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README.md

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+---
+license: mit
+---
+# GRN: Generative Refinement Networks
+
+[![arXiv](https://img.shields.io/badge/arXiv%20paper-2604.13030-b31b1b.svg)](https://arxiv.org/abs/2604.13030)
+[![Hugging Face Space](https://img.shields.io/badge/🤗%20Hugging%20Face-Space-yellow.svg)](https://huggingface.co/spaces/hanjian/GRN)
+[![License](https://img.shields.io/badge/License-MIT-blue.svg)](LICENSE)
+[![GitHub stars](https://img.shields.io/github/stars/MGenAI/GRN?style=social)](https://github.com/MGenAI/GRN)
+
+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.
+
+---
+
+## 📋 Table of Contents
+
+- [🌟 Introduction](#-introduction)
+- [� Demo](#-demo)
+- [� Open-Source Plan](#-open-source-plan)
+- [🛠️ Installation](#️-installation)
+- [🖼️ Class-to-Image](#️-class-to-image)
+  - [Dataset](#dataset)
+  - [Training](#training)
+  - [Evaluation](#evaluation)
+- [📧 Contact](#-contact)
+- [🤗 Acknowledgements](#-acknowledgements)
+- [📝 Citation](#-citation)
+
+---
+
+## 🚀 Demo
+
+Try our interactive Text-to-Image demo on 🤗 Hugging Face Space:
+
+**[GRN T2I Demo](https://huggingface.co/spaces/hanjian/GRN)**
+
+Experience the power of Generative Refinement Networks firsthand by generating images from text prompts directly in your browser!
+
+---
+
+## 🌟 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.
+
+---
+
+<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>
+
+---
+
+##  Open-Source Plan
+
+GRN adopts a minimalist and self-contained design. This implementation is in PyTorch + GPU.
+
+| Task | Checkpoints | Inference Code | Training Code |
+|------|:-----------:|:--------------:|:-------------:|
+| T2V  |     ⬜      |       ⬜        |      ✅       |
+| T2I  |     ⬜      |       ⬜        |      ✅       |
+| C2I  |     ⬜      |       ✅        |      ✅       |
+
+---
+
+## 🛠️ Installation
+
+### Step 1: Clone the repository
+```bash
+git clone https://github.com/MGenAI/GRN
+cd GRN
+```
+
+### Step 2: Create conda environment
+A suitable [conda](https://conda.io/) environment named `GRN` can be created and activated with:
+```bash
+conda env create -f environment.yaml
+conda activate GRN
+```
+
+### Troubleshooting
+If you get `undefined symbol: iJIT_NotifyEvent` when importing `torch`, simply:
+```bash
+pip uninstall torch
+pip install torch==2.5.1 --index-url https://download.pytorch.org/whl/cu124
+```
+Check this [issue](https://github.com/conda/conda/issues/13812#issuecomment-2071445372) for more details.
+
+---
+
+## 🖼️ Class-to-Image
+
+### Dataset
+Download [ImageNet](http://image-net.org/download) 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.
+
+| Model | Training Script | GPUs Required |
+|-------|:-------------:|:-------------:|
+| GRN_ind_B | `bash scripts/c2i/train_GRN_ind_B.sh` | 8x80GB |
+| GRN_bit_B | `bash scripts/c2i/train_GRN_bit_B.sh` | 8x80GB |
+| GRN_ind_L | `bash scripts/c2i/train_GRN_ind_L.sh` | 8x80GB |
+| GRN_ind_H | `bash scripts/c2i/train_GRN_ind_H.sh` | 16x80GB |
+| GRN_ind_G | `bash scripts/c2i/train_GRN_ind_G.sh` | 32x80GB |
+
+### Evaluation
+
+PyTorch pre-trained models are available [here]().
+
+All evaluation scripts are located in `scripts/c2i/`. We suggest using 8x80GB vRAM GPUs.
+
+| Model | Evaluation Script |
+|-------|:--------------:|
+| GRN_ind_B | `bash scripts/c2i/eval_GRN_ind_B.sh` |
+| GRN_bit_B | `bash scripts/c2i/eval_GRN_bit_B.sh` |
+| GRN_ind_L | `bash scripts/c2i/eval_GRN_ind_L.sh` |
+| GRN_ind_H | `bash scripts/c2i/eval_GRN_ind_H.sh` |
+| GRN_ind_G | `bash scripts/c2i/eval_GRN_ind_G.sh` |
+
+We use [torch-fidelity](https://github.com/LTH14/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`.
+
+---
+
+## 📧 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:** [hanjian.thu123@bytedance.com](mailto:hanjian.thu123@bytedance.com)
+
+---
+
+## 🤗 Acknowledgements
+
+- Thanks to [JiT](https://github.com/LTH14/JiT), [Infinity](https://github.com/FoundationVision/Infinity) and [InfinityStar](https://github.com/FoundationVision/InfinityStar) for their wonderful work and codebase!
+
+---
+
+## 📝 Citation
+
+If you find our work useful, please consider citing:
+
+```bibtex
+@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}, 
+}
+```