Controlnet Pose

# ControlNet & Pose

Condition image or video generation on a pose, skeleton, or motion reference. This skill routes across the pose-driven Model API endpoints reachable today and points the agent at ComfyUI workflows for richer ControlNet rigs.

[runcomfy.com](https://www.runcomfy.com/?utm_source=skills.sh&utm_medium=skill&utm_campaign=controlnet-pose) · [Kling motion control](https://www.runcomfy.com/models/kling/kling-2-6/motion-control-pro?utm_source=skills.sh&utm_medium=skill&utm_campaign=controlnet-pose) · [CLI docs](https://docs.runcomfy.com/cli/introduction?utm_source=skills.sh&utm_medium=skill&utm_campaign=controlnet-pose)

## Powered by the RunComfy CLI

```bash # 1. Install (see runcomfy-cli skill for details) npm i -g @runcomfy/cli # or: npx -y @runcomfy/cli --version

# 2. Sign in runcomfy login # or in CI: export RUNCOMFY_TOKEN=<token>

# 3. Pose-conditioned generate runcomfy run <vendor>/<model> \ --input '{"reference_video_url": "...", "character_image_url": "..."}' \ --output-dir ./out ```

CLI deep dive: [`runcomfy-cli`](https://www.skills.sh/agentspace-so/runcomfy-agent-skills/runcomfy-cli) skill.

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## Pick the right model

Routes split by video pose-transfer vs image pose-conditioned generation.

### Video — motion / pose transfer

**Kling 2-6 Motion Control Pro** — `kling/kling-2-6/motion-control-pro` *(default for video pose transfer)* > Takes a reference performance video + a target character image, produces video of the target performing the reference motion / pose. > Pick for: transferring a source video's motion / blocking onto a new character; dance choreography re-shot; sports motion onto a stylized character. > Avoid for: still-image pose conditioning — use Z-Image ControlNet LoRA.

**Kling 2-6 Motion Control Standard** — [`kling/kling-2-6/motion-control-standard`](https://www.runcomfy.com/models/kling/kling-2-6/motion-control-standard?utm_source=skills.sh&utm_medium=skill&utm_campaign=controlnet-pose) > Cheaper Kling Motion Control tier. > Pick for: drafts, iteration on motion-control compositions. > Avoid for: final delivery — use Pro.

**Wan 2-2 Animate (video-to-video)** — [`community/wan-2-2-animate/video-to-video`](https://www.runcomfy.com/models/community/wan-2-2-animate/video-to-video?utm_source=skills.sh&utm_medium=skill&utm_campaign=controlnet-pose) > Community-published variant on Wan 2-2. Audio-driven character animation that also accepts pose-style conditioning. > Pick for: stylized character animation, mascot work. > Avoid for: photoreal subjects — use Kling Motion Control.

### Image — pose-conditioned generation

**Z-Image Turbo ControlNet LoRA** — [`tongyi-mai/z-image/turbo/controlnet/lora`](https://www.runcomfy.com/models/tongyi-mai/z-image/turbo/controlnet/lora?utm_source=skills.sh&utm_medium=skill&utm_campaign=controlnet-pose) > Z-Image Turbo with a ControlNet LoRA — feed a control image (pose skeleton, depth map, canny) and a prompt, get a generation conditioned on that control. > Pick for: pose-locked image generation, character in specific stance, depth-locked composition. > Avoid for: complex multi-condition stacks (e.g. pose + depth + reference) — those need a ComfyUI workflow.

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## Route 1: Kling Motion Control — video pose transfer

**Model**: `kling/kling-2-6/motion-control-pro` (or `/motion-control-standard`) **Catalog**: [motion-control-pro](https://www.runcomfy.com/models/kling/kling-2-6/motion-control-pro?utm_source=skills.sh&utm_medium=skill&utm_campaign=controlnet-pose) · [`kling` collection](https://www.runcomfy.com/models/collections/kling?utm_source=skills.sh&utm_medium=skill&utm_campaign=controlnet-pose)

### Invoke

```bash runcomfy run kling/kling-2-6/motion-control-pro \ --input '{ "reference_video_url": "https://your-cdn.example/source-performance.mp4", "character_image_url": "https://your-cdn.example/target-character.png" }' \ --output-dir ./out ```

### Tips

- **Reference video provides the motion / blocking / camera**; character image provides the identity / appearance. - **Clean, well-framed reference** works best — a single subject performing one continuous action, no scene cuts. - **Stylized characters** (illustration, anime) are handled cleanly; photoreal target faces may need additional face-swap pass for identity-tight delivery.

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## Route 2: Z-Image ControlNet LoRA — image pose-conditioned generation

**Model**: `tongyi-mai/z-image/turbo/controlnet/lora` **Catalog**: [Z-Image controlnet LoRA](https://www.runcomfy.com/models/tongyi-mai/z-image/turbo/controlnet/lora?utm_source=skills.sh&utm_medium=skill&utm_campaign=controlnet-pose)

### Invoke

```bash runcomfy run tongyi-mai/z-image/turbo/controlnet/lora \ --input '{ "prompt": "A samurai in battle stance, traditional armor, cherry-blossom forest background, cinematic 35mm", "control_image_url": "https://your-cdn.example/openpose-skeleton.png" }' \ --output-dir ./out ```

### Tips

- **The control image type matter