Generate Qwen images through DashScope with normalized requests, responses, and saved run evidence.
Design & media
Alibaba Cloud AI Video Wan Video
Generate Wan text-to-video and image-to-video through a normalized DashScope Python interface.
What it does
Generate Wan text-to-video and image-to-video with the DashScope Python SDK and exact supported model IDs. The normalized `video.generate` contract maps prompts, timing, size, seed, motion, and reference images to asynchronous tasks, then returns the video URL and metadata. Task IDs, polling responses, final URLs, and an end-to-end log can be saved as evidence.
When to use it
- Adding Wan generation to a video-agent pipeline
- Implementing asynchronous video task polling
- Standardizing t2v and i2v request and response fields
- Troubleshooting missing image inputs or unsupported sizes
The skill document
Category: provider
Model Studio Wan Video
Validation
mkdir -p output/alicloud-ai-video-wan-video
python -m py_compile skills/ai/video/alicloud-ai-video-wan-video/scripts/generate_video.py && echo "py_compile_ok" > output/alicloud-ai-video-wan-video/validate.txt
Pass criteria: command exits 0 and output/alicloud-ai-video-wan-video/validate.txt is generated.
Output And Evidence
- Save task IDs, polling responses, and final video URLs to
output/alicloud-ai-video-wan-video/. - Keep one end-to-end run log for troubleshooting.
Provide consistent video generation behavior for the video-agent pipeline by standardizing video.generate inputs/outputs and using DashScope SDK (Python) with the exact model name.
Critical model names
Use one of these exact model strings:
wan2.2-t2v-pluswan2.2-t2v-flashwan2.6-i2v-flashwan2.6-i2vwan2.6-i2v-uswan2.6-t2v-uswanx2.1-t2v-turbo
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_KEYin your environment, or adddashscope_api_keyto~/.alibabacloud/credentials(env takes precedence).
Normalized interface (video.generate)
Request
prompt(string, required)negative_prompt(string, optional)duration(number, required) secondsfps(number, required)size(string, required) e.g.1280*720seed(int, optional)reference_image(string | bytes, optional for t2v, required for i2v family models)motion_strength(number, optional)
Response
video_url(string)duration(number)fps(number)seed(int)
Quick start (Python + DashScope SDK)
Video generation is usually asynchronous. Expect a task ID and poll until completion.
Note: Wan i2v models require an input image; pure t2v models can omit reference_image.
import os
from dashscope import VideoSynthesis
# Prefer env var for auth: export DASHSCOPE_API_KEY=...
# Or use ~/.alibabacloud/credentials with dashscope_api_key under [default].
def generate_video(req: dict) -> dict:
payload = {
"model": req.get("model", "wan2.6-i2v-flash"),
"prompt": req["prompt"],
"negative_prompt": req.get("negative_prompt"),
"duration": req.get("duration", 4),
"fps": req.get("fps", 24),
"size": req.get("size", "1280*720"),
"seed": req.get("seed"),
"motion_strength": req.get("motion_strength"),
"api_key": os.getenv("DASHSCOPE_API_KEY"),
}
if req.get("reference_image"):
# DashScope expects img_url for i2v models; local files are auto-uploaded.
payload["img_url"] = req["reference_image"]
response = VideoSynthesis.call(**payload)
# Some SDK versions require polling for the final result.
# If a task_id is returned, poll until status is SUCCEEDED.
result = response.output.get("results", [None])[0]
return {
"video_url": None if not result else result.get("url"),
"duration": response.output.get("duration"),
"fps": response.output.get("fps"),
"seed": response.output.get("seed"),
}
Async handling (polling)
import os
from dashscope import VideoSynthesis
task = VideoSynthesis.async_call(
model=req.get("model", "wan2.6-i2v-flash"),
prompt=req["prompt"],
img_url=req["reference_image"],
duration=req.get("duration", 4),
fps=req.get("fps", 24),
size=req.get("size", "1280*720"),
api_key=os.getenv("DASHSCOPE_API_KEY"),
)
final = VideoSynthesis.wait(task)
video_url = final.output.get("video_url")
Operational guidance
- Video generation can take minutes; expose progress and allow cancel/retry.
- Cache by
(prompt, negative_prompt, duration, fps, size, seed, reference_image hash, motion_strength). - Store video assets in object storage and persist only URLs in metadata.
reference_imagecan be a URL or local path; the SDK auto-uploads local files.- If you get
Field required: input.img_url, the reference image is missing or not mapped.
Size notes
- Use
WxHformat (e.g.1280*720). - Prefer common sizes; unsupported sizes can return 400.
Output location
- Default output:
output/alicloud-ai-video-wan-video/videos/ - Override base dir with
OUTPUT_DIR.
Anti-patterns
- Do not invent model names or aliases; use official Wan i2v model IDs only.
- Do not block the UI without progress updates.
- Do not retry blindly on 4xx; handle validation failures explicitly.
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.mdfor DashScope SDK mapping and async handling notes. -
Source list:
references/sources.md
Questions people ask
- Which Wan model IDs can I use?
- Supported exact strings are `wan2.2-t2v-plus`, `wan2.2-t2v-flash`, `wan2.6-i2v-flash`, `wan2.6-i2v`, `wan2.6-i2v-us`, `wan2.6-t2v-us`, and `wanx2.1-t2v-turbo`.
- Is a reference image always required?
- It is required for i2v-family models and optional for pure t2v models. Supply a URL or local path; the SDK auto-uploads local files and maps the value to `img_url`.
- How are asynchronous jobs handled?
- DashScope returns a task that can be polled or passed to `VideoSynthesis.wait` until completion. The workflow records task IDs, polling responses, final video URLs, and one end-to-end troubleshooting log.
Related skills
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Generate 3–15 second Kling V3 videos from text or images with configurable format, mode, and sound.
Create and export videos from scripts, URLs, documents, visuals, ideas, or speech.