We propose a diffusion-based framework for zero-shot image editing that unifies text-guided and reference-guided approaches without requiring fine-tuning. Our method leverages diffusion inversion and timestep-specific null-text embeddings to preserve the structural integrity of the source image. By introducing a stage-wise latent injection strategy-shape injection in early steps and attribute injection in later steps-we enable precise, fine-grained modifications while maintaining global consistency. Cross-attention with reference latents facilitates semantic alignment between the source and reference. Extensive experiments across expression transfer, texture transformation, and style infusion demonstrate state-of-the-art performance, confirming the method's scalability and adaptability to diverse image editing scenarios.
View on arXiv@article{jeong2025_2504.15723,
title={ Structure-Preserving Zero-Shot Image Editing via Stage-Wise Latent Injection in Diffusion Models },
author={ Dasol Jeong and Donggoo Kang and Jiwon Park and Hyebean Lee and Joonki Paik },
journal={arXiv preprint arXiv:2504.15723},
year={ 2025 }
}