Alicloud Ai Image Qwen Image Edit

Name: Alicloud Ai Image Qwen Image Edit
Author: cinience

cinience/alicloud-skills

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

alicloud-ai-image-qwen-image-edit is a Claude Code skill that calls Alibaba Cloud Qwen image edit endpoints for developers who need inpainting, outpainting, and prompt-guided photo edits on uploads.

About

alicloud-ai-image-qwen-image-edit is a Claude Code skill from cinience/alicloud-skills that integrates Alibaba Cloud Qwen image editing APIs into agent workflows. The skill covers inpainting masked regions, outpainting canvas extensions, background replacement, and prompt-guided transformations on user-uploaded images. Developers reach for it when building apps, internal tools, or automation pipelines that must send images to Qwen edit endpoints with correct parameters, auth, and response handling instead of manual console uploads. Sessions typically wire upload handlers, construct edit prompts, choose edit modes, and parse returned image assets. Use alicloud-ai-image-qwen-image-edit when Alicloud credentials are available and you need repeatable, code-backed image edit calls from Claude Code or Cursor during feature development. Parameter guidance covers mask regions for inpainting, canvas bounds for outpainting, and response parsing for edited PNG or JPEG assets. The skill keeps Alicloud credential handling and retry logic inside the integration layer agents generate.

Mask- and reference-guided edits
Inpaint, outpaint, and style transfer
Upload, preprocess, and result fetch
Parameter guardrails for fidelity
User-media pipeline integration

Alicloud Ai Image Qwen Image Edit by the numbers

335 all-time installs (skills.sh)
Ranked #488 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 alicloud-ai-image-qwen-image-edit

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Installs	335
repo stars	★ 396
Last updated	July 18, 2026
Repository	cinience/alicloud-skills ↗

How do you integrate Qwen image edit APIs?

Enable inpainting, outpainting, background swap, and prompt-guided edits on user uploads using Qwen image edit endpoints.

Who is it for?

Developers building image-editing features on Alibaba Cloud who need agent-guided Qwen inpainting and outpainting integration.

Skip if: Teams not on Alicloud or projects needing only static image hosting without generative edit endpoints.

When should I use this skill?

The user asks for Qwen image edit, Alicloud inpainting, outpainting, or background swap on uploaded images.

What you get

Working Qwen image edit API calls, edited image assets, and upload-to-edit handler code with Alicloud auth wiring.

Image edit API integration code
Edited image output files

Files

SKILL.mdMarkdownGitHub ↗

Category: provider

Model Studio Qwen Image Edit

Validation

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

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

Output And Evidence

Save edit request payloads, result URLs, and model parameters under output/alicloud-ai-image-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/alicloud-ai-image-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/alicloud-ai-image-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 $alicloud-ai-image-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()

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FAQ

What edit modes does alicloud-ai-image-qwen-image-edit support?

alicloud-ai-image-qwen-image-edit covers Qwen inpainting, outpainting, background swap, and prompt-guided edits on user uploads through Alibaba Cloud image edit endpoints.

When should developers use this Alicloud image skill?

Developers use alicloud-ai-image-qwen-image-edit when wiring upload handlers to Qwen edit APIs during feature build, especially for masked inpainting or automated background replacement.

Generative Mediallmautomation