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Alicloud Ai Image Qwen Image Edit

核心功能是基于指令对现有图像进行编辑,支持修复、替换、风格迁移、局部编辑等操作,涵盖多款指定通义图像编辑模型,可留存请求响应映射以保证可复现。典型场景包括修改现有图像、保持主体一致性、记录编辑请求与响应。输入需提供提示词、源图像,可选遮罩、尺寸等参数,需先配置API密钥并安装SDK;输出返回编辑后图像URL、种子、请求ID,结果保存至指定目录。

cinience
cinience
热度 83.67
最近更新 2026-05-11 16:28:08
分类:创作依赖:第三方 API / 读写文件 / 代码执行应用场景:图像生成

安装技能

复制下面的话术发给你的 Agent,它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill: npx secure-skills-cli add alicloud-ai-image-qwen-image-edit
Metadata
name
alicloud-ai-image-qwen-image-edit
description
Edit images with Alibaba Cloud Model Studio Qwen Image Edit models (qwen-image-edit, qwen-image-edit-plus, qwen-image-edit-max and snapshots). Use when modifying existing images (inpaint, replace, style transfer, local edits), preserving subject consistency, or documenting image edit request/response mappings.
version
1.0.0

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-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