vllm-omni/recipes/Helios/Helios.md

Helios for video generation

Summary

• Vendor: Helios
• Model: BestWishYsh/Helios-Base, BestWishYsh/Helios-Mid, BestWishYsh/Helios-Distilled
• Task: Text-to-video generation
• Mode: Offline inference with the shared text_to_video example
• Maintainer: Community

When to use this recipe

Use this recipe when you want a known-good starting point for running Helios video generation with vLLM-Omni. The concrete command below focuses on BestWishYsh/Helios-Base text-to-video generation on one NVIDIA H20 GPU via the shared text_to_video.py example. The same example also supports Helios-Mid and Helios-Distilled through the generic --extra-body flag (see below). Image-to-video and video-to-video require image/video conditioning inputs and are not covered by the text-to-video example.

References

• Upstream repository: https://github.com/PKU-YuanGroup/Helios
• Model weights: https://huggingface.co/BestWishYsh/Helios-Base
• Related example under examples/: examples/offline_inference/text_to_video/text_to_video.md

Hardware Support

GPU

1x NVIDIA H20

Environment

• OS: Ubuntu Linux x86_64
• Python: 3.12.12
• Driver / runtime: NVIDIA driver 580.126.20, CUDA 13.0
• Hardware: 1x NVIDIA H20 GPU from an 8x H20 host
• vLLM version: 0.19.0
• vLLM-Omni version or commit: a3903810

Command

Run the baseline Helios-Base text-to-video example from the repository root:

cd examples/offline_inference/text_to_video

python text_to_video.py \
  --model BestWishYsh/Helios-Base \
  --prompt "A dynamic time-lapse video showing the rapidly moving scenery from the window of a speeding train." \
  --guidance-scale 5.0 \
  --output helios_t2v_base.mp4

To use cache-dit acceleration, run on a vLLM-Omni checkout that includes Helios cache-dit support and enable the cache backend:

cd examples/offline_inference/text_to_video

python text_to_video.py \
  --cache-backend cache_dit \
  --enable-cache-dit-summary \
  --model BestWishYsh/Helios-Base \
  --prompt "A dynamic time-lapse video showing the rapidly moving scenery from the window of a speeding train." \
  --guidance-scale 5.0 \
  --output helios_t2v_base.mp4

Verification

The script should print the resolved generation configuration, total generation time, and output path. A successful run ends with output similar to:

Total generation time: <seconds> seconds (<milliseconds> ms)
Saved generated video to helios_t2v_base.mp4

Important flags and deploy config

• --model BestWishYsh/Helios-Base selects the base Helios checkpoint used by this recipe.
• --guidance-scale 5.0 matches the Helios-Base recommendation. Use --guidance-scale 1.0 for Helios-Distilled.
• --cache-backend cache_dit enables the cache-dit acceleration path.
• --enable-cache-dit-summary prints cache-dit summary information after diffusion forward passes.
• No separate deploy config is required for this offline recipe; the shared text_to_video.py example configures the pipeline through its arguments.
• Helios-specific knobs (declared in vllm_omni/model_extras/helios.py) are passed via the generic --extra-body JSON flag:
  • Helios-Mid: --extra-body '{"is_enable_stage2": true, "pyramid_num_inference_steps_list": [20, 20, 20], "use_cfg_zero_star": true, "use_zero_init": true, "zero_steps": 1}'
  • Helios-Distilled: --extra-body '{"is_enable_stage2": true, "pyramid_num_inference_steps_list": [2, 2, 2], "is_amplify_first_chunk": true}'

Known limitations

• Helios generates video in 33-frame chunks. For best performance, set --num-frames to a multiple of 33; non-multiple values are rounded up to the nearest multiple of 33.