| | --- |
| | license: mit |
| | tags: |
| | - stable-diffusion |
| | - stable-diffusion-diffusers |
| | inference: false |
| | --- |
| | # Improved Autoencoders |
| |
|
| | ## Utilizing |
| | These weights are intended to be used with the [🧨 diffusers library](https://github.com/huggingface/diffusers). If you are looking for the model to use with the original [CompVis Stable Diffusion codebase](https://github.com/CompVis/stable-diffusion), [come here](https://huggingface.co/stabilityai/sd-vae-ft-mse-original). |
| |
|
| | #### 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` |
| | ```py |
| | from diffusers.models import AutoencoderKL |
| | from diffusers import StableDiffusionPipeline |
| | |
| | model = "CompVis/stable-diffusion-v1-4" |
| | vae = AutoencoderKL.from_pretrained("stabilityai/sd-vae-ft-mse") |
| | pipe = StableDiffusionPipeline.from_pretrained(model, vae=vae) |
| | ``` |
| |
|
| | ## Decoder Finetuning |
| | We publish two kl-f8 autoencoder versions, finetuned from the original [kl-f8 autoencoder](https://github.com/CompVis/latent-diffusion#pretrained-autoencoding-models) on a 1:1 ratio of [LAION-Aesthetics](https://laion.ai/blog/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. |
| |
|
| | _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._ |
| |
|
| | <p align="center"> |
| | <br> |
| | <b> |
| | 256x256: ft-EMA (left), ft-MSE (middle), original (right)</b> |
| | </p> |
| |
|
| | <p align="center"> |
| | <img src=https://huggingface.co/stabilityai/stable-diffusion-decoder-finetune/resolve/main/eval/ae-decoder-tuning-reconstructions/merged/00025_merged.png /> |
| | </p> |
| |
|
| | <p align="center"> |
| | <img src=https://huggingface.co/stabilityai/stable-diffusion-decoder-finetune/resolve/main/eval/ae-decoder-tuning-reconstructions/merged/00011_merged.png /> |
| | </p> |
| |
|
| | <p align="center"> |
| | <img src=https://huggingface.co/stabilityai/stable-diffusion-decoder-finetune/resolve/main/eval/ae-decoder-tuning-reconstructions/merged/00037_merged.png /> |
| | </p> |
| |
|
| | <p align="center"> |
| | <img src=https://huggingface.co/stabilityai/stable-diffusion-decoder-finetune/resolve/main/eval/ae-decoder-tuning-reconstructions/merged/00043_merged.png /> |
| | </p> |
| |
|
| | <p align="center"> |
| | <img src=https://huggingface.co/stabilityai/stable-diffusion-decoder-finetune/resolve/main/eval/ae-decoder-tuning-reconstructions/merged/00053_merged.png /> |
| | </p> |
| |
|
| | <p align="center"> |
| | <img src=https://huggingface.co/stabilityai/stable-diffusion-decoder-finetune/resolve/main/eval/ae-decoder-tuning-reconstructions/merged/00029_merged.png /> |
| | </p> |
| |
|
