AI image editing prompting patterns for Qwen-Image-Edit. 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 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 Key Files prompting.md Prompt patterns and structure examples.md Good/bad examples from experiments parameters.md Tuning steps, guidance, negative prompts Tool Location tools/image edit.py CLI wrapper for the Modal ( docker/modal image edit/ ) and RunPod ( docker/runpod qwen edit/ ) endpoints Related Docs docs/qwen edit patterns.md Character transformation patterns .ai dev/qwen edit research.md Research notes

qwen-edit