WanCameraImageToVideo - ComfyUI Built-in Node Documentation

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

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The WanCameraImageToVideo node prepares conditioning and latent data for video generation from images. It takes positive and negative conditioning prompts, along with optional starting images and camera controls, and outputs modified conditioning and an empty latent tensor ready for a video model to fill in.

Inputs

Parameter	Data Type	Required	Range	Description
positive	CONDITIONING	Yes	-	Positive conditioning prompts for video generation
negative	CONDITIONING	Yes	-	Negative conditioning prompts to avoid in video generation
vae	VAE	Yes	-	VAE model for encoding images to latent space
width	INT	Yes	16 to MAX_RESOLUTION	Output video width in pixels (default: 832, step: 16)
height	INT	Yes	16 to MAX_RESOLUTION	Output video height in pixels (default: 480, step: 16)
length	INT	Yes	1 to MAX_RESOLUTION	Number of frames in the video sequence (default: 81, step: 4)
batch_size	INT	Yes	1 to 4096	Number of videos to generate simultaneously (default: 1)
clip_vision_output	CLIP_VISION_OUTPUT	No	-	Optional CLIP vision output for additional conditioning
start_image	IMAGE	No	-	Optional starting image to initialize the video sequence. When provided, the first frames of the video will be based on this image, with a mask applied to blend the starting frames with generated content. The image is resized to match the specified width and height.
camera_conditions	WAN_CAMERA_EMBEDDING	No	-	Optional camera embedding conditions for video generation. When provided, these conditions are applied to both positive and negative conditioning.

Note: When start_image is provided, the node uses it to initialize the video sequence and applies masking to blend the starting frames with generated content. The camera_conditions and clip_vision_output parameters are optional but when provided, they modify the conditioning for both positive and negative prompts.

Outputs

Output Name	Data Type	Description
positive	CONDITIONING	Modified positive conditioning with applied camera conditions, clip vision outputs, and/or starting image data
negative	CONDITIONING	Modified negative conditioning with applied camera conditions, clip vision outputs, and/or starting image data
latent	LATENT	Generated empty video latent representation for use with video models. The latent tensor has dimensions [batch_size, 16, frames, height/8, width/8] where frames is calculated as ((length - 1) // 4) + 1.

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