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

Key advantage: Output [1] (latent) eliminates the need for a separate EmptyLatentImage or VAEEncode node — the Advanced node handles latent creation internally at the correct resolution.

Other Conditioning Variants

• TextEncodeQwenImageEditPlus (Phr00t v2, built-in) — Simpler: 4 image inputs, outputs only CONDITIONING. Requires separate EmptyLatentImage. Good for quick edits.
• TextEncodeQwenImageEditPlus_lrzjason — 5 image inputs, resize toggles, but less control than Advance
• TextEncodeQwenImageEditPlusPro_lrzjason — Per-image VL resize selection via vl_resize_indexs string, main_image_index control

Lightning LoRAs (Fast Generation)

4-Step Lightning (2511 Edit)

{
  "class_type": "LoraLoaderModelOnly",
  "inputs": {
    "model": ["<unet_node>", 0],
    "lora_name": "Qwen-Image-Edit-2511-Lightning-4steps-V1.0-bf16.safetensors",
    "strength_model": 1.0
  }
}

Settings: steps=4, cfg=1.0, sampler=euler, scheduler=simple, denoise=1.0

4-Step Lightning (General Qwen)

For non-edit models (txt2img, 2512):

• Qwen-Image-Lightning-4steps-V1.0.safetensors (strength 1.0)

8-Step Lightning

• Qwen-Image-Lightning-8steps-V1.0.safetensors — Higher detail than 4-step

Sampler Settings

Preset	Steps	CFG	Sampler	Scheduler	Denoise	LoRA
Lightning 4-step (2511 edit)	4	1.0	euler	simple	1.0	2511-Lightning-4steps
Lightning 8-step	8	1.0	euler	simple	1.0	Lightning-8steps
Standard edit	40	4.0	euler	simple	0.75	none
Quality edit	50	4.0	euler	simple	0.5-0.8	none

Denoise for editing: Lower denoise = closer to source. 0.5-0.8 range for standard editing. Lightning uses 1.0 (model handles fidelity internally).

Resolutions

Qwen operates at ~1.6 megapixels natively:

Aspect	Resolution	Use Case
Square	1328x1328	General
Portrait 3:4	1104x1472	Portraits
Portrait 9:16	928x1664	Phone format
Landscape 4:3	1472x1104	Landscape scenes
Landscape 16:9	1664x928	Widescreen
Video-ready	832x480	For WAN 2.2 FLF pipeline

For video pipelines: Use 832x480 to match WAN 2.2's default resolution.

Prompt Patterns

Edit Instructions (Natural Language)

"Change the black cat into a cute girl with a black bodysuit and jeans"
"Make the sky a dramatic sunset with orange and purple clouds"
"Add a red sports car parked in front of the house"
"Remove the person on the left and fill with the background"

Multi-Angle LoRA (qwen-image-edit-2511-multiple-angles-lora)

Uses <sks> token with structured angle/distance prompts:

<sks> front view eye-level shot close-up
<sks> front-right quarter view low-angle shot medium shot
<sks> back view elevated shot wide shot

Template: <sks> {direction} view {angle} shot {distance}

Directions: front, front-right quarter, right side, back-right quarter, back, back-left quarter, left side, front-left quarter Angles: low-angle, eye-level, elevated, high-angle Distances: close-up, medium shot, wide shot

Negative Conditioning

Always use ConditioningZeroOut for negative conditioning with Qwen edit:

{
  "class_type": "ConditioningZeroOut",
  "inputs": { "conditioning": ["<positive_cond_node>", 0] }
}

Complete Workflow: Lightning Edit (Advanced Node)

Uses TextEncodeQwenImageEditPlusAdvance_lrzjason which outputs the latent directly — no EmptyLatentImage needed.

{
  "1": { "class_type": "UNETLoader", "inputs": { "unet_name": "qwen_image_edit_2511_bf16.safetensors", "weight_dtype": "default" }},
  "2": { "class_type": "LoraLoaderModelOnly", "inputs": { "model": ["1", 0], "lora_name": "Qwen-Image-Edit-2511-Lightning-4steps-V1.0-bf16.safetensors", "strength_model": 1 }},
  "3": { "class_type": "CLIPLoader", "inputs": { "clip_name": "qwen_2.5_vl_7b_fp8_scaled.safetensors", "type": "qwen_image" }},
  "4": { "class_type": "VAELoader", "inputs": { "vae_name": "qwen_image_vae.safetensors" }},
  "5": { "class_type": "LoadImage", "inputs": { "image": "<source_image.png>" }},
  "6": { "class_type": "TextEncodeQwenImageEditPlusAdvance_lrzjason", "inputs": {
    "clip": ["3", 0], "prompt": "<edit instruction>", "vae": ["4", 0],
    "vl_resize_image1": ["5", 0],
    "target_size": 1024, "target_vl_size": 384,
    "upscale_method": "lanczos", "crop_method": "pad"
  }},
  "7": { "class_type": "ConditioningZeroOut", "inputs": { "conditioning": ["6", 0] }},
  "8": { "class_type": "KSampler", "inputs": {
    "model": ["2", 0],
    "positive": ["6", 0],
    "negative": ["7", 0],
    "latent_image": ["6", 1],
    "seed": 42, "steps": 4, "cfg": 1, "sampler_name": "euler", "scheduler": "simple", "denoise": 1
  }},
  "9": { "class_type": "VAEDecode", "inputs": { "samples": ["8", 0], "vae": ["4", 0] }},
  "10": { "class_type": "SaveImage", "inputs": { "images": ["9", 0], "filename_prefix": "qwen_edit" }}
}

Key connections:

• "latent_image": ["6", 1] — KSampler gets its latent directly from the Advanced node's output [1]
• "positive": ["6", 0] — conditioning_with_full_ref from output [0]
• "vl_resize_image1": ["5", 0] — source image goes into VL-resize slot (downscaled for vision encoder)

Simpler Alternative (Phr00t v2)

If qweneditutils custom node is unavailable, use the built-in TextEncodeQwenImageEditPlus with a separate EmptyLatentImage:

{
  "6": { "class_type": "TextEncodeQwenImageEditPlus", "inputs": {
    "clip": ["3", 0], "prompt": "<edit instruction>", "vae": ["4", 0], "image1": ["5", 0]
  }},
  "8": { "class_type": "EmptyLatentImage", "inputs": { "width": 1024, "height": 1024, "batch_size": 1 }}
}

Replace node 6 and add node 8 — KSampler latent_image connects to ["8", 0] instead of ["6", 1].

Basic Variant (Official ComfyUI Example)

The official "Qwen 2511 Edit Simple" example uses newer built-in nodes for model patching and image scaling:

Additional nodes in the official pipeline:

• ModelSamplingAuraFlow (shift=3.1) — Flow matching shift applied to the UNET. Used instead of ModelSamplingSD3.
• CFGNorm (strength=1) — Normalizes CFG guidance for more stable generation. Applied after ModelSamplingAuraFlow.
• FluxKontextImageScale — Auto-scales input images to the correct resolution for Qwen. No manual size parameters needed.
• FluxKontextMultiReferenceLatentMethod (method=index_timestep_zero) — Applied to both positive and negative conditioning. Handles multi-reference latent indexing.
• VAEEncode — Encodes the scaled image to latent (instead of EmptyLatentImage).

Official pipeline flow:

UNETLoader → [LoraLoaderModelOnly] → ModelSamplingAuraFlow (shift=3.1) → CFGNorm (strength=1) → MODEL
CLIPLoader (qwen_image) → CLIP
VAELoader → VAE

LoadImage → FluxKontextImageScale → scaled_image
  ├─ TextEncodeQwenImageEditPlus (positive) → FluxKontextMultiReferenceLatentMethod → positive CONDITIONING
  ├─ TextEncodeQwenImageEditPlus (negative, empty) → FluxKontextMultiReferenceLatentMethod → negative CONDITIONING
  └─ VAEEncode → LATENT

KSampler → VAEDecode → SaveImage

Official sampler settings:

Variant	Steps	CFG	Sampler	Scheduler	Denoise	LoRA
Standard	40	4.0	euler	simple	1.0	none
Lightning	4	1.0	euler	simple	1.0	2511-Lightning-4steps

Note: The FluxKontextMultiReferenceLatentMethod and FluxKontextImageScale nodes may not be needed when using Comfy's official model files directly, but may be required with community-repackaged models.

XY Plot Technique (from Widgets.json)

For batch-testing multiple edit variations, use the Easy Nodes XY Plot system:

1. Text Multiline nodes define parameter lists (e.g., directions, angles)
2. Split String breaks them into indexed options
3. easy textIndexSwitch selects one at a time
4. easy promptReplace substitutes {X}, {Y}, {Z} placeholders in the base prompt
5. easy XYPlotAdvanced + easy XYInputs: PromptSR drives the sweep
6. easy pipeIn bundles model/clip/vae/latent into a pipeline

This produces a grid image showing all combinations — useful for finding optimal angle/distance/style for a given subject.

VRAM Considerations

• Qwen 2511 edit bf16: ~10GB VRAM
• CLIP (fp8): ~7GB VRAM
• VAE: ~200MB
• Total: ~17-18GB — fits comfortably on 24GB GPUs
• Always clear_vram before loading if switching from another model family

Tips

1. Upload source images first with upload_image before building the workflow
2. Match output resolution to the next pipeline step (e.g., 832x480 for WAN FLF)
3. Lightning LoRA + denoise 1.0 works well — the model handles structure preservation through conditioning
4. For img2img editing (denoise < 1.0), use VAEEncode on the source image instead of EmptyLatentImage
5. The lrzjason Pro variant is best for multi-image compositions where you need fine control over which images get VL-resized
6. Use analyze_workflow to understand any saved Qwen edit workflow before modifying or executing it — returns a structured summary, not raw JSON. Only use get_workflow when you need the actual JSON for enqueue_workflow or modify_workflow.