Inputs
| Parameter | Description | Data Type | Required | Range |
|---|---|---|---|---|
positive | Positive conditioning input for guiding the generation | CONDITIONING | Yes | - |
negative | Negative conditioning input for guiding the generation | CONDITIONING | Yes | - |
vae | VAE model for encoding images to latent space | VAE | Yes | - |
width | Width of the output video (default: 832, step: 16) | INT | Yes | 16 to MAX_RESOLUTION |
height | Height of the output 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 a batch (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 generation. When provided, the image is resized to match the specified width and height, and the first frames of the video are initialized from this image. The remaining frames are filled with neutral gray (0.5) values. | IMAGE | No | - |
start_image is provided, the node encodes the image sequence using the VAE and applies a mask to the conditioning inputs. The mask covers all frames except those initialized by the starting image, allowing the generation to build upon the provided image. The clip_vision_output parameter, when provided, adds vision-based conditioning to both positive and negative inputs.
Outputs
| Output Name | Description | Data Type |
|---|---|---|
positive | Modified positive conditioning with image and vision data incorporated | CONDITIONING |
negative | Modified negative conditioning with image and vision data incorporated | CONDITIONING |
latent | Empty latent space tensor ready for video generation, with shape [batch_size, 16, ((length-1)//4)+1, height//8, width//8] | LATENT |
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