(TEST Version) ERIE (WRTN) STYLE LoRA
LoRA FEATURES
- Learned Style: This LoRA is trained on the art style of ERIE, the mascot character for WRTN's character chat service, 'CRACK'.
- Dual Functionality (Style vs. Chibi): This LoRA was trained with the dual goal of recreating the ERIE character and serving as a general art style LoRA. However, in its current version, using it purely as a style LoRA (without the 'chibi' tag) often yields anatomically superior and more stable results.
- Test Version: This is a test version. Some tag contamination has been observed when using the 'chibi' tag.
TRAINING & COMPATIBILITY
Base Model: NoobAI v1.1 EPS
Compatible Models: Works well with models based on ilxl, noobai, and most other anime-style checkpoints.
HOW TO USE
Trigger Word: erie (wrtn)
(Using just 'erie' might also work)
Recommended Tags: chibi, short
(Without these, the output will mainly mimic the simple color palette of the original ERIE art. Use 'short' depending on the context.)
To Generate the ERIE Character:
For a high probability of generating ERIE, include:
white hair, red eyes, low ponytail
Recommended Weights:
- LoRA Weight: 0.7 ~ 1.5
- 'chibi' Tag Weight: 0.7 ~ 1.5
CURRENT LIMITATIONS (Test Version)
(These can be somewhat mitigated by adjusting weights)
- Anatomical Instability (Chibi): Anatomical errors are more likely to occur, especially when using the 'chibi' tag to generate the character. For better anatomy, consider using it as a style LoRA without the 'chibi' tag.
- Backgrounds: The chibi style is more likely to break when generating complex backgrounds (i.e., anything other than 'white background' or 'simple background').
- Tag Complexity: The chibi style may degrade as prompt complexity increases.
- Line Art: High LoRA weights can result in messy or broken line art. Post-processing, such as using Hires. fix, is recommended to clean up the lines.
REASONS FOR LIMITATIONS (Excuses!)
- New Techniques: I used unfamiliar settings while experimenting with new training methods.
- Small Dataset: The model was trained on only 3 images, so it relies heavily on the base checkpoint's knowledge, which limits its own stylistic variation.
- Image Quality: The original ERIE images were mostly low-resolution. The process of upscaling and preparing them for training may have introduced some unwanted noise patterns.
