  Fine-tuning facilitates the adaptation of text-to-image generative models to
novel concepts (e.g., styles and portraits), empowering users to forge
creatively customized content. Recent efforts on fine-tuning focus on reducing
training data and lightening computation overload but neglect alignment with
user intentions, particularly in manual curation of multi-modal training data
and intent-oriented evaluation. Informed by a formative study with fine-tuning
practitioners for comprehending user intentions, we propose IntentTuner, an
interactive framework that intelligently incorporates human intentions
throughout each phase of the fine-tuning workflow. IntentTuner enables users to
articulate training intentions with imagery exemplars and textual descriptions,
automatically converting them into effective data augmentation strategies.
Furthermore, IntentTuner introduces novel metrics to measure user intent
alignment, allowing intent-aware monitoring and evaluation of model training.
Application exemplars and user studies demonstrate that IntentTuner streamlines
fine-tuning, reducing cognitive effort and yielding superior models compared to
the common baseline tool.
