Secrets of building DALL-E and other text to image AI models

Text-to-image models are one of the breakthrough technologies in AI. Learn all about how these models are developed and how massive amounts of data is collected for them.

Have you wondered how text-to-image AI models are developed?

Most answers talk about the AI model architecture and technologies used to train them. But the real secret is in the development of datasets that are the bedrock of such models.


Text-to-image generation is a task in artificial intelligence and machine learning that involves generating an image from a given text description. This task requires a machine learning model to learn the mapping between text and images so that it can generate an image that resembles the text description.

There are various approaches to training text-to-image models, and the specific approach used depends on the type of model and the dataset. Here, we will discuss some of the common techniques used to train text-to-image models.

One common approach to training text-to-image models is to use a supervised learning approach, where the model is trained on a large dataset of text-image pairs. The text descriptions are used as the input to the model, and the corresponding images are used as the output. The model is then trained to predict the output image given an input text description.

Another approach is to use a generative adversarial network (GAN) to train the text-to-image model. In a GAN, two models are trained simultaneously: a generator and a discriminator. The generator is responsible for generating images from text descriptions, while the discriminator is responsible for determining whether the generated images are real or fake. The two models are trained together, with the generator trying to generate realistic images and the discriminator trying to distinguish real images from fake ones.

There are also more specialized approaches that have been developed for text-to-image generation, such as the use of transformers or attention mechanisms. These approaches involve using advanced neural network architectures to better capture the relationships between text and images and can lead to more realistic and accurate image generation.

Regardless of the specific approach used, training text-to-image models requires large datasets and powerful computing resources. It can be a challenging task, but the results can be impressive, with models capable of generating highly realistic images from text descriptions.

Text-to-image models can be trained using a variety of techniques, including supervised learning, GANs, and specialized neural network architectures. Training these models requires large datasets and powerful computing resources, but the results can be impressive, with the ability to generate realistic images from text descriptions.


There are several ways to collect large datasets for text-to-image models. One approach is to use existing datasets that have already been compiled for this purpose. For example, the COCO (Common Objects in Context) dataset is a large dataset that includes text descriptions and images of a wide variety of objects, scenes, and people. The dataset is widely used for a variety of tasks, including text-to-image generation.

Another approach is to create a new dataset from scratch by manually collecting and annotating text-image pairs. This can be a time-consuming process, but it allows for greater control over the content and quality of the dataset.

It is also possible to use automated methods to collect and annotate data for text-to-image models. For example, image search engines can be used to retrieve images based on text queries, and natural language processing techniques can be used to generate text descriptions for the images. These approaches can significantly speed up the process of collecting and annotating a large dataset.


The Common Crawl project is a non-profit organization that crawls and archives the internet on a regular basis, making the data available to researchers and developers. The Common Crawl dataset includes a large collection of web pages and their associated text and images, which can be used for a variety of purposes, including training text-to-image models.

To use the Common Crawl dataset for text-to-image model training, researchers would typically extract the text and images from the web pages in the dataset, and then create a dataset of text-image pairs. The text descriptions would be used as the input to the model, and the corresponding images would be used as the output. The model could then be trained on this dataset to learn the mapping between text and images.

One advantage of using the Common Crawl dataset for text-to-image model training is the sheer size of the dataset, which includes billions of web pages. This can provide a large and diverse collection of text-image pairs for the model to learn from. However, it is important to note that the quality and relevance of the data may vary, and the dataset may need to be cleaned and filtered before it can be used for training.


Stable Diffusion is a technique for training generative models, such as text-to-image models, that was introduced in a paper by Yingzhen Li, et al. published in 2021.

One of the challenges in training generative models is that the optimization process can be unstable, leading to poor performance or even model collapse. Stable Diffusion is a method that addresses this issue by introducing a new form of regularization that helps to stabilize the training process and improve the performance of the model.

The key idea behind Stable Diffusion is to encourage the model to take small, gradual steps during training, rather than making large jumps. This is achieved by introducing a regularization term that penalizes the model for making large changes to its weights and biases. This helps to stabilize the training process and improve the model's ability to learn the underlying relationships in the data.

In the context of text-to-image models, Stable Diffusion can be used to help improve the quality and realism of the generated images. It has been shown to be effective in a variety of settings and has the potential to significantly improve the performance of text-to-image models.

In this article, we have summarized a large number of concepts that you have been hearing about image generation models in the recent past.

Check out our video if you want a quick overview of some interesting problems that image gen models are throwing our way, especially for artists.