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open-calm-small

cyberagent

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。

基础资料已收录 · 缺失资料后续补充

文本生成
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CyberRealistic

cyberdelia

**CyberRealistic** is a photorealistic image generation model built upon the **Stable Diffusion 1.5 (SD 1.5)** architecture. Developed by Cyberdelia, this model excels at producing lifelike portraits and scenes with minimal prompt engineering, making it ideal for creators seeking high-quality outputs without complex configurations. - **Model Type**: Text-to-Image Generation - **Base Model**: Stable Diffusion 1.5 (SD 1.5) - **Format**: `safetensors` - **Version**: v8.0 - **Creator**: Cyberdelia - **File Size**: ~6.3 GB - **License**: CreativeML Open RAIL++-M License - **Photorealism**: Generates highly detailed and realistic images, particularly effective for human subjects. - **Ease of Use**: Designed for simplicity; achieves impressive results with straightforward prompts. - **Integrated VAE**: Comes with a baked-in Variational Autoencoder for enhanced image quality. - **Versatility**: Suitable for various applications, including portraits, fashion, and cinematic scenes.

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图像生成
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**CyberRealistic Flux** CyberRealistic Flux (FLUX.1 dev)! It’s designed to make realistic images, both safe-for-work and not-so-safe-for-work. It’s not perfect yet, but it’s a solid start and sets things up for what’s coming next.

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图像生成
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**CyberRealistic Pony** is the awesome Pony Diffusion with some CyberRealistic elements. - **Photorealism**: Generates highly detailed and realistic pony images, capturing intricate textures and lighting. - **Ease of Use**: Achieves impressive results with straightforward prompts. - **Integrated VAE**: Comes with a baked-in Variational Autoencoder for enhanced image quality. - **Versatility**: Suitable for various applications, including character design, illustrations, and concept art. - May produce content that could be considered sensitive; use responsibly. - Some prompts involving abstract or non-pony content may not perform as well due to the model's specialized training. - Lighting and textures may occasionally be too clean or smooth depending on sampling choices.

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图像生成
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**CyberRealistic XL** is a photorealistic image generation model based on **Stable Diffusion XL 1.0 (SDXL)**. It is designed to produce highly realistic, cinematic-quality outputs with minimal prompt engineering. This checkpoint is optimized for portraits, fashion, and editorial-style scenes, but also handles a wide range of creative prompts with remarkable fidelity. - **Model Type**: Image generation (Text-to-Image) - **Base Model**: Stable Diffusion XL 1.0 - **Format**: `safetensors` - **Latest Version**: v5.7 - **Creator**: Cyberdelia - **File Size**: ~6.31 GB - **License**: CreativeML Open RAIL++-M License - ⚡ **Ultra-Photorealism** – Exceptional detail in faces, lighting, and textures. - 🧩 **Effortless Prompting** – Works well with short prompts, but supports complex ones too. - 🪞 **Skin & Eye Fidelity** – Improved clarity in eyes, skin detail, and facial structure. - 🔧 **Built-in VAE** – No need for an external VAE; optimized out of the box.

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图像生成
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**CyberRealistic XL Desire** is a photorealistic image generation model based on **Stable Diffusion XL 1.0 (SDXL)**. It is designed to produce highly realistic, cinematic-quality outputs with minimal prompt engineering. This checkpoint is optimized for portraits, fashion, and editorial-style scenes, but also handles a wide range of creative prompts with remarkable fidelity.

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图像生成
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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SecureBERT-NER

CyberPeace-Institute

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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文本分析
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This model is based on DistilBERT and has been fine-tuned for multilabel classification of Emails and URLs as safe or potentially phishing. - __Base Architecture:__ DistilBERT - __Task:__ Multilabel Classification - __Fine-tuning Framework:__ Hugging Face Trainer API - __Training Duration:__ 3 epochs - __Accuracy:__ 99.58 - __F1-score:__ 99.579 - __Precision:__ 99.583 - __Recall:__ 99.58

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文本分析
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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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