GRN

GRN: Generative Refinement Networks

This is the official implementation of the paper Generative Refinement Networks for Visual Synthesis. Neither diffusion nor autoregressive — GRN is a third way. 🧠 Refines globally like an artist. ⚡ Generates adaptively by complexity. 🏆 New SOTA across image & video. The visual generation paradigm just got rewritten.

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📋 Table of Contents

• 🌟 Introduction
• � Demo
• � Open-Source Plan
• 🛠️ Installation
• 🖼️ Class-to-Image
  • Dataset
  • Training
  • Evaluation
• 📧 Contact
• 🤗 Acknowledgements
• 📝 Citation

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🚀 Demo

Try our interactive Text-to-Image demo on 🤗 Hugging Face Space:

GRN T2I Demo

Experience the power of Generative Refinement Networks firsthand by generating images from text prompts directly in your browser!

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🌟 Introduction

Diffusion models dominate visual generation but they allocate uniform computational effort to samples with varying levels of complexity. Autoregressive (AR) models are complexity-aware, as evidenced by their variable likelihoods, but suffer from lossy tokenization and error accumulation.

We introduce Generative Refinement Networks (GRN), a new visual synthesis paradigm that addresses these issues:

• Near-lossless tokenization via Hierarchical Binary Quantization (HBQ)
• Global refinement mechanism that progressively perfects outputs like a human artist
• Entropy-guided sampling for complexity-aware, adaptive-step generation

GRN achieves state-of-the-art results on ImageNet reconstruction and class-conditional generation, and scales effectively to text-to-image and text-to-video tasks.

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<figure align="center"> <figcaption><strong><em>Generative Refinement Framework</em></strong></figcaption> <img src="demo/framework.jpg" width="100%" alt="Framework"> </figure> <p align="center"> Starting from a random token map, GRN randomly selects more predictions at each step and refines all input tokens. For example, compared to the second step, the third step filled six new tokens (<span style="color: rgb(220, 120, 117);">pink</span>), kept two tokens (<span style="color: rgb(88, 160, 227);">blue</span>), erased two tokens (<span style="color: rgb(240, 180, 40);">yellow</span>), and left six tokens blank (<span style="color: rgb(128, 138, 151);">gray</span>). </p> <figure align="center"> <figcaption><strong><em>Class-to-Image Examples</em></strong></figcaption> <img src="demo/c2i_examples.jpg" width="100%" alt="Class-to-Image Examples"> </figure> <figure align="center"> <figcaption><strong><em>Text-to-Image Examples</em></strong></figcaption> <img src="demo/t2i_examples.jpg" width="100%" alt="Text-to-Image Examples"> </figure> <figure align="center"> <figcaption><strong><em>Text-to-Video Examples</em></strong></figcaption> <video src="https://github.com/user-attachments/assets/8ce16018-0f86-4dfc-b51b-69075e8d0f15" width="100%" controls autoplay muted loop playsinline></video> </figure>

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Open-Source Plan

GRN adopts a minimalist and self-contained design. This implementation is in PyTorch + GPU.

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🛠️ Installation

Step 1: Clone the repository

git clone https://github.com/MGenAI/GRN
cd GRN

Step 2: Create conda environment

A suitable conda environment named GRN can be created and activated with:

conda env create -f environment.yaml
conda activate GRN

Troubleshooting

If you get undefined symbol: iJIT_NotifyEvent when importing torch, simply:

pip uninstall torch
pip install torch==2.5.1 --index-url https://download.pytorch.org/whl/cu124

Check this issue for more details.

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🖼️ Class-to-Image

Dataset

Download ImageNet dataset, and place it in your IMAGENET_PATH.

Training

All training scripts are located in scripts/c2i/. We suggest using 8x80GB GPUs for most models.

Evaluation

PyTorch pre-trained models are available here.

All evaluation scripts are located in scripts/c2i/. We suggest using 8x80GB vRAM GPUs.

We use torch-fidelity to evaluate FID and IS against a reference image folder or statistics. We use the JiT's pre-computed reference stats under grn/utils_c2i/fid_stats.

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📧 Contact

If you are interested in scaling GRN for image generation / image editing / video generation / video editing / unified model directions, please feel free to reach out!

📧 Email: [email protected]

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🤗 Acknowledgements

• Thanks to JiT, Infinity and InfinityStar for their wonderful work and codebase!

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📝 Citation

If you find our work useful, please consider citing:

@misc{han2026grn,
      title={Generative Refinement Networks for Visual Synthesis}, 
      author={Jian Han and Jinlai Liu and Jiahuan Wang and Bingyue Peng and Zehuan Yuan},
      year={2026},
      eprint={2604.13030},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2604.13030}, 
}