alt.freeweights.image / sd-vae-ft-ema
From: stabilityai <huggingface.co/stabilityai>Newsgroups: alt.freeweights.imageSubject:

sd-vae-ft-ema

Date: 2026-11-14Commit: f04b2c4Signed: 2DC28CC7B26A283B ✓

Model with 84M parameters. Safetensors weights, 669 MB in all. Published by stabilityai under MIT.

84M params669MBsd-vae-ft-emasafetensorspermissive
FactsParams84MSize669 MBFormatssafetensorsTasknot taggedLicenseMITFrom sourcepermissive
$ fw get stabilityai/sd-vae-ft-ema
# manifest sha256 b86e541bd336… · 669 MB · 5 files
$ fw verify ./sd-vae-ft-ema stabilityai/sd-vae-ft-ema
From sourceHosted (signed)stabilityai/sd-vae-ft-emaLast check: 2026-11-15 02:25 UTC

Get help: custom fine-tune or private deploy. We review your setup before we quote.

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Runs with

Ranked by fit to this model (safetensors), then by GitHub stars.

Overview

Size
669 MB
Weights
335 MB (safetensors)
Parameters
84M
Formats
safetensors
Task
not tagged
License
MIT (permissive)
Files
5, each with a sha256
Publisher
stabilityai
Added
2026-11-14
Section
alt.freeweights.image
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Model card

From the publisher at commit f04b2c4. Shown as plain text with links; images open at the publisher.

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.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.

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)

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Files

5 files · 669 MBWeights 335 MB (safetensors). Sizes and sha256 from the signed manifest.
Hide all 5 files with their sha256
FileSizesha256
.gitattributes1.46 KB15bff3ac97756db161d7bae5a5aa24899c4c01fc02effcc3f0499cb642a3618b
README.md6.84 KB814a88d18bd11ff1d8fe5ee9848562b779226a32d96c3aabfc9f58aac2e57f24
config.json547 B92d3dfb746fca211a2c9e019e285f8597412211728dce3c5bcf4eda0f2d62e7e
diffusion_pytorch_model.bin335 MB7c98ebcd7ca5cb69d47b2ae287feba0695689fbf2c8fead2fab05fd3e0c28303
diffusion_pytorch_model.safetensors335 MB32db726da04f06c1b6b14c0043ce115cc87a501482945c5add89a40d838fcb46
VersionsEach time the pinned upstream commit or file list changed in this repo. Newest first.
Upstream commitDateContents
f04b2c4b98 pinned now2026-11-15 02:25 UTC5 files · 669 MB

Use it

$ fw get stabilityai/sd-vae-ft-ema
# manifest sha256 b86e541bd336… · 669 MB · 5 files
$ fw verify ./sd-vae-ft-ema stabilityai/sd-vae-ft-ema
fw.pyChecks the signature and every sha256, tries each source in order, resumes.
$ python3 fw.py get https://freeweights.allternit.com/manifests/stabilityai/sd-vae-ft-ema.json --dir ./sd-vae-ft-ema $ python3 fw.py verify ./sd-vae-ft-ema ./stabilityai__sd-vae-ft-ema.json
hfThe Hugging Face CLI with one setting. Same repo id.
$ HF_ENDPOINT=https://freeweights.allternit.com hf download stabilityai/sd-vae-ft-ema --revision f04b2c4b98319346dad8c65879f680b1997b204a --local-dir ./sd-vae-ft-ema
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License

Name
MIT License
Id
MIT
Class
Permissive
Redistribution
yes
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License as declared by the publisher. No person read it. 2026-11-14
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README.md
Text sha256
814a88d18bd11ff1d8fe5ee9848562b779226a32d96c3aabfc9f58aac2e57f24
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Provenance

From: stabilityai <huggingface.co/stabilityai>Newsgroups: alt.freeweights.imageSubject: sd-vae-ft-emaDate: 2026-11-14Commit: f04b2c4Signed: 2DC28CC7B26A283B ✓
Upstream
stabilityai/sd-vae-ft-ema
Pinned commit
f04b2c4b98319346dad8c65879f680b1997b204a
Manifest
sd-vae-ft-ema.json · .minisig
Manifest sha256
b86e541bd3364b86be1b4b3cf7514491a64ded3e40560d413bae2037b56a36aa
Signature
minisign, Ed25519, key 2DC28CC7B26A283B
Created
2026-11-15 02:25 UTC
Generator
freeweights-manifest/0.1.0
Takedowns
None.

How to verify

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