Instructions to use SceneWorks/scail2-dpo-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- SCAIL-2
How to use SceneWorks/scail2-dpo-lora with SCAIL-2:
# No code snippets available yet for this library. # To use this model, check the repository files and the library's documentation. # Want to help? PRs adding snippets are welcome at: # https://github.com/huggingface/huggingface.js
- MLX
How to use SceneWorks/scail2-dpo-lora with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir scail2-dpo-lora SceneWorks/scail2-dpo-lora
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
| 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) β 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) β 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 β q/k/v from `query_key_value`; k/v from `key_value`; `dense` | |
| β the output projection `o`; `mlp.dense_h_to_4h` / `dense_4h_to_h` β `ffn.0` / `ffn.2`; `down` / `up` | |
| β `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. | |