metadata
license: apache-2.0
base_model: Wan-AI/Wan2.2-TI2V-5B-Diffusers
tags:
- video-generation
- world-model
- uav
- lora
- pose-conditioned
SkyWander — UAV World Model Checkpoints
Checkpoints for SkyWander, a pose-conditioned UAV video world model built on Wan2.2-TI2V-5B (Apache-2.0). These weights are only meaningful together with the SkyWander inference code (private research repository; to be released with the paper).
Contents
| Path | Size | What |
|---|---|---|
teachers/full_r80_ws72_ddp4/step=20000.ckpt |
2.5G | teacher LoRA r80, sim-only, GemDepth depth condition (baseline) |
teachers/mixdepth_r80_ws72_ddp4/step=20000.ckpt |
2.5G | teacher LoRA, per-sample mixed depth sources (GemDepth + VGGT conf30/50/70) |
teachers/mixreal_r80_ws72_ddp4/step=4000.ckpt |
2.5G | teacher LoRA, sim+real continuation of the mixdepth teacher |
merged/uav_teacher_merged_step20000.pt |
9.4G | mixdepth teacher LoRA folded into the Wan2.2 base + pose modules (full transformer state dict) |
student_stage1/stage1_student_step10000_weights_bf16.pt |
10.7G | causal-AR student, full-finetune SFT step 10000, weights-only bf16 export (no optimizer state — inference/eval only) |
prompt_embeds/prompt_embeds_umt5xxl_len512_*.pt |
4×8M | cached UMT5-XXL prompt embeddings (skip loading the text encoder at inference) |
pva_planner/ckpt_final.pt + norm_stats.json |
233M | PVA→pose flow-matching planner (15M DiT-1D, EMA included) + required normalization stats |
Teacher LoRA checkpoints are PyTorch-Lightning monoliths and require the Wan2.2 base
model at load time. The student export contains state_dict with transformer.* and
pose-module keys and loads into the SkyWander causal transformer with strict prefix
matching.
Trained on the NUS Hopper cluster (H100/H200), 2026.