Papers › Swinv2-Imagen: Hierarchical Vision Transformer Diffusion Models for Text-to-Image Generation
Swinv2-Imagen: Hierarchical Vision Transformer Diffusion Models for Text-to-Image Generation
Ruijun Li, Weihua Li, Yi Yang, Hanyu Wei, Jianhua Jiang, Quan Bai
Recently, diffusion models have been proven to perform remarkably well in text-to-image synthesis tasks in a number of studies, immediately presenting new study opportunities for image generation. Google's Imagen follows this research trend and outperforms DALLE2 as the best model for text-to-image generation. However, Imagen merely uses a T5 language model for text processing, which cannot ensure learning the semantic information of the text. Furthermore, the Efficient UNet leveraged by Imagen is not the best choice in image processing. To address these issues, we propose the Swinv2-Imagen, a novel text-to-image diffusion model based on a Hierarchical Visual Transformer and a Scene Graph incorporating a semantic layout. In the proposed model, the feature vectors of entities and relationships are extracted and involved in the diffusion model, effectively improving the quality of generated images. On top of that, we also introduce a Swin-Transformer-based UNet architecture, called Swinv2-Unet, which can address the problems stemming from the CNN convolution operations. Extensive experiments are conducted to evaluate the performance of the proposed model by using three real-world datasets, i.e., MSCOCO, CUB and MM-CelebA-HQ. The experimental results show that the proposed Swinv2-Imagen model outperforms several popular state-of-the-art methods.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Text-to-Image Generation | COCO (Common Objects in Context) | Swinv2-Imagen | FID | 7.21 | #14 of 69 | Archive leaderboard | report |
| Text-to-Image Generation | COCO (Common Objects in Context) | Swinv2-Imagen | Inception score | 31.46 | #14 of 69 | Archive leaderboard | report |
| Text-to-Image Generation | CUB | Swinv2-Imagen | FID | 9.78 | #3 of 20 | Archive leaderboard | report |
| Text-to-Image Generation | CUB | Swinv2-Imagen | Inception score | 8.44 | #3 of 20 | Archive leaderboard | report |
| Text-to-Image Generation | Multi-Modal-CelebA-HQ | Swinv2-Imagen | FID | 10.31 | #1 of 10 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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