Qwen Image Edit

---
name: qwen-image-edit
description: Build Qwen Image Edit workflows — model loading, conditioning, LoRAs, prompt patterns, and XY plot testing
globs:
  - "**/*.json"
---

# 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)

```json
{
  "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:

```json
{
  "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.

```json
{
  "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`:

```json
{
  "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`.