Aliyun Qwen Image Edit

Name: Aliyun Qwen Image Edit
Author: cinience

cinience/alicloud-skills

82 installs
396 repo stars
Updated July 18, 2026
cinience/alicloud-skills

Edit existing images with Alibaba Cloud Model Studio Qwen Image Edit models for inpaint, replace, style transfer, and local edits.

About

Uses Model Studio Qwen Image Edit models for instruction-based image editing while preserving subject consistency. A developer uses it to modify existing images rather than generate from scratch.

qwen-image-edit, plus/max, and 2.0 series variants
Instruction-based editing: inpaint, replace, style transfer

Aliyun Qwen Image Edit by the numbers

82 all-time installs (skills.sh)
Ranked #818 of 1,335 Generative Media skills by installs in the Skillselion catalog
Data as of Aug 5, 2026 (Skillselion catalog sync)

npx skills add https://github.com/cinience/alicloud-skills --skill aliyun-qwen-image-edit

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Installs	82
repo stars	★ 396
Last updated	July 18, 2026
Repository	cinience/alicloud-skills ↗

What it does

Edit existing images with Alibaba Cloud Model Studio Qwen Image Edit models for inpaint, replace, style transfer, and local edits.

Files

SKILL.mdMarkdownGitHub ↗

Category: provider

Model Studio Qwen Image Edit

Validation

mkdir -p output/aliyun-qwen-image-edit
python -m py_compile skills/ai/image/aliyun-qwen-image-edit/scripts/prepare_edit_request.py && echo "py_compile_ok" > output/aliyun-qwen-image-edit/validate.txt

Pass criteria: command exits 0 and output/aliyun-qwen-image-edit/validate.txt is generated.

Output And Evidence

Save edit request payloads, result URLs, and model parameters under output/aliyun-qwen-image-edit/.
Keep one sample request/response pair for reproducibility.

Use Qwen Image Edit models for instruction-based image editing instead of text-to-image generation.

Critical model names

Use one of these exact model strings:

qwen-image-edit
qwen-image-edit-plus
qwen-image-edit-max
qwen-image-2.0
qwen-image-2.0-pro
qwen-image-2.0-2026-03-03
qwen-image-2.0-pro-2026-03-03
qwen-image-edit-plus-2025-12-15
qwen-image-edit-max-2026-01-16

Prerequisites

Install SDK in a virtual environment:

python3 -m venv .venv
. .venv/bin/activate
python -m pip install dashscope

Set DASHSCOPE_API_KEY in your environment, or add dashscope_api_key to ~/.alibabacloud/credentials.

Normalized interface (image.edit)

Request

prompt (string, required)
image (string | bytes, required) source image URL/path/bytes
mask (string | bytes, optional) inpaint region mask
size (string, optional) e.g. 1024*1024
seed (int, optional)

Response

image_url (string)
seed (int)
request_id (string)

Operational guidance

Keep prompts task-oriented: describe what to change and what to preserve.
Use masks for deterministic local edits.
Save output assets to object storage and persist only URLs.
For subject consistency, provide explicit constraints in prompt.

Local helper script

Prepare a normalized request JSON and validate response schema:

.venv/bin/python skills/ai/image/aliyun-qwen-image-edit/scripts/prepare_edit_request.py \
  --prompt "Replace the sky with sunset, keep buildings unchanged" \
  --image "https://example.com/input.png"

Output location

Default output: output/aliyun-qwen-image-edit/images/
Override base dir with OUTPUT_DIR.

Workflow

1) Confirm user intent, region, identifiers, and whether the operation is read-only or mutating. 2) Run one minimal read-only query first to verify connectivity and permissions. 3) Execute the target operation with explicit parameters and bounded scope. 4) Verify results and save output/evidence files.

References

references/sources.md

interface:
  display_name: "Alibaba Cloud AI Image Qwen Image Edit"
  short_description: "Qwen Image Edit (Max) workflows"
  default_prompt: "Use $aliyun-qwen-image-edit to complete this ai/image editing task on Alibaba Cloud."

#!/usr/bin/env python3
"""Prepare and validate normalized request/response for Qwen Image Edit."""

from __future__ import annotations

import argparse
import json
import sys
from pathlib import Path


def _load_json(path: str) -> dict:
    return json.loads(Path(path).read_text(encoding="utf-8"))


def main() -> None:
    parser = argparse.ArgumentParser(description="Prepare image.edit request and validate response shape")
    parser.add_argument("--prompt", required=True)
    parser.add_argument("--image", required=True)
    parser.add_argument("--mask")
    parser.add_argument("--size", default="1024*1024")
    parser.add_argument("--seed", type=int)
    parser.add_argument("--output", default="output/ai-image-qwen-image-edit/request.json")
    parser.add_argument("--validate-response", help="Path to JSON response file")
    args = parser.parse_args()

    req = {
        "prompt": args.prompt,
        "image": args.image,
        "size": args.size,
    }
    if args.mask:
        req["mask"] = args.mask
    if args.seed is not None:
        req["seed"] = args.seed

    out = Path(args.output)
    out.parent.mkdir(parents=True, exist_ok=True)
    out.write_text(json.dumps(req, ensure_ascii=False, indent=2), encoding="utf-8")

    result = {"ok": True, "request_path": str(out)}

    if args.validate_response:
        resp = _load_json(args.validate_response)
        missing = [k for k in ["image_url"] if k not in resp]
        if missing:
            result = {"ok": False, "error": f"missing keys: {missing}"}
            print(json.dumps(result, ensure_ascii=False))
            sys.exit(1)
        result["response_valid"] = True

    print(json.dumps(result, ensure_ascii=False))


if __name__ == "__main__":
    main()