Update README.md

README.md

@@ -1,88 +1,89 @@
 ---
-license: openrail
+许可证: 开放式轨道
+base_model:
+- black-forest-labs/FLUX.1-dev
 ---
 
-This is the pretrained weights and some other detector weights of ControlNet.
+这是ControlNet的预训练权重和其他一些检测器权重。
 
-See also: https://github.com/lllyasviel/ControlNet
+另见:https://github.com/lllyasviel/ControlNet
 
-# Description of Files
+#文件描述
 
-ControlNet/models/control_sd15_canny.pth
+ControlNet/models/control _ sd15 _ canny . PTH
 
-- The ControlNet+SD1.5 model to control SD using canny edge detection.
+-使用canny边缘检测控制标清的ControlNet+SD1.5模型。
 
-ControlNet/models/control_sd15_depth.pth
+control net/models/control _ sd15 _ depth . PTH
 
-- The ControlNet+SD1.5 model to control SD using Midas depth estimation.
+-使用Midas深度估计控制SD的ControlNet+SD1.5模型。
 
-ControlNet/models/control_sd15_hed.pth
+ControlNet/models/control _ sd15 _ hed . PTH
 
-- The ControlNet+SD1.5 model to control SD using HED edge detection (soft edge).
+-ControlNet+SD1.5模型使用HED边缘检测(软边缘)来控制SD。
 
-ControlNet/models/control_sd15_mlsd.pth
+ControlNet/models/control _ sd15 _ mlsd . PTH
 
-- The ControlNet+SD1.5 model to control SD using M-LSD line detection (will also work with traditional Hough transform).
+-ControlNet+SD1.5模型使用M-LSD线路检测来控制标清(也适用于传统的Hough变换)。
 
-ControlNet/models/control_sd15_normal.pth
+ControlNet/models/control _ sd15 _ normal . PTH
 
-- The ControlNet+SD1.5 model to control SD using normal map. Best to use the normal map generated by that Gradio app. Other normal maps may also work as long as the direction is correct (left looks red, right looks blue, up looks green, down looks purple). 
+-使用法线映射控制SD的ControlNet+SD1.5模型。最好使用Gradio应用程序生成的法线贴图。其他法线贴图只要方向正确也可能行得通(左看红，右看蓝，上看绿，下看紫)。
 
-ControlNet/models/control_sd15_openpose.pth
+control net/models/control _ sd15 _ open pose . PTH
 
-- The ControlNet+SD1.5 model to control SD using OpenPose pose detection. Directly manipulating pose skeleton should also work.
+-使用OpenPose姿态检测控制SD的ControlNet+SD1.5模型。直接操纵姿势骨骼应该也可以。
 
-ControlNet/models/control_sd15_scribble.pth
+control net/models/control _ sd15 _ scribble . PTH
 
-- The ControlNet+SD1.5 model to control SD using human scribbles. The model is trained with boundary edges with very strong data augmentation to simulate boundary lines similar to that drawn by human.
+-ControlNet+SD1.5模型使用人工涂鸦来控制SD。用具有非常强的数据扩充的边界边缘来训练该模型，以模拟类似于人绘制的边界线。
 
-ControlNet/models/control_sd15_seg.pth
+ControlNet/models/control _ sd15 _ seg . PTH
 
-- The ControlNet+SD1.5 model to control SD using semantic segmentation. The protocol is ADE20k.
+-使用语义分段控制SD的ControlNet+SD1.5模型。协议是ADE20k。
 
-ControlNet/annotator/ckpts/body_pose_model.pth
+control net/annotator/CK pts/body _ pose _ model . PTH
 
-- Third-party model: Openpose’s pose detection model.
+-第三方模型:Openpose的姿态检测模型。
 
-ControlNet/annotator/ckpts/hand_pose_model.pth
+control net/annotator/CK pts/hand _ pose _ model . PTH
 
-- Third-party model: Openpose’s hand detection model.
+-第三方模型:Openpose的手部检测模型。
 
-ControlNet/annotator/ckpts/dpt_hybrid-midas-501f0c75.pt
+control net/annotator/CK pts/DPT _ hybrid-MIDAS-501 f0c 75 . pt
 
-- Third-party model: Midas depth estimation model.
+-第三方模型:Midas深度估计模型。
 
-ControlNet/annotator/ckpts/mlsd_large_512_fp32.pth
+control net/annotator/CK pts/mlsd _ large _ 512 _ fp32 . PTH
 
-- Third-party model: M-LSD detection model.
+-第三方模型:M-LSD检测模型。
 
-ControlNet/annotator/ckpts/mlsd_tiny_512_fp32.pth
+control net/annotator/CK pts/mlsd _ tiny _ 512 _ fp32 . PTH
 
-- Third-party model: M-LSD’s another smaller detection model (we do not use this one).
+-第三方模型:M-LSD的另一个更小的检测模型(我们不使用这个)。
 
-ControlNet/annotator/ckpts/network-bsds500.pth
+control net/annotator/CK pts/network-bsds 500 . PTH
 
-- Third-party model: HED boundary detection.
+-第三方模型:HED边界检测。
 
-ControlNet/annotator/ckpts/upernet_global_small.pth
+control net/annotator/CK pts/upernet _ global _ small . PTH
 
-- Third-party model: Uniformer semantic segmentation.
+-第三方模型:统一语义分割。
 
-ControlNet/training/fill50k.zip
+control net/training/fill 50k . zip
 
-- The data for our training tutorial.
+-我们培训教程的数据。
 
-# Related Resources
+#相关资源
 
-Special Thank to the great project - [Mikubill' A1111 Webui Plugin](https://github.com/Mikubill/sd-webui-controlnet) !
+特别感谢这个伟大的项目-[Mikubill' A1111 Webui插件](https://github.com/Mikubill/sd-webui-controlnet) !
 
-We also thank Hysts for making [Gradio](https://github.com/gradio-app/gradio) demo in [Hugging Face Space](https://huggingface.co/spaces/hysts/ControlNet) as well as more than 65 models in that amazing [Colab list](https://github.com/camenduru/controlnet-colab)! 
+我们也感谢海斯特公司[格拉迪欧](https://github.com/gradio-app/gradio)演示在[拥抱面部空间](https://huggingface.co/spaces/hysts/ControlNet)以及超过65个模型[Colab列表](https://github.com/camenduru/controlnet-colab)! 
 
-Thank haofanwang for making [ControlNet-for-Diffusers](https://github.com/haofanwang/ControlNet-for-Diffusers)!
+感谢好饭网的制作[扩散器控制网络](https://github.com/haofanwang/ControlNet-for-Diffusers)!
 
-We also thank all authors for making Controlnet DEMOs, including but not limited to [fffiloni](https://huggingface.co/spaces/fffiloni/ControlNet-Video), [other-model](https://huggingface.co/spaces/hysts/ControlNet-with-other-models), [ThereforeGames](https://github.com/AUTOMATIC1111/stable-diffusion-webui/discussions/7784), [RamAnanth1](https://huggingface.co/spaces/RamAnanth1/ControlNet), etc!
+我们还感谢所有制作Controlnet演示的作者，包括但不限于[fffiloni](https://huggingface.co/spaces/fffiloni/ControlNet-Video), [其他-模型](https://hugging face . co/spaces/hysts/control net-with-other-models), [因此游戏](https://github . com/automatic 1111/stable-diffusion-webui/discussions/7784), [RamAnanth1](https://huggingface.co/spaces/RamAnanth1/ControlNet)，等等！
 
-# Misuse, Malicious Use, and Out-of-Scope Use
-
-The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
+#误用、恶意使用和超范围使用
 
+该模型不应用于故意创建或传播给人们制造敌对或疏远环境的图像。这包括生成人们可以预见会觉得不安、痛苦或令人不快的图像；或传播历史或当前刻板印象的内容。