sd-vae-ft-ema-original
Maintainer: stabilityai
146
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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 sd-vae-ft-ema-original is an improved autoencoder model developed by stabilityai that builds upon the original kl-f8 autoencoder used in Stable Diffusion. The model was fine-tuned on a combination of the LAION-Aesthetics and an unreleased LAION-Humans dataset to enhance reconstruction of human faces. Two versions were published - ft-EMA, which uses the same loss as the original but with EMA weights, and ft-MSE, which puts more emphasis on MSE reconstruction.
Compared to the original kl-f8 VAE, the fine-tuned versions show slightly improved performance on COCO 2017 and LAION-Aesthetics benchmarks. The ft-EMA model has a lower rFID score of 4.42 vs 4.99 for the original, while the ft-MSE model produces somewhat smoother outputs.
Model inputs and outputs
Inputs
- Images to be encoded into a latent representation
Outputs
- Reconstructed images from the latent representation
- Evaluation metrics like rFID, PSNR, SSIM, and PSIM
Capabilities
The sd-vae-ft-ema-original model is an improved autoencoder that can be used as a drop-in replacement for the original autoencoder in the Stable Diffusion codebase. The fine-tuning on human-centric datasets leads to better reconstruction of faces and overall more aesthetically pleasing outputs compared to the original.
What can I use it for?
The model can be used as part of the Stable Diffusion image generation pipeline, providing a higher-quality latent representation that may lead to improved downstream generation results. Additionally, the model could be used for applications like image compression, editing, or other tasks that benefit from high-fidelity image reconstructions.
Things to try
Experiment with using the ft-EMA and ft-MSE models in place of the original kl-f8 VAE in the Stable Diffusion pipeline. Observe any differences in the quality and consistency of generated outputs, especially for images containing human faces or other complex subject matter. Additionally, try fine-tuning the model further on domain-specific datasets to see if you can achieve even better performance for your particular use case.
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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