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。
输入 图像→ 输出 图像
价格待核验多模态与渠道费用须分别核对
查看详情 ↗DISCOVER & COMPARE
探索 AI 模型 26
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.
译文排队中 · 当前显示来源原文
输入 图像→ 输出 图像
价格待核验多模态与渠道费用须分别核对
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} }
译文排队中 · 当前显示来源原文
输入 图像→ 输出 图像
价格待核验多模态与渠道费用须分别核对
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.
译文排队中 · 当前显示来源原文
输入 图像→ 输出 图像
价格待核验多模态与渠道费用须分别核对
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.
译文排队中 · 当前显示来源原文
输入 图像→ 输出 图像
价格待核验多模态与渠道费用须分别核对
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.
译文排队中 · 当前显示来源原文
输入 图像→ 输出 图像
价格待核验多模态与渠道费用须分别核对
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.
译文排队中 · 当前显示来源原文
输入 图像→ 输出 图像
价格待核验多模态与渠道费用须分别核对
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} }
译文排队中 · 当前显示来源原文
输入 图像→ 输出 图像
价格待核验多模态与渠道费用须分别核对
查看详情 ↗control_v11p_sd15_normalbae
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} }
译文排队中 · 当前显示来源原文
输入 图像→ 输出 图像
价格待核验多模态与渠道费用须分别核对
查看详情 ↗control_v11p_sd15_openpose
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.
译文排队中 · 当前显示来源原文
输入 图像→ 输出 图像
价格待核验多模态与渠道费用须分别核对
查看详情 ↗control_v11p_sd15_scribble
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} }
译文排队中 · 当前显示来源原文
输入 图像→ 输出 图像
价格待核验多模态与渠道费用须分别核对
查看详情 ↗control_v11p_sd15_seg
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} }
译文排队中 · 当前显示来源原文
输入 图像→ 输出 图像
价格待核验多模态与渠道费用须分别核对
查看详情 ↗control_v11p_sd15_softedge
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} }
译文排队中 · 当前显示来源原文
输入 图像→ 输出 图像
价格待核验多模态与渠道费用须分别核对
查看详情 ↗control_v11p_sd15s2_lineart_anime
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.
译文排队中 · 当前显示来源原文
输入 图像→ 输出 图像
价格待核验多模态与渠道费用须分别核对
查看详情 ↗FLUX.1-dev-gguf
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。
输入 文本→ 输出 图像
价格待核验多模态与渠道费用须分别核对
查看详情 ↗FLUX.1-schnell-gguf
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。
输入 文本→ 输出 图像
价格待核验多模态与渠道费用须分别核对
查看详情 ↗flux_redux_bfl
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。
基础资料已收录 · 缺失资料后续补充
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 ↗FramePack_F1_I2V_HY_20250503
lllyasvielThis is the forward-only FramePack-F1 with `f1k1_x_td_f16k4f2k2f1k1_g9`, trained with anti-drifting regulations.
译文排队中 · 当前显示来源原文
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 ↗FramePackI2V_HY
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。
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 ↗sd-controlnet-canny
lllyasvielControlNet is a neural network structure to control diffusion models by adding extra conditions. This checkpoint corresponds to the ControlNet conditioned on **Canny edges**. It can be used in combination with Stable Diffusion. - **Developed by:** Lvmin Zhang, Maneesh Agrawala - **Model type:** Diffusion-based text-to-image generation model - **Language(s):** English - **License:** The CreativeML OpenRAIL M license is an Open RAIL M license, adapted from the work that BigScience and the RAIL Initiative are jointly carrying in the area of responsible AI licensing. See also the article about the BLOOM Open RAIL license on which our license is based. - **Resources for more information:** GitHub Repository, Paper. - **Cite as:**
译文排队中 · 当前显示来源原文
输入 图像→ 输出 图像
价格待核验多模态与渠道费用须分别核对
查看详情 ↗sd-controlnet-depth
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。
输入 图像→ 输出 图像
价格待核验多模态与渠道费用须分别核对
查看详情 ↗sd-controlnet-hed
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。
基础资料已收录 · 缺失资料后续补充
输入 图像→ 输出 图像
价格待核验多模态与渠道费用须分别核对
查看详情 ↗sd-controlnet-mlsd
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。
基础资料已收录 · 缺失资料后续补充
输入 图像→ 输出 图像
价格待核验多模态与渠道费用须分别核对
查看详情 ↗sd-controlnet-openpose
lllyasvielControlNet is a neural network structure to control diffusion models by adding extra conditions. This checkpoint corresponds to the ControlNet conditioned on **Human Pose Estimation**. @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.
译文排队中 · 当前显示来源原文
输入 图像→ 输出 图像
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。