ComfyUI桥接器
comfyui-bridge
by bortlesboat
Generate images, faceswap, edit photos, animate expressions, and do style transfer via a self-hosted ComfyUI instance on your LAN. Your GPU, your models.
安装
claude skill add --url github.com/openclaw/skills/tree/main/skills/bortlesboat/comfyui-bridge文档
ComfyUI Bridge
Generate images, faceswap, animate expressions, and do style transfer via a self-hosted ComfyUI instance running on your LAN. No cloud API — your GPU, your models.
Requirements
- ComfyUI Desktop (or server) running somewhere on your LAN
- ComfyUI Bridge server running on the same machine as ComfyUI (see Setup)
uvinstalled on the machine running OpenClaw
Setup
1. Install the bridge server (on your ComfyUI machine)
The bridge is a lightweight FastAPI server that wraps ComfyUI's API:
git clone https://github.com/Bortlesboat/comfyui-bridge
cd comfyui-bridge
pip install -r requirements.txt
python bridge_server.py
# Listening on http://0.0.0.0:8100
2. Configure the skill
Set the bridge URL as an environment variable on your OpenClaw machine:
export COMFYUI_BRIDGE_URL=http://YOUR_COMFYUI_MACHINE_IP:8100
Or add it to your LaunchAgent/systemd service environment.
3. Required ComfyUI custom nodes (for all features)
Install via ComfyUI Manager:
- ReActor — faceswap
- ComfyUI-LivePortrait — expression animation
- ComfyUI_IPAdapter_plus — style transfer
- WAS Node Suite — utilities
- ComfyUI-GGUF — GGUF model support (optional, for FLUX)
- rgthree-comfy — workflow utilities
4. Recommended models
| Model | Use |
|---|---|
| Juggernaut XL Ragnarok | Architecture, objects, general |
| RealVisXL V5.0 Lightning | People, portraits, fast |
| FLUX.1 Dev Q5 GGUF | Maximum photorealism (slow, VRAM-heavy) |
Usage
All commands use the comfyui_generate.py script via uv run. Replace SKILL_SCRIPTS with the path to this skill's scripts/ directory.
Always use --no-media — include one MEDIA: /full/path/to/output.png in your text response instead.
1. Text to Image
uv run $SKILL_SCRIPTS/comfyui_generate.py \
--prompt "your description" \
--filename ~/.openclaw/media/outbound/output.png \
--no-media
2. Image to Image
uv run $SKILL_SCRIPTS/comfyui_generate.py \
--prompt "make it sunset" \
-i /path/to/input.png \
--strength 0.5 \
--filename ~/.openclaw/media/outbound/output.png \
--no-media
3. Faceswap (pipeline — best quality)
Swaps a face then runs img2img cleanup for natural blending. ~30 seconds total.
uv run $SKILL_SCRIPTS/comfyui_generate.py \
--faceswap-pipeline \
--source-face /path/to/source_face.png \
-i /path/to/target.png \
--cleanup-strength 0.40 \
--filename ~/.openclaw/media/outbound/output.png \
--no-media
4. Faceswap (basic)
uv run $SKILL_SCRIPTS/comfyui_generate.py \
--faceswap \
--source-face /path/to/source_face.png \
-i /path/to/target.png \
--filename ~/.openclaw/media/outbound/output.png \
--no-media
5. Targeted Faceswap (specific face in group photo)
uv run $SKILL_SCRIPTS/comfyui_generate.py \
--targeted-faceswap \
--source-face /path/to/source_face.png \
-i /path/to/group_photo.png \
--target-face-index "1" \
--filename ~/.openclaw/media/outbound/output.png \
--no-media
Face indices: 0 = leftmost, 1 = second from left, "0,2" = first and third.
6. LivePortrait (expression animation)
uv run $SKILL_SCRIPTS/comfyui_generate.py \
--liveportrait \
-i /path/to/portrait.png \
--expression-preset smile \
--filename ~/.openclaw/media/outbound/output.png \
--no-media
Presets: smile, surprised, wink, suspicious, derp, angry, sleepy
Fine-grained control: --smile (-0.3 to 1.3), --blink-val (-20 to 5), --eyebrow-val (-10 to 15), --aaa (-30 to 120, mouth open), --pitch/--yaw/--roll (-20 to 20, head rotation).
7. Style Transfer
Generate a new image in the style of a reference:
uv run $SKILL_SCRIPTS/comfyui_generate.py \
--style-transfer \
--style-ref /path/to/reference.png \
--prompt "a portrait of a man" \
--style-weight 0.85 \
--filename ~/.openclaw/media/outbound/output.png \
--no-media
8. Restyle
Apply a reference image's style to an existing photo:
uv run $SKILL_SCRIPTS/comfyui_generate.py \
--restyle \
--style-ref /path/to/reference.png \
-i /path/to/photo.png \
--style-weight 0.85 \
--strength 0.65 \
--filename ~/.openclaw/media/outbound/output.png \
--no-media
9. Enhanced mode
Add --enhanced to any command for FaceDetailer + 4x-UltraSharp upscale (net ~2x resolution). Works with txt2img and faceswap.
Quality Gates (built-in)
The script includes two automatic quality checks on faceswap outputs:
Gate 1 — Size check: ReActor blank outputs when no face is detected (~2KB). Any faceswap output under 10KB automatically retries once. If still blank, exits with FACESWAP_BLANK: error — no garbage delivered.
Gate 2 — Vision QA: If you have Ollama running locally with gemma3:12b, faceswap outputs are checked with vision QA before delivery. PASS → deliver normally. FAIL → file renamed _qa_flagged and still delivered. Add ~10-20s but catches glitchy outputs. Disable by not having Ollama/gemma3 installed (fails open).
Offline Queue
When the bridge is unreachable, requests are automatically queued to ~/.openclaw/faceswap-queue/. A companion daemon (queue_processor.py) polls every 5 minutes and delivers via iMessage when the bridge comes back online.
Tell users: "Got it — the system is offline right now but your request is queued and will be sent automatically when it comes back."
Options Reference
| Flag | Default | Description |
|---|---|---|
--prompt / -p | — | Text description |
--filename / -f | required | Output path (use ~/.openclaw/media/outbound/) |
-i / --input-image | — | Input image (img2img target / faceswap target / portrait) |
--source-face | — | Source face image (faceswap modes) |
--faceswap | false | Basic faceswap |
--faceswap-pipeline | false | Faceswap + cleanup (best quality) |
--cleanup-strength | 0.40 | Pipeline cleanup denoise strength |
--targeted-faceswap | false | Swap specific face in multi-face image |
--target-face-index | 0 | Which face(s) to replace (comma-separated) |
--liveportrait | false | Expression animation mode |
--expression-preset | — | smile / surprised / wink / suspicious / derp / angry / sleepy |
--style-transfer | false | Generate in reference style |
--restyle | false | Apply reference style to existing photo |
--style-ref | — | Style reference image |
--style-weight | 0.85 | Style influence (0.5–1.0) |
--model / -m | juggernaut | juggernaut, flux, realvis |
--aspect-ratio / -a | 1:1 | 1:1, 4:5, 9:16, 16:9, 5:4 |
--strength / -s | 0.6 | img2img denoise strength |
--seed | -1 | Seed (-1 = random) |
--enhanced / -e | false | FaceDetailer + 4x upscale |
--no-media | false | Suppress MEDIA: stdout line (always use this) |
Routing Guide
| User says | Mode |
|---|---|
| "generate an image of..." | txt2img |
| "make this look like..." (with image) | img2img |
| "put [person]'s face on this" | --faceswap-pipeline |
| "swap the second face" | --targeted-faceswap --target-face-index 1 |
| "make him smile / look surprised" | --liveportrait --expression-preset |
| "generate something that looks like this painting" | --style-transfer |
| "make this photo look like a painting" | --restyle |
| "high quality / best quality" | add --enhanced |
Timing Reference
| Mode | Approximate time |
|---|---|
| txt2img (realvis) | ~5 seconds |
| txt2img (juggernaut) | ~5 minutes |
| txt2img (flux) | ~10 minutes |
| faceswap pipeline | ~30 seconds |
| liveportrait | ~7 seconds (21s first run) |
| style transfer / restyle | ~5 minutes |
| +enhanced | +10-30 seconds |
相关 Skills
MCP构建
by anthropics
聚焦高质量 MCP Server 开发,覆盖协议研究、工具设计、错误处理与传输选型,适合用 FastMCP 或 MCP SDK 对接外部 API、封装服务能力。
✎ 想让 LLM 稳定调用外部 API,就用 MCP构建:从 Python 到 Node 都有成熟指引,帮你更快做出高质量 MCP 服务器。
Slack动图
by anthropics
面向Slack的动图制作Skill,内置emoji/消息GIF的尺寸、帧率和色彩约束、校验与优化流程,适合把创意或上传图片快速做成可直接发送的Slack动画。
✎ 帮你快速做出适配 Slack 的动图,内置约束规则和校验工具,少踩上传与播放坑,做表情包和演示都更省心。
MCP服务构建器
by alirezarezvani
从 OpenAPI 一键生成 Python/TypeScript MCP server 脚手架,并校验 tool schema、命名规范与版本兼容性,适合把现有 REST API 快速发布成可生产演进的 MCP 服务。
✎ 帮你快速搭建 MCP 服务与后端 API,脚手架完善、扩展顺手,尤其适合想高效验证服务能力的开发者。
相关 MCP 服务
Slack 消息
编辑精选by Anthropic
Slack 是让 AI 助手直接读写你的 Slack 频道和消息的 MCP 服务器。
✎ 这个服务器解决了团队协作中需要 AI 实时获取 Slack 信息的痛点,特别适合开发团队让 Claude 帮忙汇总频道讨论或发送通知。不过,它目前只是参考实现,文档有限,不建议在生产环境直接使用——更适合开发者学习 MCP 如何集成第三方服务。
by netdata
io.github.netdata/mcp-server 是让 AI 助手实时监控服务器指标和日志的 MCP 服务器。
✎ 这个工具解决了运维人员需要手动检查系统状态的痛点,最适合 DevOps 团队让 Claude 自动分析性能数据。不过,它依赖 NetData 的现有部署,如果你没用过这个监控平台,得先花时间配置。
by d4vinci
Scrapling MCP Server 是专为现代网页设计的智能爬虫工具,支持绕过 Cloudflare 等反爬机制。
✎ 这个工具解决了爬取动态网页和反爬网站时的头疼问题,特别适合需要批量采集电商价格或新闻数据的开发者。不过,它依赖外部浏览器引擎,资源消耗较大,不适合轻量级任务。