SvdImg2vidConditioning - ComfyUI Built-in Node Documentation

Complete documentation for the SvdImg2vidConditioning node in ComfyUI. Learn its inputs, outputs, parameters and usage.

This node is designed for generating conditioning data for video generation tasks, specifically tailored for use with SVD_img2vid models. It takes various inputs including initial images, video parameters, and a VAE model to produce conditioning data that can be used to guide the generation of video frames.

Inputs

Parameter	Description	Comfy dtype
clip_vision	Represents the CLIP vision model used for encoding visual features from the initial image, playing a crucial role in understanding the content and context of the image for video generation.	CLIP_VISION
init_image	The initial image from which the video will be generated, serving as the starting point for the video generation process.	IMAGE
vae	A Variational Autoencoder (VAE) model used for encoding the initial image into a latent space, facilitating the generation of coherent and continuous video frames.	VAE
width	The desired width of the video frames to be generated, allowing for customization of the video’s resolution.	INT
height	The desired height of the video frames, enabling control over the video’s aspect ratio and resolution.	INT
video_frames	Specifies the number of frames to be generated for the video, determining the video’s length.	INT
motion_bucket_id	An identifier for categorizing the type of motion to be applied in the video generation, aiding in the creation of dynamic and engaging videos.	INT
fps	The frames per second (fps) rate for the video, influencing the smoothness and realism of the generated video.	INT
augmentation_level	A parameter controlling the level of augmentation applied to the initial image, affecting the diversity and variability of the generated video frames.	FLOAT

Outputs

Parameter	Description	Comfy dtype
positive	The positive conditioning data, consisting of encoded features and parameters for guiding the video generation process in a desired direction.	CONDITIONING
negative	The negative conditioning data, providing a contrast to the positive conditioning, which can be used to avoid certain patterns or features in the generated video.	CONDITIONING
latent	Latent representations generated for each frame of the video, serving as a foundational component for the video generation process.	LATENT

This documentation was AI-generated. If you find any errors or have suggestions for improvement, please feel free to contribute! Edit on GitHub