The rapid development of diffusion-based generative models has significantly advanced the field of text-to-image generation, enabling high-quality image creation for diverse applications such as photography, digital arts, and advertising. However, generating legible text within images and controlling its style and appearance remains a substantial challenge, limiting their utility in tasks such as advertisements and posters. This work addresses these limitations by proposing a novel approach that combines controllable image generation with a model capable of generating black-and-white text images in specific styles, conditioned on reference images. We leverage the advancements in diffusion models and text generation techniques, offering a solution that provides good legibility of generated text and enables accurate font style imitation.