stable-diffusion VS Dreambooth-Stable-Diffusion

Looks like Doggettx is a fork of CompVis/stable-diffusion, as a proof of concept:

Don't use a UI if you want terminal access. Use a project meant for terminal. https://github.com/Doggettx/stable-diffusion/tree/autocast-improvements

The feature is just called prompt editing or prompt2prompt. It is also implemented in the Automatic1111 webui.

Depends on what fork you are using but its just means you are running out of vram since it states you only have 4gb of it. You may need to use the optimizedsd scripts and use the Doggettx's attention.py, you can find this in ldm/modules/attention.py (I personally have 2 of those in my own folder since I need to switch them but typically you require 6gb min for sd.

I installed SD from the CompVis GitHub repo and then swapped in modifications (namely attention.py and main.py) done by u/Doggettx that can be found here to overcome CUDA Out Of Memory issues. Going to try larger image sizes next. I wish you all good luck with concentrating on real work with this imaginatron around! 🤠

using optimized attention.py and model.py from this github issue.

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