Architecture

Frame is built on Tauri v2, combining a Rust backend for performance-critical operations with a modern Svelte 5 frontend for reactive UI. This architecture enables native-level performance while maintaining cross-platform compatibility.

Overview

Backend Architecture (Rust)

The Rust backend handles all media processing, process management, and system integration.

Core Modules

Located in src-tauri/src/conversion/, the backend is organized into specialized modules:

Module	File	Responsibility
Manager	`manager.rs`	Async task queue, concurrency control, process lifecycle
Worker	`worker.rs`	FFmpeg process execution, progress parsing
Probe	`probe.rs`	Media metadata extraction via FFprobe
Upscale	`upscale.rs`	AI upscaling pipeline (frame extraction → upscale → encode)
Args	`args.rs`	FFmpeg argument construction
Codec	`codec.rs`	Video/audio codec configuration
Filters	`filters.rs`	Video/audio filter chain building
Types	`types.rs`	Shared data structures
Commands	`commands.rs`	Tauri command handlers (IPC entry points)

Conversion Manager

The ConversionManager (manager.rs:35-199) is the core orchestrator:

pub struct ConversionManager {
    sender: mpsc::Sender<ManagerMessage>,
    max_concurrency: Arc<AtomicUsize>,
    active_tasks: Arc<Mutex<HashMap<String, u32>>>,
    cancelled_tasks: Arc<Mutex<HashSet<String>>>,
}

Key Features:

Async Task Queue: Uses tokio::mpsc::channel for message passing (manager.rs:44)
Concurrency Control: Limits concurrent FFmpeg processes (default: 2) via AtomicUsize (manager.rs:46)
Process Management: Tracks PIDs for pause/resume/cancel operations (manager.rs:38)
Cross-Platform Signals:
- Unix: SIGSTOP/SIGCONT/SIGKILL via libc (manager.rs:273-314)
- Windows: NtSuspendProcess/NtResumeProcess via Win32 API (manager.rs:374-410)

Message Flow

Reference: manager.rs:53-191

FFmpeg Worker

The worker (worker.rs) executes FFmpeg as a Tauri sidecar process:

pub async fn run_ffmpeg_worker(
    app: AppHandle,
    tx: mpsc::Sender<ManagerMessage>,
    task: ConversionTask,
) -> Result<(), ConversionError>

Process:

Build FFmpeg arguments from ConversionConfig (worker.rs:26-32)
Spawn FFmpeg sidecar via tauri-plugin-shell (worker.rs:34-42)
Parse stderr for progress (worker.rs:88-136):
- Extract current time via regex: time=(\d{2}:\d{2}:\d{2}\.\d{2}) (utils.rs)
- Calculate percentage: (current_time / total_duration) * 100
Emit IPC events to frontend (worker.rs:50, 127)

Reference: worker.rs:15-158

Probe Module

Media metadata extraction via FFprobe (probe.rs:8-133):

pub async fn probe_media_file(
    app: &AppHandle,
    file_path: &str,
) -> Result<ProbeMetadata, ConversionError>

Execution:

Spawn FFprobe with JSON output: -print_format json -show_format -show_streams (probe.rs:12-20)
Parse JSON into FfprobeOutput struct via serde_json (probe.rs:37)
Extract metadata:
- Video: codec, resolution, frame rate, bitrate (probe.rs:49-73)
- Audio: tracks, channels, sample rate, language (probe.rs:75-97)
- Subtitles: codec, language, labels (probe.rs:99-113)

Reference: probe.rs:8-133

AI Upscaling Pipeline

The upscale worker (upscale.rs) implements a multi-stage pipeline: Stage 1: Frame Extraction (upscale.rs:311-430)

Decode video to PNG sequence via FFmpeg
Apply pre-upscale filters (crop, deinterlace, etc.)
Output to temp directory: $TEMP/frame_upscale_{id}/input/frame_%08d.png

Stage 2: AI Upscaling (upscale.rs:456-556)

Spawn realesrgan-ncnn-vulkan sidecar
Models: realesr-animevideov3-x2 (2x) or x4 (4x)
Thread config: load:process:save based on resolution (upscale.rs:148-171)
Parse stderr for frame completion: → or -> indicators (upscale.rs:525-543)

Stage 3: Re-encoding (upscale.rs:558-636)

Encode upscaled PNG sequence back to video
Mux with original audio/subtitle tracks from source file
Apply post-upscale video codec settings

Reference: upscale.rs:238-636

Sidecar Execution Model

Frame bundles FFmpeg, FFprobe, and Real-ESRGAN as Tauri sidecars:

Binary Resolution: Tauri resolves platform-specific binaries from src-tauri/binaries/
Spawn: app.shell().sidecar("ffmpeg") launches subprocess (worker.rs:34-38)
IPC: Communicate via stdin/stdout/stderr (CommandEvent::Stderr)
Lifecycle: Track PID for pause/resume/kill operations

Configuration in src-tauri/tauri.conf.json:

"bundle": {
  "externalBin": [
    "binaries/ffmpeg",
    "binaries/ffprobe",
    "binaries/realesrgan-ncnn-vulkan"
  ]
}

Frontend Architecture (SvelteKit)

The frontend is built with Svelte 5 (Runes API) and SvelteKit as the meta-framework.

Directory Structure

src/
├── routes/
│   ├── +page.svelte          # Main application view
│   └── splash/+page.svelte   # Splash screen
├── lib/
│   ├── components/           # UI components
│   │   ├── file-list/        # File list and rows
│   │   ├── settings/         # Settings panel and tabs
│   │   ├── logs/             # Logs viewer
│   │   └── ui/               # Reusable UI primitives
│   ├── stores/               # Svelte stores ($state)
│   ├── services/             # Tauri IPC services
│   ├── features/             # Feature modules (update, etc.)
│   ├── i18n/                 # Internationalization
│   └── types.ts              # TypeScript types

State Management

Frame uses Svelte 5 Runes for reactive state:

// Example: Conversion state store
let conversions = $state<Map<string, ConversionState>>(new Map());
let activeConversions = $derived(Array.from(conversions.values()));

function addConversion(task: ConversionTask) {
  conversions.set(task.id, {
    ...task,
    progress: 0,
    status: 'queued'
  });
}

Key Runes:

$state: Mutable reactive state
$derived: Computed values (like Svelte 4’s $:)
$effect: Side effects (replaces lifecycle hooks)
$props: Component props

IPC Communication

Frontend communicates with Rust backend via Tauri commands and events: Commands (frontend → backend):

import { invoke } from '@tauri-apps/api/core';

// Queue a conversion task
await invoke('queue_conversion', { task });

// Pause/resume/cancel
await invoke('pause_conversion', { id });
await invoke('resume_conversion', { id });
await invoke('cancel_conversion', { id });

Events (backend → frontend):

import { listen } from '@tauri-apps/api/event';

// Listen for progress updates
await listen<ProgressPayload>('conversion-progress', (event) => {
  updateProgress(event.payload.id, event.payload.progress);
});

await listen<LogPayload>('conversion-log', (event) => {
  appendLog(event.payload.id, event.payload.line);
});

Reference: src-tauri/src/lib.rs:156-168 for command registration

Concurrent Processing Design

Task Queue System

Frame implements a bounded concurrency queue:

// manager.rs:201-246
async fn process_queue(
    queue: &mut VecDeque<ConversionTask>,
    running_tasks: &mut HashMap<String, ()>,
    max_concurrency: Arc<AtomicUsize>,
) {
    let limit = max_concurrency.load(Ordering::SeqCst).max(1);
    
    while running_tasks.len() < limit {
        if let Some(task) = queue.pop_front() {
            running_tasks.insert(task.id.clone(), ());
            tokio::spawn(run_ffmpeg_worker(task));
        } else {
            break;
        }
    }
}

Concurrency Control:

Default: 2 concurrent FFmpeg processes
User-adjustable: set_max_concurrency(value) command
Reason: FFmpeg is CPU/GPU intensive; too many concurrent processes cause thrashing

Reference: manager.rs:201-246, types.rs:71 (DEFAULT_MAX_CONCURRENCY)

Async Runtime

Frame uses Tokio as the async runtime:

# Cargo.toml
tokio = "1.49.0"

Key Patterns:

tokio::sync::mpsc: Message passing between manager and workers
tokio::spawn: Spawn async tasks
tauri::async_runtime::spawn: Tauri’s wrapper for spawning tasks

Reference: manager.rs:53-191

Dependencies

Rust (Backend)

Crate	Version	Purpose
`tauri`	2.9.5	Application framework
`tauri-plugin-shell`	2.3.4	Sidecar process execution
`tauri-plugin-dialog`	2.6.0	Native file dialogs
`tauri-plugin-fs`	2.4.5	Filesystem operations
`tauri-plugin-store`	2.3.0	Persistent key-value storage
`tokio`	1.49.0	Async runtime
`serde` / `serde_json`	1.0	Serialization
`regex`	1.12.2	FFmpeg output parsing
`thiserror`	2.0.18	Error handling

Reference: src-tauri/Cargo.toml

Frontend (TypeScript/Svelte)

Package	Version	Purpose
`@sveltejs/kit`	2.50.0	Meta-framework
`svelte`	5.47.1	UI framework (Runes)
`@tauri-apps/api`	2.9.1	Tauri IPC bindings
`tailwindcss`	4.1.18	CSS framework
`svelte-i18n`	4.0.1	Internationalization
`typescript`	5.9.3	Type safety

Reference: package.json

Platform-Specific Code

Frame includes platform-specific implementations:

Window Effects

// lib.rs:27-52
#[cfg(target_os = "macos")]
fn apply_window_effect(window: &tauri::WebviewWindow) {
    window.set_effects(
        EffectsBuilder::new()
            .effect(Effect::HudWindow)  // macOS vibrancy
            .state(EffectState::Active)
            .radius(16.0)
            .build(),
    );
}

#[cfg(target_os = "windows")]
fn apply_window_effect(window: &tauri::WebviewWindow) {
    window.set_effects(
        EffectsBuilder::new()
            .effect(Effect::Acrylic)  // Windows Acrylic
            .build(),
    );
}

Process Control

Unix: libc::kill(pid, SIGSTOP/SIGCONT/SIGKILL) (manager.rs:273-314)
Windows: NtSuspendProcess/NtResumeProcess via ntdll.dll (manager.rs:374-410)

Reference: src-tauri/src/lib.rs:27-52, manager.rs:266-372

Performance Optimizations

Release Build Profile

# Cargo.toml:35-40
[profile.release]
opt-level = "z"         # Optimize for size
lto = true              # Link-time optimization
codegen-units = 1       # Single codegen unit for better optimization
strip = true            # Strip symbols
panic = "abort"         # Reduce binary size

Regex Compilation

FFmpeg output parsing uses precompiled regexes via once_cell:

// utils.rs
use once_cell::sync::Lazy;
use regex::Regex;

pub static TIME_REGEX: Lazy<Regex> = 
    Lazy::new(|| Regex::new(r"time=(\d{2}:\d{2}:\d{2}\.\d{2})").unwrap());
pub static DURATION_REGEX: Lazy<Regex> = 
    Lazy::new(|| Regex::new(r"Duration: (\d{2}:\d{2}:\d{2}\.\d{2})").unwrap());

Reference: src-tauri/src/conversion/utils.rs

Next Steps

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Features

Guides

Development

Architecture

Overview

Backend Architecture (Rust)

Core Modules

Conversion Manager

Message Flow

FFmpeg Worker

Probe Module

AI Upscaling Pipeline

Sidecar Execution Model

Frontend Architecture (SvelteKit)

Directory Structure

State Management

IPC Communication

Concurrent Processing Design

Task Queue System

Async Runtime

Dependencies

Rust (Backend)

Frontend (TypeScript/Svelte)

Platform-Specific Code

Window Effects

Process Control

Performance Optimizations

Release Build Profile

Regex Compilation

Next Steps

Building from Source

Contributing

Build docs developers (and LLMs) love

Get Started

Features

Guides

Development

Documentation Index

​Overview

​Backend Architecture (Rust)

​Core Modules

​Conversion Manager

​Message Flow

​FFmpeg Worker

​Probe Module

​AI Upscaling Pipeline

​Sidecar Execution Model

​Frontend Architecture (SvelteKit)

​Directory Structure

​State Management

​IPC Communication

​Concurrent Processing Design

​Task Queue System

​Async Runtime

​Dependencies

​Rust (Backend)

​Frontend (TypeScript/Svelte)

​Platform-Specific Code

​Window Effects

​Process Control

​Performance Optimizations

​Release Build Profile

​Regex Compilation

​Next Steps

Building from Source

Contributing

Build docs developers (and LLMs) love

Overview

Backend Architecture (Rust)

Core Modules

Conversion Manager

Message Flow

FFmpeg Worker

Probe Module

AI Upscaling Pipeline

Sidecar Execution Model

Frontend Architecture (SvelteKit)

Directory Structure

State Management

IPC Communication

Concurrent Processing Design

Task Queue System

Async Runtime

Dependencies

Rust (Backend)

Frontend (TypeScript/Svelte)

Platform-Specific Code

Window Effects

Process Control

Performance Optimizations

Release Build Profile

Regex Compilation

Next Steps