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README.md
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[](https://www.hiascend.com/)
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[](https://github.com/huggingface/diffusers)
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[](https://github.com/vllm-project/vllm-omni)
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[](https://github.com/sgl-project/sglang)
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* β³β³β³ Release the [Technical Report](https://github.com/PKU-YuanGroup/Helios-Page/blob/main/helios_technical_report.pdf) on arXiv.
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* `[2025.03.04]` π Day-0 support for [Ascend-NPU](https://www.hiascend.com)οΌwith sincere gratitude to the Ascend Team for their support.
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* `[2025.03.04]` π Day-0 support for [Diffusers](https://github.com/huggingface/diffusers)οΌwith special thanks to the HuggingFace Team for their support.
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* `[2025.03.04]` π Day-0 support for [vLLM-Omni](https://github.com/vllm-project/vllm-omni)οΌwith heartfelt gratitude to the vLLM Team for their support.
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* `[2025.03.04]` π Day-0 support for [SGLang-Diffusion](https://github.com/sgl-project/sglang)οΌwith huge thanks to the SGLang Team for their support.
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* `[2025.03.04]` π₯ We've released the training/inference code and weights of **Helios-Base**, **Helios-Mid** and **Helios-Distilled**.
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## π₯ Friendly Links
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If your work has improved **Helios** and you would like more people to see it, please inform us.
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| Helios-Base | π€ [Huggingface](https://huggingface.co/BestWishYsh/Helios-Base) π€ [ModelScope](https://modelscope.cn/datasets/BestWishYSH/Helios-Base) | T2V β
I2V β
V2V β
Interactive β
| Best Quality, with v-prediction, standard CFG and custom HeliosScheduler. |
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| Helios-Mid | π€ [Huggingface](https://huggingface.co/BestWishYsh/Helios-Mid) π€ [ModelScope](https://modelscope.cn/datasets/BestWishYSH/Helios-Mid) | T2V β
I2V β
V2V β
Interactive β
| Intermediate Ckpt, with v-prediction, CFG-Zero* and custom HeliosScheduler. |
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| Helios-Distilled | π€ [Huggingface](https://huggingface.co/BestWishYsh/Helios-Distilled) π€ [ModelScope](https://modelscope.cn/datasets/BestWishYSH/Helios-Distilled) | T2V β
I2V β
V2V β
Interactive β
| Best Efficiency, with x0-prediction and custom HeliosDMDScheduler. |
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> π‘Note:
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> * All three models share the same architecture, but Helios-Mid and Helios-Distilled use a more aggressive multi-scale sampling pipeline to achieve better efficiency.
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> * Helios-Mid is an intermediate checkpoint generated in the process of distilling Helios-Base into Helios-Distilled, and may not meet expected quality.
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modelscope download BestWishYSH/Helios-Distilled --local-dir BestWishYSH/HeliosDistillede
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```
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## π Inference
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pip install git+https://github.com/sgl-project/sglang.git
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```
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For example, let's take Helios-
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<details>
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<summary>Click to expand the code</summary>
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```bash
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```
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</details>
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```BibTeX
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@article{helios,
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title={Helios: Real-Time Long Video Generation
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author={Yuan, Shenghai and Yin, Yuanyang and Li, Zongjian and Huang, Xinwei and Yang, Xiao and Yuan, Li},
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journal={arXiv preprint arXiv:2603.xxxxx},
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year={2026}
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}
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```
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[](https://www.hiascend.com/)
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[](https://github.com/huggingface/diffusers)
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[](https://github.com/vllm-project/vllm-omni/pull/1604)
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[](https://github.com/sgl-project/sglang/pull/19782)
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* β³β³β³ Release the [Technical Report](https://github.com/PKU-YuanGroup/Helios-Page/blob/main/helios_technical_report.pdf) on arXiv.
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* `[2025.03.04]` π Day-0 support for [Ascend-NPU](https://www.hiascend.com)οΌwith sincere gratitude to the Ascend Team for their support.
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* `[2025.03.04]` π Day-0 support for [Diffusers](https://github.com/huggingface/diffusers)οΌwith special thanks to the HuggingFace Team for their support.
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+
* `[2025.03.04]` π Day-0 support for [vLLM-Omni](https://github.com/vllm-project/vllm-omni/pull/1604)οΌwith heartfelt gratitude to the vLLM Team for their support.
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* `[2025.03.04]` π Day-0 support for [SGLang-Diffusion](https://github.com/sgl-project/sglang/pull/19782)οΌwith huge thanks to the SGLang Team for their support.
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* `[2025.03.04]` π₯ We've released the training/inference code and weights of **Helios-Base**, **Helios-Mid** and **Helios-Distilled**.
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## π₯ Friendly Links
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If your work has improved **Helios** and you would like more people to see it, please inform us.
|
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| Helios-Base | π€ [Huggingface](https://huggingface.co/BestWishYsh/Helios-Base) π€ [ModelScope](https://modelscope.cn/datasets/BestWishYSH/Helios-Base) | T2V β
I2V β
V2V β
Interactive β
| Best Quality, with v-prediction, standard CFG and custom HeliosScheduler. |
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| Helios-Mid | π€ [Huggingface](https://huggingface.co/BestWishYsh/Helios-Mid) π€ [ModelScope](https://modelscope.cn/datasets/BestWishYSH/Helios-Mid) | T2V β
I2V β
V2V β
Interactive β
| Intermediate Ckpt, with v-prediction, CFG-Zero* and custom HeliosScheduler. |
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| Helios-Distilled | π€ [Huggingface](https://huggingface.co/BestWishYsh/Helios-Distilled) π€ [ModelScope](https://modelscope.cn/datasets/BestWishYSH/Helios-Distilled) | T2V β
I2V β
V2V β
Interactive β
| Best Efficiency, with x0-prediction and custom HeliosDMDScheduler. |
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> π‘Note:
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> * All three models share the same architecture, but Helios-Mid and Helios-Distilled use a more aggressive multi-scale sampling pipeline to achieve better efficiency.
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> * Helios-Mid is an intermediate checkpoint generated in the process of distilling Helios-Base into Helios-Distilled, and may not meet expected quality.
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modelscope download BestWishYSH/Helios-Distilled --local-dir BestWishYSH/HeliosDistillede
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```
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## π Inference
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pip install git+https://github.com/sgl-project/sglang.git
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```
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For example, let's take Helios-Base. **(Native Support)**
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<details>
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<summary>Click to expand the code</summary>
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```bash
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sglang generate \
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--model-path BestWishYsh/Helios-Base \
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--prompt "A cat walking on the beach at sunset, cinematic lighting, high quality" \
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--negative-prompt "Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards" \
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--height 384 \
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--width 640 \
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--num-frames 33 \
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--num-inference-steps 50 \
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--guidance-scale 5.0
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```
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</details>
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For example, let's take Helios-Base. **(Diffusers Backend)**
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<details>
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<summary>Click to expand the code</summary>
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```bash
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sglang generate \
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--model-path BestWishYsh/Helios-Base \
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--prompt "A cat walking on the beach at sunset, cinematic lighting, high quality" \
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--negative-prompt "Bright tones, overexposed, static, blurred details, subtitles, style, works, paintings, images, static, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, misshapen limbs, fused fingers, still picture, messy background, three legs, many people in the background, walking backwards" \
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--height 384 \
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--width 640 \
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--num-frames 33 \
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--num-inference-steps 50 \
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--guidance-scale 5.0 \
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--backend diffusers
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```
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</details>
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```BibTeX
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@article{helios,
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title={Helios: Real Real-Time Long Video Generation Model},
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author={Yuan, Shenghai and Yin, Yuanyang and Li, Zongjian and Huang, Xinwei and Yang, Xiao and Yuan, Li},
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journal={arXiv preprint arXiv:2603.xxxxx},
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year={2026}
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}
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```
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