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aliyun-qwen-image-edit

aliyun-qwen-image-edit

github.com/cinienceCreative & media39697 installsUpdated 11w ago
Repository

About aliyun-qwen-image-edit

A skill for preparing instruction-based edits with Alibaba Cloud Model Studio Qwen Image Edit models. It helps users submit image edits with optional masks and validate the normalized response.

Key features

  1. 01Prepare normalized image edit requests
  2. 02Accept image URLs, paths, or bytes
  3. 03Support optional masks, size, and seed
  4. 04Validate response schema with a helper script

Use cases

  1. 01Replace or modify regions in an existing image
  2. 02Apply localized edits using a mask

Install

Copy the command and run it in a terminal.

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

After installing, type/aliyun-qwen-image-editin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

agents/
openai.yaml
references/
sources.md
scripts/
prepare_edit_request.py

Details

From the project README

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

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