ResEdit: Residual embeddings for precise generative image editing

• URL: http://arxiv.org/abs/2606.16457v1
• Date: Mon, 15 Jun 2026 09:30:03 GMT
• Title: ResEdit: Residual embeddings for precise generative image editing
• Abstract summary: Conditional diffusion image generators can be repurposed for editing through inversion.<n>We demonstrate that incorporating a residual image encoding as additional conditioning enables both improved identity preservation and better editability.
• Score: 23.52777212647601
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Conditional diffusion image generators can be repurposed for editing through inversion, without the need for large-scale paired fine-tuning data. However, producing high-quality, targeted edits while maintaining image identity and global consistency remains challenging, as weakly conditioned inversion often embeds conflicting image features into the noise. We demonstrate that incorporating a residual image encoding as additional conditioning enables both improved identity preservation and better editability. We optimize this residual encoding to provide a strong conditioning signal for reconstruction, thereby reducing the reliance on inversion and susceptibility to its aforementioned pitfalls. To ensure this residual does not interfere with desired edits, we incorporate a gradient reversal-based optimization strategy that disentangles the residual from the edited condition. We illustrate our method's ability to produce high-fidelity results across precise intrinsic-based editing and relighting, and show proof-of-concept text-guided manipulation.

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