> ## Documentation Index
> Fetch the complete documentation index at: https://docs.sglang.io/llms.txt
> Use this file to discover all available pages before exploring further.

# LongLive 2.0

> Serve LongLive 2.0 distilled text-to-video and image-to-video models with SGLang-diffusion.

## 1. Model Introduction

[LongLive 2.0](https://nvlabs.github.io/LongLive/LongLive2/) is a distilled few-step text-to-video and image-to-video model from NVIDIA, built on Wan2.2-TI2V-5B. SGLang serves the Diffusers-format conversion for single-prompt and multi-shot video generation.

For more details, check the [LongLive 2.0 paper](https://arxiv.org/abs/2605.18739) and [LongLive 2.0 GitHub](https://github.com/NVlabs/LongLive). The model weights are released under the NVIDIA Open Model License.

## 2. SGLang-diffusion Installation

Please refer to the [official SGLang-diffusion installation guide](/docs/sglang-diffusion/installation) for installation instructions.

## 3. Deployment

```bash Command theme={null}
sglang serve --model-path Rabinovich/LongLive-2.0-5B-Diffusers
```

If the GPU runs out of memory, move the text encoder, VAE, and DiT to CPU between stages:

```bash Command theme={null}
sglang serve \
  --model-path Rabinovich/LongLive-2.0-5B-Diffusers \
  --dit-cpu-offload \
  --text-encoder-cpu-offload \
  --vae-cpu-offload
```

`Rabinovich/LongLive-2.0-5B-Diffusers` is the Diffusers-format conversion of the official `Efficient-Large-Model/LongLive-2.0-5B` weights.

## 4. Generation

### 4.1 Single prompt

Generate one clip without starting a server:

```bash Command theme={null}
sglang generate \
  --model-path Rabinovich/LongLive-2.0-5B-Diffusers \
  --prompt "A quiet street at dusk" \
  --num-frames 61 \
  --save-output \
  --output-path outputs
```

61 frames is 16 latent frames, which is two causal blocks of 8.

### 4.2 Multi-shot long video

Multi-shot prompts are sampling parameters, so pass them through the Python API:

```python Python theme={null}
from sglang import DiffGenerator

gen = DiffGenerator.from_pretrained("Rabinovich/LongLive-2.0-5B-Diffusers")
result = gen.generate(sampling_params_kwargs={
    "shot_prompts": [
        "A husky walks down a sunlit hallway.",
        "The husky turns and looks at the camera.",
        "Two dogs play together on a carpet.",
    ],
    "chunks_per_shot": 4,
    "num_frames": 381,  # 3 shots x 4 chunks x 8 = 96 latent frames -> 381 frames
    "scene_cut_prefix": "The scene transitions. ",
    "multi_shot_sink": True,
    "multi_shot_rope_offset": 8.0,
    "save_output": True,
    "output_path": "outputs",
})
```

Each shot runs for `chunks_per_shot` causal blocks before the next prompt is used. The multi-shot defaults mirror the original LongLive prompt-block settings.

### 4.3 Key parameters

These are SGLang request parameters. Original LongLive configs use latent-frame `num_output_frames`; SGLang exposes output-video `num_frames`.

* `num_frames`: 61 in the examples. This maps to 16 latent frames, while the original release config defaults to 128 latent frames.
* `num_inference_steps`: 4, matching original `sampling_steps`.
* `guidance_scale`: 1.0, matching the original inference config.
* `height` / `width`: 704 / 1280 by default, matching original latent H/W 44 / 80 with 16x spatial compression.
* `shot_prompts`, `chunks_per_shot`, `scene_cut_prefix`, `multi_shot_sink`, and `multi_shot_rope_offset`: SGLang request fields for the original prompt-block and multi-shot behavior.

### 4.4 Image-to-video

Pass a first frame with `--image-path` to condition the clip on an image:

```bash Command theme={null}
sglang generate \
  --model-path Rabinovich/LongLive-2.0-5B-Diffusers \
  --prompt "A quiet street at dusk" \
  --image-path first_frame.png \
  --num-frames 61 \
  --save-output \
  --output-path outputs
```

The image is used as the first-frame condition.

## 5. Notes

* `num_frames` must map to a whole number of causal blocks. The latent frame count is `(num_frames - 1) / 4 + 1` and must be divisible by 8. For example, 61, 125, and 189 frames give 16, 32, and 48 latent frames.
* SGLang supports T2V sizes 1280x704, 704x1280, 832x480, and 480x832.
* I2V request images follow the Wan TI2V preprocessing path in SGLang. This is different from the original LongLive dataset resize path.
* For multi-shot runs, set `num_frames` to match `len(shot_prompts) * chunks_per_shot * 8` latent frames, that is `num_frames = (len(shot_prompts) * chunks_per_shot * 8 - 1) * 4 + 1`.