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https://github.com/THUDM/CogView2
official code repo for paper "CogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformers"
https://github.com/THUDM/CogView2
pretrained-models pytorch text-to-image transformer
Last synced: about 2 months ago
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official code repo for paper "CogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformers"
- Host: GitHub
- URL: https://github.com/THUDM/CogView2
- Owner: THUDM
- License: apache-2.0
- Created: 2022-04-25T10:15:47.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2022-08-03T17:38:41.000Z (over 2 years ago)
- Last Synced: 2024-08-01T13:29:38.350Z (5 months ago)
- Topics: pretrained-models, pytorch, text-to-image, transformer
- Language: Python
- Homepage:
- Size: 148 KB
- Stars: 934
- Watchers: 34
- Forks: 77
- Open Issues: 25
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
Generate vivid Images for Chinese / English text
CogView2 is a hierarchical transformer (6B-9B-9B parameters) for text-to-image generation in general domain. This implementation is based on the [SwissArmyTransformer](https://github.com/THUDM/SwissArmyTransformer) library (v0.2).
* **Read** our paper [CogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformers](https://arxiv.org/abs/2204.14217) on ArXiv for a formal introduction. The *LoPAR* accelarate the generation and *CogLM* enables the model for bidirectional completion.
* **Run** our pretrained models from text-to-image generation or text-guided completion! Please use A100 GPU.
* **Cite** our paper if you find our work is helpful~
```
@article{ding2022cogview2,
title={CogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformers},
author={Ding, Ming and Zheng, Wendi and Hong, Wenyi and Tang, Jie},
journal={arXiv preprint arXiv:2204.14217},
year={2022}
}
```
## Web Demo
- Thank the Huggingface team for integrating CogView2 into [Huggingface Spaces 🤗](https://huggingface.co/spaces) using [Gradio](https://github.com/gradio-app/gradio). Try out the Web Demo: [](https://huggingface.co/spaces/THUDM/CogView2)
- Thank the Replicate team to deploy a web demo! Try at [](https://replicate.com/thudm/cogview2) .
## Getting Started
### Setup
* Hardware: Linux servers with Nvidia 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: install dependencies via `pip install -r requirements.txt`.
* LocalAttention: Make sure you have CUDA installed and compile the local attention kernel.
```shell
git clone https://github.com/Sleepychord/Image-Local-Attention
cd Image-Local-Attention && python setup.py install
```
If you don't install this kernel, you can also run the first stage (20*20 tokens) via `--only-first-stage` for text-to-image generation.
### Download
Our code will automatically download or detect the models into the path defined by envrionment variable `SAT_HOME`. You can download from [here](https://model.baai.ac.cn/model-detail/100041) and place them (folders named `coglm`/`cogview2-dsr`/`cogview2-itersr`) under `SAT_HOME`.
### Text-to-Image Generation
```
./text2image.sh --input-source input.txt
```
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å©`, \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.
* `--inverse-prompt`. Use the perplexity to generate the original text to sort the generated images.
* `--only-first-stage`.
* `--style`. The style of the generated images, choices=['none', 'mainbody', 'photo', 'flat', 'comics', 'oil', 'sketch', 'isometric', 'chinese', 'watercolor']. The default style is `mainbody`, usually an isolated object with white background.
You'd better specify a environment variable `SAT_HOME` to specify the path to store the downloaded model.
Chinese input is usually much better than English input.
### Text-guided Completion
```
./text_guided_completion.sh --input-source input_comp.txt
```
The format of input is `text image_path h0 w0 h1 w1`, where all the separation are **TAB** (**NOT space**). The image at `image_path` will be center-cropped to `480*480` pixels and mask the square from `(h0,w0)`to `(h1,w1)`. These coordinations are range from 0 to 1. The model will fill the square with object described in `text`. Please use a square much **larger than the desired region**.
## Gallery
