NoHumansRequired: Autonomous High-Quality Image Editing Triplet Mining

TL;DR

This paper introduces an automated pipeline for mining high-quality image editing triplets from generative models, enabling large-scale training data creation without human labeling, and releases a substantial dataset and a fine-tuned model.

Contribution

The authors develop a modular, automated system for extracting pixel-accurate image editing triplets, significantly reducing manual effort and enabling large-scale high-fidelity training data generation.

Findings

The pipeline successfully mines 720k high-quality triplets.

It outperforms all public alternatives in cross-dataset evaluation.

The approach enables large-scale training without human labeling effort.

Abstract

Recent advances in generative modeling enable image editing assistants that follow natural language instructions without additional user input. Their supervised training requires millions of triplets (original image, instruction, edited image), yet mining pixel-accurate examples is hard. Each edit must affect only prompt-specified regions, preserve stylistic coherence, respect physical plausibility, and retain visual appeal. The lack of robust automated edit-quality metrics hinders reliable automation at scale. We present an automated, modular pipeline that mines high-fidelity triplets across domains, resolutions, instruction complexities, and styles. Built on public generative models and running without human intervention, our system uses a task-tuned Gemini validator to score instruction adherence and aesthetics directly, removing any need for segmentation or grounding models. Inversion and compositional bootstrapping enlarge the mined set by approx. 2.6x, enabling large-scale high-fidelity training data. By automating the most repetitive annotation steps, the approach allows a new scale of training without human labeling effort. To democratize research in this resource-intensive area, we release NHR-Edit, an open dataset of 720k high-quality triplets, curated at industrial scale via millions of guided generations and validator passes, and we analyze the pipeline's stage-wise survival rates, providing a framework for estimating computational effort across different model stacks. In the largest cross-dataset evaluation, it surpasses all public alternatives. We also release Bagel-NHR-Edit, a fine-tuned Bagel model with state-of-the-art metrics.