Personalized image synthesis through text-to-image generation is explored using auto-regressive models, which have been less studied compared to diffusion models. The paper presents a two-stage training strategy that optimizes text embeddings and fine-tunes transformer layers, demonstrating that auto-regressive models can achieve comparable fidelity and prompt adherence to existing methods. This research opens new avenues for improving personalized image generation techniques.