wan2.2-ti2v-5b-diffusers-8bit

This repository contains MLX-Gen saved weights for Wan-AI/Wan2.2-TI2V-5B-Diffusers. The checkpoint is designed for local Apple Silicon inference with mlx-gen.

It uses the mflux/MLX saved-weight layout and MLX quantization tensors. It is not a Diffusers or Transformers from_pretrained() checkpoint.

Source Model

Original model: Wan-AI/Wan2.2-TI2V-5B-Diffusers.

License and Access

This quantized derivative follows the Apache 2.0 license of the source model.

Quantization

This is an MLX q8 checkpoint for Wan2.2 TI2V. MLX-Gen uses 8-bit quantization for Wan modules where MLX supports quantization:

• q8 for quantizable Wan transformer modules.
• q8 for quantizable Wan VAE modules.
• BF16 for the UMT5 text encoder, scheduler metadata, tokenizer files, norms, and other non-quantizable parameters.

Wan q4 quality and any possible mixed q4/q8 policy are still under validation. Prefer q8 for publishable Wan checkpoints until the q4 policy is documented.

See the MLX-Gen quantization docs for compatibility notes.

Compatibility

Requires mlx-gen >= 0.18.6.

Generated with mlx-gen 0.18.6.

Use the mlxgen command and Python import path for new MLX-Gen projects.

Usage

python -m pip install -U mlx-gen

mlxgen download --model AbstractFramework/wan2.2-ti2v-5b-diffusers-8bit

mlxgen generate \
  --model AbstractFramework/wan2.2-ti2v-5b-diffusers-8bit \
  --task text-to-video \
  --prompt "Your video prompt here" \
  --width 1280 \
  --height 704 \
  --frames 121 \
  --steps 50 \
  --guidance 5 \
  --fps 24 \
  --seed 42 \
  --output video.mp4

Attribution

MLX-Gen is based on mflux by Filip Strand and the original mflux contributors. This model card is generated by MLX-Gen so derived checkpoints keep that attribution visible.

Quantized and contributed by @lpalbou.