Stablezero123ConditioningBatched - ComfyUI Built-in Node Documentation - ComfyUI

This node is designed to process conditioning information in a batched manner specifically tailored for the StableZero123 model. It focuses on efficiently handling multiple sets of conditioning data simultaneously, optimizing the workflow for scenarios where batch processing is crucial.

Inputs

Parameter	Description	Data Type
`clip_vision`	The CLIP vision embeddings that provide visual context for the conditioning process.	`CLIP_VISION`
`init_image`	The initial image to be conditioned upon, serving as a starting point for the generation process.	`IMAGE`
`vae`	The variational autoencoder used for encoding and decoding images in the conditioning process.	`VAE`
`width`	The width of the output image.	`INT`
`height`	The height of the output image.	`INT`
`batch_size`	The number of conditioning sets to be processed in a single batch.	`INT`
`elevation`	The elevation angle for 3D model conditioning, affecting the perspective of the generated image.	`FLOAT`
`azimuth`	The azimuth angle for 3D model conditioning, affecting the orientation of the generated image.	`FLOAT`
`elevation_batch_increment`	The incremental change in elevation angle across the batch, allowing for varied perspectives.	`FLOAT`
`azimuth_batch_increment`	The incremental change in azimuth angle across the batch, allowing for varied orientations.	`FLOAT`

Outputs

Parameter	Description	Data Type
`positive`	The positive conditioning output, tailored for promoting certain features or aspects in the generated content.	`CONDITIONING`
`negative`	The negative conditioning output, tailored for demoting certain features or aspects in the generated content.	`CONDITIONING`
`latent`	The latent representation derived from the conditioning process, ready for further processing or generation steps.	`LATENT`

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