I'm curious about how the core algorithms behind this technology work. Specifically, which architectures are used for text-to-image synthesis (like diffusion models, GANs, etc.) and how are they integrated? Could you give a general explanation without going into too much detail?
How do you generate images from text using artificial intelligence?
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When comparing the most well-known methods for generating images from text, **Generative Adversarial Networks (GANs)** and **Diffusion Models** stand out. Since their introduction in 2014, GANs have long been the leaders in this field. They consist of two networks: one generates fake images, while the other tries to distinguish whether these images are real or fake. This competition leads to increasingly realistic images. However, training GANs can be unstable, and they may face the "mode collapse" problem, where all outputs become identical.
Diffusion models, on the other hand, have gained popularity in recent years, especially with Stability AI's Stable Diffusion and OpenAI's DALL·E 2. This approach is based on the principle of "cleaning up noise": starting from random noise and gradually using text prompts to build the image. Compared to GANs, diffusion models offer a more stable training process and generally produce better results in terms of image quality. They also excel at reflecting textual details in visuals. Of course, these models require more computational power for training and inference, which can be a disadvantage in terms of cost and accessibility.