MinImagen - Build Your Own Imagen Text-to-Image Model

This tutorial will guide you through training and using a simplified version of Imagen called MinImagen. Imagen is an advanced text-to-image model developed by Google, which uses large language models to generate high-quality images based on given captions. MinIMagen is designed as a lightweight and accessible alternative to the original Imagen model, making it easier for developers to understand how such models work. This tutorial assumes that you have a basic understanding of Python and Pytorch. The code provided in this tutorial should be run in an environment with these dependencies installed. You can install them using pip: ```bash pip install torch transformers matplotlib skimage numpy ``` The full code for MinIMagen is available on GitHub, but the scripts have been simplified and modified for the purposes of this tutorial. To follow along, you can clone the repository or download the necessary files: ```bash git clone https://github.com/google-research/imagen.git cd imagen/minimagen # Download the Conceptual Captions dataset (for training) bash ./scripts/download_dataset.sh ``` Now, let's move on to setting up the environment and running the scripts! Setting Up The Environment To start, create a new Python virtual environment: ```bash python3 -m venv .venv source .venv/bin/activate pip install -r requirements.txt ``` Now that your environment is set up, you can run the train script to generate "trained" MinIMagen instance weights: ```bash python minimagen_train.py ``` Generating Images with MinIMagen Once training is complete, you will see a new Training Directory, which stores all of the information from the training including model configurations and weights. To use this trained MinIMagen instance to generate images, run the inference script: ```bash python minimagen_inference.py ``` This should result in a new directory called generated_images_<TIMESTAMP>, which stores the captions used to generate the images, the Training Directory used to generate images, and the images themselves. The number in each image's filename corresponds to the index of the caption that was used to generate it. And that's it! You now have a basic understanding of how State-of-the-Art text-to-image models work and can use MinIMagen as a foundation for further exploration into this exciting field. For more Machine Learning content, feel free to check out more of our blog or YouTube channel. Alternatively, follow us on Twitter or follow our newsletter to stay in the loop for future content we drop. Follow the AssemblyAI Newsletter