IPGO: Indirect Prompt Gradient Optimization on Text-to-Image Generative Models with High Data Efficiency

Text-to-Image Diffusion models excel at generating images from text prompts but often lack optimal alignment with content semantics, aesthetics, and human preferences. To address these issues, in this study we introduce a novel framework, Indirect Prompt Gradient Optimization (IPGO), for prompt-level fine-tuning. IPGO enhances prompt embeddings by injecting continuously differentiable tokens at the beginning and end of the prompt embeddings, while exploiting low-rank benefits and flexibility from rotations. It allows for gradient-based optimization of injected tokens while enforcing value, orthonormality, and conformity constraints, facilitating continuous updates and empowering computational efficiency. To evaluate the performance of IPGO, we conduct prompt-wise and prompt-batch training with three reward models targeting image aesthetics, image-text alignment, and human preferences under three datasets of different complexity. The results show that IPGO consistently matches or outperforms cutting-edge benchmarks, including stable diffusion v1.5 with raw prompts, training-based approaches (DRaFT and DDPO), and training-free methods (DPO-Diffusion, Promptist, and ChatGPT-4o). Furthermore, we demonstrate IPGO's effectiveness in enhancing image generation quality while requiring minimal training data and limited computational resources.

@article{ye2025_2503.21812,
  title={ IPGO: Indirect Prompt Gradient Optimization on Text-to-Image Generative Models with High Data Efficiency },
  author={ Jianping Ye and Michel Wedel and Kunpeng Zhang },
  journal={arXiv preprint arXiv:2503.21812},
  year={ 2025 }
}