Flickies

io.github.psyb0t由 GitHub 账号 psyb0t 发布v0.3.17更新于 Oct 31, 2026

Self-hosted MCP server: video lipsync, face restore, and ffmpeg ops (trim, concat, transcode).

已验证STDIO仅桌面媒体与设计AI 与机器学习

概览

AI 生成的概览

自托管的视频工具 MCP 服务器,提供口型同步、人脸修复以及裁剪、拼接、转码等 ffmpeg 操作。

可以做什么
  • 用给定音轨对视频做口型同步,并用 GFPGAN 修复或锐化人脸。
  • 裁剪、拼接、转码、缩放,以及修改帧率和编码,包括输出 GIF。
  • 混流或提取音轨,并生成缩略图网格。
  • 读取 ffprobe 元数据,如时长、编码、帧率、分辨率和码率。
  • 在运行任务前列出可用引擎及其状态。
适用场景
适合希望助手在本地完成视频制作或编辑、不依赖云服务的场景,例如给片段配上新音频、修复人脸,或做常规的 ffmpeg 转换与剪辑。适用于可自行运行容器、并通过挂载数据目录共享文件的自托管环境。
运行要求
以 Docker 容器运行,暴露带 MCP 路径的 HTTP 端点,助手连接该端点,也可通过本地 stdio 桥接插件接入。需要挂载数据目录以存放文件、模型权重和日志;首次使用需联网从 Hugging Face 下载模型权重,除非已离线预置。LatentSync 与 GFPGAN 需要 CUDA GPU,CPU 镜像仅支持 ffmpeg 操作和 Wav2Lip。可选通过 FLICKIES_AUTH_TOKEN 设置 Bearer 令牌。
安装前请注意
未设置 FLICKIES_AUTH_TOKEN 时认证关闭,任何能访问该端点的人都可以使用。任务会向挂载的数据目录写入文件,并可能从 Hugging Face 下载大型模型权重。启用 Wav2Lip 需接受非商业许可限制。日志写入数据目录下的轮转文件,敏感字段会被脱敏,但请求细节仍会被记录。

安装

在 SourceWeft 中

  1. 打开 控制台中的 Flickies,将其添加到工作区。
  2. 为需要使用其工具的对话启用该服务。

Desktop only,通过 STDIO。 STDIO 服务会启动本地进程,因此需要 SourceWeft 桌面宿主。

其他 MCP 客户端

参照 仓库 中的启动说明。

README

flickies

[CI] [version] [license] [Docker Pulls]

Video toolkit. One port. Zero cloud. Lipsync, face restore, ffmpeg. Fire-and-forget async jobs. Webhooks. Spec-first OpenAPI; typed Go + Python clients generated from the same spec.

The video sibling of audiolla (audio) and talkies (speech). Same wire format, same async-job model, same bind-mount-/data story, same Makefile shape, same :latest + :latest-cuda split, same opt-in non-commercial gate.

POST a JSON body. Get a video back. Drive it from curl, shell scripts, the generated Go/Python clients, or point an LLM agent at the MCP endpoint.

No account. No subscription. docker run and you're done.


What's in the box

👄 LipsyncLatentSync 1.5 (ByteDance, Apache-2.0, default on CUDA) + Wav2Lip / Wav2Lip-GAN (Rudrabha, fast/low-VRAM, behind FLICKIES_ENABLE_NONCOMMERCIAL=1)
🧹 Face restoreGFPGAN v1.4 (TencentARC, Apache-2.0) — chains after Wav2Lip to fix the soft 96×96 mouth crop, or use standalone
⚙️ ffmpeg opsTrim · concat · transcode (incl. gif + fps + codec change) · scale · mux audio · extract audio · thumbnail grid — pure ffmpeg, CPU
📋 Infoffprobe metadata at /v1/video/info — duration, codec, fps, dimensions, bitrate
🔗 MCP serverAll endpoints exposed as MCP tools so function-calling LLMs can drive the pipeline
📜 Spec-firstopenapi.yaml is the single source of truth — server-side Pydantic, Go client, and Python client all regenerated from one file
🐳 Hot-swap eviction + idle unloadOne GPU pool. Different model requested → current model evicted. Idle longer than FLICKIES_IDLE_UNLOAD_SECS (default 600s) → unloaded by the sweeper.

Quick start

bash
docker run -d --name flickies \  -v $HOME/flickies-data:/data \  -p 8000:8000 \  psyb0t/flickies:latest
curl -s -X POST http://localhost:8000/v1/video/info \  -H "Content-Type: application/json" \  -d '{"file_path": "uploads/clip.mp4"}' | jq

CUDA image at psyb0t/flickies:latest-cuda runs every engine at usable speed. CPU image runs all ffmpeg ops (trim/concat/transcode incl. gif/scale/mux/extract/thumbnail-grid/info) + Wav2Lip-CPU (~44s for a 3s clip; OK for short ones). GFPGAN + LatentSync 1.5 are CUDA-only — CPU image refuses to load them.

Weights live in the standard HuggingFace cache layout under /data/hf/hub/models--<org>--<name>/{blobs,snapshots,refs}/… — content-addressed blobs, snapshot-named symlinks, reusable by any other HF-aware tool sharing the bind mount (not just flickies). Sources:

engineHF repo
wav2lip / wav2lip-ganNekochu/Wav2Lip
S3FD detectorByteDance/LatentSync-1.5 (bundled in auxiliary/)
gfpganleonelhs/gfpgan
latentsync-1.5ByteDance/LatentSync-1.5

Lazy by default — each engine fetches its repo on first request. Set FLICKIES_ENABLED_ENGINES=wav2lip,gfpgan (or FLICKIES_PREFETCH_ALL=1) to pull at boot before uvicorn starts. FLICKIES_OFFLINE=1 disables auto-download (operators stage the snapshot dir manually).

Auth

Bearer token set via env. Any string works:

bash
docker run -e FLICKIES_AUTH_TOKEN=testme ...# clients then send: curl -H "Authorization: Bearer testme" ...

Unset → auth disabled. /healthz is always probe-exempt.

Logging

Structured JSON to both stderr AND a rotating file at FLICKIES_LOG_FILE (default /data/logs/flickies.log, 50 MB × 5 backups). Every line carries time (ISO 8601 UTC sub-ms), level, logger, file, line, func, msg, trace_id, request_id + typed extras.

Inbound X-Request-Id (UUID v4 OR ULID; garbage → server mints fresh) threads onto the logging scope via ContextVar + echoes back on the response. Outbound httpx fetches forward X-Request-Id + X-Trace-Id so the next hop's logs correlate. Sensitive keys (authorization, cookie, *token*, *secret*, hf_*, sk-ant-*) get [REDACTED] automatically at format time.

Default level is INFO; set FLICKIES_LOG_LEVEL=DEBUG for reconstruction-grade tracing: every ffmpeg/ffprobe command + result, each transform's decision (e.g. trim stream_copy vs precise_reencode) + output size, engine inference timing (wall_secs), URL fetch/upload byte counts, and job lifecycle. Logged URLs are stripped of their query string so presigned credentials never reach the logs.

MCP

Eleven tools at /v1/mcp via streamable-HTTP JSON-RPC: list_engines, info, lipsync, restore, transcode, trim, concat, scale, mux_audio, extract_audio, thumbnail_grid. Point a function-calling LLM at it (LibreChat, Cursor, Claude desktop with the MCP connector) and it drives the pipeline.

Hardware ceiling

Tested target: RTX 3060 12 GB. Fits LatentSync 1.5 (~8 GB) with headroom. Wav2Lip + GFPGAN chain peaks at ~5 GB. One engine resident at a time — different model request triggers hot-swap eviction.

License posture

Wav2Lip variants are trained on LRS2 (non-commercial). The server refuses to load them unless FLICKIES_ENABLE_NONCOMMERCIAL=1 is set in the server env. LatentSync 1.5 (Apache-2.0) is the commercial-safe default — no gate.

EngineLicenseGate
LatentSync 1.5Apache-2.0none
Wav2Lip / Wav2Lip-GANLRS2 non-commercialFLICKIES_ENABLE_NONCOMMERCIAL=1
GFPGANApache-2.0none
ffmpeg / ffprobe (not an engine; standard CPU helper)LGPL (ffmpeg)none

Same pattern as audiolla's MusicGen / matchering gates.

Spec-first

Every request/response shape lives in openapi.yaml. The Pydantic models in src/flickies/schema/_generated.py, the Go client in pkg/clients/go/client.gen.go, and the Python client in pkg/clients/python/flickies-client/ are all generated from that single file.

bash
make generate              # regenerate all three (server models + Go client + Python client)make generate-models       # just server-side Pydanticmake generate-client-go    # just the Go clientmake generate-client-python # just the Python clientmake generate-check        # CI gate — fail if generated files drift from openapi.yaml

Never hand-edit generated files. Edit openapi.yaml, run make generate, commit everything together.

Generated clients

Go

bash
go get github.com/psyb0t/docker-flickies/pkg/clients/go@latest
go
import flickies "github.com/psyb0t/docker-flickies/pkg/clients/go"
c, _ := flickies.NewClient("http://localhost:8000")resp, err := c.PostVideoLipsync(ctx, flickies.VideoLipsyncRequest{...})

Python

bash
pip install "git+https://github.com/psyb0t/docker-flickies.git#subdirectory=pkg/clients/python/flickies-client"
python
from flickies_client import Clientfrom flickies_client.api.lipsync import post_video_lipsyncfrom flickies_client.models import VideoLipsyncRequest
client = Client(base_url="http://localhost:8000")result = post_video_lipsync.sync(client=client, body=VideoLipsyncRequest(...))

Agent integrations

The skill works in any agent that reads .agents/skills/ (such as peen), and installs natively in the clients below.

Claude Code

bash
claude plugin marketplace add psyb0t/agentsclaude plugin install flickies@psyb0t

Claude Code prompts for the flickies URL and, if auth is enabled, the token — the token is stored in your OS keychain.

Codex

bash
codex plugin marketplace add psyb0t/agentscodex plugin add flickies@psyb0t

Installed via the marketplace, the skill invokes as $flickies:flickies. Codex also picks the skill up automatically with no install in any repo containing .agents/skills/, where it invokes as plain $flickies.

OpenClaw

The skill is published to ClawHub on every release:

bash
openclaw skills install @psyb0t/flickies

For MCP clients that speak local stdio, the @psyb0t/flickies plugin bridges to flickies' /v1/mcp endpoint:

bash
openclaw plugins install clawhub:@psyb0t/flickies

Then set FLICKIES_URL (and FLICKIES_AUTH_TOKEN if the server requires one).

aigate integration

Mounts in aigate at /flickies/ and /flickies-cuda/ behind the same nginx → make run-bg lives. FLICKIES=1 and FLICKIES_CUDA=1 toggle the variants.

License

WTFPL for flickies itself. Bundled models follow their upstream licenses — review before commercial redistribution.

来源:README.md,提交 d8b19e5

工具

0
SourceWeft 尚未在沙箱中启动过该软件包,因此其工具未知。

版本历史

2
  1. v0.3.17最新Oct 10, 2026
  2. v0.3.16Sep 16, 2026

评分

已满足适用检查项的 50%

该评分衡量此收录条目的完整程度,依据注册中心条目、端点与公开来源计算得出。它不是对服务器本身的评价,也不表示服务器安全或当前可用:可连通性是带日期的检查结果,评分也不包含安装量或用户评价。

  • 近 30 天内可连通25 分

    请保持端点能够响应 MCP 握手;SourceWeft 每 30 天探测一次。

  • 已声明认证方式10 分

    请在注册中心条目中声明凭据请求头,或保持端点能够响应探测,以便判定其认证方式。

  • 已列出工具15 分

    请让服务器在无凭据时响应 tools/list,或发布可在沙箱中无凭据启动的软件包。

  • 描述必需15 分
  • 图标10 分
  • 文档15 分
  • 许可证10 分