OpenAI’s unCLIP Text-to-Image System Leverages Contrastive and Diffusion Models to Achieve SOTA Performance
In the new paper Hierarchical Text-Conditional Image Generation with CLIP Latents, an OpenAI research team combines the advantages of contrastive and diffusion models for text-conditional image generation tasks. Their proposed unCLIP model improves image diversity with minimal loss in photorealism and caption similarity, and produces image quality comparable to the state-of-the-art text-to-image system GLIDE.