This LoRA is designed to capture the distinct look and feel of early 80s fantasy movies.The trigger words are ArsMovieStill, 80s Fantasy Movie StillZ-IMAGE UPDATE: V2 dataset officially became my go-to test dataset when training a model for the first time. Experimental! Default ComfyUI LoRA Loader Reports Errors, but actually applys the LoRA. Recommended usage settings:<ol><li>Your favourite spot.</li><li>Listen to Castle Rat as background.</li><li>Enjoy!</li></ol><div data-youtube-video><iframe width="640" height="480" allowfullscreen="true" autoplay="false" disablekbcontrols="false" enableiframeapi="false" endtime="0" ivloadpolicy="0" loop="false" modestbranding="false" origin playlist src="https://www.youtube.com/embed/iWKmiTwjjHA?rel=1" start="0"></iframe></div> MJ7 UPDATE: Building each dataset is a joy, so with the release of MidJourney 7, I naturally started this one again from scratch.This version stays true to the original aesthetic but with improved fidelity and an even stronger 80s vibe.It’s trained on a fresh, carefully curated set of ~250 images, created by referencing the best results from the previous versions. V3 UPDATE: The training dataset has been further expanded to 1,000 curated images, pushing it to the limits of Civit’s on-site training capabilities.This update include broader selection of fantasy environments, mythical creatures, and epic scenes, enhancing the fidelity to the iconic 80s fantasy aesthetic. V2 UPDATE: Added to the dataset a huge amount of Fantasy Characters, beasts and scenes. Use v1 for stronger effects and v2/v3 for better versatility.

80s Fantasy Movie

This is my first Wan2.2 LoRA, because it was requested by few people. Given the rumors that Wan3 is coming soon, might be the last one :) This is STRICTLY Text2Image. It will have almost no effect on actual video generation as it is Trained on 1 Frame with the V2 Dataset. This is Low LoRA, so if you use Workflow that uses Both High and Low - make sure that you have attached it to the correct place. Have fun.

Files with SHA256: 9780f7be04c43b026ddb4ace2357d5dce9f36703116771746ea200b5de3b60cb