Instructions to use lightx2v/Minimax-h3-Turbo-SLA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Inference
- Notebooks
- Google Colab
- Kaggle
MiniMax-H3 Turbo-SLA
MiniMax-H3 Turbo-SLA is a 4-step distilled FL2V checkpoint for MiniMax-H3, enhanced with SLA (Sparse–Linear Attention) for more efficient inference.
The model uses an 85% attention sparsity ratio. In our tested LightX2V setup, it delivers approximately 2.5× inference acceleration on an NVIDIA RTX 5090 while retaining competitive visual quality.
This repository contains LoRA weights and requires the original MiniMax-H3 model for inference. Actual performance may vary with resolution, video length, software environment, and hardware configuration.
Demo
Side-by-side comparison between the 30-step MiniMax-H3 base model and the 4-step MiniMax-H3 Turbo-SLA model:
Model Highlights
- 4-step distillation for substantially reduced denoising steps.
- SLA sparse attention with an 85% sparsity ratio.
- Approximately 2.5× inference acceleration on RTX 5090 in our tested LightX2V setup.
- 768p FL2V generation support.
- Native LightX2V and ComfyUI-compatible BF16 checkpoints.
Available Checkpoints
| Checkpoint | Format | Description |
|---|---|---|
minimax_h3_fl2v_turbo_4step_v0.1_768p_sla_bf16.safetensors |
LightX2V | Native BF16 LoRA checkpoint for LightX2V inference. |
minimax_h3_fl2v_turbo_4step_v0.1_768p_sla_comfyui_bf16.safetensors |
ComfyUI | Converted BF16 LoRA checkpoint for ComfyUI workflows. |
LightX2V Inference
For installation and general MiniMax-H3 inference instructions, refer to the LightX2V MiniMax-H3 examples.
Use the following configuration for SLA-enabled inference on an RTX 5090:
minimax_h3_fp8_4step_5090_with_fp8_vae_sla.json
Key SLA-related settings in this configuration include:
{
"attn_type": "dynamic_sparse_attn",
"dynamic_sparse_attn_setting": {
"sparsity_ratio": 0.85,
"operator": "sage2"
},
"video_flow_shift": 6.0,
"audio_flow_shift": 3.0,
"h3_step_update": "training_euler"
}
Use the configuration file linked above as the source of truth for the complete inference setup, including FP8 DiT/VAE options and checkpoint paths.
ComfyUI
For ComfyUI workflows, use:
minimax_h3_fl2v_turbo_4step_v0.1_768p_sla_comfyui_bf16.safetensors
The ComfyUI checkpoint is a converted version of the SLA-enabled 4-step LoRA and is intended for compatible MiniMax-H3 ComfyUI workflows.
Related Projects
Acknowledgements
This work builds on MiniMax-H3, LightX2V, and SLA. We thank the respective authors and contributors for making their work available to the community.
Citation
If you use SLA in your work, please cite:
@article{zhang2025sla,
title={SLA: Beyond Sparsity in Diffusion Transformers via Fine-Tunable Sparse-Linear Attention},
author={Zhang, Jintao and Wang, Haoxu and Jiang, Kai and Yang, Shuo and Zheng, Kaiwen and Xi, Haocheng and Wang, Ziteng and Zhu, Hongzhou and Zhao, Min and Stoica, Ion and others},
journal={arXiv preprint arXiv:2509.24006},
year={2025}
}
License
The adapter weights in this repository are released under the Apache 2.0 License. Use of the MiniMax-H3 base model is also subject to its corresponding license and terms of use.
Model tree for lightx2v/Minimax-h3-Turbo-SLA
Base model
MiniMaxAI/MiniMax-H3