Product Information
What is Controlnet?
Add conditional control to the official implementation of text-to-image diffusion models. ControlNet is a neural network structure that enables the control of diffusion models by incorporating additional conditions.
It encodes the weights of neural network blocks into both a "locked" copy and a "trainable" copy.
The "trainable" copy learns your conditions, while the "locked" one preserves your model.
Thus, training on a small image dataset won’t compromise the pre-trained diffusion model.
"Zero convolutions" are 1×1 convolutions with weights and biases initialized to zero.
Before training, all zero convolutions output zero, ensuring ControlNet introduces no distortion.
No layers are trained from scratch—you're still fine-tuning. Your original model remains secure.
This facilitates training on small-scale or even personal devices.
It also supports merging, replacing, or offsetting models/weights/blocks/layers in a user-friendly manner.
How to use Controlnet?
ControlNet is a neural network structure that controls diffusion models by adding additional conditions. Its core value lies in allowing training with small datasets while protecting production-ready diffusion models from corruption.
Core Functions of Controlnet
Text-to-image, AI-driven
Usage Scenarios of Controlnet
- Train models with a small number of image pairs
- Conduct model training on small or personal devices
- Perform model merging, replacement, or offset operations
Common Questions about Controlnet
What does ControlNet do?
How do I use ControlNet?
What are the core features of ControlNet?
What are the use cases for ControlNet?
