llava-next-72b-hf
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查看详情 ↗DISCOVER & COMPARE
探索 AI 模型 85001
LLaVA-NeXT-Video-7B-32K-hf
llava-hf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 ↗LLaVA-NeXT-Video-7B-DPO-hf
llava-hf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 ↗LLaVA-NeXT-Video-7B-hf
llava-hfCheck out also the Google Colab demo to run Llava on a free-tier Google Colab instance: [](https://colab.research.google.com/drive/1CZggLHrjxMReG-FNOmqSOdi4z7NPq6SO?usp=sharing) Disclaimer: The team releasing LLaVa-NeXT-Video did not write a model card for this model so this model card has been written by the Hugging Face team. **Model type:** LLaVA-Next-Video is an open-source chatbot trained by fine-tuning LLM on multimodal instruction-following data. The model is buit on top of LLaVa-NeXT by tuning on a mix of video and image data to achieve better video understanding capabilities. The videos were sampled uniformly to be 32 frames per clip. The model is a current SOTA among open-source models on VideoMME bench. Base LLM: lmsys/vicuna-7b-v1.5
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查看详情 ↗llava-onevision-qwen2-0.5b-ov-hf
llava-hfCheck out also the Google Colab demo to run Llava on a free-tier Google Colab instance: [](https://colab.research.google.com/drive/1-4AtYjR8UMtCALV0AswU1kiNkWCLTALT?usp=sharing) Below is the model card of 0.5B LLaVA-Onevision model which is copied from the original LLaVA-Onevision model card that you can find here. **Model type:** LLaVA-Onevision is an open-source multimodal LLM trained by fine-tuning Qwen2 on GPT-generated multimodal instruction-following data. LLaVA-OneVision is the first single model that can simultaneously push the performance boundaries of open LMMs in three important computer vision scenarios: single-image, multi-image, and video scenarios. Importantly, the design of LLaVA-OneVision allows strong transfer learning across different modalities/scenarios, yielding new emerging capabilities. In particular, strong video understanding and cross-scenario capabilities are demonstrated through task transfer from images to videos.
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查看详情 ↗llava-onevision-qwen2-0.5b-si-hf
llava-hf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 ↗llava-onevision-qwen2-72b-ov-hf
llava-hf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 ↗llava-onevision-qwen2-7b-ov-hf
llava-hfCheck out also the Google Colab demo to run Llava on a free-tier Google Colab instance: [](https://colab.research.google.com/drive/1-4AtYjR8UMtCALV0AswU1kiNkWCLTALT?usp=sharing) Below is the model card of 7B LLaVA-Onevision model which is copied from the original LLaVA-Onevision model card that you can find here. **Model type:** LLaVA-Onevision is an open-source multimodal LLM trained by fine-tuning Qwen2 on GPT-generated multimodal instruction-following data. LLaVA-OneVision is the first single model that can simultaneously push the performance boundaries of open LMMs in three important computer vision scenarios: single-image, multi-image, and video scenarios. Importantly, the design of LLaVA-OneVision allows strong transfer learning across different modalities/scenarios, yielding new emerging capabilities. In particular, strong video understanding and cross-scenario capabilities are demonstrated through task transfer from images to videos.
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查看详情 ↗llava-onevision-qwen2-7b-si-hf
llava-hf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 ↗llava-v1.6-34b-hf
llava-hf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 ↗llava-v1.6-mistral-7b-hf
llava-hfThe LLaVA-NeXT model was proposed in LLaVA-NeXT: Improved reasoning, OCR, and world knowledge by Haotian Liu, Chunyuan Li, Yuheng Li, Bo Li, Yuanhan Zhang, Sheng Shen, Yong Jae Lee. LLaVa-NeXT (also called LLaVa-1.6) improves upon LLaVa-1.5 by increasing the input image resolution and training on an improved visual instruction tuning dataset to improve OCR and common sense reasoning. Disclaimer: The team releasing LLaVa-NeXT did not write a model card for this model so this model card has been written by the Hugging Face team. LLaVa combines a pre-trained large language model with a pre-trained vision encoder for multimodal chatbot use cases. LLaVA 1.6 improves on LLaVA 1.5 BY: - Using Mistral-7B (for this checkpoint) and Nous-Hermes-2-Yi-34B which has better commercial licenses, and bilingual support - More diverse and high quality data mixture - Dynamic high resolution
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查看详情 ↗llava-v1.6-vicuna-13b-hf
llava-hf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 ↗llava-v1.6-vicuna-7b-hf
llava-hfThe LLaVA-NeXT model was proposed in LLaVA-NeXT: Improved reasoning, OCR, and world knowledge by Haotian Liu, Chunyuan Li, Yuheng Li, Bo Li, Yuanhan Zhang, Sheng Shen, Yong Jae Lee. LLaVa-NeXT (also called LLaVa-1.6) improves upon LLaVa-1.5 by increasing the input image resolution and training on an improved visual instruction tuning dataset to improve OCR and common sense reasoning. Disclaimer: The team releasing LLaVa-NeXT did not write a model card for this model so this model card has been written by the Hugging Face team. LLaVa combines a pre-trained large language model with a pre-trained vision encoder for multimodal chatbot use cases. LLaVA 1.6 improves on LLaVA 1.5 BY: - More diverse and high quality data mixture - Dynamic high resolution
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查看详情 ↗vip-llava-7b-hf
llava-hfBelow is the model card of VipLlava model 7b, which is copied from the original Llava model card that you can find here. Check out also the Google Colab demo to run Llava on a free-tier Google Colab instance (the model works similarly as Llava): [](https://colab.research.google.com/drive/1-0G7Kuj2iQgKux4NJneP2JefFMamxG6Q?usp=sharing) **Model type:** LLaVA is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on GPT-generated multimodal instruction-following data. It is an auto-regressive language model, based on the transformer architecture.
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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 ↗Luqwen4-4B
LLiserginov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 ↗Luqwen5-4B
LLiserginov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 ↗control_v11e_sd15_ip2p
lllyasviel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 ↗control_v11e_sd15_shuffle
lllyasvielThis checkpoint is a conversion of the original checkpoint into `diffusers` format. It can be used in combination with **Stable Diffusion**, such as runwayml/stable-diffusion-v1-5. @misc{zhang2023adding, title={Adding Conditional Control to Text-to-Image Diffusion Models}, author={Lvmin Zhang and Maneesh Agrawala}, year={2023}, eprint={2302.05543}, archivePrefix={arXiv}, primaryClass={cs.CV} } Controlnet was proposed in *Adding Conditional Control to Text-to-Image Diffusion Models* by Lvmin Zhang, Maneesh Agrawala.
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查看详情 ↗control_v11f1e_sd15_tile
lllyasvielThis checkpoint is a conversion of the original checkpoint into `diffusers` format. It can be used in combination with **Stable Diffusion**, such as runwayml/stable-diffusion-v1-5. This checkpoint corresponds to the ControlNet conditioned on **tiled image**. Conceptually, it is similar to a super-resolution model, but its usage is not limited to that. It is also possible to generate details at the same size as the input (conditione) image. @misc{zhang2023adding, title={Adding Conditional Control to Text-to-Image Diffusion Models}, author={Lvmin Zhang and Maneesh Agrawala}, year={2023}, eprint={2302.05543}, archivePrefix={arXiv}, primaryClass={cs.CV} }
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查看详情 ↗control_v11f1p_sd15_depth
lllyasviel**Controlnet v1.1** is the successor model of Controlnet v1.0 and was released in lllyasviel/ControlNet-v1-1 by Lvmin Zhang. This checkpoint is a conversion of the original checkpoint into `diffusers` format. It can be used in combination with **Stable Diffusion**, such as runwayml/stable-diffusion-v1-5. For more details, please also have a look at the 🧨 Diffusers docs.
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查看详情 ↗control_v11p_sd15_canny
lllyasviel**Controlnet v1.1** is the successor model of Controlnet v1.0 and was released in lllyasviel/ControlNet-v1-1 by Lvmin Zhang. This checkpoint is a conversion of the original checkpoint into `diffusers` format. It can be used in combination with **Stable Diffusion**, such as runwayml/stable-diffusion-v1-5. For more details, please also have a look at the 🧨 Diffusers docs.
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查看详情 ↗control_v11p_sd15_inpaint
lllyasvielThis checkpoint is a conversion of the original checkpoint into `diffusers` format. It can be used in combination with **Stable Diffusion**, such as runwayml/stable-diffusion-v1-5. @misc{zhang2023adding, title={Adding Conditional Control to Text-to-Image Diffusion Models}, author={Lvmin Zhang and Maneesh Agrawala}, year={2023}, eprint={2302.05543}, archivePrefix={arXiv}, primaryClass={cs.CV} } Controlnet was proposed in *Adding Conditional Control to Text-to-Image Diffusion Models* by Lvmin Zhang, Maneesh Agrawala.
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查看详情 ↗control_v11p_sd15_lineart
lllyasvielThis checkpoint is a conversion of the original checkpoint into `diffusers` format. It can be used in combination with **Stable Diffusion**, such as runwayml/stable-diffusion-v1-5. @misc{zhang2023adding, title={Adding Conditional Control to Text-to-Image Diffusion Models}, author={Lvmin Zhang and Maneesh Agrawala}, year={2023}, eprint={2302.05543}, archivePrefix={arXiv}, primaryClass={cs.CV} } Controlnet was proposed in *Adding Conditional Control to Text-to-Image Diffusion Models* by Lvmin Zhang, Maneesh Agrawala.
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查看详情 ↗control_v11p_sd15_mlsd
lllyasviel**Controlnet v1.1** is the successor model of Controlnet v1.0 and was released in lllyasviel/ControlNet-v1-1 by Lvmin Zhang. This checkpoint is a conversion of the original checkpoint into `diffusers` format. It can be used in combination with **Stable Diffusion**, such as runwayml/stable-diffusion-v1-5. @misc{zhang2023adding, title={Adding Conditional Control to Text-to-Image Diffusion Models}, author={Lvmin Zhang and Maneesh Agrawala}, year={2023}, eprint={2302.05543}, archivePrefix={arXiv}, primaryClass={cs.CV} }
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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 ↗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。