16ch-vae
Maintainer: AuraDiffusion
63
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
The 16ch-VAE is a fully open-source 16-channel Variational Autoencoder (VAE) reproduction for the Stable Diffusion 3 (SD3) model. It was developed by AuraDiffusion, who maintains the model on the Hugging Face platform. The 16ch-VAE is useful for those building their own image generation models who need an off-the-shelf VAE. It is natively trained in fp16 precision.
Compared to other VAE models like the SDXL-VAE and the SD1.5 VAE, the 16ch-VAE demonstrates improved performance on key metrics such as rFID, PSNR, and LPIPS.
Model inputs and outputs
Inputs
- Images
Outputs
- Latent representations of input images
Capabilities
The 16ch-VAE model is capable of encoding input images into a 16-channel latent space, which can then be used for various image-to-image tasks. Its improved performance over other VAE models makes it a compelling option for those looking to build their own image generation pipelines.
What can I use it for?
The 16ch-VAE can be used as a drop-in replacement for the VAE component in Stable Diffusion 3 or other diffusion-based image generation models. By leveraging the improved latent representations, users may be able to achieve better generation quality and downstream task performance. Additionally, the model can be finetuned or adapted for specific applications, such as image inpainting, super-resolution, or style transfer.
Things to try
One interesting aspect of the 16ch-VAE is its native support for fp16 precision, which can enable faster inference and reduced memory footprint on compatible hardware. Users may want to experiment with different fp16 deployment strategies to find the optimal balance of quality and performance for their use case.
Additionally, the maintainer has provided a variant of the 16ch-VAE that incorporates Fast Fourier Transform (FFT) preprocessing. This version may be worth exploring for users interested in further improving the model's performance on specific tasks or datasets.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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