mlx-video/mlx_video/models/wan2

Wan2.1 / Wan2.2

Both Wan2.1 and Wan2.2 are text-to-video diffusion models built on a DiT (Diffusion Transformer) backbone with a T5 text encoder and 3D VAE.

They share the same model architecture — the difference is in the inference pipeline:

	Wan2.1	Wan2.2 T2V-14B	Wan2.2 I2V-14B	Wan2.2 TI2V-5B
Task	Text-to-Video	Text-to-Video	Image-to-Video	Text+Image-to-Video
Pipeline	Single model	Dual model	Dual model	Single model
Sizes	1.3B, 14B	14B	14B	5B
Resolution	480P (1.3B), 720P (14B)	720P	720P	720P
Steps	50	40	40	40
Guidance	5.0 (fixed)	3.0 / 4.0	3.5 / 3.5	5.0 (fixed)
Shift	5.0	12.0	5.0	5.0
VAE	Wan2.1 (z=16)	Wan2.1 (z=16)	Wan2.1 (z=16) + encoder	Wan2.2 (z=48)

Step 1: Download Weights

Download the original PyTorch checkpoints from HuggingFace using the huggingface-cli tool (install with pip install huggingface_hub):

Wan2.1

# Text-to-Video 1.3B (fast, fits in ~4 GB)
huggingface-cli download Wan-AI/Wan2.1-T2V-1.3B --local-dir ./Wan2.1-T2V-1.3B

# Text-to-Video 14B
huggingface-cli download Wan-AI/Wan2.1-T2V-14B --local-dir ./Wan2.1-T2V-14B

Wan2.2

# Text-to-Video 14B
huggingface-cli download Wan-AI/Wan2.2-T2V-A14B --local-dir ./Wan2.2-T2V-A14B

# Image-to-Video 14B
huggingface-cli download Wan-AI/Wan2.2-I2V-A14B --local-dir ./Wan2.2-I2V-A14B

# Text+Image-to-Video 5B (uses a different VAE — z_dim=48)
huggingface-cli download Wan-AI/Wan2.2-TI2V-5B --local-dir ./Wan2.2-TI2V-5B

Each downloaded directory will have this structure:

Wan2.1-T2V-*/
├── models_t5_umt5-xxl-enc-bf16.pth       # T5 text encoder
├── Wan2.1_VAE.pth                         # 3D VAE
└── diffusion_pytorch_model*.safetensors   # transformer (single)

Wan2.2-T2V-A14B/ or Wan2.2-I2V-A14B/
├── models_t5_umt5-xxl-enc-bf16.pth
├── Wan2.1_VAE.pth
├── low_noise_model/                       # dual-model low-noise transformer
└── high_noise_model/                      # dual-model high-noise transformer

Wan2.2-TI2V-5B/
├── models_t5_umt5-xxl-enc-bf16.pth
├── Wan2.2_VAE.pth                         # different VAE (z_dim=48)
└── diffusion_pytorch_model*.safetensors   # transformer (single)

Wan2.2 I2V-14B shares the same directory structure as Wan2.2 T2V. The conversion script auto-detects I2V from the model's config.json (model_type: "i2v", in_dim: 36).

Step 2: Convert to MLX Format

The conversion script auto-detects the model version from the directory structure (presence of low_noise_model/ → Wan2.2 dual model) and the model type from config.json (I2V vs T2V).

Wan2.1 T2V 1.3B

python -m mlx_video.wan2.convert \
    --checkpoint-dir ./Wan2.1-T2V-1.3B \
    --output-dir ./Wan2.1-T2V-1.3B-MLX

Wan2.1 T2V 14B

python -m mlx_video.wan2.convert \
    --checkpoint-dir ./Wan2.1-T2V-14B \
    --output-dir ./Wan2.1-T2V-14B-MLX

Wan2.2 T2V 14B

python -m mlx_video.wan2.convert \
    --checkpoint-dir ./Wan2.2-T2V-A14B \
    --output-dir ./Wan2.2-T2V-A14B-MLX

Wan2.2 I2V 14B

python -m mlx_video.wan2.convert \
    --checkpoint-dir ./Wan2.2-I2V-A14B \
    --output-dir ./Wan2.2-I2V-A14B-MLX

The I2V model is auto-detected from config.json; the output will include a vae_encoder.safetensors used to encode the conditioning image.

Wan2.2 TI2V 5B

python -m mlx_video.wan2.convert \
    --checkpoint-dir ./Wan2.2-TI2V-5B \
    --output-dir ./Wan2.2-TI2V-5B-MLX

The TI2V model uses a different VAE (z_dim=48, vae_stride=(4,16,16)) and is auto-detected during conversion.

---

You can also pass --model-version 2.1 or --model-version 2.2 to force the version instead of relying on auto-detection.

Conversion Options

Option	Default	Description
--checkpoint-dir	(required)	Path to original PyTorch checkpoint directory
--output-dir	wan_mlx_model	Output path for MLX model
--dtype	bfloat16	Target dtype (float16, float32, bfloat16)
--model-version	auto	Model version: 2.1, 2.2, or auto
--quantize	off	Quantize transformer weights for reduced memory
--bits	4	Quantization bits: 4 or 8
--group-size	64	Quantization group size: 32, 64, or 128

The converter produces:

wan_mlx/
├── config.json                    # Model configuration
├── t5_encoder.safetensors         # T5 UMT5-XXL text encoder
├── vae.safetensors                # 3D VAE decoder
├── vae_encoder.safetensors        # 3D VAE encoder (I2V-14B only)
├── model.safetensors              # (Wan2.1) Single transformer
├── low_noise_model.safetensors    # (Wan2.2) Low-noise transformer
└── high_noise_model.safetensors   # (Wan2.2) High-noise transformer

Step 3: Generate Video

Wan2.1 T2V 1.3B

python -m mlx_video.wan2.gemer \
    --model-dir ./Wan2.1-T2V-1.3B-MLX \
    --prompt "A cat playing piano in a cozy living room, cinematic lighting" \
    --width 832 --height 480 --num-frames 81 \
    --steps 50 --guide-scale 5.0 \
    --seed 42 \
    --output-path wan21_1b.mp4

Wan2.1 T2V 14B

python -m mlx_video.wan2.gemer \
    --model-dir ./Wan2.1-T2V-14B-MLX \
    --prompt "A woman walks through a misty forest at dawn, slow motion, cinematic" \
    --width 1280 --height 704 --num-frames 81 \
    --steps 50 --guide-scale 5.0 \
    --seed 42 \
    --output-path wan21_14b.mp4

Tip

: If the first few frames look washed out or have color artifacts, add --trim-first-frames 1 to generate 4 extra frames at the start and discard them. With the unipc scheduler (default), 10 steps often gives satisfying results — useful for quick iteration.

Wan2.2 T2V 14B

Wan2.2 uses a dual-model pipeline (separate high-noise and low-noise transformers) and takes guidance as a high,low pair:

python -m mlx_video.wan2.generate \
    --model-dir ./Wan2.2-T2V-A14B-MLX \
    --prompt "Two astronauts playing chess on the surface of the moon, dramatic lighting, 8K" \
    --negative-prompt "low quality, blurry, distorted" \
    --width 1280 --height 704 --num-frames 81 \
    --steps 40 --guide-scale "3.0,4.0" \
    --seed 42 \
    --output-path wan22_t2v.mp4

Tip

: With the unipc scheduler (default), 10 steps often produces satisfying results for 14B models — a significant speed-up with minimal quality loss. Try --steps 10 for quick iterations.

Wan2.2 I2V 14B

Image-to-video: animates a starting image guided by a text prompt. Pass the image with --image:

python -m mlx_video.wan2.generate \
    --model-dir ./Wan2.2-I2V-A14B-MLX \
    --image ./my_photo.png \
    --prompt "The person slowly turns their head and smiles, cinematic, natural lighting" \
    --negative-prompt "low quality, blurry, distorted" \
    --width 1280 --height 704 --num-frames 81 \
    --steps 40 --guide-scale "3.5,3.5" \
    --seed 42 \
    --output-path wan22_i2v.mp4

Tip

: As with T2V, --steps 10 with the unipc scheduler is often sufficient for fast prototyping.

Wan2.2 TI2V 5B

Text+image-to-video: a single-model variant with a larger VAE (z_dim=48). Resolution must be divisible by 32 (not 16 as with other models):

python -m mlx_video.wan2.generate \
    --model-dir ./Wan2.2-TI2V-5B-MLX \
    --image ./my_photo.png \
    --prompt "The subject waves hello, warm sunlight, film grain" \
    --width 1280 --height 704 --num-frames 41 \
    --steps 40 --guide-scale 5.0 \
    --seed 42 \
    --output-path wan22_ti2v.mp4

Note

: The 5B model is fast — 40 steps run quickly and are recommended for best quality.

Frame count: --num-frames must satisfy 4n+1 for all models (e.g. 5, 9, 13, 21, 41, 81, 101 …).

Resolution: Always use the model's native resolution. While generation will succeed at other sizes, mismatched resolutions or aspect ratios are likely to produce visual artifacts. Preferred resolutions are:

• 480P — 832×480 (landscape) or 480×832 (portrait) — for Wan2.1 1.3B
• 720P — 1280×704 (landscape) or 704×1280 (portrait) — for Wan2.1 14B, Wan2.2 T2V/I2V/TI2V

Generation Options

Option	Default	Description
--model-dir	(required)	Path to converted MLX model directory
--prompt	(required)	Text prompt
--image	—	Input image path (I2V and TI2V modes)
--negative-prompt	config default	Negative guidance prompt
--width	1280	Output width in pixels
--height	704	Output height in pixels
--num-frames	81	Number of frames (must be 4n+1)
--steps	config default	Diffusion steps
--guide-scale	config default	Guidance scale; use "high,low" pair for Wan2.2 dual models
--shift	config default	Noise schedule shift
--seed	-1 (random)	Random seed for reproducibility
--output-path	output.mp4	Output video file path
--scheduler	unipc	Solver: euler, dpm++, or unipc
--trim-first-frames	0	Drop N leading frames (fixes first-frame artifacts on 14B models)
--tiling	auto	VAE tiling: auto, none, spatial, temporal

Quantization (Reduced Memory)

Quantize the transformer weights to reduce memory usage by ~3.4×. Quantization is supported for all model variants and is especially important for running 14B models on devices with limited unified memory:

# Convert with 4-bit quantization (works for any variant)
python -m mlx_video.wan2.convert \
    --checkpoint-dir ./Wan2.1-T2V-1.3B \
    --output-dir ./Wan2.1-T2V-1.3B-MLX-Q4 \
    --quantize --bits 4 --group-size 64

python -m mlx_video.wan2.convert \
    --checkpoint-dir ./Wan2.1-T2V-14B \
    --output-dir ./Wan2.1-T2V-14B-MLX-Q4 \
    --quantize --bits 4 --group-size 64

python -m mlx_video.wan2.convert \
    --checkpoint-dir ./Wan2.2-T2V-A14B \
    --output-dir ./Wan2.2-T2V-A14B-MLX-Q4 \
    --quantize --bits 4 --group-size 64

python -m mlx_video.wan2.convert \
    --checkpoint-dir ./Wan2.2-I2V-A14B \
    --output-dir ./Wan2.2-I2V-A14B-MLX-Q4 \
    --quantize --bits 4 --group-size 64

python -m mlx_video.wan2.convert \
    --checkpoint-dir ./Wan2.2-TI2V-5B \
    --output-dir ./Wan2.2-TI2V-5B-MLX-Q4 \
    --quantize --bits 4 --group-size 64

You can also quantize an already-converted MLX model without re-converting from PyTorch:

python -m mlx_video.wan2.convert \
    --checkpoint-dir ./Wan2.2-T2V-A14B-MLX \
    --output-dir ./Wan2.2-T2V-A14B-MLX-Q4 \
    --quantize-only --bits 4

Quantized models are used exactly the same way — the quantization is auto-detected from config.json:

python -m mlx_video.wan2.generate \
    --model-dir ./Wan2.2-T2V-A14B-MLX-Q4 \
    --prompt "A cat playing piano"

What gets quantized: Self-attention (Q/K/V/O), cross-attention (Q/K/V/O), and FFN (fc1/fc2) — 10 layers × N blocks = ~95% of model weights. Embeddings, norms, and the output head remain in bfloat16 for precision.

Model	BF16 Size	4-bit Size	Notes
1.3B	2.7 GB	799 MB	~3.4x smaller
14B	~28 GB	~8 GB	Enables running on 16GB devices

Note

: On Apple Silicon, the 1.3B model fits comfortably in unified memory at bf16. Quantization reduces memory but may not speed up inference for small models. For the 14B model, quantization is essential to fit in memory and will also improve speed.

Wan Model Specifications

Transformer (14B)

• 40 layers, 40 attention heads, dim 5120, head dim 128
• 3-way factorized RoPE (temporal + spatial)
• 14.29B parameters

Transformer (1.3B, Wan2.1 only)

• 30 layers, 12 attention heads, dim 1536, head dim 128
• Same architecture, smaller scale

Text Encoder — UMT5-XXL (5.68B parameters)

• 24 layers, 64 heads, dim 4096, vocab 256K

VAE — 3D causal convolution decoder (72.6M parameters)

• Latent channels: 16
• Compression: 4× temporal, 8× spatial

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LoRA Support

LoRA's can be used with the --lora-high and --lora-low command line switches.

For example, for using the the distilled Wan2.2-Lightning LoRA, use the following command. Lightning speeds up generation by using only 4 steps and a CFG scale of 1.

python -m mlx_video.wan2.generate \
    --model-dir /Volumes/SSD/Wan-AI/Wan2.2-T2V-A14B-MLX \
    --width 480 \
    --height 704 \
    --num-frames 41 \
    --prompt "Two dogs of the poodle breed sitting on a beach wearing sunglasses, nodding with their heads, close up, cinematic, sunset" \
    --steps 4 \
    --guide-scale 1 \
    --trim-first-frames 1 \
    --seed 2391784614 \
    --lora-high /Volumes/SSD/Wan-AI/lightx2v/Wan2.2-Lightning/Wan2.2-T2V-A14B-4steps-lora-rank64-Seko-V2.0/high_noise_model.safetensors 1 \
    --lora-low /Volumes/SSD/Wan-AI/lightx2v/Wan2.2-Lightning/Wan2.2-T2V-A14B-4steps-lora-rank64-Seko-V2.0/low_noise_model.safetensors 1

Enjoy