Anchor-Constrained Optimization Improves Perceptual Quality in Diffusion Models
A recent study published on arXiv (2604.26348) introduces an optimization framework constrained by anchors to integrate no-reference perceptual quality into the training of diffusion models. Traditionally, diffusion models depend on full-reference objectives that focus on pixel-wise alignment with ground-truth images, which may not fully reflect subjective visual perception or the semantic consistency between text and images. A significant hurdle is that directly optimizing no-reference image quality assessment (NR-IQA) signals can lead to a mismatch with the original diffusion objectives, resulting in training instability and distributional drift. The proposed approach employs a learned NR-IQA model as a perceptual guidance signal within an anchor-constrained framework, facilitating stable perceptual adaptation. The paper can be accessed at https://arxiv.org/abs/2604.26348.
Key facts
- Paper arXiv:2604.26348 proposes anchor-constrained perceptual optimization for diffusion models.
- Full-reference objectives in diffusion training may not ensure subjective visual quality.
- Directly optimizing no-reference perceptual signals causes training instability.
- The framework uses a learned NR-IQA model as perceptual guidance.
- Anchor constraints enable stable adaptation without distributional drift.
- The paper addresses text-image semantic consistency.
- Published on arXiv under cross type.
- Available at https://arxiv.org/abs/2604.26348.
Entities
Institutions
- arXiv