ControlNetApply - ComfyUI Built-in Node Documentation

Complete documentation for the ControlNetApply node in ComfyUI. Learn its inputs, outputs, parameters and usage.

Using controlNet requires preprocessing of input images. Since ComfyUI initial nodes do not come with preprocessors and controlNet models, please first install ControlNet preprocessors download preprocessors here and corresponding controlNet models.

Inputs

Parameter	Data Type	Function
positive	CONDITIONING	Positive conditioning data, from CLIP Text Encoder or other conditioning inputs
negative	CONDITIONING	Negative conditioning data, from CLIP Text Encoder or other conditioning inputs
control_net	CONTROL_NET	The controlNet model to apply, typically input from ControlNet Loader
image	IMAGE	Image for controlNet application, needs to be processed by preprocessor
vae	VAE	Vae model input
strength	FLOAT	Controls the strength of network adjustments, value range 010. Recommended values between 0.51.5 are reasonable. Lower values allow more model freedom, higher values impose stricter constraints. Too high values may result in strange images. You can test and adjust this value to fine-tune the control network’s influence.
start_percent	FLOAT	Value 0.000~1.000, determines when to start applying controlNet as a percentage, e.g., 0.2 means ControlNet guidance will start influencing image generation at 20% of the diffusion process
end_percent	FLOAT	Value 0.000~1.000, determines when to stop applying controlNet as a percentage, e.g., 0.8 means ControlNet guidance will stop influencing image generation at 80% of the diffusion process

Outputs

Parameter	Data Type	Function
positive	CONDITIONING	Positive conditioning data processed by ControlNet, can be output to next ControlNet or K Sampler nodes
negative	CONDITIONING	Negative conditioning data processed by ControlNet, can be output to next ControlNet or K Sampler nodes