SVD_img2vid_Conditioning - ComfyUI Built-in Node Documentation

Complete documentation for the SVD_img2vid_Conditioning node in ComfyUI. Learn its inputs, outputs, parameters and usage.

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The SVD_img2vid_Conditioning node prepares conditioning data for video generation using Stable Video Diffusion. It takes an initial image and processes it through CLIP vision and VAE encoders to create positive and negative conditioning pairs, along with an empty latent space for video generation. This node sets up the necessary parameters for controlling motion, frame rate, and augmentation levels in the generated video.

Inputs

Parameter	Data Type	Required	Range	Description
clip_vision	CLIP_VISION	Yes	-	CLIP vision model for encoding the input image
init_image	IMAGE	Yes	-	Initial image to use as the starting point for video generation
vae	VAE	Yes	-	VAE model for encoding the image into latent space
width	INT	Yes	16 to MAX_RESOLUTION	Output video width (default: 1024, step: 8)
height	INT	Yes	16 to MAX_RESOLUTION	Output video height (default: 576, step: 8)
video_frames	INT	Yes	1 to 4096	Number of frames to generate in the video (default: 14)
motion_bucket_id	INT	Yes	1 to 1023	Controls the amount of motion in the generated video (default: 127)
fps	INT	Yes	1 to 1024	Frames per second for the generated video (default: 6)
augmentation_level	FLOAT	Yes	0.0 to 10.0	Level of noise augmentation to apply to the input image (default: 0.0, step: 0.01)

Outputs

Output Name	Data Type	Description
positive	CONDITIONING	Positive conditioning data containing image embeddings and video parameters
negative	CONDITIONING	Negative conditioning data with zeroed embeddings and video parameters
latent	LATENT	Empty latent space tensor ready for video generation