A high-performance guide and toolset for converting static images into dynamic, cinematic videos using state-of-the-art AI motion models.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install image-to-video
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install image-to-video using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Image to Video Conversion?
This skill empowers developers and creators to transform static imagery into fluid, high-quality video content using the inference.sh CLI. By leveraging industry-leading models like Wan 2.5 and Seedance, it provides a streamlined path from a single frame to a fully realized cinematic sequence. Whether you are building marketing assets, product animations, or artistic cinemagraphs, this integration within Openclaw Skills ensures you have the right model and prompt structure for every visual scenario.
The toolset covers the entire lifecycle of video production, from model selection (such as Fabric 1.0 for textiles or Wan 2.5 for landscapes) to advanced motion prompting. It simplifies complex AI workflows into actionable CLI commands, making it easier than ever to bring static photos to life with professional-grade camera movements and subject physics.
Image to Video Conversion Use Cases
- Animating landscape photography with realistic environmental effects like rippling water or drifting clouds.
- Creating professional product showcase videos with smooth 360-degree orbit camera movements.
- Generating subtle character animations for portraits, including natural eye blinks and breathing.
- Developing physics-based fabric simulations for fashion or textile visualizations.
- Building automated media pipelines that generate, animate, upscale, and add audio to visual content.
How Image to Video Conversion Works
- Install the inference.sh CLI and authenticate to access the cloud-based AI inference engine.
- Provide a source image path or generate a high-quality starter image using models like FLUX or Seedream.
- Select the optimal video model (e.g., Wan 2.5 for realism or Fabric 1.0 for cloth physics) based on the content type.
- Define motion prompts encompassing camera movement, subject action, and atmospheric effects.
- Execute the generation command via the CLI to produce an MP4 file with the specified motion parameters.
- Optionally chain additional tasks like upscaling, audio generation, and media merging for a final production-ready video.
Image to Video Conversion Setup
To begin using this capability within Openclaw Skills, install the inference.sh CLI:
curl -fsSL https://cli.inference.sh | sh && infsh login
Once logged in, you can verify your installation by running a test generation with a motion model:
infsh app run falai/wan-2-5-i2v --input '{"prompt": "gentle ripples on water", "image": "path/to/your-image.png"}'
Image to Video Conversion Data Schema & Taxonomy
The skill processes JSON-based input payloads and generates multimedia files. Data is structured as follows:
| Attribute | Type | Description |
|---|---|---|
| prompt | String | Detailed motion and camera instructions. |
| image | Path | Local or remote path to the source still image. |
| model | App ID | The specific AI model identifier (e.g., falai/wan-2-5-i2v). |
| output | File | A high-quality .mp4 video file. |
Workflow results are typically exported as standard media files compatible with major video editors and web players.
Image to Video Conversion Advanced Features
- Multi-model orchestration for specialized physics (e.g., using Fabric 1.0 specifically for cloth simulation).
- Sequential clip stitching using media-merger to bypass single-clip duration limits.
- Automated post-processing pipelines including AI video upscaling and Foley sound effect generation.
- Cinemagraph creation through specific 'frozen-motion' prompt engineering.
- Asynchronous execution support using the --no-wait flag for parallel processing of multiple scenes.
SKILL.md
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