# HY-WU (Part I): An Extensible Functional Neural Memory Framework and An Instantiation in Text-Guided Image Editing
## 🔥 News
- **March 6, 2025**: 🎉 **[HY-WU](https://github.com/Tencent-Hunyuan/HY-WU)** open source - Inference code and model weights publicly available.
## 🗂️ Contents
- [🔥 News](#-news)
- [📖 Introduction](#-introduction)
- [✨ Key Features](#-key-features)
- [🖼 Showcases](#-showcases)
- [📑 Open-Source Plan](#-open-source-plan)
- [🚀 Usage](#-usage)
- [🧱 Memory Requirement](#-memory-requirement)
- [📊 Evaluation](#-evaluation)
- [📚 Citation](#-citation)
---
## 📖 Introduction
We propose HY-WU: a scalable framework for on-the-fly conditional generation of low-rank (LoRA) updates.
HY-WU synthesizes instance-conditioned adapter weights from hybrid image–instruction representations and injects them into a frozen backbone during the forward pass, producing instance-specific operators without test-time optimization.
## ✨ Key Features
* 🧠 **Functional Neural Memory:**
Introduces a lightweight “neural memory” for AI. Generates conditioned model adapter per request (without finetuning!), enabling instance-level personalization while preserving the base model’s general capability.
* 🏆 **Scalable for Large Models:**
HY-WU remains practical for large foundation models (even at 80B parameters!). With structured parameter tokenization, the method naturally compatible with large-scale architectures.
* 🎨 **Strong Human Preference:**
HY-WU achieves high human preference win-rates against open-source models, exceeds strong closed-source baselines, and remains close to the latest Nano-Banana series.
## 🖼 Showcases
**Showcase 1: Cross-Domain Clothing Fusion**
**Showcase 2: Creative Cosplay and Character Outfit Migration**
**Showcase 3: High-Fidelity Face Identity Transfer**
**Showcase 4: Seamless Outfit Transfer and Virtual Try-on**
**Showcase 5: High-Quality Texture Synthesis**
## 📑 Open-source Plan
- HY-WU
- [x] Inference
- [x] HY-Image-3.0-Instruct's checkpoint
- [ ] Distilled checkpoint
- [ ] Other models' checkpoint
## 🚀 Usage
#### 🏠 Clone the repository
```bash
git clone https://github.com/Tencent-Hunyuan/HY-WU.git
cd HY-WU
```
#### 📥 Install dependencies
```bash
pip install -r requirements.txt
```
#### 🔥 Play with the code
Directly run `infer.py`
```python
python infer.py
```
Or use the code below:
```python
from wu import WUPipeline
base_model_path = "tencent/HunyuanImage-3.0-Instruct"
pg_model_path = "tencent/HY-WU"
pipeline = WUPipeline(
base_model_path=base_model_path,
pg_model_path=pg_model_path,
device_map="auto",
moe_impl="eager",
moe_drop_tokens=False,
)
prompt = "以图1为底图，将图2公仔穿的衣物换到图1人物身上；保持图1人物、姿态和背景不变，自然贴合并融合。"
# prompt_en = Using Figure 1 as the base image, replace the clothing on the character in Figure 1 with the outfit worn by the figurine in Figure 2. Keep the character, pose, and background of Figure 1 unchanged, ensuring the new clothing fits naturally and blends seamlessly.
imgs_input = ["./assets/input_1_1.png", "./assets/input_1_2.png"]
sample = pipeline.generate(prompt=prompt, imgs_input=imgs_input, diff_infer_steps=50, seed=42, verbose=2)
sample.save("./output.png")
```
#### 🎨 Interactive Gradio Demo
Launch an interactive web interface for easy image-to-image generation.
```bash
pip install gradio>=4.21.0
python gradio/app.py
```
> 🌐 **Web Interface:** Open your browser and navigate to `http://localhost:7680` or shared link.
## 🧱 Memory Requirement
| Base model param | HY-WU param | Recommended VRAM |
|--------------------| ----------- | ----------------------- |
| 80B (13B active) | 8B | ≥ 8 × 40 GB or 4 x 80GB |
Notes:
- Multi‑GPU inference is required for the base model.
## 📊 Evaluation
### 👥 **GSB (Human Evaluation)**
HY-WU substantially outperforms leading open-source models, and remain competitive with top-tier closed-source commercial systems.
While Nano Banana 2 and Nano Banana Pro achieve slightly higher overall scores (52.4\% and 53.8\%, respectively), the margin remains modest.
Given that these commercial systems are likely trained with substantially larger-scale backbones and proprietary data, the modest performance gap suggests that our operator-level conditional adaptation remains effective even under more constrained model scale.
## 📚 Citation
If you find HY-WU useful in your research, please cite our work:
```bibtex
@misc{wu2026hy-wu,
author = {Tencent HY Team, Mengxuan Wu, Xuanlei Zhao, Ziqiao Wang, Ruichfeng Feng, Atlas Wang, Qinglin Lu, and Kai Wang},
title = {HY-WU (Part I): An Extensible Functional Neural Memory Framework and An Instantiation in Text-Guided Image Editing},
year = {2026},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/Tencent-Hunyuan/HY-WU}},
note = {Preprint}
}
```
# HY-WU (Part I): An Extensible Functional Neural Memory Framework and An Instantiation in Text-Guided Image Editing
