qwen-image-edit

Qwen Image Edit Workflows

Overview

Qwen Image Edit uses a vision-language model (Qwen2.5-VL) to edit images based on natural language instructions. The model "sees" the source image through CLIP conditioning and generates an edited version.

Models

Required Components

Component	Node	Model Name	Notes
UNET	UNETLoader	qwen_image_edit_2511_bf16.safetensors	Official 2511 edit model (bf16)
CLIP	CLIPLoader (type=qwen_image)	qwen_2.5_vl_7b_fp8_scaled.safetensors	Shared across all Qwen models
VAE	VAELoader	qwen_image_vae.safetensors	Qwen-specific VAE

Alternative UNET Models

Model	Path	Focus
qwenImageEditRemix_v10	qwenImageEditRemix_v10.safetensors	Community remix, general editing
qwenUltimateRealism_v11	Qwen/imageized/qwenUltimateRealism_v11.safetensors	Product photography, hyper-realistic
copaxTimeless	Qwen/realistic/copaxTimeless_qwenUltraRealistic.safetensors	Ultra-realistic portraits
qwnImageEdit_v16Bf16	Qwen/abliterated/qwnImageEdit_v16Bf16.safetensors	Abliterated (uncensored)

Conditioning Nodes

TextEncodeQwenImageEditPlusAdvance_lrzjason (Recommended)

From the qweneditutils custom node pack. The Advanced variant is preferred because it:

• Outputs a LATENT directly (no need for separate EmptyLatentImage)
• Has separate VL-resize and non-resize image slots for fine control
• Supports target_size control for output resolution
• Includes a pad/center/disabled crop method with pad_info output

Required Inputs:
  - clip: CLIP
  - prompt: STRING — natural language edit instruction

Optional Inputs:
  - vae: VAE — needed for image encoding and latent output
  - vl_resize_image1-3: IMAGE — images that get VL-resized (downscaled for vision encoder)
  - not_resize_image1-3: IMAGE — images kept at full resolution
  - target_size: [1024, 1344, 1536, 2048, 768, 512] (default 1024)
  - target_vl_size: [392, 384] (default 384)
  - upscale_method: [lanczos, bicubic, area]
  - crop_method: [pad, center, disabled]
  - instruction: STRING — system instruction template (has sensible default)

Outputs (10):
  [0] conditioning_with_full_ref: CONDITIONING — use as positive conditioning
  [1] latent: LATENT — auto-scaled latent, feed directly to KSampler
  [2] target_image1: IMAGE — processed target-size image
  [3] target_image2: IMAGE
  [4] target_image3: IMAGE
  [5] vl_resized_image1: IMAGE — VL-resized version
  [6] vl_resized_image2: IMAGE
  [7] vl_resized_image3: IMAGE
  [8] conditioning_with_first_ref: CONDITIONING — conditioning with only first ref
  [9] pad_info: ANY — padding info for later unpadding