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The idea of we have good control over text to image models probably came across our mind one or two times because of how well we can generate now. And ever since stability AI released Sable Diffusion 2.1, we were like, yay, depth through image is going to give us one more way to control image generations other than image to image and text to image. Yes, that was pretty amazing, but have you ever thought about accurate human post to image, precise normal map to image, coherent semantic map to image, or even line R to image. Maybe something that can generalize the idea of whatever to image, that would be game changing. Let me introduce you to control net, which is a neural net structure that controls large diffusion models in a way that supports additional input conditions much better than any current existing methods. This may sound like youre every other scribble to image or semantic to image model, but actually this is something much more generalizable
