13 New developments and challenges in text-to-image generation
This chapter covers
- How state-of-the-art text-to-image generators work
- Challenges and concerns faced by text-to-image models
- Creating a model to distinguish real images from deepfakes
- Preparing a large-scale dataset of real and fake images for fine-tuning
- Testing the fine-tuned model on unseen images
By now, we have explored two ways of text-to-image generation, building models from scratch and unlocking the creative potential of modern AI. From early transformer-based generators to cutting-edge diffusion models, we’ve seen how machines can now turn simple prompts into breathtaking images based on text prompts.
Yet, with these advances come profound new challenges. As the quality and realism of generated images have skyrocketed, so too have the risks. Deepfakes, AI-generated images and videos designed to deceive, are now increasingly indistinguishable from real photographs. This presents not only technical hurdles, but also ethical, legal, and societal dilemmas. How do we ensure these powerful tools are used responsibly? Can we reliably detect AI-generated images, and what are the broader consequences when detection fails?