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. Supports multiline text and dynamic prompts.	STRING	Yes
`negative_prompt`	The text prompt describing what to avoid in the edit. Can be left empty to drop negative conditioning. Advanced parameter.	STRING	Yes
`vae`	The VAE model used to encode reference images into latent space. Needed to add reference latents to the conditioning outputs.	VAE	No
`images`	Reference image(s) to edit. Boogu focuses on one reference per sample; more are allowed.	IMAGE	No	Up to 16 images

Reference latents are added to both conditioning outputs only when vae is provided together with at least one reference image. If vae is omitted, the positive output still receives vision tokens from the reference images, but neither output includes reference latents.

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

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