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This documentation was AI-generated. If you find any errors or have suggestions for improvement, please feel free to contribute! Edit on GitHubThe 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 that the model uses to fill in the masked areas.
Inputs
| Parameter | Data Type | Required | Range | Description |
|---|---|---|---|---|
positive | CONDITIONING | Yes | - | The positive conditioning to be augmented with the inpainting latent information |
negative | CONDITIONING | Yes | - | The negative conditioning to be augmented with the inpainting latent information |
vae | VAE | Yes | - | The VAE model used to encode the mask and masked video into latent space |
video | IMAGE | Yes | - | Source video frames [T, H, W, 3] |
quadmask | MASK | Yes | - | Preprocessed quadmask from VOIDQuadmaskPreprocess [T, H, W] |
width | INT | Yes | 16 to MAX_RESOLUTION (step: 8) | The width to resize the video and mask to (default: 672) |
height | INT | Yes | 16 to MAX_RESOLUTION (step: 8) | The height to resize the video and mask to (default: 384) |
length | INT | Yes | 1 to MAX_RESOLUTION (step: 1) | 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) |
batch_size | INT | Yes | 1 to 64 | The batch size for the output noise latent (default: 1) |
Outputs
| Output Name | Data Type | Description |
|---|---|---|
positive | CONDITIONING | The positive conditioning with the inpainting latent information added |
negative | CONDITIONING | The negative conditioning with the inpainting latent information added |
latent | LATENT | A zero-filled noise latent tensor with shape [batch_size, 16, latent_t, latent_h, latent_w] |
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