Zero-shot personalized image generation models aim to produce images that align with both a given text prompt and subject image, requiring the model to effectively incorporate both sources of guidance. However, existing methods often struggle to capture fine-grained subject details and frequently prioritize one form of guidance over the other, resulting in suboptimal subject encoding and an imbalance in the generated images. In this study, we uncover key insights into achieving high-quality balances on subject identity preservation and text-following, notably that 1) the design of the subject image encoder critically influences subject identity preservation, and 2) the text and subject guidance should take effect at different denoising stages. Building on these insights, we introduce a new approach, EZIGen, that employs two main components: a carefully crafted subject image encoder based on the pretrained UNet of the Stable Diffusion model, following a process that balances the two guidances by separating their dominance stage and revisiting certain time steps to bootstrap subject transfer quality. Through these two components, EZIGen achieves state-of-the-art results on multiple personalized generation benchmarks with a unified model and 100 times less training data.