Type | VAE |
Stats | 5,941 |
Reviews | (217) |
Published | Jan 17, 2024 |
Base Model | |
Training | Steps: 150,001 |
Hash | AutoV2 24E650297C |
Huggingface Repository (includes credits, Diffusers weights, and more): https://huggingface.co/RedRocket/furception_vae
This is a VAE decoder finetune, resumed from stabilityai/sd-vae-ft-mse using images from e621. It is trained with a mixture of MAE and MSE loss to maintain an acceptable balance between sharpness and smooth outputs, and loss is calculated in Oklab color space in order to prioritize image reconstruction based on which color channels are more perceptually significant.
Our testing has shown that the VAE is good at eliminating unwanted high-frequency noise when used on models trained on similar data. Results are far more apparent on flat-colored images than they are on realistic or painterly images, but we have not noticed any obvious loss of performance on any type of image. The effects are also more noticeable on lower-resolution generated images, but there are improvements at all resolutions. It may have some generalizability to a broader range of art styles due to the variety of different styles in the dataset.
Licensing:
This VAE is available under the terms of the CC BY-NC-SA 4.0 Deed. This license applies over permissions listed on CivitAI, in cases where they may appear to conflict. This applies to the use of the model, deployment, and distribution of the model weights only. The license does not apply to images decoded by this VAE and you may release them under any license, even public domain, as long as you are not creating them for commercial purposes. You are free and encouraged to distribute this VAE with models as long as you give credit and the VAE carries this license (the rest of the model does not need to share this license, although its distribution must be non-commercial), and I would ask that you include the version number so people can know if they need to get an updated version in the future.