  Taking advantage of the many recent advances in deep learning, text-to-image
generative models currently have the merit of attracting the general public
attention. Two of these models, DALL-E 2 and Imagen, have demonstrated that
highly photorealistic images could be generated from a simple textual
description of an image. Based on a novel approach for image generation called
diffusion models, text-to-image models enable the production of many different
types of high resolution images, where human imagination is the only limit.
  However, these models require exceptionally large amounts of computational
resources to train, as well as handling huge datasets collected from the
internet. In addition, neither the codebase nor the models have been released.
It consequently prevents the AI community from experimenting with these
cutting-edge models, making the reproduction of their results complicated, if
not impossible.
  In this thesis, we aim to contribute by firstly reviewing the different
approaches and techniques used by these models, and then by proposing our own
implementation of a text-to-image model. Highly based on DALL-E 2, we introduce
several slight modifications to tackle the high computational cost induced. We
thus have the opportunity to experiment in order to understand what these
models are capable of, especially in a low resource regime. In particular, we
provide additional and analyses deeper than the ones performed by the authors
of DALL-E 2, including ablation studies.
  Besides, diffusion models use so-called guidance methods to help the
generating process. We introduce a new guidance method which can be used in
conjunction with other guidance methods to improve image quality. Finally, the
images generated by our model are of reasonably good quality, without having to
sustain the significant training costs of state-of-the-art text-to-image
models.
