SURPRISE ;)Merry Christmas, everyone. As a little Christmas gift for you all, I've been working on a new version for the SD 1.5 version of Juggernaut in my spare time for the past few weeks.I took a small portion of the JuggernautXL dataset and trained it on SD 1.5 (Base Juggernaut Final), while also updating the epiCRealism model in the merge. However, the RPG and Diva models that were added in the 'Aftermath' version have been removed.Images were created with the following settings: 512*768 Sampler: DPM++ 2M Karras Steps: 35 CFG: 7 HiRes Fix: Sampler: 4xNMKD Siax 200k Steps: 15 Denoise: 0.5 Upscale: 2Lastly, the recipe for the last Juggernaut (Aftermath) version on SD 1.5:SD 1.5 BaseTraining Set 1 (0.65)<a target="_blank" rel="ugc" href="https://civarchive.com/models/81458">Absolute Reality</a> (0.18)<a target="_blank" rel="ugc" href="https://civarchive.com/models/25694?modelVersionId=105035">epiCRealism V3</a> (0.2)<a target="_blank" rel="ugc" href="https://civarchive.com/models/98755?modelVersionId=105639">Humans</a> (0.15)Skin Enhancer (0.15)<a target="_blank" rel="ugc" href="https://civarchive.com/models/41809?modelVersionId=53221">Better Portrait Lighting</a> (0.2)Training Set 2 (0.55)<a target="_blank" rel="ugc" href="https://civarchive.com/models/1116?modelVersionId=124626">RPG 5</a> (0.38)<a target="_blank" rel="ugc" href="https://civarchive.com/models/113337?modelVersionId=122472">Divas</a> (0.15)And of course the NinjaFix made by @chillpixelThanks all those Creators for their hard work :)Of Course you can run "Juggernaut" on <a target="_blank" rel="ugc" href="https://tensor.art/u/597789882022103034">Tensor.Art</a>

Juggernaut

Big Update for Version 2I merged Juggernaut 1.9 (which was built from the scratch) with my new Trained Model (still in training/right now at 50k Steps)The New Model is trained on 4K Images. I will put it online once its finished (right now its not stable for a Standalone Release), but merging with Juggernaut already worked pretty fine :)Also the Ninja Fix got an Update.

Files with SHA256: 4ba1a308ab63fa51cec2a1591cc7e703fdeef96e0c8ec8383e6f256057d02de0