Skip to main content

​
1. Model Introduction

LongLive 2.0 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 and LongLive 2.0 GitHub. The model weights are released under the NVIDIA Open Model License.

​
2. SGLang-diffusion Installation

Please refer to the official SGLang-diffusion installation guide for installation instructions.

​
3. Deployment

Command
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:
Command
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:
Command
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
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:
Command
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.