license: mit
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
inference: false
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
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(model, vae=vae)
Decoder Finetuning
We publish two kl-f8 autoencoder versions, finetuned from the original kl-f8 autoencoder. The first, ft-EMA, was resumed from the original checkpoint, trained for 313198 steps and uses EMA weights. The second, ft-MSE, was resumed from ft-EMA and uses EMA weights and was trained for another 280k steps using a re-weighted loss, with more emphasis on MSE reconstruction (producing somewhat ``smoother'' outputs). 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.
Original kl-f8 VAE vs f8-ft-EMA vs f8-ft-MSE
Evaluation
COCO 2017 (256x256, val, 5000 images)
| Model | train steps | rFID | PSNR | SSIM | PSIM | Link | Comments |
|---|---|---|---|---|---|---|---|
| original | 246803 | 4.99 | 23.4 +/- 3.8 | 0.69 +/- 0.14 | 1.01 +/- 0.28 | https://ommer-lab.com/files/latent-diffusion/kl-f8.zip | as used in SD |
| ft-EMA | 560001 | 4.42 | 23.8 +/- 3.9 | 0.69 +/- 0.13 | 0.96 +/- 0.27 | https://huggingface.co/stabilityai/sd-vae-ft-ema-original/resolve/main/vae-ft-ema-560000-ema-pruned.ckpt | slightly better overall, with EMA |
| ft-MSE | 840001 | 4.70 | 24.5 +/- 3.7 | 0.71 +/- 0.13 | 0.92 +/- 0.27 | https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.ckpt | resumed with EMA from ft-EMA, emphasis on MSE (rec. loss = MSE + 0.1 * LPIPS), smoother outputs |
LAION-Aesthetics 5+ (256x256, subset, 10000 images)
| Model | train steps | rFID | PSNR | SSIM | PSIM | Link | Comments |
|---|---|---|---|---|---|---|---|
| original | 246803 | 2.61 | 26.0 +/- 4.4 | 0.81 +/- 0.12 | 0.75 +/- 0.36 | https://ommer-lab.com/files/latent-diffusion/kl-f8.zip | as used in SD |
| ft-EMA | 560001 | 1.77 | 26.7 +/- 4.8 | 0.82 +/- 0.12 | 0.67 +/- 0.34 | https://huggingface.co/stabilityai/sd-vae-ft-ema-original/resolve/main/vae-ft-ema-560000-ema-pruned.ckpt | slightly better overall, with EMA |
| ft-MSE | 840001 | 1.88 | 27.3 +/- 4.7 | 0.83 +/- 0.11 | 0.65 +/- 0.34 | https://huggingface.co/stabilityai/sd-vae-ft-mse-original/resolve/main/vae-ft-mse-840000-ema-pruned.ckpt | resumed with EMA from ft-EMA, emphasis on MSE (rec. loss = MSE + 0.1 * LPIPS), smoother outputs |
Visual
Visualization of reconstructions on 256x256 images from the COCO2017 validation dataset.
256x256: ft-EMA (left), ft-MSE (middle), original (right)
