---
license: apache-2.0
base_model: zai-org/SCAIL-2
library_name: safetensors
tags:
- lora
- video
- character-animation
- scail-2
- wan2.1
- mlx
- sceneworks
---

# SCAIL-2 Bias-Aware DPO LoRA (inference-format)

A quality-refinement **LoRA** for [zai-org/SCAIL-2](https://huggingface.co/zai-org/SCAIL-2) — the
end-to-end controlled character-animation / cross-identity-replacement model (Wan2.1-14B I2V). This is
the **Bias-Aware DPO** adapter zai-org ships on the `sat-scail2` branch, **converted from its native
DeepSpeed / SwissArmyTransformer (SAT) checkpoint to an inference-named `.safetensors`** so it loads
directly into native-MLX inference engines (e.g. SceneWorks) as a standard `lora_down` / `lora_up`
adapter.

- **Rank 128**, 400 LoRA pairs covering every transformer block:
  `blocks.N.{self_attn,cross_attn}.{q,k,v,o}` and `blocks.N.ffn.{0,2}`.
- A clean low-rank LoRA (no diff-patch tensors) — applies as a forward-time residual over the
  (optionally Q4 / Q8-quantized) base.
- `bf16`, ~1.2 GB.

## Conversion

The upstream `model/bias-aware-dpo-lora.pt` is a DeepSpeed checkpoint (the LoRA state lives under the
top-level `["module"]` key) with SAT module names and fused `query_key_value` / `key_value` projections
already split into per-projection `lora_layer.{idx}` sub-LoRAs. The conversion is a pure key rename to
the inference `SCAIL2Model` module names — q/k/v from `query_key_value`; k/v from `key_value`; `dense`
→ the output projection `o`; `mlp.dense_h_to_4h` / `dense_4h_to_h` → `ffn.0` / `ffn.2`; `down` / `up`
→ `lora_down` / `lora_up`. The dims confirm the mapping (e.g. `mlp.dense_h_to_4h`
`down[128,5120]` / `up[13824,128]` == SCAIL-2's `ffn.0` `[13824,5120]`).

The converter is `scripts/convert_scail2_dpo_lora.py` in the SceneWorks repository.

## License & attribution

Apache-2.0, inherited from [zai-org/SCAIL-2](https://github.com/zai-org/SCAIL-2). All credit for the
underlying model and the DPO adapter belongs to zai-org; this repository only re-hosts a format
conversion of their published weights.