SvdImg2vidConditioning - ComfyUI Built-in Node Documentation - ComfyUI

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`

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