Wan 2.2 VAE MLX

Wan 2.2 VAE — MLX port

First MLX port of the Wan 2.2 video VAE. Runs natively on Apple Silicon via MLX.

This is the VAE component used by bytedance-research/Lance (their unified multimodal model) and originally trained by the Alibaba Wan team. Useful as a standalone for any Wan-family model or for building MLX-native video diffusion pipelines on Apple Silicon.

What it is

A 3D causal video VAE:

Property	Value
Latent channels (z_dim)	48
Spatial downsample	16×
Temporal downsample	4×
Input patchify	2×
Encoder/decoder stages	4 with dim_mult=[1, 2, 4, 4]
Layout	NTHWC (MLX native)
Size	2.82 GB (float32, 196 tensors)

Encoder: (B, T, H, W, 3) RGB in [-1, 1] → (B, T', H', W', 48) latent. Decoder: latent → reconstructed RGB clamped to [-1, 1].

Validation

Single-image reconstruction PSNR vs the original PT checkpoint on a structured sinusoid test pattern: 37.99 dB (target ≥ 25 dB for parity).

Latent: shape=(1, 1, 4, 4, 48)  mean=-0.070  std=0.699
Recon : shape=(1, 1, 64, 64, 3)  range=[-0.717, 0.735]
PSNR(input, recon) = 37.99 dB

Status — v0.1.0

• Image mode (T=1): working, validated at 37.99 dB PSNR
• Video streaming-cache mode (T>1): ✅ working — chunked encode/decode with per-conv feat_cache matching the PyTorch reference. Verified at T=5 (36.17 dB overall) and T=9 (35.62 dB overall) on synthetic moving sinusoid video.

Encode pattern: first chunk is frame 0 (1 frame), then chunks of 4. T input frames → T_lat = 1 + (T-1)//4 latent frames.

Decode pattern: one latent frame at a time, expanded to 4 output frames per latent frame after the first (which is 1 frame). T_lat latent frames → T = (T_lat - 1) × 4 + 1 output frames.

Usage

Requires mlx >= 0.29, numpy, and einops (used internally for tensor rearrangement).

pip install mlx numpy einops

Verified on M3 Ultra and M4 Studio — bit-identical reconstruction (zero diff) across both. Deterministic: same input → same output on repeat runs.

Performance (M3 Ultra, steady-state)

Image mode (T=1):

Output size	Encode	Decode	Peak mem
2562	~80 ms	~200 ms	7.6 GiB
5122	~260 ms	~780 ms	13.2 GiB
7682	~620 ms	~2.2 s	17.2 GiB
10242	~1.0 s	~3.8 s	27.4 GiB

Decode scales as ~pixels1·2 (near-linear).

Video mode (T>1, streaming cache, 64×64):

T input frames	T_lat (encode)	Encode	Decode	Round-trip PSNR
5	2	30 ms	70 ms	36.17 dB
9	3	~60 ms	~140 ms	35.62 dB

Streaming cache means memory stays bounded per frame regardless of T — only the prior frame's intermediate state is retained between chunks.

Cold-start note: the first call at each new spatial resolution pays a Metal-kernel JIT compile cost (a few seconds for 10242, trivial for ≤ 7682). Warm the pipeline once at your target size before timing or batching.

Reconstruction quality (real photos)

Content	Resolution	PSNR
Smooth/cartoon content	512×288	~49 dB
iPhone photos (high-freq detail)	384×512	~34 dB
Synthetic sinusoid (baseline)	64×64	37.99 dB

Round-trip stability: successive encode→decode cycles converge to a fixed point on the latent manifold (not divergent). Latent statistics across diverse inputs: mean ≈ 0, std ≈ 0.6–0.9, no collapse.

import mlx.core as mx
from lance_mlx.vae_wan22 import Wan2_2_VAE  # from RockTalk/Lance-MLX (companion repo)

# Build
vae = Wan2_2_VAE(z_dim=48, c_dim=160,
                 dim_mult=(1, 2, 4, 4),
                 temperal_downsample=(False, True, True))

# Load
weights = mx.load("model.safetensors")
vae.model.load_weights(list(weights.items()), strict=True)

# Encode an image (T=1)
img = mx.array(image_array_in_minus1_to_plus1)[None, None, ...]  # (1, 1, H, W, 3)
mu, log_var = vae.encode(img)

# Decode
recon = vae.decode(mu)

# Encode a video clip (T>1) — uses streaming cache automatically
video = mx.array(video_array_in_minus1_to_plus1)[None, ...]      # (1, T, H, W, 3)
mu_v, log_var_v = vae.encode(video)                              # (1, T_lat, H/16, W/16, 48)

# Decode back to T frames (T = (T_lat - 1) * 4 + 1)
video_recon = vae.decode(mu_v)                                   # (1, T, H, W, 3)

Conversion source

Converted from bytedance-research/Lance/Wan2.2_VAE.pth using the open-source conversion tool at https://github.com/RockTalk/Lance-MLX (tools/convert_wan22_vae.py).

Layout transforms applied:

• Conv weights: PT (O, I, [T,] H, W) → MLX (O, [T,] H, W, I)
• RMS_norm gamma: PT (C, 1, 1, 1) → MLX (C,)
• ResidualBlock: PT Sequential.{0,2,3,6} → MLX norm1/conv1/norm2/conv2
• Encoder/decoder head: PT Sequential.{0,2} → MLX head_norm/head_conv
• Resample 2D conv: PT resample.1 → MLX spatial_conv

License

Apache 2.0 — inherited from the upstream Wan 2.2 release.

Acknowledgements

• Alibaba Wan team — original VAE training
• ByteDance Research — distribution as part of Lance
• This MLX port — RockTalk

Citation

@misc{wan22vae_mlx,
  title  = {Wan 2.2 VAE — MLX port},
  author = {RockTalk},
  year   = {2026},
  url    = {https://huggingface.co/RockTalk/Wan2.2-VAE-MLX}
}