README.md

---
license: cc-by-nc-sa-4.0
library_name: diffusers
pipeline_tag: image-to-video
base_model: Wan-AI/Wan2.2-TI2V-5B
base_model_relation: adapter
tags:
  - video generation
  - identity-preserving
  - lora
  - wan2.2
  - image-to-video
  - text-to-video
  - cvpr2026
---

<h1 align='center'>AnyID: Ultra-Fidelity Universal Identity-Preserving Video Generation from Any Visual References</h1>

<p align='center'>
    ☆ CVPR 2026 ☆
</p>

<p align="center">
    <strong><a href="https://scholar.google.com/citations?hl=en&user=zQnTBEoAAAAJ">Jiahao Wang</a><sup>1</sup></strong>
    ·
    <strong><a href="https://scholar.google.com/citations?user=73JaDUQAAAAJ&hl=en&oi=ao">Hualian Sheng</a><sup>2</sup></strong>
    ·
    <strong><a href="https://scholar.google.com/citations?user=LMVeRVAAAAAJ&hl=en&oi=ao">Sijia Cai</a><sup>2,&dagger;</sup></strong>
    ·
    <strong><a href="https://scholar.google.com/citations?hl=zh-CN&user=WwKt4BEAAAAJ">Yuxiao Yang</a><sup>3</sup></strong>
    ·
    <strong><a href="https://gr.xjtu.edu.cn/web/zhangwzh123/">Weizhan Zhang</a><sup>1,*</sup></strong>
    ·
    <strong><a href="https://gr.xjtu.edu.cn/web/yancaixia">Caixia Yan</a><sup>1</sup></strong>
    ·
    <strong><a href="https://scholar.google.com/citations?user=VQp_ye4AAAAJ&hl=zh-CN&oi=ao">Bing Deng</a><sup>2</sup></strong>
    ·
    <strong><a href="https://scholar.google.com/citations?user=T9AzhwcAAAAJ&hl=zh-CN&oi=ao">Jieping Ye</a><sup>2</sup></strong>
    <br>
    <sup>1</sup>Xi'an Jiaotong University &nbsp;&nbsp;&nbsp;&nbsp;
    <sup>2</sup>Alibaba Cloud &nbsp;&nbsp;&nbsp;&nbsp;
    <sup>3</sup>Tsinghua University
    <br>
    <sup>*</sup>Corresponding Author &nbsp;&nbsp;&nbsp;&nbsp; <sup>&dagger;</sup>Project Lead
    <br>
    <br>
        <a href="https://arxiv.org/abs/2603.25188"><img src='https://img.shields.io/badge/+-arXiv-b31b1b' alt='Paper PDF'></a>
        <a href="https://johnneywang.github.io/AnyID-webpage"><img src='https://img.shields.io/badge/+-Project_Page-blue' alt='Project Page'></a>
        <a href="https://github.com/JoHnneyWang/AnyID"><img src='https://img.shields.io/badge/+-GitHub-black'></a>
        <a href="https://huggingface.co/JonneyWang/AnyID"><img src='https://img.shields.io/badge/+-HuggingFace-yellow'></a>
    <br>
    <br>
    Please give us a star⭐ on GitHub if you like our work.
</p>

## 📝 Intro

**AnyID** is an **ultra-fidelity, universal identity-preserving video generation framework** that overcomes the limitations of single-reference methods by leveraging **multiple free-form inputs**, including faces, portraits, and video clips. By unifying heterogeneous references through a **scalable omni-referenced architecture** and employing a **primary-referenced generation paradigm with differential prompts**, AnyID achieves **precise attribute-level controllability** while maintaining **robust identity consistency** across varying angles and expressions. Further refined by **human-centric reinforcement learning**, the framework delivers superior visual quality, ensuring both **high-fidelity character preservation** and **accurate adherence to user instructions**. Hope you have fun with this demo! 🎬

![](./assets/teaser.jpg)

> ⚠️ **Preview release.** The weights hosted in this repository are a **preview version**,
> released for research and evaluation purposes. They may be updated or replaced with an
> improved version in the future.

## 🧩 Model Card

This repository hosts the **AnyID LoRA**, a lightweight adapter trained on top of the
**[Wan2.2 TI2V-5B](https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B)** video diffusion backbone.
It does **not** contain the base model — download Wan2.2 TI2V-5B separately (see [Quick Start](#-quick-start)).

| Item            | Value                                                              |
|-----------------|--------------------------------------------------------------------|
| Version         | **Preview** (subject to future updates)                            |
| Model type      | LoRA adapter (PEFT)                                                |
| Base model      | [Wan-AI/Wan2.2-TI2V-5B](https://huggingface.co/Wan-AI/Wan2.2-TI2V-5B) |
| Rank / Alpha    | 256 / 256 (scaling = 1.0)                                          |
| Target modules  | All `nn.Linear` layers (self-/cross-attention `q,k,v,o`, FFN, embeddings, head) |
| Precision       | bf16                                                               |
| Task            | Identity-preserving text/image/video → video generation            |

### Files in this repository

| File                     | Description                                  |
|--------------------------|----------------------------------------------|
| `anyid_lora.safetensors` | AnyID LoRA weights                           |
| `lora_config.json`       | PEFT LoRA config (rank / alpha / targets)    |

## 🚀 Quick Start

The inference code lives in the [GitHub repository](https://github.com/JoHnneyWang/AnyID).

**1. Get the code and set up the environment** (NVIDIA A100, CUDA 12.1):

```bash
git clone https://github.com/JoHnneyWang/AnyID.git
cd AnyID

conda create -n anyid python=3.10 -y
conda activate anyid
pip install torch torchvision --index-url https://download.pytorch.org/whl/cu121
pip install flash-attn --no-build-isolation
pip install -r requirements.txt
```

**2. Download the weights** — the base Wan2.2 TI2V-5B and this AnyID LoRA:

```bash
# Base model (T5 + VAE + base DiT)
huggingface-cli download Wan-AI/Wan2.2-TI2V-5B --local-dir checkpoints/Wan2.2-TI2V-5B
# AnyID LoRA (this repository)
huggingface-cli download JonneyWang/AnyID --local-dir checkpoints/AnyID
```

**3. Merge the LoRA into the DiT.** Injecting the LoRA dynamically takes minutes to load,
so pre-merge it into the DiT once (the merged weights are written into the LoRA directory):

```bash
PYTHONPATH=. python trainer/models/wan22/merge_lora.py \
    --ckpt_dir checkpoints/Wan2.2-TI2V-5B \
    --lora_path checkpoints/AnyID
```

**4. Run inference** — T5/VAE are loaded from the base folder, the merged DiT from the AnyID folder:

```bash
CKPT_DIR=checkpoints/Wan2.2-TI2V-5B \
MERGED_DIT=checkpoints/AnyID \
bash scripts/infer.sh
```

Or call the generator directly:

```bash
PYTHONPATH=. torchrun --nproc_per_node=1 \
    trainer/models/wan22/generate.py \
    --task ti2v-5B --size 1280*704 \
    --ckpt_dir checkpoints/Wan2.2-TI2V-5B \
    --merged_dit checkpoints/AnyID \
    --prompt prompts/example.json \
    --frame_num 121 \
    --sample_guide_scale 4.0 --sample_shift 5.0 \
    --convert_model_dtype --offload_model False \
    --out_dir outputs
```

## 📥 Input Format

The prompt file is a JSON list of items. Each item carries a **differential prompt** and a single
unified **`ref`** field — a path or a list of paths — that works for both image and video
references. The **first** reference is the primary reference; the rest are auxiliary. Use **either**
up to **five images** **or** **one video** (they cannot be mixed). See
[`prompts/example.json`](https://github.com/JoHnneyWang/AnyID/blob/main/prompts/example.json) in the
GitHub repo for a runnable example.

```json
[
  {
    "ref": ["person_primary.png", "person_auxiliary.png"],
    "prompt": "..."
  }
]
```

## 📚 Citation

If you find AnyID useful in your research, please consider citing:

```bibtex
@InProceedings{Wang_2026_CVPR,
    author    = {Wang, Jiahao and Sheng, Hualian and Cai, Sijia and Yang, Yuxiao and Zhang, Weizhan and Yan, Caixia and Deng, Bing and Ye, Jieping},
    title     = {AnyID: Ultra-Fidelity Universal Identity-Preserving Video Generation from Any Visual References},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2026},
    pages     = {12808-12817}
}
```

## 🙏 Acknowledgements

We would like to express our sincere thank to [Wan Team](https://github.com/Wan-Video) for their support.

## 📄 License

This model is released under the [CC BY-NC-SA 4.0](LICENSE) license. You are free to share and
adapt this work for **non-commercial** purposes only, with proper attribution and under the same
license.