kohaku-v2.1
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查看详情 ↗DISCOVER & COMPARE
探索 AI 模型 84999
Kohaku-XL-Delta
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查看详情 ↗Kohaku-XL-Epsilon
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查看详情 ↗Kohaku-XL-Epsilon-rev2
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查看详情 ↗Kohaku-XL-Zeta
KBlueLeaf.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)
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价格待核验多模态与渠道费用须分别核对
查看详情 ↗TIPO-100M
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查看详情 ↗TIPO-200M
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查看详情 ↗TIPO-200M-ft
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查看详情 ↗TIPO-200M-ft2
KBlueLeafIn this project, we introduce "TIPO" (**T**ext to **I**mage with text presampling for **P**rompt **O**ptimization), an innovative framework designed to significantly enhance the quality and usability of Text-to-Image (T2I) generative models. TIPO utilizes the Large Language Models (LLMs) to perform "Text Presampling" within the inference pipeline of text-to-image generative modeling. By refining and extending user input prompts, TIPO enables generative models to produce superior results with minimal user effort, making T2I systems more accessible and effective for a wider range of users. Use updated version of DTG extension (renamed to z-tipo-extension), current version of z-tipo-extension support stable-diffusion-webui, stable-diffusion-webui-forge and ComfyUI. SD-Next haven't been tested. https://github.com/KohakuBlueleaf/z-tipo-extension This model is LLaMA arch with 200M parameters, the training data is combined version of Danbooru2023, Coyo-HD-11M. The total token seen is around 50B tokens. For more information please refer to the tech report and following table.
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查看详情 ↗TIPO-500M
KBlueLeaf500M LLaMA arch model trained for TIPO. Tech Report: https://arxiv.org/abs/2411.08127 In this project, we introduce "TIPO" (**T**ext to **I**mage with text presampling for **P**rompt **O**ptimization), an innovative framework designed to significantly enhance the quality and usability of Text-to-Image (T2I) generative models. TIPO utilizes the Large Language Models (LLMs) to perform "Text Presampling" within the inference pipeline of text-to-image generative modeling. By refining and extending user input prompts, TIPO enables generative models to produce superior results with minimal user effort, making T2I systems more accessible and effective for a wider range of users. Use updated version of DTG extension (renamed to z-tipo-extension), current version of z-tipo-extension support stable-diffusion-webui, stable-diffusion-webui-forge and ComfyUI. SD-Next haven't been tested. https://github.com/KohakuBlueleaf/z-tipo-extension
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查看详情 ↗TIPO-500M-ft
KBlueLeaf500M LLaMA arch model trained for TIPO. Tech Report: https://arxiv.org/abs/2411.08127 In this project, we introduce "TIPO" (**T**ext to **I**mage with text presampling for **P**rompt **O**ptimization), an innovative framework designed to significantly enhance the quality and usability of Text-to-Image (T2I) generative models. TIPO utilizes the Large Language Models (LLMs) to perform "Text Presampling" within the inference pipeline of text-to-image generative modeling. By refining and extending user input prompts, TIPO enables generative models to produce superior results with minimal user effort, making T2I systems more accessible and effective for a wider range of users. Use updated version of DTG extension (renamed to z-tipo-extension), current version of z-tipo-extension support stable-diffusion-webui, stable-diffusion-webui-forge and ComfyUI. SD-Next haven't been tested. https://github.com/KohakuBlueleaf/z-tipo-extension
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查看详情 ↗TIPO-v2.1-1B-A200M
KBlueLeaf**TIPO-v2.1-1B-A200M** is a **1B-A200M** sparse model. 991M total parameters, ~200M active per token, plus a ~50M embedding table. It is a full retrain of TIPOv2 on a **corrected dataset**, for **longer**, at **4096 context**, in **pure fp16**. TIPO is a framework for improving Text-to-Image generation by *text presampling*: a small language model expands a short user prompt into a detailed one before the diffusion model ever sees it. Pre-sampling a smaller distribution by narrowing the range indicated by a brief prompt to a more specific description which matches the original prompt allows the diffusion model to have more information to work with, while preserving overall diversity and fidelity. Instead of asking the user to write 200 tokens of booru tags and natural language, TIPO samples that expansion from a distribution learned over real caption data. This is **v2.1**. Same architecture and same data sources as v2, retrained from scratch after three defects were found in how v2's training examples were built.
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查看详情 ↗TIPOv2-1B-A200M
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查看详情 ↗This is an uncensored version of Qwen/Qwen3.8-27B created with abliteration (see remove-refusals-with-transformers to know more about it). This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens. The newly added **Huihui-Qwen3.8-27B-abliterated-Underdog-Saluki** series come from ConwayResearch/Underdog-Saluki-27B-1.0 and ukisai/Swift-1.5-Qwen3.8-27B-GSQ-RCO-GGUF. Only layers **22 to 52 (0-based indexing)** have been ablated, while the other layers remain unablated. It may come with a small disclaimer warning. Due to ConwayResearch/Underdog-Saluki-27B-1.0 not providing an imatrix file, we will attempt to perform ablation by mixing the GGUF weights of Swift-1.5 and ConwayResearch/Underdog-Saluki-27B-1.0.
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查看详情 ↗NeoHorse-Jev-4B-GGUF
kcatjan基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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查看详情 ↗Qwen3.8-27B-OBLITERATED
kcplummer基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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查看详情 ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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查看详情 ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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价格待核验多模态与渠道费用须分别核对
查看详情 ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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查看详情 ↗Kev-27B-GGUF
kdercksen基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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查看详情 ↗基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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查看详情 ↗Mistral-Nemo-Instruct-2407-abliterated-AWQ
KebalBaguette基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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输入 待核验→ 输出 待核验
价格待核验多模态与渠道费用须分别核对
查看详情 ↗Qwen2.5-7B-Instruct-abliterated-v2-AWQ
KebalBaguette基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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价格待核验多模态与渠道费用须分别核对
查看详情 ↗Qwen3-Coder-Next-GGUF
kebengdou基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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查看详情 ↗暂未找到匹配模型
可以调整厂商、分类或输入输出条件；新模型正持续收录。
COST EXPLORER
好模型，也要算好成本。
调整月度 Token 用量，直观比较不同模型的预估 API 开支。
1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价
仅比较同一渠道的基础文本 Token 报价
使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。
MODEL HANDBOOK
读懂模型，再做选择
API 价格应该怎么比较？+
先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。
开放权重，就代表可以免费商用吗？+
不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。
排行榜第一，就是最适合我的模型吗？+
榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。