runninghub-video

Use RunningHub official standard-model APIs for image-to-video generation. Trigger when the user asks to use RunningHub, 可灵, Seedance, 万相, or other RunningHub video endpoints to turn one image or start/end frames into a video, especially when local files need to be uploaded first, tasks need polling via /openapi/v2/query, or completed videos should be downloaded automatically.

Use this skill to submit image-to-video jobs to RunningHub, poll task status, and download the finished video locally.

Quick Start

1. Confirm the user wants RunningHub image-to-video rather than HTML/CSS animation or local FFmpeg edits.
2. Use scripts/runninghub_video.py instead of hand-writing curl unless the user explicitly asks for raw API calls.
3. Accept either:
   • a public image URL
   • a local image path that should be uploaded through RunningHub's binary upload endpoint first
4. Wait for /openapi/v2/query unless the user explicitly asks for submit-only behavior.
5. Download the returned media immediately because RunningHub-hosted outputs and uploaded media links can expire.

Default Workflow

1. Pick the model

Use these stable shortcuts unless the user names a different endpoint:

• wan-2.2: default choice for general image-to-video generation in this skill
• kling-v3.0-std: strong alternative for high-quality single-image or start/end-frame generation
• seedance-2.0-global: quality-oriented alternative with resolution and audio switches
• seedance-2.0-global-fast: faster/cheaper Seedance variant
• wan-2.2: Wan 2.2 image-to-video endpoint with RunningHub's numeric field names

2. Prepare the inputs

• If the user provides a local file path, pass it directly to the script. The script uploads it to POST /openapi/v2/media/upload/binary and reuses the returned download_url.
• If the user provides a public URL or a data: URI, pass it through unchanged.
• If the user wants stronger transition control and the chosen model supports it, include an end frame.

3. Submit and wait

Run the helper from the skill directory:

powershell

python "C:\Users\Administrator\.codex\skills\runninghub-video\scripts\runninghub_video.py" `
  --image "C:\path\to\start.png" `
  --prompt "镜头缓慢推进，人物抬头微笑，风吹动头发" `
  --out-dir "C:\path\to\outputs"

For start/end frame generation:

powershell

python "C:\Users\Administrator\.codex\skills\runninghub-video\scripts\runninghub_video.py" `
  --image "C:\path\to\start.png" `
  --end-image "C:\path\to\end.png" `
  --prompt "从平静站立过渡到转身回望，镜头平滑推进" `
  --duration 5 `
  --out-dir "C:\path\to\outputs"

4. Return useful output

When you finish, report:

• which model/endpoint was used
• the taskId
• whether local images were uploaded first
• the saved output path(s)
• any prompt or parameter choices worth remembering

Command Patterns

Kling 3.0 std

powershell

python "C:\Users\Administrator\.codex\skills\runninghub-video\scripts\runninghub_video.py" `
  --model kling-v3.0-std `
  --image "C:\path\to\image.png" `
  --prompt "电影感镜头，小幅推近，人物表情逐渐变化" `
  --duration 5 `
  --cfg-scale 0.8 `
  --sound true

Seedance 2.0 global

powershell

python "C:\Users\Administrator\.codex\skills\runninghub-video\scripts\runninghub_video.py" `
  --model seedance-2.0-global `
  --image "C:\path\to\image.png" `
  --prompt "书页翻动时，文字化作发光蝴蝶飞散" `
  --resolution 720p `
  --ratio adaptive `
  --audio true `
  --real-person-mode true

Wan 2.2

powershell

python "C:\Users\Administrator\.codex\skills\runninghub-video\scripts\runninghub_video.py" `
  --image "C:\path\to\image.png" `
  --prompt "产品绕镜头缓慢旋转，补光扫过金属表面" `
  --duration 5 `
  --wan-resolution auto

Parameters Worth Tuning

• --prompt: motion, camera movement, atmosphere, and audio intent
• --duration: model-specific duration string; keep it to values shown in the official endpoint docs
• --end-image: use when the endpoint supports start/end-frame control
• --out-dir: always set an explicit output directory for easier follow-up work
• --submit-only: use when the user wants a task id without waiting
• --poll-interval and --timeout: useful for long renders

Troubleshooting

• If upload fails, verify the local file exists and the API key is valid.
• If the task returns moderation or content-verification errors, keep the same image but soften the prompt or remove risky wording.
• If the task succeeds but no download happens, inspect the raw query response and the returned results array.
• If the user asks for a model not covered by the helper yet, read references/api_reference.md, then extend the script instead of crafting a one-off request.

References

• Read references/api_reference.md for the exact endpoints, payload shapes, and polling behavior used by this skill.