---
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](https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B-Diffusers) (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.