--- 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": ["", 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 `` token with structured angle/distance prompts: ``` front view eye-level shot close-up front-right quarter view low-angle shot medium shot back view elevated shot wide shot ``` **Template**: ` {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": ["", 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": "" }}, "6": { "class_type": "TextEncodeQwenImageEditPlusAdvance_lrzjason", "inputs": { "clip": ["3", 0], "prompt": "", "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": "", "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`.