Kohaku-XL-Delta

Kohaku-XL-Delta

KBlueLeaf

SDXL-based anime model trained on 3.6M images using LyCORIS fine-tuning. Features advanced tag system, quality ratings, and specialized artist style blending capabilities.

PropertyValue
LicenseFAIR AI Public License 1.0
ArchitectureStableDiffusionXL Pipeline
Training Dataset Size3.6M images
Research PaperLyCORIS Fine-tuning Paper

What is Kohaku-XL-Delta?

Kohaku-XL-Delta is the fourth major iteration in the Kohaku XL series, representing a significant advancement in anime-style image generation. Trained on a massive dataset of 3.6 million images using LyCORIS fine-tuning, this model was developed on consumer-grade hardware while maintaining professional-grade output quality.

Implementation Details

The model utilizes the LoKr algorithm with full matrix triggering and employs factors of 2-8 for different modules. Training was conducted on dual RTX 3090s over approximately 17-18 days, processing 28,638 steps with an equivalent batch size of 128.

  • Trained using Lion8bit optimizer with carefully tuned learning rates
  • Implements mixed precision FP16 training
  • Supports resolutions from 256x256 up to 4096x4096
  • Features advanced tag system with quality, rating, and date categorization

Core Capabilities

  • Sophisticated tag handling with support for all Danbooru tags (1000+ popularity)
  • Quality-based image generation with 7 distinct quality levels
  • Specialized artist style blending capabilities
  • Support for various aspect ratios and high-resolution outputs

Frequently Asked Questions

Q: What makes this model unique?

The model's distinctive feature is its comprehensive training on a carefully curated dataset, combined with advanced LyCORIS fine-tuning techniques. It offers superior anime-style image generation while maintaining the characteristic look of AI-generated art.

Q: What are the recommended use cases?

The model excels at generating anime-style images with specific quality levels and artistic styles. It's particularly effective when blending multiple artist tags rather than replicating specific artists' styles.

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