📝 SummarXiv

Abstract

Conditional image generation models have achieved remarkable results by leveraging text-based control to generate customized images. However, the high resource demands of these models and the scarcity of well-annotated data have hindered their deployment on edge devices, leading to enormous costs and privacy concerns, especially when user data is sent to a third party. To overcome these challenges, we propose Refine-Control, a semi-supervised distillation framework. Specifically, we improve the performance of the student model by introducing a tri-level knowledge fusion loss to transfer different levels of knowledge. To enhance generalization and alleviate dataset scarcity, we introduce a semi-supervised distillation method utilizing both labeled and unlabeled data. Our experiments reveal that Refine-Control achieves significant reductions in computational cost and latency, while maintaining high-fidelity generation capabilities and controllability, as quantified by comparative metrics.