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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Text-to-Image generation. The repo for NeurIPS 2021 paper "CogView: Mastering Text-to-Image Generation via Transformers".
| Date | Stars |
|---|---|
| 2026-07-24 | 1798 |
| 2026-07-25 | 1798 |
| 2026-07-28 | 1798 |
| 2026-07-30 | 1798 |
| 2026-07-31 | 1799 |
| 2026-08-06 | 1799 |
Today
— stars today
This week
+1 stars this week
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— stars this month
Momentum
0.0
growth rate 0.06%/day
<p align="center">
<img src="assets/logo.png"/>
</p>
<p align="center">
<b>Generate vivid Images for <i>Any</i> (Chinese) text</b>
</p>
**News!** The paper of ImageReward is accepted by NeurIPS 2023!
**News!** The codes of ImageReward ([paper link](https://arxiv.org/abs/2304.05977.pdf)) have been released at https://github.com/THUDM/ImageReward! ImageReward is the first general-purpose text-to-image human preference RM.
**News!** The codes of CogView2 ([paper link](https://arxiv.org/pdf/2105.13290.pdf)) have been released at https://github.com/THUDM/CogView2!
**News!** The [demo](https://agc.platform.baai.ac.cn/CogView/index.html) for a better and faster CogView2 (formal version, March 2022) is available! The lastest model also supports English input, but to translate them into Chinese often could be better.
**News!** The [demo](https://agc.platform.baai.ac.cn/CogView/index.html) for a better and faster CogView2 (new version) is available!
**News!** The paper of CogView is accepted by NeurIPS 2021!
CogView is a pretrained (4B-param) transformer for text-to-image generation in general domain.
* **Read** our paper [CogView: Mastering Text-to-Image Generation via Transformers](https://arxiv.org/pdf/2105.13290.pdf) on ArXiv for a formal introduction. The *PB-relax* and *Sandwich-LN* can also help you train large and deep transformers stably (e.g. eliminating NaN losses).
* **Visit** our demo at [Github Page](https://thudm.github.io/CogView/index.html) or [Wudao](https://wudao.aminer.cn/CogView/)! (Without post-selection or super-resolution, currently only supports simplified Chinese input, but one can translate text from other languages into Chinese for input. Note: *Wudao* provides faster access for users from China mainland.)
* **Download** our pretrained models from [Tsinghua Cloud](https://cloud.tsinghua.edu.cn/f/df21f6d4109b4285bfd9/?dl=1).
* **Cite** our paper if you find our work is helpful~
```
@article{ding2021cogview,
title={CogView: Mastering Text-to-Image Generation via Transformers},
author={Ding, Ming and Yang, Zhuoyi and Hong, Wenyi and Zheng, Wendi and Zhou, Chang and Yin, Da and Lin, Junyang and Zou, Xu and Shao, Zhou and Yang, Hongxia and Tang, Jie},
journal={arXiv preprint arXiv:2105.13290},
year={2021}
```
* **Google Colab** Two contributors successfully setup up CogView on Colab [](https://github.com/THUDM/CogView/issues/10)!
## Getting Started
### Setup
* Hardware: Linux servers with Nvidia V100s or A100s are recommended, but it is also okay to run the pretrained models with smaller `--max-inference-batch-size` or training smaller models on less powerful GPUs.
* Environment (Option 1): Please first install PyTorch (>=1.7.0) and [apex](https://github.com/NVIDIA/apex), and then install other dependencies via `pip install -r requirements.txt`.
* Environment (Option 2): We prepare a docker image in case that you fail to handle the environments. Pull the image, create a (background) container and get into it via:
```
docker pull cogview/cuda111_torch181_deepspeed040
./env/start_docker.sh && docker exec -it bg-cogview bash
cd /root/cogview # in the container
```
### Download
0. Download the image tokenizer `vqvae_hard_biggerset_011.pt` from [BAAI website](https://resource.wudaoai.cn/home?ind=2&name=WuDao%20WenHui&id=1399364355975327744) or Tsinghua Cloud. Place the file under `pretrained/vqvae`.
```
wget 'https://cloud.tsinghua.edu.cn/f/71607a5dca69417baa8c/?dl=1' -O pretrained/vqvae/vqvae_hard_biggerset_011.pt
```
1. Download models from [Project Wudao-Wenhui](https://resource.wudaoai.cn/home?ind=2&name=WuDao%20WenHui&id=1399364355975327744).
| FileName | Discription |
| ---- | ---- |
| cogview-base.tar | The pretrained text-to-image model. |
| cogview-caption.tar | Finetuned image-to-text model, also used for reranking. |
| cogExcerpt of 7,864 characters
Read on GitHub
Sleepy_chord
10
3
Jiazheng Xu · Tsinghua University, KEG Group · China
2
Wenyi Hong · Tsinghua University
1
Somefive · Alibaba Cloud · China
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:3dbc11cf75ba9990, topic:text-to-image, desc:text-to-image, readme:text-to-image
matched fp:3dbc11cf75ba9990, topic:pytorch