Text Encode QwenImageEdit Keep Size - Vision-language conditioning with size policies

Node Purpose: Text Encode QwenImageEdit Keep Size encodes a text prompt together with one or more input images into QwenImageEdit-compatible conditioning. Each image contributes a vision token stream (fixed at 384×384), and optional VAE reference latents are attached according to a keep-size policy. This enables image-grounded editing while preserving composition.

Inputs

Name	Port	Type	Default	Range	Description
`clip`	-	CLIP	-	-	CLIP model that supports vision tokens and scheduled encoding.
`prompt`	-	STRING	``	multiline	Text instruction appended after image vision tokens.
`keep_size`	-	COMBO	`first`	`none` / `first` / `all`	Policy for VAE reference latents: `none` scales all to target area; `first` preserves the first image if already multiple-of-8; `all` preserves any image that already satisfies multiples-of-8.
`base_size`	-	COMBO	`1024`	`1024` / `1536` / `2048`	Target area side; when not preserving size or when the input size is non-multiple-of-8, latents are scaled so that width×height ≈ `base_size²` and rounded to multiples of 8.
`vae`	optional	VAE	-	-	VAE used to encode RGB reference latents attached to conditioning.
`image_1`	optional	IMAGE	-	-	First image; contributes vision tokens and may produce reference latents.
`image_2…image_N`	optional	IMAGE	-	-	Additional images recognized by numeric suffix; ordered by suffix. Up to 10 images recommended.

Outputs

Name	Type	Description
`conditioning`	CONDITIONING	QwenImageEdit-compatible conditioning with optional `reference_latents` attached.

Features

Vision tokens at 384×384: each input image is resized to 384×384 (area resample) for the CLIP tokenize step; the prompt is prefixed by image token placeholders and wrapped in a Qwen-compatible llama template.
Size-preserving latents: keep_size controls whether to retain original spatial size when already multiples-of-8, or to scale to a target area derived from base_size and round width/height to multiples of 8.
RGB-only latents: reference latents are encoded from RGB channels; alpha is omitted to keep semantics consistent.
Dynamic ordering: numeric-suffix inputs (image_1, image_2, …) are collected and ordered by suffix, ensuring stable multi-image guidance.
Scheduled encoding: tokens are fed to the CLIP for scheduled encoding; VAE latents are attached via reference_latents for downstream use.

Typical Usage

Anchor editing: provide one or more images to ground the edit and set keep_size=first to preserve the first anchor’s composition when it already meets multiples-of-8.
Preserve all anchors: set keep_size=all to retain original sizes for all anchors that meet multiples-of-8; others are scaled to the selected base_size area.
Unify latent area: set keep_size=none and choose base_size=1536 or 2048 to standardize reference latents for consistent model behavior.
Text-only conditioning: omit images to encode pure text; add images later to enrich guidance.

Notes & Tips

Multiples-of-8 constraint: when preserving size, width and height must be divisible by 8; otherwise the node scales to the chosen base_size² area and rounds to multiples of 8.
Channel handling: VAE reference latents use RGB channels; vision tokens include any channels but are internally processed from the resized image tensor.
Ordering: images are collected by image_* numeric suffix; gaps are allowed and order follows ascending suffix numbers.
Device safety: all tensors preserve dtype/device; batched inputs are supported in channels-last layout (B×H×W×C).

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