FastWAM wan package#

← Tutorial Library · policies and Models

This section introduces the general functions of LeRobot. Please configure the example according to the target robot, data set and operating environment; before using it with AlohaMini 2 / 2 Pro, you need to confirm the hardware and interface compatibility.

This package holds FastWAM’s model implementation. It mixes a small vendored subset of the official Wan2.2 source tree with FastWAM’s own code, kept flat in a single directory.

Vendored from Wan2.2#

  • Upstream repository: https://github.com/Wan-Video/Wan2.2

  • Upstream commit: 42bf4cfaa384bc21833865abc2f9e6c0e67233dc

  • License: Apache-2.0, matching the license in LICENSE.txt from the upstream repository

Copied files:

  • model.py (was wan/modules/model.py), trimmed: the flash-attention path (the vendored attention.py and the block/model forwards) was removed. FastWAM’s DiT uses SDPA instead (see video_dit.py).

  • get_sampling_sigmas in video_dit.py (was wan/utils/fm_solvers.py), inlined next to its only caller.

This subset only backs FastWAM’s custom MoT video DiT. The Wan2.2 VAE, UMT5 text encoder, and tokenizer are no longer vendored - they come from diffusers.AutoencoderKLWan, transformers.UMT5EncoderModel, and transformers.AutoTokenizer (see components.py and adapters.py).

FastWAM’s own code#

  • video_dit.py builds on model (sinusoidal_embedding_1d, rope_params, rope_apply, …) and computes attention with SDPA (fastwam_masked_attention). Its WanContinuousFlowMatchScheduler uses get_sampling_sigmas for Wan-compatible inference timesteps.

  • components.py / adapters.py load the VAE, text encoder, tokenizer, and the custom DiT weights.

  • modular.py defines the FastWAM model (ActionDiT, MoT, FastWAM, …).