On my end, it’s sitting at ~64GB (with btrfs compression shenanigans), though 60 of those are from all the models I have installed. The download would probably be ~2GB, even less if you disable downloading the “default” models with --no-download-sd-model and instead pick models off of Civit or wherever manually.
Edit: Should have mentioned. Most full models are between 2-4 GBs each. Some can be 5+ but they tend to be “full” versions intended for merging & such. LoRAs are generally smaller. Depending on how much they’re pruned they’ll be anywhere between 10-100 MBs each.
how confusing is the software to use?
There’s definitely a learning curve, yes. But there’s plenty of resources (and more importantly, examples) out there.
what kind of limitations does the software have? can i do multiple people? monsters? futa? etc.
As long as you have the correct models set up it can generate basically anything. At least with anime models, monsters and futa are a given. Your main issue will probably be multiple people, although there are solutions to that. (See the multidiffusion upscaler GitHub repo on the main post)
On my end, it’s sitting at ~64GB (with btrfs compression shenanigans), though 60 of those are from all the models I have installed. The download would probably be ~2GB, even less if you disable downloading the “default” models with
--no-download-sd-model
and instead pick models off of Civit or wherever manually.Edit: Should have mentioned. Most full models are between 2-4 GBs each. Some can be 5+ but they tend to be “full” versions intended for merging & such. LoRAs are generally smaller. Depending on how much they’re pruned they’ll be anywhere between 10-100 MBs each.
There’s definitely a learning curve, yes. But there’s plenty of resources (and more importantly, examples) out there.
As long as you have the correct models set up it can generate basically anything. At least with anime models, monsters and futa are a given. Your main issue will probably be multiple people, although there are solutions to that. (See the multidiffusion upscaler GitHub repo on the main post)