Category: provider Model Studio Qwen Image Validation mkdir -p output/alicloud-ai-image-qwen-image python -m py_compile skills/ai/image/alicloud-ai-image-qwen-image/scripts/generate_image.py && echo "py_compile_ok"

output/alicloud-ai-image-qwen-image/validate.txt Pass criteria: command exits 0 and output/alicloud-ai-image-qwen-image/validate.txt is generated. Output And Evidence Write generated image URLs, prompts, and metadata to output/alicloud-ai-image-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-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. 10241024 , 7681024 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" : "10241024" , "seed" : 1234 } Preview workflow (download then open): curl -L -o output/alicloud-ai-image-qwen-image/images/preview.png "" && open output/alicloud-ai-image-qwen-image/images/preview.png Local helper script (JSON request -> image file): python skills/ai/image/alicloud-ai-image-qwen-image/scripts/generate_image.py \ \ --request '{"prompt":"a studio product photo of headphones","size":"10241024"}' \ \ --output output/alicloud-ai-image-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. 10241024 , 7681024 . 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": } 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/alicloud-ai-image-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. 10241024 , 7681024 ). 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 Confirm user intent, region, identifiers, and whether the operation is read-only or mutating. Run one minimal read-only query first to verify connectivity and permissions. Execute the target operation with explicit parameters and bounded scope. 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/alicloud-ai-image-qwen-image-edit/ . Source list: references/sources.md