Text to image using Jupyter Notebook on Google Colab.
Overview
In one of my articles, I went through code examples on image classification using CLIP and ResNext. In this article let’s go through a few algorithms and libraries which can be used to generate an image from text.
CLIP + BigGAN
This library uses OpenAI’s CLIP and the generator from a BigGAN to generate images from text.
- From Colab, open this notebook.
- I use 100 iterations and 5 epochs to reduce the time to generate the image.
And this is the image generated for the phrase “flying cartoon elephant”.
CLIP + Siren
This library uses OpenAI’s CLIP and Siren to generate images from text.
- From Colab, open this notebook.
- I use 100 iterations and 5 epochs to reduce the time to generate the image. Increase them for a better image.
And this is the image generated for the phrase “flying cartoon elephant”.
DALL-E
OpenAI’s DALL-E is the new neural network that can be used to generate images from text. It is a huge 12-billion parameter version of GPT-3.
The DALLE-PyTorch library is an implementation of the paper. This model takes time to train but shows amazing results. A good explanation is available in this video.
- From Colab, open this notebook.
- I use the OpenAI model with a depth of 6 to reduce the time to generate the image.
And this is the image generated from the phrase “fireflies in a field under a full moon”
References
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