[Deprecated] V0 Inference

The following commands and APIs are deprecated but still supported until V1’s API can completely replace all the features in this page.

Inference StepVideo with Sliding Tile Attention

First, download the model:

python scripts/huggingface/download_hf.py --repo_id=stepfun-ai/stepvideo-t2v --local_dir=data/stepvideo-t2v --repo_type=model

Use the following scripts to run inference for StepVideo. When using STA for inference, the generated videos will have dimensions of 204×768×768 (currently, this is the only supported shape).

sh scripts/inference/inference_stepvideo_STA.sh # Inference stepvideo with STA
sh scripts/inference/inference_stepvideo.sh # Inference original stepvideo

Inference HunyuanVideo with Sliding Tile Attention

First, download the model:

python scripts/huggingface/download_hf.py --repo_id=FastVideo/hunyuan --local_dir=data/hunyuan --repo_type=model

We provide two examples in the following script to run inference with STA + TeaCache and STA only.

sh scripts/inference/inference_hunyuan_STA.sh

Video Demos using STA + Teacache

Visit our demo website to explore our complete collection of examples. We shorten a single video generation process from 945s to 317s on H100.

Inference FastHunyuan on single RTX4090

We now support NF4 and LLM-INT8 quantized inference using BitsAndBytes for FastHunyuan. With NF4 quantization, inference can be performed on a single RTX 4090 GPU, requiring just 20GB of VRAM.

# Download the model weight
python scripts/huggingface/download_hf.py --repo_id=FastVideo/FastHunyuan-diffusers --local_dir=data/FastHunyuan-diffusers --repo_type=model
# CLI inference
bash scripts/inference/inference_hunyuan_hf_quantization.sh

For more information about the VRAM requirements for BitsAndBytes quantization, please refer to the table below (timing measured on an H100 GPU):

Configuration	Memory to Init Transformer	Peak Memory After Init Pipeline (Denoise)	Diffusion Time	End-to-End Time
BF16 + Pipeline CPU Offload	23.883G	33.744G	81s	121.5s
INT8 + Pipeline CPU Offload	13.911G	27.979G	88s	116.7s
NF4 + Pipeline CPU Offload	9.453G	19.26G	78s	114.5s

For improved quality in generated videos, we recommend using a GPU with 80GB of memory to run the BF16 model with the original Hunyuan pipeline. To execute the inference, use the following section:

FastHunyuan

# Download the model weight
python scripts/huggingface/download_hf.py --repo_id=FastVideo/FastHunyuan --local_dir=data/FastHunyuan --repo_type=model
# CLI inference
bash scripts/inference/inference_hunyuan.sh

You can also inference FastHunyuan in the official Hunyuan github.

FastMochi

# Download the model weight
python scripts/huggingface/download_hf.py --repo_id=FastVideo/FastMochi-diffusers --local_dir=data/FastMochi-diffusers --repo_type=model
# CLI inference
bash scripts/inference/inference_mochi_sp.sh