Embedding a single image? Extracting style from multiple embeddings?

[Luke2642]

Firstly, Thank you everyone for the amazing work so far.

My question is inspired by:

https://pub.towardsai.net/stable-diffusion-based-image-compresssion-6f1f0a399202

Does anyone have any tips on the fastest way to train a TI embedding to reproduce a single image?
What's the smallest number of vectors you've needed?
Can anyone explain what the seed would do in this scenario, or would training have to be on a fixed seed? Or "find the seed" too?

Embeddings could be used anywhere you'd use a CLIP prompt / BLIP caption but a vector embedding rather than inefficient human language?

They also open the possibility of new features:

a) "Extract style from embeddings" to extract the common elements in each embedding, so generate an instant style?
b) "Joint object style training" A modified version of textual inversion training that jointly retrains [unique image embedding] and trains a new [joint style embedding] to reproduce each image to a higher fidelity than in the original training, thus training a meaningful joint style embedding?

I'm just thinking, if optimised, this could be a faster and deterministic approach, as the creation of the original embeddings can be done by overfitting. Or does this already exist?