MODEL PROFILE

Kohaku-XL-Zeta

KBlueLeaf · Kohaku-XL-Zeta

.image-viewer {position: relative;width: 100%;margin: 0 auto;display: flex;flex-flow: wrap;align-items: center;justify-content: center;} .image-viewer input[type="radio"] {display: none;} .image-viewer label {border-radius: 10%;padding: 20px;background-color: #B398F5;background-size: cover;background-position: center;cursor: pointer;color: black;margin: 8px;} .image-viewer label:hover {background-color: #4C88F5;padding: 24px;margin: 4px;} .image-viewer input[type="radio"]:checked + label {background-color: #6296F5;padding: 28px;margin: 0px;} .image-container {position: relative;width: 100%;height: 50vh;margin: 1rem 1rem 0 0;} .inner-container {position: absolute;width:100%;height: 100%;display: flex;align-items: center;justify-content: center;} .inner-container img {border-radius: 10px;max-height: 100%;max-width: 100%;height: 0;width: 0;opacity: 0;transition: opacity 0.5s ease, height 0.25s ease, width 0.25s ease;} As same as Kohaku XL Epsilon or Delta, but you can replace "general tags" with "natural language caption". You can also put both together. For better ability on some certain concepts, I use full danbooru dataset instead of filterd one. Than use crawled Pixiv dataset (from 3~5 tag with popularity sort) as addon dataset. Since Pixiv's search system only allow 5000 page per tag so there is not much meaningful image, and some of them are duplicated with danbooru set(but since I want to reinforce these concept I directly ignore the duplication)

Image generationlicense:other
Context windowNot disclosed
Input → OutputText → Image
Model weightsNot verified
Profile updated2026/11/2 19:16:07
OVERVIEW

Model overview

.image-viewer {position: relative;width: 100%;margin: 0 auto;display: flex;flex-flow: wrap;align-items: center;justify-content: center;} .image-viewer input[type="radio"] {display: none;} .image-viewer label {border-radius: 10%;padding: 20px;background-color: #B398F5;background-size: cover;background-position: center;cursor: pointer;color: black;margin: 8px;} .image-viewer label:hover {background-color: #4C88F5;padding: 24px;margin: 4px;} .image-viewer input[type="radio"]:checked + label {background-color: #6296F5;padding: 28px;margin: 0px;} .image-container {position: relative;width: 100%;height: 50vh;margin: 1rem 1rem 0 0;} .inner-container {position: absolute;width:100%;height: 100%;display: flex;align-items: center;justify-content: center;} .inner-container img {border-radius: 10px;max-height: 100%;max-width: 100%;height: 0;width: 0;opacity: 0;transition: opacity 0.5s ease, height 0.25s ease, width 0.25s ease;} As same as Kohaku XL Epsilon or Delta, but you can replace "general tags" with "natural language caption". You can also put both together. For better ability on some certain concepts, I use full danbooru dataset instead of filterd one. Than use crawled Pixiv dataset (from 3~5 tag with popularity sort) as addon dataset. Since Pixiv's search system only allow 5000 page per tag so there is not much meaningful image, and some of them are duplicated with danbooru set(but since I want to reinforce these concept I directly ignore the duplication)

Key features

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Limitations & considerations

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PRICING & COST

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

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Frequently asked questions

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Sources & traceability

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