The rapid advancement of generative mod- els (Roy et al. 2023; Pal et al. 2024; Roy et al. 2024) has opened new avenues for addressing critical challenges in computer vision (Dhar et al. 2021; Fazlyab et al. 2023), such as data scarcity, image quality enhancement, and person- alization. Recent progress has concentrated on improving the adaptability, efficiency, and quality of these models to meet the growing demand for parameter-efficient fine-tuning and adaptation of large vision-language and generative mod- els (Roy et al. 2025b; Pramanick, Roy, and Patel 2022). In this work, we begin by tackling the challenges of resource- constrained learning (Roy et al. 2022). We then leverage powerful vision-language models to address these issues in a parameter-efficient manner. Additionally, we aim to en- hance state-of-the-art generative models—specifically dif- fusion models—by incorporating natural image priors (Roy et al. 2023). We also explore joint concept merging through the lens of low-rank adapter merging, applying it to content- style personalization. Finally, we address the challenge of zero-shot personalization of any object without requiring additional training. We conclude by devising a frequency- guided method for training-free multi-LoRA composition, which is more appropriate for deployment on edge devices.