Dreambooth-Stable-Diffusion VS sd-enable-textual-inversion

I did search online for an answer, but I am a PC noob, I didn't know what to do when I found this solution in this link: https://github.com/XavierXiao/Dreambooth-Stable-Diffusion/issues/113

Do you know some projects or guided tutorials that could help me? How many drawings with the desired style I should then have to give to train the AI model? I found Dreambooth on Stable Diffusion but it seams to be for another use case.

This seems to be an issue with the code and or the environment itself. There is an open bug for this where some suggestions are p provided by others on how to fix. https://github.com/XavierXiao/Dreambooth-Stable-Diffusion/issues/47

Could you provide some more detail on how this works? Did you just use this GitHub repository or did you put together your own implementation?

I think the best way to do this would be to train Dreambooth on a number of photos of Ray Liotta first, and use Stable Diffusion instead. https://github.com/XavierXiao/Dreambooth-Stable-Diffusion

Dreambooth. There are other ways, but that is the gold standard. It takes even more Vram than regular stable diffusion, so if you don't have a very beefy card (e.g. 4090 with 25 GB VRAM) various websites let you do it onlin for a small fee. You then download a new model that has all the old stuff (e.g.the 4 gigabyte SD 1.5 file) plus your new images. Like I said, there are other ways that are easier, but when people show great results they are usually talking about Dreambooth.

THE CODE (unofficial implementation, for the exact wording stating how little images you need read the paper) is designed with extremely little data in mind. I don't know how else to phrase it dude, do you think the training is a magic black box that runs with snail neurons? If you train a dreambooth model the jupyter ide makes calls to python files, those are the files. That is the code

Yes, what you’re describing can be done using textual inversion.

https://github.com/hlky/sd-enable-textual-inversion

It creates a representation of an entity and allows rending it in different styles and contexts. Currently it involves model fine tuning, but I expect it will become convenient as the power of the operation becomes clear. And once it's convenient, you'll be able to do the progressive queries you're asking for (and it'll be a lot easier to create narratively coherent sets of images.)

A branch to add it to SD (which I think the original now has): https://github.com/hlky/sd-enable-textual-inversion

Hey now, there's still an option out there, this Colab notebook, it's able to run on the free tier of Colab. By default, it runs for about 2 hours per embed, and the files made can be used both in the notebook, and on Hlky's front-end, after enabling it, and setting to full precision.