metadata
license: other
license_name: base-model-terms
pipeline_tag: image-to-video
tags:
- world-model
- video-generation
- camera-control
- reinforcement-learning
- lora
base_model:
- nvidia/Lyra-2.0
- robbyant/lingbot-world-v2-14b-causal-fast
- Drexubery/UniView
datasets:
- ziqima/TrajectoryBench
LoGo checkpoints
The LoRA adapter each model was post-trained to with the LoGo reward.
| file | model | base weights |
|---|---|---|
logo-lyra2-lora.pt |
Lyra-2 (use DMD scheduler) | nvidia/Lyra-2.0 |
logo-lingbot-world-v2-lora.pt |
LingBot-World 2.0 | robbyant/lingbot-world-v2-14b-causal-fast |
logo-uniworld-view-lora.safetensors |
UniWorld-View | Drexubery/UniView + Wan-AI/Wan2.1-VACE-14B-diffusers + CausVid LoRA |
Usage, with the code at https://github.com/ziqi-ma/logo.
# Lyra-2
torchrun --standalone --nproc_per_node=8 -m lyra_2._src.rl.inference.evaluate \
--adapter logo-lyra2-lora.pt --scenes-root <scene dir> --scenes "<ids>"
# LingBot-World 2.0
python -m wan.rl.inference.gen_scenes --input_base <scene root> --videos_base <out> \
--lora_path logo-lingbot-world-v2-lora.pt
# UniWorld-View
python -m rl.inference.eval_gen --ws_prefix <out> \
--rl-ckpt logo-uniworld-view-lora.safetensors --pose-scale 1.5
Evaluation on TrajectoryBench, including the per-group generation settings, is described in
eval/README.md in that repository.