You can integrate this fine-tuned VAE decoder to your existing
diffusers
workflows, by including a
vae
argument to the
StableDiffusionPipeline
from diffusers.models import AutoencoderKL
from diffusers import StableDiffusionPipeline
model = "CompVis/stable-diffusion-v1-4"
vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-ema")
pipe = StableDiffusionPipeline.from_pretrained(model, vae=vae)
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 somewhat ``smoother'' outputs. The batch size for both versions was 192 (16 A100s, batch size 12 per GPU).
To keep compatibility with existing models, only the decoder part was finetuned; the checkpoints can be used as a drop-in replacement for the existing autoencoder.
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