I’m struggling to understand your question. Word vectors are a bunch of vectors representing words. ULMFiT is a process for training a text classification model. The models used in ULMFiT include word embeddings but that doesn’t make ULMFiT a word embedding. It’s like you’re asking how a steering wheel is different from driving a car.
Also what’s the intuition behind trying to create an “image input” with word vectors? Are you thinking of putting it through an image type CNN with 2D convolutions? Why that instead of using a 1D convolution?