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Alicloud Ai Video Wan R2v

该 Skill 面向阿里云百炼 Wan R2V 参考生视频模型，定义 video.generate_reference 归一化接口（prompt、参考视频/图像、时长、帧率、尺寸、随机种子），给出模型名、异步轮询方式与输出目录约定，并附带用于生成归一化请求 JSON、校验响应结构的本地 Python 脚本。

cinience
cinience
热度 80.31
最近更新 2026-05-11 16:28:08
分类：创作依赖：代码执行 / 读写文件 / 第三方 API应用场景：视频编辑 / 图像生成 / API 集成 / 自动化脚本

安装技能

复制下面的话术发给你的 Agent，它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill： npx secure-skills-cli add alicloud-ai-video-wan-r2v
Metadata
name
alicloud-ai-video-wan-r2v
description
Generate reference-based videos with Alibaba Cloud Model Studio Wan R2V models (wan2.6-r2v-flash, wan2.6-r2v). Use when creating multi-shot videos from reference video/image material, preserving character style, or documenting reference-to-video request/response flows.
version
1.0.0

Category: provider

Model Studio Wan R2V

Validation

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

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

Output And Evidence

  • Save reference input metadata, request payloads, and task outputs in output/alicloud-ai-video-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

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/alicloud-ai-video-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/alicloud-ai-video-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