Here is the home of the zoo for the liminal suburbs contest winner loras.
Prompt using "liminal", "suburban", "suburban liminal", and many variations. It's tagged with plain English unless specified with "tags" or "tags only", those are tagged with specific tags related to objects on the scene.
Use the model's quality tags and necessary detail tags to increase quality.
If they're SDXL based you can probably just swap them around all SDXL based models for fun results.
Suburban Liminal SD15 - 1024x1024 - Epoch 10 - 20,000 samples~ Civit
Suburban SDXL10 - 2048x2048 - Epoch 10 - 50,000 samples - Civit
Suburban SD3.5 M - 1024x1024 - Epoch 10 - 20,000 samples~ Civit
Suburban Flux1D - 1216x1216 - Epoch 10 - 50,000 samples - CIVIT
Suburban SimNoobV4-Vpred - pending
Suburban NoobSimV4-Vpred - pending
Suburban PonySimV4-Vpred - pending
Suburban SimSDXL V4 Epsilon - 1024x1024 - Epoch 10 ~ 50,000 samples - CIVIT
Tags only
I trained this one particularly because SimV4 is more stable than the others for architecture.
Suburban Liminal epiCRealism VXVI LAST FAME - Epoch 10 - 20,000 samples~ Civit
Tags only
I chose this because the quality is very high and the outcome is very high quality for uniformity with objects and humans.
Suburban SimPonyV4-Vpred - not released yet dead model
Suburban Illustrious - 1216x1216 - Epoch 10 ~ 50,000 samples - CIVIT
Redone v2 1024x1024 -> tags only, hand tagged
Suburban PonyV6 - 1216x1216 - Epoch 10 ~ 50,000 samples - CIVIT ~ Pending
Suburban is additions that were contest winners from Suburban Liminal, and they are absolutely choice.
Description
Trained specifically for epiCRealism VXVI.
I find it very difficult to get good variation on images generated with epiCRealism using this lora trained on epiCRealism directly, maybe you'll have better luck.
It definitely conforms to uniformity very well, so if you want to create very deep or complex uniform styles it's fantastic at that.
This version is trained with the exact same settings as SimV4 epsilon's Suburban lora, but the SimV4's version retains a higher variance of complexity on the output.