Structure-Preserving Zero-Shot Image Editing via Stage-Wise Latent Injection in Diffusion Models
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909616732897280 |
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| author | Jeong, Dasol Kang, Donggoo Park, Jiwon Lee, Hyebean Paik, Joonki |
| author_facet | Jeong, Dasol Kang, Donggoo Park, Jiwon Lee, Hyebean Paik, Joonki |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_15723 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Structure-Preserving Zero-Shot Image Editing via Stage-Wise Latent Injection in Diffusion Models Jeong, Dasol Kang, Donggoo Park, Jiwon Lee, Hyebean Paik, Joonki Computer Vision and Pattern Recognition 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. |
| title | Structure-Preserving Zero-Shot Image Editing via Stage-Wise Latent Injection in Diffusion Models |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.15723 |