TextEncodeMageFlowEdit - ComfyUI Built-in Node Documentation

Overview

This node encodes an edit instruction (prompt) along with one or more reference images for the Mage-Flow-Edit model. It resizes all reference images to the target output resolution, encodes them into latent space if a VAE is provided, and attaches the reference latents to the conditioning output. A blank latent tensor with the correct dimensions for sampling is also generated, ensuring the size always matches the output width and height.

Inputs

Parameter	Description	Data Type	Required	Range
`clip`	The CLIP model used to tokenize and encode the text prompts.	CLIP	Yes
`prompt`	The editing instruction (positive prompt) to apply.	STRING	Yes	multiline, dynamic prompts enabled
`negative_prompt`	The negative prompt to steer away from. Default: empty string (uses a space internally when blank).	STRING	No	multiline, dynamic prompts enabled
`vae`	VAE model to encode reference images into latent space. If not provided, no reference latents are added to the conditioning.	VAE	No
`images`	One or more reference images to edit. All images are resized to the output resolution before encoding.	IMAGE (autogrow)	No	Up to 16 images (named `image_1`…`image_16`), at least 0
`width`	Output width in pixels. If set to 0, the width of the first reference image is used. Always rounded down to a multiple of 16. Default: 0.	INT	Yes	0 to 8192 (step 16)
`height`	Output height in pixels. Same fallback behavior as width. Default: 0.	INT	Yes	0 to 8192 (step 16)
`batch_size`	Number of latent samples to generate. Default: 1.	INT	Yes	1 to 4096

Notes on parameter dependencies:

If width and/or height are 0 and no reference images are provided, they fall back to 1024 each.
The vae parameter is optional; reference latents are only generated and attached to conditioning when a VAE is connected.
The negative_prompt field is optional – if left empty, a single space is used internally as the negative text.

Outputs

Output Name	Description	Data Type
`positive`	Conditioning output containing the positive prompt tokens, plus (if a VAE was provided) the encoded reference latents.	CONDITIONING
`negative`	Conditioning output containing the negative prompt tokens, plus the same reference latents (if VAE provided).	CONDITIONING
`latent`	A blank latent tensor with shape `[batch_size, 128, height÷16, width÷16]` for use as the initial noise during sampling.	LATENT

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​Overview

​Inputs

​Outputs

Overview

Inputs

Outputs