VOIDInpaintConditioning - ComfyUI Built-in Node Documentation

The VOIDInpaintConditioning node prepares the conditioning data needed for inpainting with CogVideoX models. It takes a source video and a preprocessed quadmask, encodes them through the VAE, and combines them into a 32-channel conditioning signal (16 channels from the mask + 16 channels from the masked video) that the model uses to fill in the masked areas.

Inputs

Parameter	Description	Data Type	Required	Range
`positive`	The positive conditioning to be augmented with the inpainting latent information	CONDITIONING	Yes	-
`negative`	The negative conditioning to be augmented with the inpainting latent information	CONDITIONING	Yes	-
`vae`	The VAE model used to encode the mask and masked video into latent space	VAE	Yes	-
`video`	Source video frames [T, H, W, 3]	IMAGE	Yes	-
`quadmask`	Preprocessed quadmask from VOIDQuadmaskPreprocess [T, H, W]	MASK	Yes	-
`width`	The width to resize the video and mask to (default: 672)	INT	Yes	16 to MAX_RESOLUTION (step: 8)
`height`	The height to resize the video and mask to (default: 384)	INT	Yes	16 to MAX_RESOLUTION (step: 8)
`length`	Number of pixel frames to process. For CogVideoX-Fun-V1.5 (patch_size_t=2), latent_t must be even — lengths that produce odd latent_t are rounded down (e.g. 49 → 45) (default: 45)	INT	Yes	1 to MAX_RESOLUTION (step: 1)
`batch_size`	The batch size for the output noise latent (default: 1)	INT	Yes	1 to 64

Note: Because CogVideoX-Fun-V1.5 uses patch_size_t=2, the encoded latent must have an even temporal dimension. If length would produce an odd latent_t, the node automatically rounds it down to the nearest valid value and logs a warning. Using an odd latent_t corrupts the last frame through circular padding, which can cause visible jitter or disappearing subjects near the end of the decoded video.

Outputs

Output Name	Description	Data Type
`positive`	The positive conditioning with the inpainting latent information added	CONDITIONING
`negative`	The negative conditioning with the inpainting latent information added	CONDITIONING
`latent`	A zero-filled noise latent tensor with shape [batch_size, 16, latent_t, latent_h, latent_w]	LATENT

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Inputs

Outputs