These text-to-graph problems seem like a good candidate for someone to create a training-dataset/benchmark of.
Bear in mind that the training data for these models has been mostly images and their alt text, scraped off the web. There is a good chance that there's nothing remotely like the examples given here in the training data. (People don't caption their graphs like that.) These models are undoubtably good at doing what they have been trained to do - but I think no-one disagrees that there's plenty of room for improvement.
(And bear in mind that these text2image models only released this year, and that this tech in general has only been invented in the last couple of years, so it's very early days...)
Bear in mind that the training data for these models has been mostly images and their alt text, scraped off the web. There is a good chance that there's nothing remotely like the examples given here in the training data. (People don't caption their graphs like that.) These models are undoubtably good at doing what they have been trained to do - but I think no-one disagrees that there's plenty of room for improvement.
(And bear in mind that these text2image models only released this year, and that this tech in general has only been invented in the last couple of years, so it's very early days...)