NoHumansRequired: Autonomous High-Quality Image Editing Triplet Mining
Authors: name: Maksim Kuprashevich, name: Grigorii Alekseenko, name: Irina Tolstykh, name: Georgii Fedorov, name: Bulat Suleimanov, name: Vladimir Dokholyan, name: Aleksandr Gordeev

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 approximately 2.2x,
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 358k
high-quality triplets. In the largest cross-dataset evaluation, it surpasses
all public alternatives. We also release Bagel-NHR-Edit, an open-source
fine-tuned Bagel model, which achieves state-of-the-art metrics in our
experiments.