Add UnityVideo 5B model card

README.md

@@ -1,3 +1,82 @@
 ---
 license: apache-2.0
+library_name: unityvideo
+pipeline_tag: text-to-video
+base_model: Wan-AI/Wan2.2-TI2V-5B
+tags:
+  - video-generation
+  - multi-modal
+  - depth-estimation
+  - optical-flow
+  - controllable-generation
 ---
+
+# UnityVideo Wan2.2-TI2V-5B
+
+This repository contains the five-modality, three-task UnityVideo checkpoint
+built on Wan2.2-TI2V-5B. Source code and usage instructions are available at
+[JIA-Lab-research/UnityVideo](https://github.com/JIA-Lab-research/UnityVideo).
+
+## Capabilities
+
+- Modalities: depth, DensePose, optical flow (RAFT), segmentation, skeleton.
+- Tasks: text-to-RGB+modality, video-to-modality, modality-to-video.
+- Architecture: joint RGB/modality self-attention, split text cross-attention,
+  modality identity embeddings, and separate RGB/modality output heads.
+
+## Files
+
+| File | Size | SHA-256 | Use |
+| --- | ---: | --- | --- |
+| `checkpoints/unityvideo_wan22_ti2v_5b_step15000_ema.safetensors` | 10,020,954,352 bytes | `0df3909e312526c46f68097958afa055868f73354fe4276d693f7ebc398e6a39` | Inference |
+| `checkpoints/unityvideo_wan22_ti2v_5b_step15000.safetensors` | 10,020,954,352 bytes | `4ee83a43ffdbaee90e7ff6a25fe108d8d65d255cd0803e3972bbbfb5b01db48c` | Fine-tuning |
+
+The checkpoints contain the full two-stream DiT. Download the VAE, text encoder,
+tokenizer, and base configuration from
+[Wan-AI/Wan2.2-TI2V-5B](https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B).
+
+## Quick start
+
+```bash
+git clone https://github.com/JIA-Lab-research/UnityVideo.git
+cd UnityVideo
+pip install -e .
+
+unityvideo-infer \
+  --task video2flow \
+  --modality depth \
+  --rgb-video input.mp4 \
+  --output depth.mp4
+```
+
+The CLI downloads this checkpoint and the Wan2.2 base model on first use. See
+the GitHub README for all inference modes, local download commands, metadata
+format, and distributed training.
+
+## Training recipe
+
+- Base: Wan2.2-TI2V-5B.
+- Resolution: 256 x 256, 33 frames.
+- Modalities: depth, DensePose, RAFT, segmentation, skeleton.
+- Modality sampling: 0.2, 0.2, 0.2, 0.4, 0.4.
+- Task sampling (`text2all`, `video2flow`, `flow2video`): 0.5, 0.25, 0.25.
+- EMA decay: 0.9999.
+- Effective released step: 15,000.
+
+## Limitations
+
+The released model was evaluated at 256 x 256 with 33 frames. Higher
+resolutions, longer videos, unseen modality encodings, and safety-critical uses
+require independent validation. The historical A14B dual-expert experiments
+used an RGB+depth-only recipe and are not represented by this checkpoint.
+
+## Citation
+
+```bibtex
+@article{huang2025unityvideo,
+  title={UnityVideo: Unified Multi-Modal Multi-Task Learning for Enhancing World-Aware Video Generation},
+  author={Huang, Jiehui and Zhang, Yuechen and He, Xu and Gao, Yuan and Cen, Zhi and Xia, Bin and Zhou, Yan and Tao, Xin and Wan, Pengfei and Jia, Jiaya},
+  journal={arXiv preprint arXiv:2512.07831},
+  year={2025}
+}
+```