---
title: Monetico
canonical_url: "https://www.modelscope.cn/models/AI-ModelScope/Monetico"
md_url: "https://www.modelscope.cn/models/AI-ModelScope/Monetico.md"
repository: AI-ModelScope/Monetico
last_updated: 2024-11-02
license: apache-2.0
pipeline_tag: text-to-image-synthesis
tasks:
  - text-to-image-synthesis
parameters: 1.5B
tensor_type:
  - F32
library_name:
  - safetensors
  - diffusers
  - pytorch
frameworks:
  - pytorch
downloads: 1917
stars: 1
tags:
  - Non-Autoregressive
---

# Monetico

> Monetico - AI-ModelScope 在 ModelScope 开源的模型。Monetico: An Efficient Reproduction of Meissonic for Text-to-Image Synthesis

AI-ModelScope/Monetico 是 ModelScope 魔搭社区上的 1.5B 参数text-to-image-synthesis模型，采用 apache-2.0 许可。

- **Repository**: AI-ModelScope/Monetico
- **License**: apache-2.0
- **Tasks**: text-to-image-synthesis
- **Parameters**: 1.5B
- **Tags**: Non-Autoregressive
- **Downloads**: 1917
- **Stars**: 1
- **Last updated**: 2024-11-02

Source: https://www.modelscope.cn/models/AI-ModelScope/Monetico

---

# Monetico: An Efficient Reproduction of Meissonic for Text-to-Image Synthesis

## Introduction
Similar to Meissonic, Monetico is a non-autoregressive masked image modeling text-to-image synthesis model capable of generating high-resolution images. It is designed to run efficiently on consumer-grade graphics cards.

Monetico is an efficient reproduction of Meissonic. Trained on 8 H100 GPUs for approximately one week, Monetico can generate high-quality 512x512 images that are comparable to those produced by Meissonic and SDXL.

Monetico was developed by Collov Labs. We extend our gratitude to @MeissonFlow and @viiika for their valuable advice on efficient training.

## Usage

For detailed usage instructions, please refer to [GitHub repository](https://github.com/viiika/Meissonic).

## Citation
If you find this work helpful, please consider citing:
```bibtex
@article{bai2024meissonic,
  title={Meissonic: Revitalizing Masked Generative Transformers for Efficient High-Resolution Text-to-Image Synthesis},
  author={Bai, Jinbin and Ye, Tian and Chow, Wei and Song, Enxin and Chen, Qing-Guo and Li, Xiangtai and Dong, Zhen and Zhu, Lei and Yan, Shuicheng},
  journal={arXiv preprint arXiv:2410.08261},
  year={2024}
}
```