WanCameraImageToVideo - ComfyUI Built-in Node Documentation

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	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, 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.	IMAGE	No	-
`camera_conditions`	Optional camera embedding conditions for video generation. When provided, these conditions are applied to both positive and negative conditioning.	WAN_CAMERA_EMBEDDING	No	-

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	Description	Data Type
`positive`	Modified positive conditioning with applied camera conditions, clip vision outputs, and/or starting image data	CONDITIONING
`negative`	Modified negative conditioning with applied camera conditions, clip vision outputs, and/or starting image data	CONDITIONING
`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.	LATENT

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