vllm-omni/recipes/Tencent/HunyuanVideo-1.5.md

HunyuanVideo 1.5

Text-to-video with the HunyuanVideo-1.5 Diffusers checkpoints

Summary

• Vendor: Tencent (community Diffusers ports)
• Model: hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_t2v
• Task: Text-to-video
• Mode: Offline inference
• Maintainer: Community

When to use this recipe

Use this recipe to generate a short clip with HunyuanVideo-1.5 on a memory- constrained card, trading output size for capacity.

References

• Model card: https://huggingface.co/hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_t2v
• Shared runnable example: examples/offline_inference/text_to_video/text_to_video.py
• CPU offload guide: docs/user_guide/diffusion/cpu_offload.md

Hardware Support

This recipe documents one validated Intel XPU configuration. Extend it with more hardware sections as community validation lands.

XPU

1x Intel Arc Pro B70 (32 GB)

BF16 weights with model-level CPU offload at a reduced 320x240, 17-frame shape. The 480p and 720p T2V and I2V checkpoints all use the same command; only --model changes.

Environment

• OS: Linux
• Python: 3.10+
• torch: 2.13.0+xpu
• vLLM: 0.29.0 (98dff2a8)
• vLLM-Omni: main at 4c7a98c2

Command

python examples/offline_inference/text_to_video/text_to_video.py \
  --model hunyuanvideo-community/HunyuanVideo-1.5-Diffusers-720p_t2v \
  --prompt "A serene lakeside sunrise with mist over the water." \
  --height 240 --width 320 --num-frames 17 \
  --num-inference-steps 50 \
  --guidance-scale 6.0 \
  --enable-cpu-offload \
  --vae-use-tiling \
  --vae-use-slicing \
  --output hunyuan_video_15_output.mp4

Verification

Confirm hunyuan_video_15_output.mp4 decodes and that the sampled frames match the prompt.

Notes

• Memory usage: 18.3 GiB loaded, 25.7 GiB peak, about 39 s per clip.
• Known limitations: the native 720p geometry does not fit a 32 GB card with CPU offload. Online serving is out of scope for this profile.