Flickies

Name: Flickies
Author: psyb0t

by psyb0tversion 0.3.17i★ –iRuns on your computer

Registry nameio.github.psyb0t/flickies

Install

Overview

Description from the maker (original English)

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

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews open together with accounts in a later step
Our basic checksi1 of 1 passedChecked from public information; code not scanned yet
UsageiNot measured yet★ –

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: Streamable HTTP
Category: Design, images & audio

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 1 of 1 passed
Runs on your computer with your user’s permissions (files, network, apps).
The registry lists no key to fill in.

You need

DockerDocker installed

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "flickies": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "docker.io/psyb0t/flickies:v0.3.17"
      ]
    }
  }
}

Run this in your terminal

claude mcp add flickies -- docker run -i --rm docker.io/psyb0t/flickies:v0.3.17

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "flickies": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "docker.io/psyb0t/flickies:v0.3.17"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "flickies": {
      "type": "stdio",
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "docker.io/psyb0t/flickies:v0.3.17"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account psyb0t, which owns the repo
… Not checked yet
Still maintained
We have not read this repo from GitHub yet
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Design, images & audioi

More stars than 0% of the other 2190 servers in this category
Published to the registry more recently than 51% of the other 2190 servers in this category
Basic checks equal to or better than 100% of the other 2190 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Flickies safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (0.3.17) reached the registry 27 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Docker on your computer.

What can I use instead?

In the same category: Figma Context MCP, Claude Real Video, Figma MCP Server

Alternatives

Servers in the same category from other makers, sorted by stars.

Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Figma Context MCP

by GLips

Give your coding agent access to your Figma data. Implement designs in any framework in one-shot.

Runs on your computer★ 16,165MITUpdated 5 months ago

Install

Claude Real Video

by HUANGCHIHHUNGLeo

Let any LLM watch a video locally — and search everything it has ever watched.

Runs on your computer★ 2,284MITUpdated 2 months ago

Install

Figma MCP Server

by figma

Figma’s official server gives AI your design data so it can turn designs into code or describe them accurately. Connects to Figma’s cloud.

Runs in the cloud★ 2,152Updated 13 months ago

Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

flickies

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


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

Quick start

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-- -- /{blobs,snapshots,refs}/… — content-addressed blobs, snapshot-named symlinks, reusable by any other HF-aware tool sharing the bind mount (not just flickies). Sources:

engine	HF repo
`wav2lip` / `wav2lip-gan`	`Nekochu/Wav2Lip`
S3FD detector	`ByteDance/LatentSync-1.5` (bundled in `auxiliary/`)
`gfpgan`	`leonelhs/gfpgan`
`latentsync-1.5`	`ByteDance/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:

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.

Engine	License	Gate
LatentSync 1.5	Apache-2.0	none
Wav2Lip / Wav2Lip-GAN	LRS2 non-commercial	`FLICKIES_ENABLE_NONCOMMERCIAL=1`
GFPGAN	Apache-2.0	none
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.

make generate              # regenerate all three (server models + Go client + Python client)

make generate-models # just server-side Pydantic make generate-client-go # just the Go client make generate-client-python # just the Python client make 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

go get github.com/psyb0t/docker-flickies/pkg/clients/go@latest

import flickies "github.com/psyb0t/docker-flickies/pkg/clients/go"

c, _ := flickies.NewClient("http://localhost:8000") resp, err := c.PostVideoLipsync(ctx, flickies.VideoLipsyncRequest{...})

Python

pip install "git+https://github.com/psyb0t/docker-flickies.git#subdirectory=pkg/clients/python/flickies-client"

from flickies_client import Client

from flickies_client.api.lipsync import post_video_lipsync from 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

claude plugin marketplace add psyb0t/agents

claude 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

codex plugin marketplace add psyb0t/agents

codex 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:

openclaw skills install @psyb0t/flickies

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

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.

Flickies

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Compared with others in Design, images & audioi

Common questions

Alternatives

Figma Context MCP

Claude Real Video

Figma MCP Server

README from the maker

flickies

What's in the box

Quick start

Auth

clients then send: curl -H "Authorization: Bearer testme" ...

Logging

MCP

Hardware ceiling

License posture

Spec-first

Generated clients

Go

Python

Agent integrations

Claude Code

Codex

OpenClaw

aigate integration

License