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ControlNet. The ControlNet model was introduced in Adding Conditional Control to Text-to-Image Diffusion Models by Lvmin Zhang, Anyi Rao, Maneesh Agrawala. It provides a greater degree of control over text-to-image generation by conditioning the model on additional inputs such as edge maps, depth maps, segmentation maps, and keypoints for pose detection.
With a ControlNet model, you can provide an additional control image to condition and control Stable Diffusion generation. For example, if you provide a depth map, the ControlNet model generates an image that’ll preserve the spatial information from the depth map. It is a more flexible and accurate way to control the image generation process.
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4 de abr. de 2023 · To mitigate this issue, we have a new Stable Diffusion based neural network for image generation, ControlNet . ControlNet is a new way of conditioning input images and prompts for image generation. It allows us to control the final image generation through various techniques like pose, edge detection, depth maps, and many more.
ControlNet-v1-1. like. 3.29k. License: openrail. Model card Files Community. 120. Edit model card. This is the model files for ControlNet 1.1 . This model card will be filled in a more detailed way after 1.1 is officially merged into ControlNet.
3. ControlNet Data Link Layer 3.1. ControlNet Token Ring. Now that you have seen the basics of ControlNet, let’s have a look to the other layers and get into the specifics of how it works. As I stated earlier, it is based on a “token-passing” bus control network which implements a logical “token ring” through a coaxial cable.
12 de sept. de 2023 · Stable Diffusionを用いた画像生成は、呪文(プロンプト)が反映されないことがよくありますよね。その際にStable Diffusionで『ControlNet』という拡張機能が便利です。その『ControlNet』の使い方や導入方法を詳しく解説します！
