Text-to-Image
Diffusers
Safetensors
English
Pipeline
Non-Autoregressive
Masked-Generative-Transformer
Instructions to use MeissonFlow/Meissonic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use MeissonFlow/Meissonic with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("MeissonFlow/Meissonic", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- Draw Things
- DiffusionBee
| pipeline_tag: text-to-image | |
| license: apache-2.0 | |
| tags: | |
| - Non-Autoregressive | |
| - Masked-Generative-Transformer | |
| language: | |
| - en | |
| # Meissonic: Revitalizing Masked Generative Transformers for Efficient High-Resolution Text-to-Image Synthesis | |
| [Paper](https://arxiv.org/abs/2410.08261) | [Model](https://huggingface.co/MeissonFlow/Meissonic) | [Code](https://github.com/viiika/Meissonic) | [Demo](https://huggingface.co/spaces/MeissonFlow/meissonic) | |
|  | |
| ## Introduction | |
| Meissonic is a non-autoregressive mask image modeling text-to-image synthesis model that can generate high-resolution images. It is designed to run on consumer graphics cards. | |
| ## Usage | |
| Please refer to [github link](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} | |
| } | |
| ``` |