Text-to-image diffusion models have risen to prominence due to their ability to generate high-quality images from textual prompts, offering considerable advantages over previous models.
The incorporation of extensive image-text pairs data has allowed diffusion models to produce state-of-the-art synthesis results, demonstrating notable proficiency in aligning generated images with provided descriptions.
Despite the efficiency of textual prompts, they can struggle with generating intricate details, especially for more complex subjects like human faces, highlighting a limitation in the current methodology.
Diffusion models show superior training stability and a better capability to integrate various forms of guidance, such as text and stylized images, compared to earlier GAN-based methods.
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