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aliyun-wan-r2v

Use when generating 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.

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name
aliyun-wan-r2v
description
Use when generating 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 ```bash 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: ```bash 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: ```bash .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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