SuperAPI AI Directory
Partner with us中文AI newsFavorites
Skillsqwen-edit

qwen-edit

Design & creation

View repository

Updated 10-15 13:29

About this skill

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

# 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 and hair."

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