CompassMix XL Lightning LoRAs #

CompassMix Lightning is a refined SDXL-based model developed by Lodestone for frosting.ai, designed to deliver high-quality image generation in significantly fewer steps than traditional models. Distinguished by its ability to produce detailed outputs in just 8-16 steps while maintaining SDXL’s high resolution standards, it strikes an optimal balance between generation speed and image quality. The model features enhanced prompt adherence and supports ControlNet compatibility, making it particularly valuable for users who need quick yet high-quality image generation capabilities.

All LoRAs listed here are actually LyCORIS. This might be important in case the software you use makes you put them in separate folders or if you are generating from a cute Python script.

If you try these LoRAs out, make sure to set the CFG higher as I have diluted the distillation. 😳

LoRAs #

Characters

Character LoRAs specialize in teaching Stable Diffusion models to generate consistent representations of specific characters, including their facial features, body proportions, clothing, and distinctive attributes. These adaptations are particularly popular in the AI art community for creating consistent representations of original characters, celebrities, or fictional personalities. Character LoRAs require careful curation of training data (typically 20-50 high-quality images) that clearly show the character’s defining features from various angles and expressions. They primarily work by fine-tuning the model’s attention mechanisms to maintain consistency in facial features, body proportions, and characteristic details while allowing for natural variations in poses and expressions. The training process often focuses on the upper layers of the model responsible for high-level feature recognition and generation.

Styles

Style LoRAs for Stable Diffusion models focus on adapting the neural network to replicate specific artistic styles, visual aesthetics, or rendering techniques. These adaptations modify how the model interprets and generates visual elements like brushstrokes, color palettes, shading techniques, and overall artistic presentation. Style LoRAs can transform outputs to match anything from classical art movements (like impressionism or art nouveau) to modern digital art styles (such as anime, pixel art, or watercolor). They work by fine-tuning the model’s attention layers to recognize and reproduce distinctive visual patterns and techniques associated with the target style, while requiring relatively few training images (typically 15-50 high-quality examples) to achieve good results.