Case study: DALL·E
Definition
DALL·E (and DALL·E 2) are text-to-image models from OpenAI. They generate images from text prompts using diffusion models and language–image alignment.
They are a leading example of multimodal generation: text in, image out. The same diffusion and conditioning ideas appear in Stable Diffusion and other open models. Use case: creative and product imagery from natural language; safety and content policies apply.
How it works
Text is encoded with a language or multimodal encoder (e.g. CLIP text encoder, T5) into a text embedding. A diffusion model (e.g. UNet) is conditioned on this embedding: the denoising process is guided so the generated image matches the text. Training uses large datasets of captioned images; the model learns to associate text and image content. Sampling: start from noise, run the reverse diffusion process with the text embedding as condition, and decode to an image. Safety filters (e.g. classifier, policy) limit harmful or restricted outputs before delivery. Variants (inpainting, editing) condition on both text and an existing image or mask.
Use cases
Text-to-image models like DALL·E are used wherever you need images generated or edited from natural language (creative, product, UI).
- Creative and marketing asset generation from text prompts
- Concept art, illustration, and design exploration
- Product and UI mockups from natural language descriptions