vae-kl-f8-d16
Maintainer: ostris
59
⛏️
| 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 vae-kl-f8-d16 is a 16-channel Variational Autoencoder (VAE) with an 8x downsampling factor, created by maintainer ostris. It was trained from scratch on a balanced dataset of photos, artistic works, text, cartoons, and vector images. Compared to other VAEs like the SD3 VAE, the vae-kl-f8-d16 is lighter weight with only 57,266,643 parameters, yet it scores quite similarly on real images in terms of PSNR and LPIPS metrics. It is released under the MIT license, allowing users to use it freely.
The vae-kl-f8-d16 can be used as a drop-in replacement for the VAE in the Stable Diffusion 1.5 pipeline. It provides a more efficient alternative to the larger VAEs used in Stable Diffusion models, while maintaining similar performance.
Model inputs and outputs
Inputs
- Latent representations of images
Outputs
- Reconstructed images from the provided latent representations
Capabilities
The vae-kl-f8-d16 VAE is capable of reconstructing a wide variety of image types, including photos, artwork, text, and vector graphics, with a high level of fidelity. Its lighter weight compared to larger VAEs makes it an attractive option for those looking to reduce the computational and memory requirements of their image generation pipelines, without sacrificing too much in terms of output quality.
What can I use it for?
The vae-kl-f8-d16 VAE can be used as a drop-in replacement for the VAE component in Stable Diffusion 1.5 pipelines, as demonstrated in the provided example code. This allows for faster and more efficient image generation, while maintaining the quality of the outputs. Additionally, the open-source nature of the model means that users can experiment with it, fine-tune it, or incorporate it into their own custom image generation models and workflows.
Things to try
One interesting thing to try with the vae-kl-f8-d16 VAE is to explore how its latent space and reconstruction capabilities differ from those of larger VAEs, such as the SD3 VAE. Comparing the outputs and performance on various types of images can provide insights into the tradeoffs between model size, efficiency, and output quality. Additionally, users may want to experiment with fine-tuning the VAE on specialized datasets to tailor its performance for their specific use cases.
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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