How does DALL-E 2 work?

DALL-E 2 consists of two main parts: a prior that gets the embedding of a caption and turns it into an image embedding, and a decoder that accepts this image embedding and produces the final image. As a side note, an embedding is a mathematical representation of a piece of information. The creators of DALL-E 2 chose to use a model called CLIP to create these embeddings.

CLIP is a neural network model that learns to match images with their captions. It trains encoders such that, when given a piece of data, it creates embeddings. In the first part of DALL-E 2, once a caption is passed to the model, we use the CLIP encoder to create a CLIP text embedding and pass it through the prior to generate a CLIP image embedding.

The prior in this process is a diffusion model. Diffusion models are generative models that can create data from noise. For a more detailed understanding of the rest of the DALL-E 2 architecture, check out part 3 or visit our YouTube channel for a longer video explaining how DALL-E 2 functions.

Keywords

DALL-E 2
Embedding
Prior
Decoder
CLIP
Neural Network
Diffusion Model
Generative Models

FAQ

Q: What are the main parts of DALL-E 2? A: DALL-E 2 primarily consists of a prior and a decoder.

Q: What is embedding in the context of DALL-E 2? A: Embedding is the mathematical representation of a piece of information.

Q: Which model is used to create embeddings in DALL-E 2? A: The creators of DALL-E 2 opted to use a model called CLIP to create these embeddings.

Q: How does the prior in DALL-E 2 function? A: The prior is a diffusion model that generates a CLIP image embedding from a CLIP text embedding.

Q: What is a diffusion model? A: Diffusion models are generative models that can create data from noise.