sd-webui-supermerger VS stable-diffusion-webui-model-toolkit

Have u tried the supermerger extension ? https://github.com/hako-mikan/sd-webui-supermerger

Have u looked at https://github.com/hako-mikan/sd-webui-supermerger - it may or may not allow you to give comment. Im going on what im experiencing with this LORA experiment, it sort of updates parts of it, and if u put balanced models back in on reversing or offsetting the overcooked stage it gets to over time the model does seem to improve versus base 1.5.

I found this extension: supermerger

I merged this on Colab, so you don't need a powerful graphics card. You'll need the block merge extension,the model toolkit extension and the supermerger extension.

there is an extension for auto1111 that can clean up models. GitHub - arenasys/stable-diffusion-webui-model-toolkit: A Multipurpose toolkit for managing, editing and creating models.

It's in the available extensions list or here: https://github.com/arenasys/stable-diffusion-webui-model-toolkit

Have you tried using the https://github.com/arenasys/stable-diffusion-webui-model-toolkit extension to prune the mega mix and see what the difference is?

Pytorch2.0 seems to use a bunch more memory on my end, though more testing is necessary to confirm that. This would limit your image size. But, you can reclaim some of that back if you run your models through the model toolkit. It's an extension that cuts out unneeded data from your model files, which will save space in your VRAM. Cut realisticVision almost in half (3.7GB > 2.0GB). | You can also run a lower batch size but a higher batch count.

You might be able to use the model toolkit to remove extra bloat from models to get it under that VRAM requirement.