12 A minimal implementation of DALL-E
This chapter covers
- How DALL-E is trained to generate images from text descriptions
- How a pretrained BART encoder transforms a text prompt into dense embeddings
- How a BART decoder uses those embeddings to predict image tokens
- How a VQGAN decoder converts image tokens into a high-resolution image
OpenAI’s DALL-E is one of the earliest and most influential text-to-image generators. It is a large-scale transformer that can generate high-resolution images from natural language prompts. DALL-E stands at the intersection of two technologies in modern AI: powerful autoregressive language models and vector-quantized representations of visual information. Yet, for many learners, practitioners, and researchers, the full DALL-E model remains out of reach due to its proprietary nature.
To foster deeper understanding and democratize access, the open-source community has stepped up, replicating DALLE’s core ideas with public tools and datasets. Notably, the DALL-E Mini project and its streamlined PyTorch reimplementation, min-DALL-E, have made it possible for anyone to explore the mechanics of transformer-based text-to-image generation and even build their own models from scratch.