---
library_name: diffusers
base_model: microsoft/Mage-Flow
base_model_relation: adapter
tags:
- vae
- autoencoder
- flux.2
- image-generation
---

# MageFlow VAE (diffusers)

A 🧨 diffusers-native `AutoencoderKLMage` port of the VAE from
[microsoft/Mage-Flow](https://huggingface.co/microsoft/Mage-Flow)
(DConvEncoder + DConvDenoiser/CoD decoder), usable as a drop-in adapter for
pipelines that expect a Flux.2-style VAE.

- **Encode:** one-step diffusion encoder (t=0) → `latent_dist` over `(B, 32, H/8, W/8)`
- **Decode:** DConvDenoiser + CoD decoder (t=0) → image `(B, 3, H, W)` in `[-1, 1]`
- **Latent space:** shaped *and* valued like the raw Flux.2 VAE latent — the native
  128-channel `H/16` code is 2×2-unpatchified and denormalized with the Flux.2 BN
  latent stats stored in `config.json` (anchor-latent regularization,
  [arXiv:2607.19064](https://arxiv.org/abs/2607.19064)). No dependency on the
  Flux.2 VAE at runtime (0.972 mean latent correlation over a 50-image test
  set; see the [comparison](#comparison-with-the-original-flux2-vae) below).
- **Checkpoint:** bf16, with the t=0 adaLN MLPs constant-folded at conversion time
  (`folded: true` in the config), so it is ready to run on load.

> [!NOTE]
> The latents exposed by this model are **not** the native MageVAE code. The
> native `(B, 128, H/16, W/16)` code is **2×2-unpatchified** to
> `(B, 32, H/8, W/8)` and **denormalized** with the Flux.2 BN latent statistics
> (stored in `config.json`) so that `encode`/`decode` operate directly in the
> original Flux.2 VAE latent space. Latents from this model can be decoded by
> the Flux.2 VAE and vice versa.

## Comparison with the original Flux.2 VAE

Measured against `black-forest-labs/FLUX.2-dev` (subfolder `vae`,
`AutoencoderKLFlux2`) on an NVIDIA L4, bf16, batch 1 (torch 2.8.0,
diffusers 0.37.1). Speed/memory at 1024×1024, means over 5 runs after warmup,
memory is peak CUDA allocation during the op. Quality is the mean over a
50-image test set (06JPEG, ~2040×1524 photos, center-cropped to a multiple
of 16, evaluated at native resolution with deterministic `.mode()` latents):

| | MageFlow VAE (this repo) | Flux.2 VAE |
| --- | --- | --- |
| Parameters | 100.8M | 84.0M |
| Encode time | **23 ms** (~10× faster) | 240 ms |
| Decode time | **71 ms** (~6× faster) | 441 ms |
| Encode peak memory | **0.46 GiB** | 1.90 GiB |
| Decode peak memory | **0.98 GiB** | 2.78 GiB |
| Roundtrip PSNR (50 images) | 34.1 dB | 34.7 dB |

**Quality** is on par with the original: same-VAE roundtrip reconstruction
averages within ~0.5 dB of the Flux.2 VAE over the 50-image set. The latent
spaces are interchangeable — mean latent correlation with raw Flux.2 latents
is **0.972** (stable per image, ~0.97 on every one of the 50), and
cross-decoding (`flux.decode(mage.encode(x))` at 34.0 dB,
`mage.decode(flux.encode(x))` at 33.8 dB) stays within ~0.7 dB of the
same-VAE roundtrip.

**Repeated roundtrips** hold up slightly *better* than the original: MageFlow
starts ~0.5 dB below the Flux.2 VAE at one roundtrip but degrades more
slowly, matching it by the second cycle and leading by ~1.1 dB after five —
so it is well suited to iterative editing workflows. Alternating the two
VAEs each cycle (MageFlow encode → Flux.2 decode) tracks in between
(50-image means):

| PSNR vs original (dB) | k=1 | k=2 | k=3 | k=4 | k=5 |
| --- | --- | --- | --- | --- | --- |
| MageFlow VAE ×k | 34.1 | 32.1 | **30.3** | **28.9** | **27.7** |
| Flux.2 VAE ×k | **34.7** | 32.1 | 29.9 | 28.1 | 26.5 |
| Alternating (Mage enc → Flux.2 dec) | 34.0 | 31.5 | 29.5 | 27.8 | 26.4 |

**Speed** comes from the one-step architecture: both directions run a single
t=0 forward pass of depthwise-conv (DiCo) blocks with the adaLN modulation
constant-folded into the checkpoint, instead of the Flux.2 VAE's deep
ResNet/attention encoder-decoder.

**Memory** stays flat at high resolution: the decoder's per-patch MLP tail is
chunked (`decode_chunk_size`, default 4096 = one 1024×1024 image worth of
16×16 patches), so decode peak memory is roughly constant beyond 1024×1024
instead of growing with image area.

## Files

| File | Purpose |
| --- | --- |
| `autoencoder_kl_mage.py` | Self-contained: `AutoencoderKLMage` (ModelMixin/ConfigMixin), all network blocks, and the one-time checkpoint converter |
| `config.json` | Model config, incl. Flux.2 BN latent stats |
| `diffusion_pytorch_model.safetensors` | Folded bf16 weights |

## Usage

The model class lives in this repo, so grab the code files alongside the weights:

```python
import sys

import torch
from huggingface_hub import snapshot_download

repo_dir = snapshot_download("<your-username>/MageFlow-VAE-diffusers")
sys.path.insert(0, repo_dir)

from autoencoder_kl_mage import AutoencoderKLMage

vae = AutoencoderKLMage.from_pretrained(repo_dir, torch_dtype=torch.bfloat16).to("cuda")

image = torch.rand(1, 3, 1024, 1024, device="cuda", dtype=torch.bfloat16) * 2 - 1
latents = vae.encode(image).latent_dist.sample()   # (1, 32, 128, 128), Flux.2 latent space
recon = vae.decode(latents, return_dict=False)[0]  # (1, 3, 1024, 1024) in [-1, 1]
```

Because the latents are shape- and value-compatible with the Flux.2 VAE, you can
swap this in as the `vae` of a Flux.2 pipeline:

```python
pipe.vae = vae
```

Input `H`/`W` must be multiples of 16. `from_pretrained` accepts
`fold_adaln=False` if you need the unfolded adaLN MLPs (only relevant for
unfolded checkpoints).

## Requirements

```
torch
diffusers>=0.37
safetensors
loguru
```

## Conversion

The checkpoint here was produced from the original CoD checkpoint layout with:

```bash
python autoencoder_kl_mage.py   # convert_mage_ckpt: MageFlow/vae -> MageFlow/vae_diffusers
```

which remaps `student.dconv_encoder.* -> encoder.*` and `pipeline.* -> decoder.*`,
carries the Flux.2 BN stats into the config, constant-folds the t=0 adaLN MLPs
(~74 MB smaller), and saves in bf16.