Discover THUDM/CogView Open Source project by @THUDM

Generate vivid Images for Any (Chinese) text

News! The paper of ImageReward is accepted by NeurIPS 2023!

News! The codes of ImageReward (paper link) 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) have been released at https://github.com/THUDM/CogView2!

News! The demo 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 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 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 or Wudao! (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.
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 !

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, 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

Download the image tokenizer vqvae_hard_biggerset_011.pt from BAAI website 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

Download models from Project Wudao-Wenhui.

FileName	Discription
cogview-base.tar	The pretrained text-to-image model.
cogview-caption.tar	Finetuned image-to-text model, also used for reranking.
cogview-sr.tar	Finetuned super-resolution model. (warning: it runs slow.)

Uncompress them into pretrained/cogview/. The following command should be modified based on the model name.

tar -xvf cogview-{base, sr, caption}.tar -C pretrained/cogview/

(Only for training tutorial, skip it for inference.) Download a small "bird-and-animal" example dataset from our link at Tsinghua Cloud.

wget https://cloud.tsinghua.edu.cn/f/1e4963ec8ac84941ba68/?dl=1 -O data/bird_animal.bin

Run CogView! (Model Inference)

We encapsulate the generation functions into scripts. See generate_samples.py and arguments.py for details.

Text-to-Image Generation

Write text queries (one per line) into input.txt and run:

./scripts/text2image.sh --debug

The results will in a new folder samples_text2image/.

Arguments useful in inference are mainly:

--input-source [path or "interactive"]. The path of the input file, can also be "interactive", which will launch a CLI.
--output-path [path]. The folder containing the results.
--batch-size [int]. The number of samples will be generated per query.
--max-inference-batch-size [int]. Maximum batch size per forward. Reduce it if OOM.
--debug. Only save concatenated images for all generated samples, and name them by input text and date.
--with-id. When it toggled, you must specify an "id" before each input, e.g. 001\t一个漂亮的女孩, \t denoting TAB (NOT space). It will generate batch-size split images in a folder named "id" for each input. Confict with --debug.
--device [int]. Running on which GPU.

Super-resolution

Run the following script and input text\t{image_path}, where {image_path} means the path of a previously generated image.

./scripts/super_resolution.sh

Note: It is only effective for generated images from our Image Tokenizer (due to the token distribution).

Image-to-Text

The input is "one image path per line", and will print the results to stdout.

./scripts/image2text.sh

Note: Not optimized for this task, so it might not very competitive (but okay). We will consider to release a version funetuning for a longer period on this task in the future. (TODO)

This application only takes file inputs, where each line is {text}\t{image_path1}\t{image_path2}\t{image_path3}.... The output is {output_path}/scores.txt, a line of a list of scores, following a line from inputs.

./scripts/post_selection.sh

Note: In the released codes, for simplicity, we did not expose the raw API , which supports some advanced generation modes, e.g. text and part of image.

Training

Here we use a subset of our dataset from bird-and-animal for tutorial. The binary dataset is generated by our cogdata toolkit. Please wait for a formal release with tutorials of cogdata (although it is available now).

Single Node

After downloading the dataset, directly run

./scripts/pretrain_single_node.sh

Multiple Nodes

If you want to train the models on multiple servers inter-connected by infiniband without a shared file system (you may need pdsh to accelerate this process):

On each server, use git clone to download this repo, and make sure the data (LMDB format) are moved into the data subfolder.
On each server, echo "ip1 ip2 <other IPs>" > ./docker/ip_list.txt, and then start the docker by ./env/start_docker.sh.
Get into the docker on the first node container via docker exec -it bg-cogview bash.
Get into /root/cogview and run ./scripts/pretrain_multiple_nodes.sh. You may need to change the config (especially OPTIONS_NCCL) in the shell script.

See the arguments.py for advanced functions for training. TODO

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