LongCat-Image-Dev

<div align="center"> <img src="assets/longcat-image_logo.svg" width="45%" alt="LongCat-Image" /> </div> <hr> <div align="center" style="line-height: 1;"> <a href='https://arxiv.org/pdf/2512.07584'><img src='https://img.shields.io/badge/Technical-Report-red'></a> <a href='https://github.com/meituan-longcat/LongCat-Image'><img src='https://img.shields.io/badge/GitHub-Code-black'></a> <a href='https://github.com/meituan-longcat/LongCat-Flash-Chat/blob/main/figures/wechat_official_accounts.png'><img src='https://img.shields.io/badge/WeChat-LongCat-brightgreen?logo=wechat&logoColor=white'></a> <a href='https://x.com/Meituan_LongCat'><img src='https://img.shields.io/badge/Twitter-LongCat-white?logo=x&logoColor=white'></a> </div> <div align="center" style="line-height: 1;">

<a href='https://huggingface.co/meituan-longcat/LongCat-Image'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-LongCat--Image-blue'></a> <a href='https://huggingface.co/meituan-longcat/LongCat-Image-Dev'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-LongCat--Image--Dev-blue'></a> <a href='https://huggingface.co/meituan-longcat/LongCat-Image-Edit'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-LongCat--Image--Edit-blue'></a>

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Introduction

LongCat-Image-Dev is a development variant of LongCat-Image, representing a mid-training checkpoint that is released to facilitate downstream development by the community, such as secondary fine-tuning via SFT, LoRA, and other customization methods.

<div align="center"> <img src="assets/model_struct.jpg" width="90%" alt="LongCat-Image Model Architecture" /> </div>

Key Features

• 🔧 True Developer-Ready Foundation: Unlike typical release-the-final-model-only approaches, we provide the Dev—a high-plasticity, unconstrained state that avoids RL-induced rigidity. This enables seamless fine-tuning without fighting against over-aligned parameter spaces.

• 🛠️ Full-Stack Training Framework: We ship production-ready code for SFT, LoRA fine-tuning, DPO/GRPO/MPO alignment, and specialized Edit training. Every stage from pre-training data curation to reward model integration is reproducible, empowering researchers to build on our exact pipeline rather than reverse-engineering it.