Krea2-R lokr 克系美学

Besides trigger words, please keep prompts under 30 words! Short descriptions, anime style auto-invasion.

Recommended for use in this workflow. More efficient and saves points.
https://www.RunningHub.ai/zh-cn/post/2110636832210817025

When used alone, a weight of 1 is sufficient. Since it leans towards anime invasion, using some realistic words directly might result in a more live-action look. When adding the trigger word: Cthulhu aesthetics, you can append words like digital artwork, scarlet mask, etc., afterwards to enhance the anime style.

When combined with face models, a weight of 1 is generally sufficient. Please refer to the last sample image for optional weights.

LoKr (Low-Rank Kronecker Product): Uses Kronecker product for decomposition (ΔW = C ⊗ (B·A)) (e.g., splitting 512×512 into 16×16 and 32×32). The Kronecker product has multiplicative rank properties, meaning LoKr can achieve a higher equivalent rank than LoRA with controllable parameter counts by designing the dimensions of the factor matrices, thereby introducing block-level structured repetition patterns. This structure makes LoKr typically smaller in parameter count and file size than LoRA at the same rank.

Possesses extremely strong expressive power and higher parameter efficiency. Under specific configurations, the parameter structure of LoKr is even more compact than LoRA, capable of capturing richer visual details and cross-dimensional complex interactions.

Training stability is medium, more sensitive to data quality, and usually requires high-quality, diverse training sets. It is especially suitable for generation tasks requiring style features, structured layouts, and complex spatial relationships (such as style transfer), as well as scenarios where model size needs to be significantly reduced in resource-constrained environments.

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