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

Alternative UNET Models

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) is 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

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:

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, so no EmptyLatentImage is 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:

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 the best 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 (action:"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 get_workflow (action:"analyze") to understand any saved Qwen edit workflow before modifying or executing it. It returns a structured summary, not raw JSON. Only use get_workflow when you need the actual JSON for enqueue_workflow or create_workflow (action:"modify").

Sources

• Official: none found.
• Empirical: sampler values, wiring, and prompt notes from working graphs in packs/ and observed renders; not a vendor prompting guide.