comparable to standard VAE-based latent diffusion models. |
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| Key features: |
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| - Replaces low-dimensional VAE latent space with high-dimensional semantic feature space. |
| - Includes a lightweight residual encoder for refining fine-grained details. |
| - Enables strong generation and perception performance. |
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| ## How to Use |
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| For code, and instructions, see the GitHub repository: |
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| [https://github.com/shiml20/SVG](https://github.com/shiml20/SVG) |
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| Official project page: |
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| [https://howlin-wang.github.io/svg/](https://howlin-wang.github.io/svg/) |
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| Arxiv paper: |
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| [https://arxiv.org/abs/2510.15301](https://arxiv.org/abs/2510.15301) |
