Tencent-Hunyuan/HY-WU

Tencent-Hunyuan/HY-WU

Description: HY-WU (Part I): An Extensible Functional Neural Memory Framework and An Instantiation in Text-Guided Image Editing

Language: Python

License: NOASSERTION

Stars: 295

Forks: 13

Open issues: 2

Created: 2026-03-05T08:36:50Z

Pushed: 2026-03-18T09:24:59Z

Default branch: main

Fork: no

Archived: no

README:

🔥 News

• March 10, 2026: We are hiring Interns and Full-time Employees! 🚀 (Focus: Parameter Generation. Drop your CV via [victorkwang@global.tencent.com](mailto:victorkwang@global.tencent.com))
• March 6, 2026: 🎉 [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:

HY-WU introduces a lightweight “neural memory” for AI. It 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

git clone https://github.com/Tencent-Hunyuan/HY-WU.git
cd HY-WU

📥 Install dependencies

pip install -r requirements.txt

🔥 Play with the code

Directly run infer.py

python infer.py

Or use the code below:

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.

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:

@article{wu2026hy-wu,
title={HY-WU (Part I): An Extensible Functional Neural Memory Framework and An Instantiation in Text-Guided Image Editing},
author={Tencent HY Team, Mengxuan Wu, Xuanlei Zhao, Ziqiao Wang, Ruicheng Feng, Atlas Wang, Qinglin Lu, and Kai Wang},
journal={arXiv preprint arXiv:2603.07236},
year={2026}
}