WanImageToVideo - ComfyUI Built-in Node Documentation

The WanImageToVideo node prepares conditioning and latent representations for video generation.

The WanImageToVideo node prepares conditioning and latent representations for video generation. It creates an empty latent space for the video and can optionally incorporate a starting image and CLIP vision output to guide the generation. Both the positive and negative conditioning inputs are updated with the provided image and vision data.

Inputs

Parameter	Description	Data Type	Required	Range
positive	Positive conditioning input used to guide the generation	CONDITIONING	Yes	-
negative	Negative conditioning input used to guide the generation	CONDITIONING	Yes	-
vae	VAE model used to encode images into latent space	VAE	Yes	-
width	Width of the generated video (default: 832, step: 16)	INT	Yes	16 to MAX_RESOLUTION
height	Height of the generated video (default: 480, step: 16)	INT	Yes	16 to MAX_RESOLUTION
length	Number of frames in the video (default: 81, step: 4)	INT	Yes	1 to MAX_RESOLUTION
batch_size	Number of videos to generate in one batch (default: 1)	INT	Yes	1 to 4096
clip_vision_output	Optional CLIP vision output added as extra conditioning to both the positive and negative inputs	CLIP_VISION_OUTPUT	No	-
start_image	Optional starting image used to initialize the video. When provided, it is resized to the specified width and height and placed at the beginning of the frame sequence; any frames beyond length are ignored. The remaining frames are filled with neutral gray (0.5) values.	IMAGE	No	-

Note: When start_image is provided, the frame sequence is encoded with the VAE and a mask is applied to the conditioning. The mask is set to 0 for the frames covered by the starting image and 1 for the remaining frames, so generation continues from the provided image. Only the first three color channels (RGB) of the image are used during encoding. Both positive and negative conditioning receive the same concatenated latent image, mask, and (if supplied) CLIP vision output.

Outputs

Output Name	Description	Data Type
positive	Positive conditioning, updated with the image and vision data	CONDITIONING
negative	Negative conditioning, updated with the image and vision data	CONDITIONING
latent	Empty latent tensor ready for video generation, with shape [batch_size, 16, ((length-1)//4)+1, height//8, width//8]	LATENT

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