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AI 모델 298
“Stable Diffusion API”
control_v11p_sd15_inpaint
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} } Controlnet was proposed in *Adding Conditional Control to Text-to-Image Diffusion Models* by Lvmin Zhang, Maneesh Agrawala.
stable-diffusion-3-medium-GGUF
stable-diffusion-xl-inpainting-1.0-GGUF
sd-controlnet-canny
ControlNet 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:**
PowerPaint-V1-stable-diffusion-inpainting
stable-diffusion-3.5-large
stable-video-diffusion-img2vid-xt-1-1
stable-diffusion-v1.5-webnn
control_v11p_sd15_seg
**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} }