ProCreate, Dont Reproduce! Propulsive Energy Diffusion for Creative Generation

Jack Lu, Ryan Teehan, Mengye Ren

ProCreate, Dont Reproduce! Propulsive Energy Diffusion for Creative Generation: 11 upvotes on Hugging Face Daily Papers, #9 of 14 papers on 2024-08-06. Day-by-day upvote history.

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.

Paper page on Hugging Face · arXiv

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