sd-webui-lobe-theme VS kohya_ss

upscayl is very approachable, but lacked many features i needed. i ended up using https://github.com/AUTOMATIC1111/stable-diffusion-webui after upscaling became part of my regular workflow, but for someone who just needs a few images enhanced, it's an ideal tool.

For the Git alternative, simply right-click on the location you want to put the Stable Diffusion and select “Git Bash Here”, then paste this on the CLI: git clone https://github.com/AUTOMATIC1111/stable-diffusion-webui

ComfyUI is similar to Houdini in complexity, but immensely powerful. It's a joy to use.

There are also a large amount of resources available for it on YouTube, GitHub (https://github.com/comfyanonymous/ComfyUI_examples), reddit (https://old.reddit.com/r/comfyui), CivitAI, Comfy Workflows (https://comfyworkflows.com/), and OpenArt Flow (https://openart.ai/workflows/).

I still use AUTO1111 (https://github.com/AUTOMATIC1111/stable-diffusion-webui) and the recently released and heavily modified fork of AUTO1111 called Forge (https://github.com/lllyasviel/stable-diffusion-webui-forge).

Seems like an interesting project. Regarding the name, is there permission to use something so similar to AUTOMATIC1111 [1]?

> Diffusers will Cuda out of memory/perform very slowly for huge generations, like 2048x2048 images, while Auto 1111 SDK won't.

Do we have some numbers on this? I have seen AUTOMATIC1111 fall-over whilst using only half the available of GPU VRAM - there seems to be some weirdness where it tries to allocate before de-allocating the last batch or something.

> You can use any of the 6 compatible RealEsrgran models/weights with our RealEsrgran pipeline for upscaling images. Here are the model ids:

I've previously had trouble trying to use AUTOMATIC1111 upscalers, it seems like it needs more GPU VRAM than just generating the image already upscaled.

[1] https://github.com/AUTOMATIC1111/stable-diffusion-webui

You might be thinking of Fooocus: https://github.com/lllyasviel/Fooocus

The Stable Diffusion web interface that got a lot of people's attention originally was Automatic1111: https://github.com/AUTOMATIC1111/stable-diffusion-webui

Fooocus is definitely more beginner friendly. It does a lot of the prompt engineering for you. Automatic1111 has a ton of plugins, most notably ControlNet which gives you fine grained control over the images, but there is a learning curve.

Stable Diffusion https://github.com/AUTOMATIC1111/stable-diffusion-webui

Make it into a stable diffusion server!

There's no link version, you have to run it locally. You install it from here

Repository: AUTOMATIC1111/stable-diffusion-webui · Tag: v1.7.0-RC · Commit: 48fae7c · Released by: AUTOMATIC1111

Many of the options are explained here https://github.com/bmaltais/kohya_ss/wiki/LoRA-training-parameters

training in BF16 might solve this issue from what I saw in this ticket. I know other people ran into the issue too https://github.com/bmaltais/kohya_ss/issues/1382

for lycoris loras you can use the command-line script from the kohya-ss repo https://github.com/bmaltais/kohya_ss/blob/master/networks/merge_lora.py i have an older version checked out from late july, it had a separate merge_lycoris.py for for this purpose, it's probably unified now in a single file

I usually do that with kohya_ss, a tool made for making LoRAs and finetunes. It might be a bit of a pain to set up just to do this one task, but if nobody gives you an easier method, look into it. https://github.com/bmaltais/kohya_ss

After that, make your staging directory, and do the git clone https://github.com/bmaltais/kohya_ss.git, and navigate inside of it. Now, here's where things can become a pain. I used pyenv to set my system level python to 3.10.6 with pyenv global 3.10.6, though you can probably just use "local" and do it for the current shell. You NEED it to be active however before you set up your venv. If you do python --version and get 3.10.6, you're ready for this next part. Make your venv with python -m venv venv. This is the simplest way, it'll create a virtual environment in your current folder named venv. You'll do a source venv/bin/activate and then do which python to make sure it's using the python from the venv. Now for the fun part. The included setup scripts have been flaky for me, so I just went through the requirements and installed everything by hand. I'm going to do this guide right now for nvidia, because I just got a 4090 for this stuff. If this ends up working well for others and there's demand, I'll try to reproduce this for AMD (But I'll be honest, I got an nvidia card because bitsandbytes doesn't have full rocm support, nor do most libraries, so it's not very reliable). After installing everything and testing it works at least at a basic level for dreambooth training, my finished requirements.txt for pip is as below:

Earlier I created a post where I asked for recommendations for LoRA model training tutorials. The first one I looked at used the kohya_ss GUI. That GitHub repo already has two tutorials, which are quite good, so I ended up using those:

I have tried my best to research this issue and have not come up with much. It is obvious that its a backend issue right? The guides that I used https://github.com/bmaltais/kohya_ss and https://github.com/pyenv-win/pyenv-win/

I am training on a 12GB 3060 using kohya_ss. Is there a setting or something I'm missing?