Alignment of Diffusion Model and Flow Matching for Text-to-Image Generation

• URL: http://arxiv.org/abs/2602.00413v1
• Date: Sat, 31 Jan 2026 00:06:55 GMT
• Title: Alignment of Diffusion Model and Flow Matching for Text-to-Image Generation
• Authors: Yidong Ouyang, Liyan Xie, Hongyuan Zha, Guang Cheng,
• Abstract summary: Diffusion models and flow matching have demonstrated remarkable success in text-to-image generation.<n>We propose a novel alignment framework by leveraging the underlying nature of the alignment problem.<n>We achieve comparable performance to finetuning-based models with one-step generation with at least a 60% reduction in computational cost.
• Score: 39.484148941369234
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diffusion models and flow matching have demonstrated remarkable success in text-to-image generation. While many existing alignment methods primarily focus on fine-tuning pre-trained generative models to maximize a given reward function, these approaches require extensive computational resources and may not generalize well across different objectives. In this work, we propose a novel alignment framework by leveraging the underlying nature of the alignment problem -- sampling from reward-weighted distributions -- and show that it applies to both diffusion models (via score guidance) and flow matching models (via velocity guidance). The score function (velocity field) required for the reward-weighted distribution can be decomposed into the pre-trained score (velocity field) plus a conditional expectation of the reward. For the alignment on the diffusion model, we identify a fundamental challenge: the adversarial nature of the guidance term can introduce undesirable artifacts in the generated images. Therefore, we propose a finetuning-free framework that trains a guidance network to estimate the conditional expectation of the reward. We achieve comparable performance to finetuning-based models with one-step generation with at least a 60% reduction in computational cost. For the alignment on flow matching, we propose a training-free framework that improves the generation quality without additional computational cost.

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