LatRef-Diff: Latent and Reference-Guided Diffusion for Facial Attribute Editing and Style Manipulation

2026-04-24 · Source: cs.CV updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision · Depth: Expert, extended

Summary

LatRef-Diff is a novel diffusion-based framework for facial attribute editing and style manipulation, addressing limitations of prior conditional GANs and diffusion models. It replaces traditional semantic directions with style codes, generated via latent or reference guidance, and integrates them into target images using a style modulation module. This module incorporates learnable vectors, cross-attention mechanisms, and a hierarchical design to enhance accuracy and image quality. To improve training stability and eliminate the need for paired images, LatRef-Diff employs a forward-backward consistency training strategy. This strategy approximately removes a target attribute using image-specific semantic directions and then restores it via style modulation, guided by perceptual and classification losses. Extensive experiments on the CelebA-HQ dataset demonstrate LatRef-Diff's state-of-the-art performance in both qualitative and quantitative evaluations for facial attribute editing and style manipulation.

Key takeaway

For research scientists developing advanced image editing models, LatRef-Diff offers a robust approach to overcome limitations in facial attribute and style manipulation. You should consider adopting its style code-based modulation and forward-backward consistency training to achieve higher accuracy and image quality, especially when paired training data is scarce. This framework provides a stable alternative to GAN-based methods and enhances control beyond traditional diffusion models.

Key insights

LatRef-Diff uses style codes and a novel modulation module for precise facial attribute and style manipulation.

Principles

• Style codes enhance expressiveness over semantic directions.
• Forward-backward consistency stabilizes training without paired images.
• Hierarchical design minimizes attribute interference.

Method

The method involves generating style codes via latent or reference guidance, injecting them into images using a style modulation module, and training with a forward-backward consistency strategy using perceptual and classification losses.

In practice

• Use latent guidance for random style manipulation.
• Employ reference guidance for customized style transfer.
• Integrate cross-attention for improved image quality.

Topics

• LatRef-Diff
• Facial Attribute Editing
• Style Manipulation
• Diffusion Models
• Style Modulation Module

Code references

• WeMiHuang/LatRef-Diff

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CV updates on arXiv.org.