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ffmpeg-render-pro

by beeswaxpat
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  ║  GPU: AUTO   DASHBOARD: LIVE   CONCAT: INSTANT       ║
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ffmpeg-render-pro

License: MIT Platform: Cross-platform Node.js MCP Server

Parallel video rendering with live dashboard, GPU auto-detection, checkpoint system, and stream-copy concat. The most powerful free ffmpeg rendering toolkit.

Built by Beeswax Pat with Claude Code · Free and open source forever

Features

  • Parallel rendering — Split frames across N worker threads, concat with zero re-encoding

  • GPU auto-detection — Probes NVENC, VideoToolbox, AMF, VA-API, QSV with 1-frame validation

  • Live dashboard — Auto-opens in your browser with per-worker progress, FPS chart, ETA

  • Checkpoint system — 93% reduction in fast-forward overhead for long renders

  • Color grading — 5 built-in presets (noir, warm, cool, cinematic, vintage) + custom filters

  • Audio merge — Combine video + audio with loudness normalization, no video re-encode

  • Deterministic output — Seeded RNG ensures parallel workers produce identical results to sequential

  • MCP server — Model Context Protocol server with 6 tools, works with Claude Code, Claude Desktop, and any MCP client

  • Cross-platform — Windows, macOS, Linux. Any GPU or CPU-only. Requires Node.js >= 18 + ffmpeg.

Requirements

  • Node.js >= 18

  • ffmpeg installed and on PATH

Quick Start

# Clone or install
git clone https://github.com/beeswaxpat/ffmpeg-render-pro.git
cd ffmpeg-render-pro

# Run the benchmark (5s test render, dashboard auto-opens)
node examples/render-test.js

# Run a longer test
node examples/render-test.js --duration=30

# YouTube Shorts format (vertical 1080x1920)
node examples/render-test.js --width=1080 --height=1920 --fps=30 --duration=60

# Check your GPU
node bin/ffmpeg-render-pro.js detect-gpu

# System info (workers, RAM, CPU)
node bin/ffmpeg-render-pro.js info

CLI

ffmpeg-render-pro detect-gpu          # Probe hardware encoders
ffmpeg-render-pro info                # Show system config
ffmpeg-render-pro render <worker.js>  # Render with your worker script
ffmpeg-render-pro benchmark           # Quick 5s test render

API

const {
  renderParallel,    // Core: parallel rendering engine
  createEncoder,     // Pipe raw frames to ffmpeg
  detectGPU,         // Cross-platform GPU detection
  getConfig,         // Auto-tune workers, codec selection
  concatSegments,    // Stream-copy segment joining
  colorGrade,        // Apply color grades (presets or custom)
  mergeAudio,        // Combine video + audio
  startDashboard,    // Live progress dashboard
  saveCheckpoint,    // Checkpoint serialization
  loadCheckpoint,    // Checkpoint restoration
} = require('ffmpeg-render-pro');

renderParallel(options)

The main entry point. Splits a render across workers, shows a live dashboard, and produces a final MP4.

await renderParallel({
  workerScript: './my-worker.js',  // Your frame generator
  outputPath: './output.mp4',
  width: 1920,
  height: 1080,
  fps: 60,
  duration: 60,        // seconds
  title: 'My Render',
  autoOpen: true,      // auto-open dashboard in browser
});

Writing a Worker

Workers receive frame ranges via workerData and pipe raw BGRA frames to ffmpeg:

const { workerData, parentPort } = require('worker_threads');
const { spawn } = require('child_process');

const { width, height, fps, startFrame, endFrame, segmentPath, workerId } = workerData;

// Spawn ffmpeg encoder
const ffmpeg = spawn('ffmpeg', [
  '-y', '-f', 'rawvideo', '-pixel_format', 'bgra',
  '-video_size', `${width}x${height}`, '-framerate', String(fps),
  '-i', 'pipe:0',
  '-c:v', 'libx264', '-preset', 'fast', '-crf', '20',
  '-pix_fmt', 'yuv420p', '-movflags', '+faststart',
  segmentPath,
], { stdio: ['pipe', 'pipe', 'pipe'] });

const buffer = Buffer.alloc(width * height * 4);

for (let f = startFrame; f < endFrame; f++) {
  // Fill buffer with your frame data (BGRA format)
  renderMyFrame(f, buffer);

  // Write with backpressure
  const ok = ffmpeg.stdin.write(buffer);
  if (!ok) await new Promise(r => ffmpeg.stdin.once('drain', r));

  // Report progress
  parentPort.postMessage({ type: 'progress', workerId, pct: ..., fps: ..., frame: ..., eta: ... });
}

ffmpeg.stdin.end();
ffmpeg.on('close', () => parentPort.postMessage({ type: 'done', workerId }));

See examples/basic-worker.js for a complete working example.

Modules

Module

Purpose

parallel-renderer

N-worker thread pool with progress tracking

encoder

Raw frame pipe to ffmpeg with backpressure

gpu-detect

Cross-platform hardware encoder discovery + validation

config

Auto-tune workers based on resolution, RAM, CPU

concat

Stream-copy segment joining (instant)

color-grade

ffmpeg video filter presets + custom chains

audio-merge

Video + audio merge with loudnorm support

dashboard-server

Zero-dep HTTP server with auto-open browser

progress

Per-worker terminal + JSON progress tracking

checkpoint

State serialization for long renders

Benchmarks

Run your own:

node examples/render-test.js --duration=5
node examples/render-test.js --duration=30
node examples/render-test.js --duration=60 --width=1080 --height=1920

MCP Server

ffmpeg-render-pro includes a Model Context Protocol (MCP) server with 6 tools. Works with Claude Code, Claude Desktop, and any MCP client.

Add to Claude Code

claude mcp add --transport stdio ffmpeg-render-pro -- npx -y ffmpeg-render-pro-mcp
# Or if installed locally:
claude mcp add --transport stdio ffmpeg-render-pro -- node /path/to/src/mcp-server.mjs

Add to Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "ffmpeg-render-pro": {
      "command": "node",
      "args": ["/path/to/ffmpeg-render-pro/src/mcp-server.mjs"]
    }
  }
}

MCP Tools

Tool

Description

detect_gpu

Probe hardware encoders (NVENC, VideoToolbox, AMF, VA-API, QSV)

system_info

Show CPU cores, RAM, recommended workers, ffmpeg version

render_video

Parallel render with live dashboard

color_grade

Apply presets (noir, warm, cool, cinematic, vintage) or custom filters

merge_audio

Combine video + audio with loudness normalization

concat_videos

Stream-copy join multiple videos (instant, no re-encode)

Claude Code Skill

This repo includes a ready-to-use Claude Code skill. To install it, copy the skill folder into your Claude skills directory:

# macOS / Linux
cp -r .claude/skills/ffmpeg-render-pipeline ~/.claude/skills/

# Windows
xcopy .claude\skills\ffmpeg-render-pipeline %USERPROFILE%\.claude\skills\ffmpeg-render-pipeline\ /E /I

Once installed, Claude Code will automatically use the skill when you ask it to render video or audio with ffmpeg.

License

MIT

Author

Beeswax Pat

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