1hew-ComfyUI-1hewNodes/web/docs/1hew_TextEncodeQwenImageEdit/en.md

Text Encode Qwen Image Edit - Qwen image-edit prompt encoder

Node Purpose: Text Encode Qwen Image Edit builds a Qwen Image Edit-compatible prompt with one or more image placeholders, resizes images for the vision encoder, and outputs a CONDITIONING. With an optional VAE, it also attaches reference_latents for image-reference guidance.

Inputs

Name	Port	Type	Default	Range	Description
clip	-	CLIP	-	-	Qwen Image Edit-compatible CLIP model used for tokenize and encode.
vae	optional	VAE	-	-	When connected, the node generates reference_latents for each provided image.
image_1	optional	IMAGE	-	-	First input image. The UI supports dynamic image_2..image_10 ports.
prompt	-	STRING(multiline)	""	-	User instruction appended after the image placeholders.
reference_skip_prep	-	COMBO	first	none / first / all	Reference-latents strategy for preserving original size on 8-aligned inputs.
reference_sq_area	-	INT	1024	64-8192	Side length used to derive the target area (reference_sq_area²) for reference-latents normalization.
vision_embed	-	COMBO	stretch	crop / pad / stretch / area	Vision-image resize strategy used by clip.tokenize(images=...).

Outputs

Name	Type	Description
conditioning	CONDITIONING	Text/image conditioning. When vae is connected, reference_latents are appended into conditioning values.

Features

• Multi-image prompt: assembles Picture 1..N with Qwen vision placeholders and feeds them into clip.tokenize(images=...).
• Vision resize modes: provides crop, pad, stretch, and area for stable vision-encoder input preparation.
• Reference latents: generates per-image VAE latents and attaches them to the conditioning for reference-based image editing.
• 8-alignment aware: preserves original VAE encode size on 8-aligned images according to reference_skip_prep.

vision_embed Modes

• area: aspect-preserving resize to a target area of 384×384 pixels; output width/height follow sqrt((384²)/(W×H)).
• crop: aspect-preserving resize that covers 384×384, then center-crop to 384×384.
• pad: aspect-preserving resize that fits inside 384×384, then center-pad to 384×384 with zeros.
• stretch: direct resize to 384×384, allowing aspect change.

reference_latents Rules

• Generation scope:
  • none: all images use area-normalized resize to reference_sq_area² and 8-alignment, then VAE encode.
  • first: the first image uses original size VAE encode when width/height align to 8; remaining images follow none behavior.
  • all: each image uses original size VAE encode when width/height align to 8; each remaining image follows none behavior.
• Normalization behavior (area + 8-alignment):
  • scale = sqrt((reference_sq_area²) / (W×H))
  • W' = round((W×scale)/8)×8, H' = round((H×scale)/8)×8
  • VAE encodes the resized RGB image (:3 channels).

Typical Usage

• Text-only conditioning: connect clip, set prompt, and keep vae empty.
• Image-edit conditioning: connect clip, connect one or more image_*, set vision_embed, and provide an instruction in prompt.
• Reference-guided editing: connect vae and tune reference_skip_prep plus reference_sq_area for reference-latents behavior.

Notes & Tips

• Image ports are consumed in numeric order: image_1, image_2, image_3, ...
• For consistent vision behavior across images, use the same vision_embed mode for a workflow.