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aliyun-wan-r2v

aliyun-wan-r2v

github.com/cinienceCreative & media39662 installsUpdated 11w ago
Repository

About aliyun-wan-r2v

aliyun-wan-r2v is a Model Studio Wan R2V skill for reference-to-video generation in video creation workflows. It prepares requests from a prompt and reference video, optionally with a reference image, and supports polling asynchronous tasks.

Key features

  1. 01Generate video from a reference video
  2. 02Use prompts and optional reference images
  3. 03Set duration, frame rate, size, and seed
  4. 04Prepare requests and validate response schema
  5. 05Poll asynchronous task status

Use cases

  1. 01Create clips based on reference video
  2. 02Generate montages with consistent character style
  3. 03Submit asynchronous video generation tasks
  4. 04Save request and task result records

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/cinience/alicloud-skills --skill aliyun-wan-r2v

After installing, type/aliyun-wan-r2vin Claude Code to confirm it loaded.

Bundled files

References and scripts installed along with SKILL.md.

agents/
openai.yaml
references/
sources.md
scripts/
prepare_r2v_request.py

Details

From the project README

Category: provider

Model Studio Wan R2V

Validation

mkdir -p output/aliyun-wan-r2v
python -m py_compile skills/ai/video/aliyun-wan-r2v/scripts/prepare_r2v_request.py && echo "py_compile_ok" > output/aliyun-wan-r2v/validate.txt

Pass criteria: command exits 0 and output/aliyun-wan-r2v/validate.txt is generated.

Output And Evidence

  • Save reference input metadata, request payloads, and task outputs in output/aliyun-wan-r2v/.
  • Keep at least one polling result snapshot.

Use Wan R2V for reference-to-video generation. This is different from i2v (single image to video).

Critical model names

Use one of these exact model strings:

  • wan2.6-r2v-flash
  • wan2.6-r2v

Newer official releases may prefer the flash variant for lower latency and lower cost.

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 (video.generate_reference)

Request
  • prompt (string, required)
  • reference_video (string | bytes, required)
  • reference_image (string | bytes, optional)
  • duration (number, optional)
  • fps (number, optional)
  • size (string, optional)
  • seed (int, optional)
Response
  • video_url (string)
  • task_id (string, when async)
  • request_id (string)

Async handling

  • Prefer async submission for production traffic.
  • Poll task result with 15-20s intervals.
  • Stop polling when SUCCEEDED or terminal failure status is returned.

Local helper script

Prepare a normalized request JSON and validate response schema:

.venv/bin/python skills/ai/video/aliyun-wan-r2v/scripts/prepare_r2v_request.py \
  --prompt "Generate a short montage with consistent character style" \
  --reference-video "https://example.com/reference.mp4"

Output location

  • Default output: output/aliyun-wan-r2v/videos/
  • 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

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