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

2026-04-23 · Source: Takara TLDR - Daily AI Papers · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Computer Vision · Depth: Expert, medium

Summary

LatRef-Diff, a novel diffusion-based framework, addresses challenges in facial attribute editing and style manipulation, crucial for applications like virtual avatars and photo editing. Traditional conditional GANs face accuracy and training instability issues, while existing diffusion models struggle with style manipulation due to limited semantic direction expressiveness. LatRef-Diff replaces semantic directions with style codes, generated via latent and reference guidance. It integrates these style codes into target images using a style modulation module, which includes learnable vectors, cross-attention, and a hierarchical design for improved accuracy and image quality. The framework also employs a forward-backward consistency training strategy, eliminating the need for paired images and enhancing stability by first removing and then restoring attributes via style modulation, guided by perceptual and classification losses. Experiments on CelebA-HQ demonstrate state-of-the-art performance.

Key takeaway

For research scientists developing advanced image editing tools, LatRef-Diff offers a robust approach to facial attribute and style manipulation. You should consider adopting its style code integration and forward-backward consistency training strategy to overcome limitations of traditional GANs and existing diffusion models, particularly when precise control and training stability without paired data are critical for your applications.

Key insights

LatRef-Diff uses style codes and a novel training strategy for precise, stable facial attribute and style manipulation.

Principles

• Style codes enhance diffusion model expressiveness.
• Forward-backward consistency improves training stability.
• Hierarchical design refines image quality.

Method

LatRef-Diff generates style codes via latent/reference guidance, integrates them with a style modulation module, and uses a forward-backward consistency training strategy with perceptual and classification losses.

In practice

• Apply style codes for fine-grained control.
• Use consistency training to avoid paired data.
• Integrate cross-attention for better image quality.

Topics

• Facial Attribute Editing
• Style Manipulation
• Diffusion Models
• Latent and Reference Guidance
• Style Modulation

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Takara TLDR - Daily AI Papers.