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

sd-vae-ft-mse

Date: 2026-11-14Commit: 31f26fdSigned: 2DC28CC7B26A283B ✓

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

84M params669MBsd-vae-ft-msesafetensorspermissive
FactsParams84MSize669 MBFormatssafetensorsTasknot taggedLicenseMITFrom sourcepermissive
$ fw get stabilityai/sd-vae-ft-mse
# manifest sha256 7f0e8375aa0a… · 669 MB · 5 files
$ fw verify ./sd-vae-ft-mse stabilityai/sd-vae-ft-mse
From sourceHosted (signed)stabilityai/sd-vae-ft-mseLast check: 2026-11-15 02:25 UTC

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

Works with

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
Lineage

Original release from the publisher.

Where these bytes come from
Publisher at the pinned commit→You

Sources are tried in order. A file counts only when its size and sha256 match the manifest. Sources are checked when you ask for them, not on a timer.

Sources
BadgeHostLast checkResult
From sourcehuggingface.co2026-11-15 02:25 UTCpassed

Model card

From the publisher at commit 31f26fd. 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-mse")
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)

Image: image

Image: image

Image: image

Image: image

Image: image

Image: image

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 KB8ac69483c18a45d0fee531e08b6ede97ee9b4c79e875659c1b0e6a5903ca3400
config.json547 B92d3dfb746fca211a2c9e019e285f8597412211728dce3c5bcf4eda0f2d62e7e
diffusion_pytorch_model.bin335 MB1b4889b6b1d4ce7ae320a02dedaeff1780ad77d415ea0d744b476155c6377ddc
diffusion_pytorch_model.safetensors335 MBa1d993488569e928462932c8c38a0760b874d166399b14414135bd9c42df5815
VersionsEach time the pinned upstream commit or file list changed in this repo. Newest first.
Upstream commitDateContents
31f26fdeee pinned now2026-11-15 02:25 UTC5 files · 669 MB

Use it

$ fw get stabilityai/sd-vae-ft-mse
# manifest sha256 7f0e8375aa0a… · 669 MB · 5 files
$ fw verify ./sd-vae-ft-mse stabilityai/sd-vae-ft-mse
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-mse.json --dir ./sd-vae-ft-mse $ python3 fw.py verify ./sd-vae-ft-mse ./stabilityai__sd-vae-ft-mse.json
hfThe Hugging Face CLI with one setting. Same repo id.
$ HF_ENDPOINT=https://freeweights.allternit.com hf download stabilityai/sd-vae-ft-mse --revision 31f26fdeee1355a5c34592e401dd41e45d25a493 --local-dir ./sd-vae-ft-mse
Packaged for ComfyUI. Most image, video and music models run in ComfyUI . Check the publisher's page for the workflow. Freeweights does not host it.

License

Name
MIT License
Id
MIT
Class
Permissive
Redistribution
yes
Listing
Hosted (signed)
Decided
License as declared by the publisher. No person read it. 2026-11-14
Text in
README.md
Text sha256
8ac69483c18a45d0fee531e08b6ede97ee9b4c79e875659c1b0e6a5903ca3400
Terms gate
No extra terms beyond the license.

Read the license text

Licenses are checked against the license table. A custom license passes only after a person reads it.

Provenance

From: stabilityai <huggingface.co/stabilityai>Newsgroups: alt.freeweights.imageSubject: sd-vae-ft-mseDate: 2026-11-14Commit: 31f26fdSigned: 2DC28CC7B26A283B ✓
Upstream
stabilityai/sd-vae-ft-mse
Pinned commit
31f26fdeee1355a5c34592e401dd41e45d25a493
Manifest
sd-vae-ft-mse.json · .minisig
Manifest sha256
7f0e8375aa0a0d754ee0f6b5187bf624ec74aa169d7ef29f810ec84593ed0601
Signature
minisign, Ed25519, key 2DC28CC7B26A283B
Created
2026-11-15 02:25 UTC
Generator
freeweights-manifest/0.1.0
Takedowns
None.

How to verify

$ curl -O https://freeweights.allternit.com/manifests/stabilityai/sd-vae-ft-mse.json $ curl -O https://freeweights.allternit.com/manifests/stabilityai/sd-vae-ft-mse.json.minisig $ minisign -Vm sd-vae-ft-mse.json -P RWQ7KGqyx4zCLXtUFOqvfv8L3g+8fpKJ1FsuxPyOYMF+4KufbUxKq1Oq $ shasum -a 256 <file> # compare with the Files tab

A manifest whose signature fails must be rejected. A file whose size or sha256 does not match must be discarded. Verify a download

Model tree

No related models in this catalog.Nothing here lists this model as its parent, and it names no parent.
Get it
Download
$ HF_ENDPOINT=https://freeweights.allternit.com hf download stabilityai/sd-vae-ft-mse
Open in Freeweights Studio

Pull, verify and run it in the Allternit app on Desktop, web or phone.

Signed manifest
minisign, key 2DC28CC7B26A283B. sd-vae-ft-mse.json. Provenance. Takedown history: none.
From: Comfy-Org <huggingface.co/Comfy-Org>Subject: Boogu-Image
From: Comfy-Org <huggingface.co/Comfy-Org>Subject: Chroma1-HD
From: lodestones <huggingface.co/lodestones>Subject: Chroma1-HD
From: Comfy-Org <huggingface.co/Comfy-Org>Subject: Chroma1-Radiance
From: Comfy-Org <huggingface.co/Comfy-Org>Subject: ERNIE-Image
From: baidu <huggingface.co/baidu>Subject: ERNIE-Image