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10 de feb. de 2023 · We present ControlNet, a neural network architecture to add spatial conditioning controls to large, pretrained text-to-image diffusion models. ControlNet locks the production-ready large diffusion models, and reuses their deep and robust encoding layers pretrained with billions of images as a strong backbone to learn a diverse set of conditional controls. The neural architecture is connected ...
Nodes for scheduling ControlNet strength across timesteps and batched latents, as well as applying custom weights and attention masks. The ControlNet nodes here fully support sliding context sampling, like the one used in the ComfyUI-AnimateDiff-Evolved nodes. Currently supports ControlNets, T2IAdapters, ControlLoRAs, ControlLLLite, SparseCtrls, SVD-ControlNets, and Reference.
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El soporte de control de movimiento viene con la tecnología PROFINET desde tres aspectos principales: 1) La capacidad de control en tiempo real dependiendo de la aplicación del drive. 2) Mayor determinismo a través de la utilización del ancho de banda y la programación. 3) La opción del Perfil de Aplicación PROFIdrive.
arXiv.org e-Print archive
Controlnet - v1.1 - depth Version 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.. For more details, please also have a look at the 🧨 Diffusers ...
In this repository, you will find a basic example notebook that shows how this can work. The key trick is to use the right value of the parameter controlnet_conditioning_scale - while value of 1.0 often works well, it is sometimes beneficial to bring it down a bit when the controlling image does not fit the selected text prompt very well.
