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With ControlNet, users can easily condition the generation with different spatial contexts such as a depth map, a segmentation map, a scribble, keypoints, and so on! We can turn a cartoon drawing into a realistic photo with incredible coherence. Realistic Lofi Girl. Or even use it as your interior designer.
16 de oct. de 2023 · ControlNet changes the game by allowing an additional image input that can be used for conditioning (influencing) the final image generation. This could be anything from simple scribbles to detailed depth maps or edge maps. By conditioning on these input images, ControlNet directs the Stable Diffusion model to generate images that align closely ...
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This is the official release of ControlNet 1.1. ControlNet 1.1 has the exactly same architecture with ControlNet 1.0. We promise that we will not change the neural network architecture before ControlNet 1.5 (at least, and hopefully we will never change the network architecture). Perhaps this is the best news in ControlNet 1.1.
1 de abr. de 2023 · Let's get started. 1. Download ControlNet Models. Download the ControlNet models first so you can complete the other steps while the models are downloading. Keep in mind these are used separately from your diffusion model. Ideally you already have a diffusion model prepared to use with the ControlNet models.
The ControlNet input image will be stretched (or compressed) to match the height and width of the text2img (or img2img) settings. This will alter the aspect ratio of the Detectmap. Crop and Resize. The ControlNet Detectmap will be cropped and re-scaled to fit inside the height and width of the txt2img settings.
Text-to-Image Generation with ControlNet Conditioning Overview Adding Conditional Control to Text-to-Image Diffusion Models by Lvmin Zhang and Maneesh Agrawala.. Using the pretrained models we can provide control images (for example, a depth map) to control Stable Diffusion text-to-image generation so that it follows the structure of the depth image and fills in the details.
