Delta-Adapter: Scalable Exemplar-Based Image Editing with Single-Pair Supervision

Hugging Face Daily Papers Papers
image-editing exemplar-based semantic-delta perceiver-adapter single-pair-supervision vision-encoder

Summary

Delta-Adapter enables exemplar-based image editing using single-pair supervision by extracting semantic deltas from pre-trained vision encoders and injecting them via Perceiver-based adapters, improving accuracy and generalization.

Exemplar-based image editing applies a transformation defined by a source-target image pair to a new query image. Existing methods rely on a pair-of-pairs supervision paradigm, requiring two image pairs sharing the same edit semantics to learn the target transformation. This constraint makes training data difficult to curate at scale and limits generalization across diverse edit types. We propose Delta-Adapter, a method that learns transferable editing semantics under single-pair supervision, requiring no textual guidance. Rather than directly exposing the exemplar pair to the model, we leverage a pre-trained vision encoder to extract a semantic delta that encodes the visual transformation between the two images. This semantic delta is injected into a pre-trained image editing model via a Perceiver-based adapter. Since the target image is never directly visible to the model, it can serve as the prediction target, enabling single-pair supervision without requiring additional exemplar pairs. This formulation allows us to leverage existing large-scale editing datasets for training. To further promote faithful transformation transfer, we introduce a semantic delta consistency loss that aligns the semantic change of the generated output with the ground-truth semantic delta extracted from the exemplar pair. Extensive experiments demonstrate that Delta-Adapter consistently improves both editing accuracy and content consistency over four strong baselines on seen editing tasks, while also generalizing more effectively to unseen editing tasks. Code will be available at https://delta-adapter.github.io.
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Paper page - Delta-Adapter: Scalable Exemplar-Based Image Editing with Single-Pair Supervision

Source: https://huggingface.co/papers/2605.07940

Abstract

Delta-Adapter enables image editing with single-pair supervision by extracting semantic deltas from pre-trained vision encoders and injecting them into editing models via Perceiver-based adapters, improving accuracy and generalization.

Exemplar-based image editingapplies a transformation defined by a source-target image pair to a new query image. Existing methods rely on a pair-of-pairs supervision paradigm, requiring two image pairs sharing the same edit semantics to learn the target transformation. This constraint makes training data difficult to curate at scale and limits generalization across diverse edit types. We propose Delta-Adapter, a method that learns transferable editing semantics undersingle-pair supervision, requiring no textual guidance. Rather than directly exposing the exemplar pair to the model, we leverage apre-trained vision encoderto extract asemantic deltathat encodes the visual transformation between the two images. Thissemantic deltais injected into a pre-trainedimage editing modelvia aPerceiver-based adapter. Since the target image is never directly visible to the model, it can serve as the prediction target, enablingsingle-pair supervisionwithout requiring additional exemplar pairs. This formulation allows us to leverage existing large-scale editing datasets for training. To further promote faithful transformation transfer, we introduce asemantic delta consistency lossthat aligns the semantic change of the generated output with the ground-truthsemantic deltaextracted from the exemplar pair. Extensive experiments demonstrate that Delta-Adapter consistently improves both editing accuracy and content consistency over four strong baselines on seen editing tasks, while also generalizing more effectively to unseen editing tasks. Code will be available at https://delta-adapter.github.io.

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