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@@ -34,6 +34,8 @@ By repeating the above simple structure 14 times, we can control stable diffusio
![img](github_page/sd.png)
In this way, the ControlNet can **reuse** the SD encoder as a **deep, strong, robust, and powerful** backbone to learn diverse controls. Many evidences (like [this](https://jerryxu.net/ODISE/) and [this](https://vpd.ivg-research.xyz/)) validate that the SD encoder is an excellent backbone.
Note that the way we connect layers is computational efficient. The original SD encoder does not need to store gradients (the locked original SD Encoder Block 1234 and Middle). The required GPU memory is not much larger than original SD, although many layers are added. Great!
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