metadata
license: other
library_name: pytorch
pipeline_tag: text-to-video
tags:
- longlive-2.0
- robotics
- autoregressive
LongLive 2.0 Robot-S
Autoregressive robot-video generation weights. S is trained on short sequences of up to a few seconds. S and M describe training sequence length, not model size.
Download
hf download Efficient-Large-Model/LongLive2.0-Robot-S --local-dir ./weights/LongLive2.0-Robot-S
Inference
Instantiate the matching LongLive 2.0 generator, then load the FP32 weights:
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. Optimizer and other training states are not included.
Original upstream model terms continue to apply.