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, image-to-video, and video-to-video generation
• Mode: Offline inference with the bundled Helios 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. The same example script also supports Helios-Mid, Helios-Distilled, image-to-video, and video-to-video; see the linked example docs for those model-specific flags.

References

• Upstream repository: https://github.com/PKU-YuanGroup/Helios
• Model weights: https://huggingface.co/BestWishYsh/Helios-Base
• Upstream or canonical docs: docs/user_guide/examples/offline_inference/helios.md
• Related example under examples/: examples/offline_inference/helios/README.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/helios

python end2end.py \
  --model BestWishYsh/Helios-Base \
  --sample-type t2v \
  --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/helios

python end2end.py \
  --cache-backend cache_dit \
  --enable-cache-dit-summary \
  --model BestWishYsh/Helios-Base \
  --sample-type t2v \
  --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 stage configs

• --model BestWishYsh/Helios-Base selects the base Helios checkpoint used by this recipe.
• --sample-type t2v selects text-to-video generation. Use i2v with --image-path for image-to-video, or v2v with --video-path for video-to-video.
• --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 stage config is required for this offline recipe; the bundled Helios example configures the pipeline through end2end.py arguments.
• For Helios-Mid, add --is-enable-stage2, --pyramid-num-inference-steps-list 20 20 20, --use-cfg-zero-star, --use-zero-init, and --zero-steps 1.
• For Helios-Distilled, add --is-enable-stage2, --pyramid-num-inference-steps-list 2 2 2, and --is-amplify-first-chunk.

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.