CreatCtrl: Controllable Creative Graphic Design Generation from References

Creativity is essential in graphic design. Despite recent advances in graphic design generation, controlling the creativity of generated designs remains unexplored. In this paper, we investigate the problem of creativity control for graphic design generation, and propose a model, CreatCtrl, which learns to elevate the creativity of reference designs in a controllable manner. Our model takes as input a textual design intention, a reference design in raster format, and a continuous creativity parameter, and generates more creative, layered graphic designs. Our model enables users to control the creativity degree of the generated designs by adjusting the creativity parameter in a continuous way, which allows them to explore a wide range of creative variants of the reference to find design inspiration. To train our model, we propose a dataset construction method to synthesize training tuples, each consisting of a reference design, its creative variant, and a parameter representing the creativity gap between them, a reliability weight of the creativity parameter. Our experiments demonstrate that our method outperforms baselines in design quality and creativity controllability, showing the effectiveness and potential of our creativity‑controllable generation paradigm for real‑world design applications.