Case study: DALL·E
定义
DALL·E (and DALL·E 2) are 来自 OpenAI 的文本到图像模型. They generate images from text prompts using diffusion models and language–image alignment.
它们是 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.
工作原理
Text is encoded with a language or multimodal encoder (例如 CLIP text encoder, T5) into a text embedding. A diffusion model (例如 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 (例如 classifier, policy) limit harmful or restricted outputs before delivery. Variants (inpainting, editing) condition on both text and an existing image or mask.
应用场景
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 设计 exploration
- Product and UI mockups from natural language descriptions