The DALL·E Model

Learn about the DALL·E transformer, another task-agnostic transformer model that can process images and text.

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Overview
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Overview

DALL·E, like CLIP, is a task-agnostic model. CLIP processed text-image pairs. DALL·E processes the text and image tokens differently. DALL·E’s input is a single stream of text and image of 1,280 tokens. 256 tokens are for the text, and 1,024 tokens are used for the image. DALL·E is a foundation model like CLIP.

DALL·E was named after Salvador Dali and Pixar’s WALL-E. When using DALL·E, we enter a text prompt and produce an image. However, DALL·E must first learn how to generate images with text.

DALL·E is a 12-billion-parameter version of GPT-3.

This transformer generates images from text descriptions using a dataset of text-image pairs.

The basic architecture of DALL·E

Unlike CLIP, DALL·E concatenates up to 256 BPE-encoded text tokens with 32×32 = 1,024 image tokens, as shown in the figure below:

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Course Introduction

Basics of Transformers

Architecture of the Transformer Model

Fine-Tuning BERT Models

Pretraining a RoBERTa Model from Scratch

Downstream NLP Tasks with Transformers

Machine Translation with the Transformer

The Rise of Transformers with GPT-3 Engines

Applying Transformers for AI Text Summarization

Matching Tokenizers and Datasets

Transformers for NLP—Exam 1

Semantic Role Labeling with BERT-Based Transformers

Your Data Speaks: Story, Questions, and Answers

Detecting Customer Emotions to Make Predictions

Analyzing Fake News with Transformers

Interpreting Black Box Transformer Models

From NLP to Task-Agnostic Transfomer Models

The Emergence of Transformer-Driven Copilots

Transformers for NLP—Exam 2

Conclusion

Appendix

The DALL·E Model

Overview

The basic architecture of DALL·E