VOIDInpaintConditioning - ComfyUI Built-in Node Documentation

The VOIDInpaintConditioning node prepares the conditioning data needed for inpainting with CogVideoX models.

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

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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