ProCreate, Don't Reproduce! Propulsive Energy Diffusion for Creative Generation
<|reference_start|>ProCreate, Don't Reproduce! Propulsive Energy Diffusion for Creative Generation: In this paper, we propose ProCreate, a simple and easy-to-implement method to improve sample diversity and creativity of diffusion-based image generative models and to prevent training data reproduction. ProCreate operates on a set of reference images and actively propels the generated image embedding away from the reference embeddings during the generation process. We propose FSCG-8 (Few-Shot Creative Generation 8), a few-shot creative generation dataset on eight different categories -- encompassing different concepts, styles, and settings -- in which ProCreate achieves the highest sample diversity and fidelity. Furthermore, we show that ProCreate is effective at preventing replicating training data in a large-scale evaluation using training text prompts. Code and FSCG-8 are available at https://github.com/Agentic-Learning-AI-Lab/procreate-diffusion-public. The project page is available at https://procreate-diffusion.github.io.<|reference_end|>
@article{lu2024procreate,,
    title={ProCreate, Don't Reproduce! Propulsive Energy Diffusion for Creative
  Generation},
    author={Jack Lu and Ryan Teehan and Mengye Ren},
    journal={arXiv preprint arXiv:2408.02226},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.02226},
    primaryClass={cs.CV}
}