Wan22FunControlToVideo - ComfyUI Built-in Node Documentation

Complete documentation for the Wan22FunControlToVideo node in ComfyUI. Learn its inputs, outputs, parameters and usage.

This documentation was AI-generated. If you find any errors or have suggestions for improvement, please feel free to contribute! Edit on GitHub

The Wan22FunControlToVideo node prepares conditioning and latent representations for video generation using the Wan video model architecture. It processes positive and negative conditioning inputs along with optional reference images and control videos to create the necessary latent space representations for video synthesis. The node handles spatial scaling and temporal dimensions to generate appropriate conditioning data for video models.

Inputs

Parameter	Data Type	Required	Range	Description
positive	CONDITIONING	Yes	-	Positive conditioning input for guiding the video generation
negative	CONDITIONING	Yes	-	Negative conditioning input for guiding the video generation
vae	VAE	Yes	-	VAE model used for encoding images to latent space
width	INT	No	16 to MAX_RESOLUTION	Output video width in pixels (default: 832, step: 16)
height	INT	No	16 to MAX_RESOLUTION	Output video height in pixels (default: 480, step: 16)
length	INT	No	1 to MAX_RESOLUTION	Number of frames in the video sequence (default: 81, step: 4)
batch_size	INT	No	1 to 4096	Number of video sequences to generate (default: 1)
ref_image	IMAGE	No	-	Optional reference image for providing visual guidance
control_video	IMAGE	No	-	Optional control video for guiding the generation process

Note: The length parameter is processed in chunks of 4 frames, and the node automatically handles temporal scaling for the latent space. When ref_image is provided, it influences the conditioning through reference latents. When control_video is provided, it directly affects the concat latent representation used in conditioning.

Outputs

Output Name	Data Type	Description
positive	CONDITIONING	Modified positive conditioning with video-specific latent data
negative	CONDITIONING	Modified negative conditioning with video-specific latent data
latent	LATENT	Empty latent tensor with appropriate dimensions for video generation