$2b

3. Pose-conditioned generate

runcomfy run /

--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.

---

## 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.

---

## 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

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.

## 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

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