As someone with ~0 knowledge in this field, I think this has to do with a concept called "transfer learning" in which you once train with that kazillion of images, then use that same "coefficients" for further run of the NN.
The goal is usually to have the more fundamental parts of your model already working and you thus need way less domain specific data.
Here, you're not training anything, you're running the models (both the CLIP language model and the unet) in feedforward. That's just deploying your model, not transfer learning.