add initial support for Textual Inversion models

[ssube]

https://huggingface.co/docs/diffusers/training/text_inversion

[ssube]

#199 adds support for the sd-concepts family of inversions, but does not support all of the options available from Civitai.

[ssube]

#209 adds support for many of the inversions on Civitai.

The main difference is the shape of the tensor data, with some having an additional axis:

>>> t2["<jungle-punk>"].shape
torch.Size([768])

vs

>>> t = torch.load("autumn.ckpt")
>>> t["string_to_param"]["*"].shape
torch.Size([5, 768])
>>> t = torch.load("ghostly.ckpt")
>>> t["string_to_param"]["*"].shape
torch.Size([10, 768])

Looping over that first axis and generating a token for each layer works, and allows them to be controlled/weighted separately as well. I came across https://www.reddit.com/r/StableDiffusion/comments/zl81na/how_to_use_embeddings_with_pytorch/, and they were kind enough to point out https://github.com/kohya-ss/sd-scripts/blob/main/gen_img_diffusers.py#L2174, which does pretty much the same thing.

This still needs to be documented in the user guide, and may need some token replacement logic, but it definitely works.

[ForserX]

I looked. It turned out to get something similar by throwing a pack of tokens into the prompt. There is a suspicion that they need to be combined somehow.

[ssube]

The best solution for this is probably a single token that automatically expands into the correct number, #179, but that will require something like model_config.json in the inversion directories.

Until then, I've implemented infinite-length prompts with a range expansion syntax: autumn-{0,4} or winter-{0,9} for the simple case, with an optional third parameter for the step, even-steps-{0,99,2}.

With docs, too:

• https://github.com/ssube/onnx-web/blob/main/docs/converting-models.md#converting-textual-inversion-embeddings
• https://github.com/ssube/onnx-web/blob/main/docs/user-guide.md#using-and-controlling-textual-inversions