PoGDiff: Product-of-Gaussians Diffusion Models for Imbalanced Text-to-Image Generation

• URL: http://arxiv.org/abs/2502.08106v2
• Date: Wed, 19 Feb 2025 16:18:04 GMT
• Title: PoGDiff: Product-of-Gaussians Diffusion Models for Imbalanced Text-to-Image Generation
• Authors: Ziyan Wang, Sizhe Wei, Xiaoming Huo, Hao Wang,
• Abstract summary: We propose a general fine-tuning approach, dubbed PoGDiff, to address this challenge.<n>Experiments on real-world datasets demonstrate that our method effectively addresses the imbalance problem in diffusion models.
• Score: 13.07145194221385
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diffusion models have made significant advancements in recent years. However, their performance often deteriorates when trained or fine-tuned on imbalanced datasets. This degradation is largely due to the disproportionate representation of majority and minority data in image-text pairs. In this paper, we propose a general fine-tuning approach, dubbed PoGDiff, to address this challenge. Rather than directly minimizing the KL divergence between the predicted and ground-truth distributions, PoGDiff replaces the ground-truth distribution with a Product of Gaussians (PoG), which is constructed by combining the original ground-truth targets with the predicted distribution conditioned on a neighboring text embedding. Experiments on real-world datasets demonstrate that our method effectively addresses the imbalance problem in diffusion models, improving both generation accuracy and quality.

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