WanCameraImageToVideo - ComfyUI Built-in Node Documentation

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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Inputs

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