ProCreate, Don't Reproduce! Propulsive Energy Diffusion for Creative Generation (2408.02226v2)

Abstract: 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.

• The paper presents ProCreate, an innovative energy-based method that guides latent representations to boost image diversity and prevent training data replication.
• It evaluates ProCreate on the FSCG-8 dataset using metrics like FID, KID, and Vendi Score to validate its performance in few-shot creative generation.
• The approach empowers ethical AI design by generating high-quality, diverse images and expanding the practical applications of diffusion-based models.

ProCreate: Propulsive Energy Diffusion for Creative Generation

Summary

This essay critically examines "ProCreate, Don't Reproduce! Propulsive Energy Diffusion for Creative Generation," which proposes a novel method—ProCreate—to enhance sample diversity and creativity in diffusion-based image generative models while preventing training data replication. ProCreate manipulates the latent representations, repelling them from reference embeddings during inference. The effectiveness of ProCreate is showcased through the Few-Shot Creative Generation 8 (FSCG-8) dataset and large-scale training data replication experiments.

Key Contributions

1. Energetic and Propulsive Guidance: ProCreate utilizes an energy-based method to impose a force on the latent representations during inference, ensuring that generated images diverge from the reference set while maintaining conceptual alignment. This innovative approach addresses both the issue of over-replication of training data and the general problem of low diversity in generative model samples.
2. FSCG-8 Dataset: FSCG-8 comprises images across eight diverse categories, including paintings, architecture, cartoon characters, and fashion, with a focus on enhancing the creative capacity of generative models. This fine-grained dataset serves as a robust testbed for evaluating few-shot generation.
3. Quantitative and Qualitative Evaluation: The paper rigorously evaluates ProCreate through standard metrics such as FID, KID, Precision, Recall, MSS, Vendi Score, and Prompt Fidelity. Across various experimental setups—standard fine-tuning, DreamBooth fine-tuning, and replication prevention—ProCreate consistently demonstrates superior performance in generating diverse, high-quality samples.

Experimental Highlights

1. Few-Shot Creative Generation:
   • Qualitative Insights: ProCreate significantly enhances diversity and maintains high prompt fidelity in generated samples across all categories. For instance, in the prompt "an Amedeo Modigliani painting of a girl in blue," ProCreate produces varied postures, facial features, and clothing, unlike the more homogeneous outputs from DDIM and CADS.
   • Quantitative Metrics: Across the FSCG-8 dataset, ProCreate outperforms baseline methods on diversity-focused metrics (Recall, MSS, Vendi) and those evaluating distribution matching (FID, KID). It remains competitive in quality (Precision, Prompt Fidelity), making it a robust solution for creative generation.
2. Training Data Replication Prevention:
   • Large-Scale Evaluation with LAION12M: ProCreate demonstrates substantial improvement in preventing the replication of training images, as evidenced by significantly lower Top-1 SSCD scores. For instance, while DDIM and CADS methods occasionally yield perceptually similar outputs to the training set, ProCreate effectively minimizes this replication.
   • Improved Sample Quality: Beyond replication prevention, ProCreate also enhances overall sample quality, as indicated by lower FID and KID scores compared to other sampling methods.

Implications and Speculations

ProCreate has significant practical and theoretical implications. Practically, it empowers designers and content creators to leverage generative models for AI-assisted design without facing legal or ethical issues regarding replication of copyrighted materials. It also pushes the boundaries of what can be achieved in low-data settings, making generative modeling more accessible and versatile.

Theoretically, ProCreate opens avenues for further research in energy-based generative model guidance, possibly extending into other modalities such as text, audio, and video. Future developments could involve fine-tuning the propulsive energy function for even more nuanced control over sample generation or integrating ProCreate with other state-of-the-art generative models across multi-modal data forms.

Conclusion

ProCreate stands as a significant advancement in the domain of diffusion-based generative models, addressing critical challenges of diversity and data replication. Its applicability across different few-shot learning settings and large-scale diffusion models heralds a new paradigm for AI-assisted creative generation. Areas for future research include optimizing ProCreate's computational efficiency and exploring its adaptability across various AI domains. This work lays a solid foundation for enhancing both the ethical use and the creative potential of generative AI technologies.