Papers › Controllable Text-to-Image Generation
Controllable Text-to-Image Generation
Bowen Li, Xiaojuan Qi, Thomas Lukasiewicz, Philip H. S. Torr
In this paper, we propose a novel controllable text-to-image generative adversarial network (ControlGAN), which can effectively synthesise high-quality images and also control parts of the image generation according to natural language descriptions. To achieve this, we introduce a word-level spatial and channel-wise attention-driven generator that can disentangle different visual attributes, and allow the model to focus on generating and manipulating subregions corresponding to the most relevant words. Also, a word-level discriminator is proposed to provide fine-grained supervisory feedback by correlating words with image regions, facilitating training an effective generator which is able to manipulate specific visual attributes without affecting the generation of other content. Furthermore, perceptual loss is adopted to reduce the randomness involved in the image generation, and to encourage the generator to manipulate specific attributes required in the modified text. Extensive experiments on benchmark datasets demonstrate that our method outperforms existing state of the art, and is able to effectively manipulate synthetic images using natural language descriptions. Code is available at https://github.com/mrlibw/ControlGAN.
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Tasks
1 archive task tag without a task page not shown.
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Text-to-Image Generation | CUB | Attention-driven Generator (perceptual loss) | Inception score | 4.58 | #17 of 20 | Archive leaderboard | report |
| Text-to-Image Generation | Multi-Modal-CelebA-HQ | ControlGAN | Acc | 14.6 | #7 of 10 | Archive leaderboard | report |
| Text-to-Image Generation | Multi-Modal-CelebA-HQ | ControlGAN | FID | 116.32 | #7 of 10 | Archive leaderboard | report |
| Text-to-Image Generation | Multi-Modal-CelebA-HQ | ControlGAN | LPIPS | 0.522 | #7 of 10 | Archive leaderboard | report |
| Text-to-Image Generation | Multi-Modal-CelebA-HQ | ControlGAN | Real | 13.1 | #7 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.
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