---
license: other
library_name: pytorch
pipeline_tag: image-to-video
tags: [longlive-2.0, robotics, autoregressive]
---

# LongLive 2.0 Robot-L

Autoregressive robot-video generation weights.
L continues from Robot-M with long-sequence training; this is the final Stage-L checkpoint at step 3,972.
S, M and L describe training sequence length, not model size.

## Download

```bash
hf download Efficient-Large-Model/LongLive2.0-Robot-L --local-dir ./weights/LongLive2.0-Robot-L
```

## Inference

Instantiate the matching LongLive 2.0 generator, then load the FP32 weights:

```python
import torch

state_dict = torch.load("model.pt", map_location="cpu", mmap=True, weights_only=True)
model.load_state_dict(state_dict, strict=True)
```

Run the loading example from the downloaded directory. The matching inference
runtime, VAE and text encoder are required separately; this is not a standalone
pipeline. This release contains the online generator only (825 FP32 tensors,
4,999,787,712 parameters). Optimizer, EMA and other training states are not included.

All 20 Stage-L fixed-video validation gates were accepted; the final panel contains
24 videos evaluated with 50 sampling steps, CFG 3.0 and base seed 20260711.
These checks are release validation, not a robot-control safety or benchmark claim.
See the [Long-WAM Stage 1 guide](https://github.com/NVlabs/LongLive/tree/main/Long-WAM/stage1)
for the matching runtime and inference instructions.

Original upstream model terms continue to apply.