WanCameraImageToVideo - ComfyUI Built-in Node Documentation

The WanCameraImageToVideo node prepares conditioning and latent data for camera-controlled video generation from images.

The WanCameraImageToVideo node prepares conditioning and latent data for camera-controlled video generation from images. It takes positive and negative conditioning prompts, along with optional inputs such as a starting image, CLIP vision output, and camera conditions, and outputs updated conditioning plus an empty latent tensor ready for a video model to fill in.

Inputs

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

Note: When start_image is provided, the node sets concat_latent_image and concat_mask values on both positive and negative conditioning. The camera_conditions and clip_vision_output parameters are optional, but when provided, they modify the conditioning for both the positive and negative prompts.

Outputs

Output Name	Description	Data Type
positive	Modified positive conditioning with applied camera conditions, clip vision output, and/or starting image data	CONDITIONING
negative	Modified negative conditioning with applied camera conditions, clip vision output, and/or starting image data	CONDITIONING
latent	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.	LATENT

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