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ControlNet

This is the pretrained weights and some other detector weights of ControlNet.

TipoOtherVersãoRevision e78a8c4Bnota

Fonte do modelo

Tipo
Other
Tamanho do arquivo
2,49 MB–5,32 GB
Versão
vRevision e78a8c4
Atualizado
6 de out. de 2026
Downloads
0
Fonte
Hugging Face

Descrição da fonte

This is the pretrained weights and some other detector weights of ControlNet.

See also: https://github.com/lllyasviel/ControlNet

Description of Files

ControlNet/models/control_sd15_canny.pth

  • The ControlNet+SD1.5 model to control SD using canny edge detection.

ControlNet/models/control_sd15_depth.pth

  • The ControlNet+SD1.5 model to control SD using Midas depth estimation.

ControlNet/models/control_sd15_hed.pth

  • The ControlNet+SD1.5 model to control SD using HED edge detection (soft edge).

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/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).

ControlNet/models/control_sd15_openpose.pth

  • The ControlNet+SD1.5 model to control SD using OpenPose pose detection. Directly manipulating pose skeleton should also work.

ControlNet/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/models/control_sd15_seg.pth

  • The ControlNet+SD1.5 model to control SD using semantic segmentation. The protocol is ADE20k.

ControlNet/annotator/ckpts/body_pose_model.pth

  • Third-party model: Openpose’s pose detection model.

ControlNet/annotator/ckpts/hand_pose_model.pth

  • Third-party model: Openpose’s hand detection model.

ControlNet/annotator/ckpts/dpt_hybrid-midas-501f0c75.pt

  • Third-party model: Midas depth estimation model.

ControlNet/annotator/ckpts/mlsd_large_512_fp32.pth

  • Third-party model: M-LSD detection model.

ControlNet/annotator/ckpts/mlsd_tiny_512_fp32.pth

  • Third-party model: M-LSD’s another smaller detection model (we do not use this one).

ControlNet/annotator/ckpts/network-bsds500.pth

  • Third-party model: HED boundary detection.

ControlNet/annotator/ckpts/upernet_global_small.pth

  • Third-party model: Uniformer semantic segmentation.

ControlNet/training/fill50k.zip

  • The data for our training tutorial.

Related Resources

Special Thank to the great project - Mikubill' A1111 Webui Plugin !

We also thank Hysts for making Gradio demo in Hugging Face Space as well as more than 65 models in that amazing Colab list!

Thank haofanwang for making ControlNet-for-Diffusers!

We also thank all authors for making Controlnet DEMOs, including but not limited to fffiloni, other-model, ThereforeGames, RamAnanth1, etc!

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.

Fontes

1 fonte
Hugging FacePrimárioVersão e78a8c4a5052a238198043ee5c0cb44e22abb9f7
huggingface.co/lllyasviel/ControlNet/tree/e78a8c4a5052a238198043ee5c0cb44e22abb9f7

Verificado 6 de out.

Artefatos de modelo

15 artefatos

annotator/ckpts/body_pose_model.pth

pth · 200 MB · SHA-256 25a948c16078…b746 · Hugging Face

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annotator/ckpts/dpt_hybrid-midas-501f0c75.pt

pt · 470 MB · SHA-256 501f0c75b3bc…0853 · Hugging Face

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annotator/ckpts/hand_pose_model.pth

pth · 141 MB · SHA-256 b76b00d17509…c600 · Hugging Face

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annotator/ckpts/mlsd_large_512_fp32.pth

pth · 6,05 MB · SHA-256 5696f168eb2c…a082 · Hugging Face

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annotator/ckpts/mlsd_tiny_512_fp32.pth

pth · 2,49 MB · SHA-256 3f2323cfb9fa…0b94 · Hugging Face

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annotator/ckpts/network-bsds500.pth

pth · 56,1 MB · SHA-256 58a858782f5f…1a94 · Hugging Face

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annotator/ckpts/upernet_global_small.pth

pth · 197 MB · SHA-256 bebfa1264c10…d970 · Hugging Face

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models/control_sd15_canny.pth

pth · 5,32 GB · SHA-256 4de384b16bc2…3f45 · Hugging Face

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models/control_sd15_depth.pth

pth · 5,32 GB · SHA-256 726cd0b472c4…60bf · Hugging Face

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models/control_sd15_hed.pth

pth · 5,32 GB · SHA-256 00ad5ff2c3ce…5d9c · Hugging Face

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models/control_sd15_mlsd.pth

pth · 5,32 GB · SHA-256 4520b2240075…b6eb · Hugging Face

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models/control_sd15_normal.pth

pth · 5,32 GB · SHA-256 1188b9ac6754…a0d0 · Hugging Face

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models/control_sd15_openpose.pth

pth · 5,32 GB · SHA-256 d19ffffeeaff…f591 · Hugging Face

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models/control_sd15_scribble.pth

pth · 5,32 GB · SHA-256 591bc5a70a16…9ad3 · Hugging Face

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models/control_sd15_seg.pth

pth · 5,32 GB · SHA-256 2c772486812f…3a37 · Hugging Face

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Trechos de fonte

3 trechos

--- license: openrail --- This is the pretrained weights and some other detector weights of ControlNet. See also: https://github.com/lllyasviel/ControlNet # Description of Files ControlNet/models/control_sd15_canny.pth - The ControlNet+SD1.5 model to control SD using canny edge detection. ControlNet/models/control_sd15_depth.pth - The ControlNet+SD1.5 model to control SD using Midas depth estimation. ControlNet/models/control_sd15_hed.pth - The ControlNet+SD1.5 model to control SD using HED edge detection (soft edge). 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/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). ControlNet/models/control_sd15_openpose.pth - The ControlNet+SD1.5 model to control SD using OpenPose pose detection. Directly manipulating pose skeleton should also work. ControlNet/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/models/control_sd15_seg.pth - The ControlNet+SD1.5 model to control SD using semantic segmentation. The protocol is ADE20k. ControlNet/annotator/ckpts/body_pose_model.pth - Third-party model: Openpose’s pose detection model. ControlNet/annotator/ckpts/hand_pose_model.pth - Third-party model: Openpose’s hand detection model. ControlNet/annotator/ckpts/dpt_hybrid-midas-501f0c75.pt - Third-party model: Midas depth estimation model. ControlNet/annotator/ckpts/mlsd_large_512_fp32.pth - Third-party model: M-LSD detection model. ControlNet/annotator/ckpts/mlsd_tiny_512_fp32.pth - Third-party model: M-LSD’s another smaller detection model (we do not use this one). ControlNet/annotator/ckpts/network-bsds500.pth - Third-party model: HED boundary detection. ControlNet/annotator/ckpts/upernet_global_small.pth - Third-party model: Uniformer semantic segmentation. ControlNet/training/fill50k.zip - The data for our training tutorial. # Related Resour...

Source context: 0 downloads · 3867 likes · Repo lllyasviel/ControlNet

Source context: 5 downloads · 3832 likes · Repo lllyasviel/ControlNet