Improved Autoencoders Utilizing These weights are intended to be used with the 🧨 diffusers library. If you are looking for the model to use with the original CompVis Stable Diffusion codebase, come here. How to use with 🧨 diffusers You can integrate this fine tuned VAE decoder to your existing diffusers workflows, by including a vae argument to the StableDiffusionPipeline Decoder Finetuning We publish two kl f8 autoencoder versions, finetuned from the original kl f8 autoencoder on a 1:1 ratio of LAION Aesthetics and LAION Humans, an unreleased subset containing only SFW images of humans. The intent was to fine tune on the Stable Diffusion training set (the autoencoder was originally trained on OpenImages) but also enrich the dataset with images of humans to improve the reconstruction of faces. The first, ft EMA , was resumed from the original checkpoint, trained for 313198 steps and uses EMA weights. It uses the same loss configuration as the original checkpoint (L1 + LPIPS). The second, ft MSE , was resumed from ft EMA and uses EMA weights and was trained for another 280k steps using a different loss, with more emphasis on MSE reconstruction (MSE + 0.1 LPIPS). It produces somewh…
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