aliyun-qwen-image

name	aliyun-qwen-image
description	Use when generating images with Model Studio DashScope SDK using Qwen Image generation models (qwen-image, qwen-image-plus, qwen-image-max, qwen-image-2.0 series and snapshots). Use when implementing or documenting image.generate requests/responses, mapping prompt/negative_prompt/size/seed/reference_image, or integrating image generation into the video-agent pipeline.
version	1.0.0

Category: provider

Model Studio Qwen Image

Validation

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

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

Output And Evidence

• Write generated image URLs, prompts, and metadata to output/aliyun-qwen-image/.
• Keep at least one sample JSON response per run.

Build consistent image generation behavior for the video-agent pipeline by standardizing image.generate inputs/outputs and using DashScope SDK (Python) with the exact model name.

Prerequisites

• Install SDK (recommended in a venv to avoid PEP 668 limits):

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 (env takes precedence).

Critical model names

Use one of these exact model strings:

• qwen-image
• qwen-image-plus
• qwen-image-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-max-2025-12-30
• qwen-image-plus-2026-01-09

Normalized interface (image.generate)

Request

• prompt (string, required)
• negative_prompt (string, optional)
• size (string, required) e.g. 1024*1024, 768*1024
• style (string, optional)
• seed (int, optional)
• reference_image (string | bytes, optional)

Response

• image_url (string)
• width (int)
• height (int)
• seed (int)

Quickstart (normalized request + preview)

Minimal normalized request body:

{
  "prompt": "a cinematic portrait of a cyclist at dusk, soft rim light, shallow depth of field",
  "negative_prompt": "blurry, low quality, watermark",
  "size": "1024*1024",
  "seed": 1234
}

Preview workflow (download then open):

curl -L -o output/aliyun-qwen-image/images/preview.png "<IMAGE_URL_FROM_RESPONSE>" && open output/aliyun-qwen-image/images/preview.png

Local helper script (JSON request -> image file):

python skills/ai/image/aliyun-qwen-image/scripts/generate_image.py \\
  --request '{"prompt":"a studio product photo of headphones","size":"1024*1024"}' \\
  --output output/aliyun-qwen-image/images/headphones.png \\
  --print-response

Parameters at a glance

Field	Required	Notes
prompt	yes	Describe a scene, not just keywords.
negative_prompt	no	Best-effort, may be ignored by backend.
size	yes	WxH format, e.g. 1024*1024, 768*1024.
style	no	Optional stylistic hint.
seed	no	Use for reproducibility when supported.
reference_image	no	URL/file/bytes, SDK-specific mapping.

Quick start (Python + DashScope SDK)

Use the DashScope SDK and map the normalized request into the SDK call. Note: For qwen-image-max, the DashScope SDK currently succeeds via ImageGeneration (messages-based) rather than ImageSynthesis. If the SDK version you are using expects a different field name for reference images, adapt the input mapping accordingly.

import os
from dashscope.aigc.image_generation import ImageGeneration

# Prefer env var for auth: export DASHSCOPE_API_KEY=...
# Or use ~/.alibabacloud/credentials with dashscope_api_key under [default].


def generate_image(req: dict) -> dict:
    messages = [
        {
            "role": "user",
            "content": [{"text": req["prompt"]}],
        }
    ]

    if req.get("reference_image"):
        # Some SDK versions accept {"image": <url|file|bytes>} in messages content.
        messages[0]["content"].insert(0, {"image": req["reference_image"]})

    response = ImageGeneration.call(
        model=req.get("model", "qwen-image-max"),
        messages=messages,
        size=req.get("size", "1024*1024"),
        api_key=os.getenv("DASHSCOPE_API_KEY"),
        # Pass through optional parameters if supported by the backend.
        negative_prompt=req.get("negative_prompt"),
        style=req.get("style"),
        seed=req.get("seed"),
    )

    # Response is a generation-style envelope; extract the first image URL.
    content = response.output["choices"][0]["message"]["content"]
    image_url = None
    for item in content:
        if isinstance(item, dict) and item.get("image"):
            image_url = item["image"]
            break
    return {
        "image_url": image_url,
        "width": response.usage.get("width"),
        "height": response.usage.get("height"),
        "seed": req.get("seed"),
    }

Error handling

Error	Likely cause	Action
401/403	Missing or invalid DASHSCOPE_API_KEY	Check env var or ~/.alibabacloud/credentials, and access policy.
400	Unsupported size or bad request shape	Use common WxH and validate fields.
429	Rate limit or quota	Retry with backoff, or reduce concurrency.
5xx	Transient backend errors	Retry with backoff once or twice.

Output location

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

Operational guidance

• Store the returned image in object storage and persist only the URL in metadata.
• Cache results by (prompt, negative_prompt, size, seed, reference_image hash) to avoid duplicate costs.
• Add retries for transient 429/5xx responses with exponential backoff.
• Some backends ignore negative_prompt, style, or seed; treat them as best-effort inputs.
• If the response contains no image URL, surface a clear error and retry once with a simplified prompt.

Size notes

• Use WxH format (e.g. 1024*1024, 768*1024).
• Prefer common sizes; unsupported sizes can return 400.

Anti-patterns

• Do not invent model names or aliases; use official model IDs only.
• Do not store large base64 blobs in DB rows; use object storage.
• Do not omit user-visible progress for long generations.

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

• See references/api_reference.md for a more detailed DashScope SDK mapping and response parsing tips.

• See references/prompt-guide.md for prompt patterns and examples.

• For edit workflows, use skills/ai/image/aliyun-qwen-image-edit/.

• Source list: references/sources.md