https://github.com/matrix-alpha/Accountable-Textual-Visual-Chat
The official repository for Accountable Textual-Visual Chat Learns to Reject Human Instructions in Image Re-creation.
https://github.com/matrix-alpha/Accountable-Textual-Visual-Chat
Last synced: about 1 month ago
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The official repository for Accountable Textual-Visual Chat Learns to Reject Human Instructions in Image Re-creation.
- Host: GitHub
- URL: https://github.com/matrix-alpha/Accountable-Textual-Visual-Chat
- Owner: matrix-alpha
- Created: 2023-06-13T10:59:31.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2023-06-17T15:02:05.000Z (almost 2 years ago)
- Last Synced: 2024-08-03T01:25:58.832Z (10 months ago)
- Language: Shell
- Homepage:
- Size: 669 KB
- Stars: 7
- Watchers: 1
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-llm-and-aigc - matrix-alpha/Accountable-Textual-Visual-Chat - alpha/Accountable-Textual-Visual-Chat?style=social"/> : "Accountable Textual-Visual Chat Learns to Reject Human Instructions in Image Re-creation". (**[arXiv 2023](https://arxiv.org/abs/2303.05983)**). [https://matrix-alpha.github.io/](https://matrix-alpha.github.io/) (Datasets / 数据集)
- awesome-llm-and-aigc - matrix-alpha/Accountable-Textual-Visual-Chat - alpha/Accountable-Textual-Visual-Chat?style=social"/> : "Accountable Textual-Visual Chat Learns to Reject Human Instructions in Image Re-creation". (**[arXiv 2023](https://arxiv.org/abs/2303.05983)**). [https://matrix-alpha.github.io/](https://matrix-alpha.github.io/) (Datasets / 数据集)
README
## Accountable Textual-Visual Chat Learns to Reject Human Instructions in Image Re-creation
The *official* repository for [Accountable Textual-Visual Chat Learns to Reject
Human Instructions in Image Re-creation](https://arxiv.org/pdf/2303.05983.pdf).
### Requirements
- Python 3.8
- matplotlib == 3.1.1
- numpy == 1.19.4
- pandas == 0.25.1
- scikit_learn == 0.21.3
- torch == 1.8.0
### Installation
We provide an environment file; ``environment.yml`` containing the required dependencies. Clone the repo and run the following command in the root of this directory:
```
conda env create -f environment.yml
```
### Dataset
Please refer to [DOWNLOAD.md](data/DOWNLOAD.md) for dataset preparation.
### Pretrained Models
Please refer to [pretrained-models](pretrained-models/README.md) to download the released models.
### Train
#### Training commands
+ To train the first stage:
```shell
bash dist_train_vae.sh ${DATA_NAME} ${NODES} ${GPUS}
```
+ To train the second stage:
```shell
bash dist_train_atvc.sh ${VAE_PATH} ${DATA_NAME} ${NODES} ${GPUS}
```
#### Arguments
+ `${VAE_PATH}`: path of pretrained vae model.
+ `${DATA_NAME}`: dataset for training, e.g. `CLEVR-ATVC`, `Fruit-ATVC`.
+ `${NODES}`: number of node.
+ `${GPUS}`: number of gpus for each node.
### Test
#### Testing commands
+ To test image reconstruction ability of the first stage:
```shell
bash gen_vae.sh ${GPU} ${VAE_PATH} ${IMAGE_PATH}
```
+ To test atvc final model:
```shell
bash gen_atvc.sh ${GPU} ${ATVC_PATH} ${TEXT_QUERY} ${IMAGE_PATH}
```
#### Arguments
+ `${GPU}`: id of one gpu, e.g. `0`.
+ `${VAE_PATH}`: path of pretrained vae model.
+ `${IMAGE_PATH}`: image path for reconstrction, e.g. `input.png`.
+ `${ATVC_PATH}`: path of pretrained atvc model.
+ `${TEXT_QUERY}`: text-based query, e.g. `"Please put the small blue cube on top of the small yellow cylinder."`.
### License
`ATVC` is released under the [Apache 2.0 license](LICENSE).
### Citation
If you find this code useful for your research, please cite our paper
```
@article{zhang2023accountable,
title={Accountable Textual-Visual Chat Learns to Reject Human Instructions in Image Re-creation},
author={Zhang, Zhiwei and Liu, Yuliang},
journal={arXiv preprint arXiv:2303.05983},
year={2023}
}
```
## Acknowledgement
Our code is learned from [DALLE-pytorch](https://github.com/lucidrains/DALLE-pytorch) and [CLIP](https://github.com/openai/CLIP). We would like to thank all the people who help label text-image pairs and participate in human evaluation experiments. We hope our explorations and findings contribute valuable insights regarding the accountability of textual-visual generative models.
## Contact
This project is developed by Zhiwei Zhang ([@zzw-zwzhang](https://github.com/zzw-zwzhang)) and Yuliang Liu ([@Yuliang-Liu](https://github.com/Yuliang-Liu)).