Do you ever wish you could just describe an image in your mind and have it magically appear on your screen? Well, thanks to recent advances in artificial intelligence, we’re getting closer than ever to making that sci-fi dream a reality. In this post, we’ll explore the history of how AI has evolved to generate increasingly stunning and creative images from text descriptions alone.
The rise of GANs for Image Generation
It all started with an exciting breakthrough in 2014 from researcher Ian Goodfellow and his colleagues. They introduced an AI technique called generative adversarial networks, or GANs for short. GANs pit two neural networks against each other in a competitive game of counterfeiting. One AI generates fake images while the other tries to detect the fakes. Through this adversarial training process, the “generator” keeps getting better at producing realistic images that can fool its partner AI. GANs were like a creative spark that ignited the field of neural art generation.
Suddenly, GANs were creating photorealistic pictures of everything from human faces to stunning landscapes. Researchers also adapted GANs for applications like transferring artistic styles from one image to another. However, GANs had their limitations. They were tricky to train properly and often got stuck churning out a limited variety of similar-looking images.
Transformers & CLIP
The AI community turned to a different technique to overcome these challenges – Transformers. Originally created for processing language in applications like chatbots, Transformers proved to be a key ingredient in the next generation of text-to-image models. In 2018, OpenAI introduced GPT-2 which used the Transformer architecture to generate remarkably human-like text.
Researchers soon realized they could train Transformers like GPT-2 on massive datasets of image and caption pairs. The result was DALL-E in 2021, which could generate diverse and realistic images from text prompts. DALL-E’s outputs were still a bit rough around the edges though.
This brings us to CLIP, another pivotal model in text-to-image generation by OpenAI. CLIP provided the missing link between understanding text and images. By training on captioned images, CLIP learned to embed text and images into a common mathematical space. This enabled better alignment between text descriptions and the generated image results.
CLIP acted as a guiding hand for image generation AIs like DALL-E, dramatically improving the quality and accuracy of the images produced from text prompts. But CLIP was still hungry for more data and processing power to reach its full potential.
Diffusion Models for Image Generation
That’s where diffusion models came to the rescue! Diffusion models simulate the natural process of particles diffusing and coalescing to gradually transform random noise into coherent images. Researchers discovered that running the diffusion process in a compressed latent space made image generation much more efficient.
Latent Diffusion combined with guidance from CLIP resulted in huge leaps in quality and creativity. Now high-resolution images poured out of the models with incredible detail and precision tailored to the text prompts. Services like DALL-E 2 from OpenAI brought these advanced text-to-image models directly into the hands of everyday users through intuitive apps and websites.
Of course, the story doesn’t end here. Generative AI is advancing rapidly with new techniques like Stable Diffusion making high-quality image generation widely accessible and customizable. There are still challenges around consistency, coherence and photorealism, but the future looks bright as research continues.
The journey so far has been remarkable. In less than a decade, AI has evolved from simply classifying images to creatively synthesizing them. Who knows what new innovations and applications the next decade may bring as generative models continue to mature. But one thing’s for sure – the worlds of art, media and communication will never be the same!
Which part of this incredible AI image journey excites you the most? Let me know in the comments! I’d love to hear your thoughts.
Marina Mele has experience in artificial intelligence implementation and has led tech teams for over a decade. On her personal blog (marinamele.com), she writes about personal growth, family values, AI, and other topics she’s passionate about. Marina also publishes a weekly AI newsletter featuring the latest advancements and innovations in the field (marinamele.substack.com)