---
language: en
license: mit
library_name: diffusers
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
datasets: yuntian-deng/im2latex-100k
metrics: []
---

# latex2im_ss_finetunegptneo

## Model description

Details of this model can be found in [our paper on markup-to-image generation](https://arxiv.org/pdf/2210.05147.pdf). Our code is built on top of HuggingFace [diffusers](https://github.com/huggingface/diffusers) and [transformers](https://github.com/huggingface/transformers).

## Online Demo: [https://huggingface.co/spaces/yuntian-deng/latex2im](https://huggingface.co/spaces/yuntian-deng/latex2im).

## Model Details
- **Developed by:** Yuntian Deng, Noriyuki Kojima, Alexander M. Rush
- **Model type:** Diffusion-based text-to-image generation model
- **Language(s):** English
- **License:** [MIT](https://github.com/da03/markup2im/blob/main/LICENSE).
- **Model Description:** This is a model that can be used to generate math formula images based on LaTeX prompts.
- **Resources for more information:** [GitHub Repository](https://github.com/da03/markup2im), [Paper](https://arxiv.org/abs/2210.05147).
- **Cite as:**

      @inproceedings{
        deng2023markuptoimage,
        title={Markup-to-Image Diffusion Models with Scheduled Sampling},
        author={Yuntian Deng and Noriyuki Kojima and Alexander M Rush},
        booktitle={The Eleventh International Conference on Learning Representations },
        year={2023},
        url={https://openreview.net/forum?id=81VJDmOE2ol}
      }