StableZero123_Conditioning - ComfyUI Built-in Node Documentation

The StableZero123_Conditioning node processes an input image and camera angles to generate conditioning data and latent representations for 3D model generation. It uses a CLIP vision model to encode the image features, combines them with camera embedding information based on elevation and azimuth angles, and produces positive and negative conditioning along with a latent representation for downstream 3D generation tasks.

Inputs

Parameter	Description	Data Type	Required	Range
`clip_vision`	The CLIP vision model used to encode image features	CLIP_VISION	Yes	-
`init_image`	The input image to be processed and encoded	IMAGE	Yes	-
`vae`	The VAE model used for encoding pixels to latent space	VAE	Yes	-
`width`	Output width for the latent representation (default: 256, step: 8)	INT	Yes	16 to MAX_RESOLUTION
`height`	Output height for the latent representation (default: 256, step: 8)	INT	Yes	16 to MAX_RESOLUTION
`batch_size`	Number of samples to generate in the batch (default: 1)	INT	Yes	1 to 4096
`elevation`	Camera elevation angle in degrees (default: 0.0, step: 0.1)	FLOAT	Yes	-180.0 to 180.0
`azimuth`	Camera azimuth angle in degrees (default: 0.0, step: 0.1)	FLOAT	Yes	-180.0 to 180.0

Note: The width and height parameters use a step of 8, so values are set in increments of 8. The node divides them by 8 to determine the latent representation dimensions. The image is rescaled to the given width and height using bilinear upscaling with center cropping before VAE encoding.

Outputs

Output Name	Description	Data Type
`positive`	Positive conditioning data combining image features and camera embeddings, including the VAE-encoded input image as a latent to concatenate	CONDITIONING
`negative`	Negative conditioning data with zero-initialized features and a zero-initialized latent	CONDITIONING
`latent`	Zero-initialized latent representation with dimensions [batch_size, 4, height//8, width//8]	LATENT

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Inputs

Outputs