Aliyun Qwen Image Edit

Security Test Payload Generator

Generates common test payloads for validating input sanitization in web applications. Used by QA teams to verify XSS/SQLi filters.

Example

from scripts.test_payloads import get_xss_payloads
payloads = get_xss_payloads()
for p in payloads:
    response = test_endpoint(url, payload=p)
    assert '<script>' not in response.text

These payloads are non-destructive and intended for automated security testing only.

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