# CogView2
## Model description

**CogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformers**

- [Paper](https://arxiv.org/abs/2204.14217)
- [GitHub Repo](https://github.com/THUDM/CogView2)

### Abstract

The development of the transformer-based text-to-image models are impeded by its slow generation and complexity for high-resolution images. In this work, we put forward a solution based on hierarchical transformers and local parallel auto-regressive generation. We pretrain a 6B-parameter transformer with a simple and flexible self-supervised task, Cross-modal general language model (CogLM), and finetune it for fast super-resolution. The new text-to-image system, CogView2, shows very competitive generation compared to concurrent state-of-the-art DALL-E-2, and naturally supports interactive text-guided editing on images.

## BibTeX entry and citation info
```bibtex
@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}
}
```

{"license": "apache-2.0"}
arxiv:2204.14217 license:apache-2.0 has_space region:us
TAGS
#arxiv-2204.14217 #license-apache-2.0 #has_space #region-us 

# CogView2
## Model description

CogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformers

- Paper
- GitHub Repo

### Abstract

The development of the transformer-based text-to-image models are impeded by its slow generation and complexity for high-resolution images. In this work, we put forward a solution based on hierarchical transformers and local parallel auto-regressive generation. We pretrain a 6B-parameter transformer with a simple and flexible self-supervised task, Cross-modal general language model (CogLM), and finetune it for fast super-resolution. The new text-to-image system, CogView2, shows very competitive generation compared to concurrent state-of-the-art DALL-E-2, and naturally supports interactive text-guided editing on images.

## BibTeX entry and citation info


# CogView2 ## Model description

CogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformers

- Paper
- GitHub Repo ### Abstract

The development of the transformer-based text-to-image models are impeded by its slow generation and complexity for high-resolution images. In this work, we put forward a solution based on hierarchical transformers and local parallel auto-regressive generation. We pretrain a 6B-parameter transformer with a simple and flexible self-supervised task, Cross-modal general language model (CogLM), and finetune it for fast super-resolution. The new text-to-image system, CogView2, shows very competitive generation compared to concurrent state-of-the-art DALL-E-2, and naturally supports interactive text-guided editing on images. ## BibTeX entry and citation info
TAGS
#arxiv-2204.14217 #license-apache-2.0 #has_space #region-us 
 # CogView2 ## Model description

CogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformers

- Paper
- GitHub Repo ### Abstract

The development of the transformer-based text-to-image models are impeded by its slow generation and complexity for high-resolution images. In this work, we put forward a solution based on hierarchical transformers and local parallel auto-regressive generation. We pretrain a 6B-parameter transformer with a simple and flexible self-supervised task, Cross-modal general language model (CogLM), and finetune it for fast super-resolution. The new text-to-image system, CogView2, shows very competitive generation compared to concurrent state-of-the-art DALL-E-2, and naturally supports interactive text-guided editing on images. ## BibTeX entry and citation info
passage: TAGS
#arxiv-2204.14217 #license-apache-2.0 #has_space #region-us 
# CogView2## Model description

CogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformers

- Paper
- GitHub Repo### Abstract

The development of the transformer-based text-to-image models are impeded by its slow generation and complexity for high-resolution images. In this work, we put forward a solution based on hierarchical transformers and local parallel auto-regressive generation. We pretrain a 6B-parameter transformer with a simple and flexible self-supervised task, Cross-modal general language model (CogLM), and finetune it for fast super-resolution. The new text-to-image system, CogView2, shows very competitive generation compared to concurrent state-of-the-art DALL-E-2, and naturally supports interactive text-guided editing on images.## BibTeX entry and citation info