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---
license: other
license_name: nvidia-open-model-license
license_link: >-
  https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/
pipeline_tag: text-to-video
tags:
  - text-to-video
  - multi-shot
  - video-generation
  - diffusion
  - long-video
  - longlive2
  - wan2.2
---

<p align="center">
  <img src="logo.png" alt="LongLive2.0 logo" width="100%">
</p>

# LongLive2.0 5B

[![Paper](https://img.shields.io/badge/ArXiv-Paper-brown)](https://arxiv.org/abs/2605.18739)
[![Code](https://img.shields.io/badge/GitHub-Code-blue)](https://github.com/NVlabs/LongLive)
[![Video](https://img.shields.io/badge/YouTube-Video-red)](https://www.youtube.com/watch?v=7oQALy32fiU)
[![Models](https://img.shields.io/badge/Model-BF16-yellow)](https://huggingface.co/Efficient-Large-Model/LongLive-2.0-5B)
[![Models](https://img.shields.io/badge/Model-NVFP4-orange)](https://huggingface.co/Efficient-Large-Model/LongLive-2.0-5B-NVFP4-S4)
[![Demo](https://img.shields.io/badge/Demo-Page-brightgreen)](https://nvlabs.github.io/LongLive/LongLive2/)
[![Docs](https://img.shields.io/badge/Full-Documentation-green)](https://nvlabs.github.io/LongLive/LongLive2/docs/)

This repository hosts temporary LongLive2.0 5B BF16 checkpoints for inference with
the LongLive2.0 release code:

https://github.com/NVlabs/LongLive

The checkpoint package contains two parts:

- **Base generator checkpoint**: the AR-trained Wan2.2-TI2V-5B generator.
- **LoRA checkpoint**: the DMD-distilled few-step LoRA adapter.

LongLive2.0 inference loads the base generator first, applies the LoRA modules,
and then loads the LoRA weights.

## Installation

```bash
git clone https://github.com/wileewang/LongLive2.0.git
cd LongLive2.0

conda create -n longlive2 python=3.10 -y
conda activate longlive2
pip install torch==2.8.0 torchvision==0.23.0 --index-url https://download.pytorch.org/whl/cu128
pip install -r requirements.txt
pip install flash-attn --no-build-isolation
```

The released LongLive2.0 checkpoint is sufficient for standard inference. You
only need to download the original Wan2.2-TI2V-5B components if you want to run
training, initialize from the original Wan weights, or use code paths that
explicitly load the base Wan model files:

```bash
huggingface-cli download Wan-AI/Wan2.2-TI2V-5B \
  --local-dir wan_models/Wan2.2-TI2V-5B
```

Download this checkpoint repository:

```bash
huggingface-cli download Perflow-Shuai/longlive_2.0_5B_tmp_20260507 \
  --local-dir checkpoints/longlive2_5b
```

## Configure Inference

Edit `configs/inference.yaml`:

```yaml
checkpoints:
  generator_ckpt: checkpoints/longlive2_5b/path/to/base_generator.pt
  lora_ckpt: checkpoints/longlive2_5b/path/to/dmd_lora.pt

adapter:
  type: lora
  rank: 128
  alpha: 128
  dropout: 0.0
  verbose: true

data:
  data_path: /path/to/inference_prompts

output_folder: videos/longlive2
num_samples: 1

inference:
  sampling_steps: 4
  sink_size: 8
  guidance_scale: 1.0
  multi_shot_sink: true
  multi_shot_rope_offset: 8
```

Replace the checkpoint filenames above with the actual files in this repository.
If the LoRA checkpoint is not used, remove the `adapter` section and leave
`lora_ckpt` unset.

## Prompt Folder

`data.data_path` is passed to `MultiTextConcatDataset` in `inference.py`. It can
be either:

- a `.txt` file, where each line is one single-shot prompt; or
- a directory of multi-shot prompt folders.

For a directory input, the code supports both of the following layouts. The
direct caption-root layout is the simplest:

```text
inference_prompts/
  robot_lab_demo/
    0.json
    1.json
    2.json
    shot_durations.txt
```

It also supports a dataset root with an outer `caption/` folder:

```text
inference_prompts/
  caption/
    robot_lab_demo/
      0.json
      1.json
      2.json
      shot_durations.txt
```

Each JSON file contains:

```json
{
  "caption": "A compact silver robot with one blue optic explores a clean robotics lab."
}
```

`shot_durations.txt` is optional. If provided, each number is the number of
temporal chunks assigned to the corresponding caption, for example:

```text
2 2 4
```

## Run

Single node, 8 GPUs:

```bash
torchrun --standalone --nnodes=1 --nproc_per_node=8 inference.py \
  --config_path configs/inference.yaml
```

Single GPU:

```bash
python inference.py --config_path configs/inference.yaml
```

Outputs are written to `output_folder`.

## Notes

- The base checkpoint and LoRA checkpoint should be loaded together for the
  few-step DMD model.
- `inference.sampling_steps` controls the number of denoising steps.
- `inference.multi_shot_sink` enables the multi-shot attention sink.
- `inference.multi_shot_rope_offset` controls the multi-shot RoPE offset.
- For NVFP4 inference, use the separate NVFP4 config and setup instructions in
  the LongLive2.0 documentation.

## License/Terms of Use

GOVERNING TERMS: This trial service is governed by the [NVIDIA API Trial Terms of Service](https://assets.ngc.nvidia.com/products/api-catalog/legal/NVIDIA%20API%20Trial%20Terms%20of%20Service.pdf). Use of this model is governed by the [NVIDIA Open Model License Agreement](https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/).

## Citation

```bibtex
@article{longlive_2,
  title={LongLive2.0: An NVFP4 Parallel Infrastructure for Long Video Generation},
  author={Chen, Yukang and Wang, Luozhou and Huang, Wei and Yang, Shuai and Zhang, Bohan and Xiao, Yicheng and Chu, Ruihang and Mao, Weian and Hu, Qixin and Liu, Shaoteng and Zhao, Yuyang and Mao, Huizi and Chen, Ying-Cong and Xie, Enze and Qi, Xiaojuan and Han, Song},
  journal={arXiv preprint arXiv},
  year={2026}
}
```