/qwen-edit

name: qwen-edit
description: AI image editing prompting patterns for Qwen-Image (Qwen-Image-Edit-2511 by default, Qwen-Image-2.1 as a non-commercial Modal opt-in). Use when editing photos while preserving identity, reframing cropped images, changing clothing or accessories, adjusting poses, applying style transfers, or character transformations. Provides prompt patterns, parameter tuning, and examples.

Qwen-Image-Edit Skill

AI-powered image editing via `tools/image_edit.py`. The model depends on the deployment:

| Deployment | Model | License | Defaults | Max inputs | |-----------|-------|---------|----------|------------| | `--cloud modal` (default) | Qwen-Image-Edit-2511 | Apache-2.0, commercial OK | 8 steps, guidance 1.0 | 3 images | | `--cloud modal`, app deployed with `IMAGE_EDIT_MODEL=qwen-image-2.1` | **Qwen-Image-2.1** (2026-09, unified gen+edit, 7B DiT, RGBA) | Qwen Research License -- **non-commercial** ("research or evaluation purposes only") | 40 steps, guidance 1.0 (no CFG) | 10 images | | `--cloud runpod` | Qwen-Image-Edit-2511 + Lightning LoRA | Apache-2.0, commercial OK | 8 steps, guidance 1.0 | 3 images |

The tool prints `Model:` and `Steps:` after each edit, so you can tell which model the Modal app runs and how many steps it took. Without `--steps`, each model runs its own default above. Opt in with `IMAGE_EDIT_MODEL=qwen-image-2.1 uv run modal deploy docker/modal-image-edit/app.py`; deploy again without it to switch back.

**Status:** Evolving - learnings being captured as we experiment. The prompt patterns and parameter findings in `examples.md` / `parameters.md` were measured on 2511; re-check them on 2.1 before relying on the exact numbers.

Qwen-Image-2.1 specifics (Modal opt-in)

• **Not step-distilled** -- 40 steps is the model card default. The old 8-step

default was tuned for 2511's Lightning LoRA, which 2.1 doesn't have here, so expect low step counts to cost quality (untested).

• **Sampled without guidance.** `--guidance 1.0` (default) is how the model is meant

to run. `>1` switches on true CFG (blank negative prompt if you pass none) and doubles the cost of every step.

• **Multi-image:** the first `--input` is the image being edited ("image 1"); the

rest are references in order ("image 2", ...). Up to 10. Output size follows the first image's aspect ratio at ~1MP.

• **Local edits:** draw a circle or paint an annotation on the image, or pass a

separate mask as an extra `--input`, and say what to change there (e.g. "Remove the watch inside the red circle").

• **RGBA:** transparent PNG inputs keep their alpha. For a cut-out, prompt

`"This is an RGBA image with transparency. <subject>. The image has alpha channel and the background is transparent."` -- the result comes back as an RGBA PNG only when the model actually produced transparency.

When to Use This Skill

Use when the user wants to:

• Edit/transform photos while preserving identity
• Reframe cropped images (fix cut-off heads, etc.)
• Change clothing, add accessories
• Change pose (arm positions, hand placement)
• Apply style transfers (cyberpunk, anime, oil painting)
• Adjust lighting/color grading
• Add/remove objects
• Character transformations (Bond, Neo, etc.)

When NOT to Use

• **Background replacement (single image)** - creates cut-out artifacts, halos
• **Face swapping** - cannot preserve identity from reference
• **Outpainting** - can't extend canvas reliably

Use With Care

• **Identity keep-clauses** - naming features to keep ("keep his glasses and beard") ADDS them to people who lack

them. Use "Keep exactly the same person with an identical face and identical hair, do not add or remove glasses, beard or stubble, photorealistic photograph" (costuming colleagues, 2026-11; `.claude/skills/parody-short/people.md`). For someone who does wear glasses, name them ("keeps wearing her round glasses"), or a new setting can drop them

• **Multi-image compositing** - CAN work with explicit identity anchors (see examples.md for prompt patterns). Requires describing distinctive features (hair texture/color, ethnicity, outfit) and using guidance ~2.0
• **Camera angle changes** - Inconsistent results. Vertical angles (low/high) work better than rotational (three-quarter view)

Quick Reference

# Basic edit
uv run tools/image_edit.py --input photo.jpg --prompt "Add sunglasses"

# With negative prompt (recommended)
uv run tools/image_edit.py --input photo.jpg \
  --prompt "Reframe as portrait with full head visible" \
  --negative "blur, distortion, artifacts"

# Style transfer
uv run tools/image_edit.py --input photo.jpg --style cyberpunk

# Background (use cautiously - often fails)
uv run tools/image_edit.py --input photo.jpg --background office

# Higher quality (RunPod / 2511 -- on Modal / 2.1 the 40-step default already is)
uv run tools/image_edit.py --input photo.jpg --prompt "..." --steps 16 --guidance 3.0 --cloud runpod

# Extract the subject onto a transparent background (Modal / 2.1)
uv run tools/image_edit.py --input product.jpg   --prompt "This is an RGBA image with transparency. The product from the photo. The image has alpha channel and the background is transparent."   --output product_cutout.png

# Multi-image composite (identity-preserving) -- settings tuned on RunPod / 2511
uv run tools/image_edit.py --input person.jpg background.jpg \
  --prompt "The [ethnicity] [gender] with [hair description] from first image is now in [scene] from second image. Same [features], [outfit]." \
  --negative "different ethnicity, different hair color, different face shape, generic stock photo" \
  --steps 16 --guidance 2.0 --cloud runpod

# Multi-image composite on Modal / 2.1 -- up to 10 references, defaults usually suffice
uv run tools/image_edit.py --input person.jpg outfit.jpg shoes.jpg background.jpg \
  --prompt "The person from image 1 wearing the outfit from image 2 and the shoes from image 3, standing in the scene from image 4. Same face