TextEncodeBooguEdit - ComfyUI Built-in Node Documentation

This node prepares conditioning for image editing with Boogu. It processes reference images to create both positive and negative conditioning outputs. The reference image is used twice: vision tokens from the image are added only to the positive conditioning to amplify the edit instruction, while a VAE reference latent is added to both positive and negative conditioning so it cancels out under CFG, preserving the original image identity. The tokenizer automatically selects the appropriate system prompt based on the presence of images and empty negative prompts.

Inputs

Parameter	Description	Data Type	Required	Range
`clip`	The CLIP model used for text encoding	CLIP	Yes
`prompt`	The text prompt describing the desired edit	STRING	Yes
`negative_prompt`	The text prompt describing what to avoid in the edit (can be empty to drop negative conditioning)	STRING	Yes
`vae`	The VAE model used to encode reference images into latent space	VAE	No
`images`	Reference image(s) to edit. Boogu focuses on one reference per sample; more are allowed.	IMAGE	No	Up to 16 images

Outputs

Output Name	Description	Data Type
`positive`	Conditioning containing both the text prompt with vision tokens and the reference latents	CONDITIONING
`negative`	Conditioning containing the negative text prompt and the reference latents	CONDITIONING

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

Source fingerprint (SHA-256): 170979acf5b2e9f25f96231a4b23a4376cfddcd4bda2fdd6e03528417e6931b0

​Inputs

​Outputs

Inputs

Outputs