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han2han-rl

cadazar

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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CAD-Coder

CADCODER

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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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animagine-xl-3.0

cagliostrolab

**Animagine XL 3.0** is the latest version of the sophisticated open-source anime text-to-image model, building upon the capabilities of its predecessor, Animagine XL 2.0. Developed based on Stable Diffusion XL, this iteration boasts superior image generation with notable improvements in hand anatomy, efficient tag ordering, and enhanced knowledge about anime concepts. Unlike the previous iteration, we focused on training the model to learn concepts rather than just aesthetics. - **Developed by**: Cagliostro Research Lab - **Model type**: Diffusion-based text-to-image generative model - **Model Description**: Animagine XL 3.0 is engineered to generate high-quality anime images from textual prompts. It features enhanced hand anatomy, better concept understanding, and prompt interpretation, making it the most advanced model in its series. - **License**: CreativeML Open RAIL++-M License - **Finetuned from model**: Animagine XL 2.0 Animagine XL 3.0 is accessible through user-friendly platforms such as Gradio and Google Colab:

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animagine-xl-3.1

cagliostrolab

.title-container { display: flex; justify-content: center; align-items: center; height: 100vh; } .title { font-size: 2.5em; text-align: center; color: #333; font-family: 'Helvetica Neue', sans-serif; text-transform: uppercase; letter-spacing: 0.1em; padding: 0.5em 0; background: transparent; } .title span { background: -webkit-linear-gradient(45deg, #7ed56f, #28b485); -webkit-background-clip: text; -webkit-text-fill-color: transparent; }

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animagine-xl-4.0

cagliostrolab

**Animagine XL 4.0**, also stylized as **Anim4gine**, is the ultimate anime-themed finetuned SDXL model and the latest installment of Animagine XL series. Despite being a continuation, the model was retrained from Stable Diffusion XL 1.0 with a massive dataset of 8.4M diverse anime-style images from various sources with the knowledge cut-off of January 7th 2025 and finetuned for approximately 2650 GPU hours. Similar to the previous version, this model was trained using tag ordering method for the identity and style training. With the release of **Animagine XL 4.0 Opt (Optimized)**, the model has been further refined with an additional dataset, improving **stability**, **anatomy accuracy**, **noise reduction**, **color saturation**, and **overall color accuracy**. These enhancements make **Animagine XL 4.0 Opt** more consistent and visually appealing while maintaining the signature quality of the series. - 2025-02-13 – Added Animagine XL 4.0 Opt - Better stability for more consistent outputs - Enhanced anatomy with more accurate proportions - Reduced noise and artifacts in generations - Fixed low saturation issues, resulting in richer colors - Improved color accuracy for more visually appealing results - 2025-01-24 – Initial release - **Developed by**: Cagliostro Research Lab - **Model type**: Diffusion-based text-to-image generative model - **License**: CreativeML Open RAIL++-M - **Model Description**: This is a model that can be used to generate and modify specifically anime-themed images based on text prompt - **Fine-tuned from**: Stable Diffusion XL 1.0

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图像生成开放权重
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It is BERT-base model pre-trained with indonesian Wikipedia and indonesian newspapers using a masked language modeling (MLM) objective. This model is uncased. This is one of several other language models that have been pre-trained with indonesian datasets. More detail about its usage on downstream tasks (text classification, text generation, etc) is available at Transformer based Indonesian Language Models This model was pre-trained with 522MB of indonesian Wikipedia and 1GB of indonesian newspapers. The texts are lowercased and tokenized using WordPiece and a vocabulary size of 32,000. The inputs of the model are then of the form:

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COST EXPLORER

好模型,也要算好成本。

费用分析

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

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

选型与常见问题
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。

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