AI image generators have made incredible strides, conjuring up stunning visuals from mere words. But there's a catch: sometimes, the image doesn't quite match the text, leaving out key objects or mixing up colors. This disconnect between text and image, known as the prompt-image alignment problem, has been a frustrating hurdle for AI artists and researchers alike. Now, a novel technique called FRAP (Faithful and Realistic Text-to-Image Generation with Adaptive Prompt Weighting) is offering a clever solution. Instead of tweaking the complex code that generates the image, FRAP adjusts the emphasis on individual words in the text prompt, boosting the importance of objects and attributes during image creation. Imagine you're prompting the AI with "a brown dog chasing a white cat." FRAP dynamically emphasizes "brown," "dog," "white," and "cat" at different stages of the image generation process, ensuring the AI doesn't forget to include them or miscolor them. This online optimization process acts like a conductor, orchestrating the image elements in real-time. The results? More faithful images that accurately represent the user's intent, all without sacrificing realism. This is a big win for AI art, as previous attempts to improve accuracy often led to less realistic, cartoonish results. Plus, FRAP works faster than other complex methods, reducing latency during image generation. Beyond improving current generators, FRAP has the potential to work with large language models (LLMs) that enhance prompts, leading to both more beautiful and more accurate AI art. This synergistic approach could be key to bridging the gap between what we ask for and what we see, ushering in a new era of precision and creativity in AI image generation.