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--- |
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language: en |
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license: mit |
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tags: |
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- diffusion |
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- autoencoder |
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- feature-space |
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- svg |
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references: |
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- https://arxiv.org/abs/2510.15301 |
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--- |
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# 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. |
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- Includes a lightweight residual encoder for refining fine-grained details. |
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- 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) |
