LTX-Video: Real-Time Video Filters and Effects

Wanted to add AI effects to livestreams in real-time. Most video models take minutes to render seconds of video. LTX-Video claimed real-time performance - was skeptical but it actually works. Built a streaming filter app that runs at 24fps.

Can't Achieve Real-Time Performance in Streaming Mode

Problem

Tried using LTX-Video for livestream filtering. Got 5-8fps at best, nowhere near the claimed 24fps real-time performance. CPU usage was low, GPU at 80%, but frame time was inconsistent - some frames 200ms, others 800ms.

Average FPS: 5.2 (Target: 24)

What I Tried

Attempt 1: Reduced resolution to 720p - slight improvement but still <10fps.
Attempt 2: Disabled temporal consistency - smoother but artifacts appeared.
Attempt 3: Used TensorRT compilation - complicated, didn't help much.

Actual Fix

The issue was that LTX-Video's streaming mode wasn't properly configured. The model needs to be in "real-time" mode explicitly, AND you need to use the async pipeline instead of the default sync one. Also, the input queue size and buffer management are critical for consistent performance. 

# Real-time streaming setup
from ltx_video import LTXVideoPipeline
import asyncio
import queue

# Use async pipeline for streaming
pipeline = LTXVideoPipeline.from_pretrained(
    "Lightricks/LTX-Video",
    mode="realtime",  # Critical: not "quality"
    torch_dtype=torch.float16,
    device="cuda"
)

# Configure streaming parameters
pipeline.configure_streaming(
    target_fps=24,
    max_frame_delay=42,  # Max ms per frame (1000/24 ≈ 42ms)
    buffer_size=3,  # Number of frames to buffer
    enable_async=True,  # Enable async processing
    prefetch_count=2  # Prefetch 2 frames
)

# Frame queue for streaming
frame_queue = queue.Queue(maxsize=5)

async def process_stream():
    """Async streaming processor"""
    while True:
        # Get frame from camera
        frame = await get_camera_frame()

        # Add to queue (non-blocking)
        try:
            frame_queue.put_nowait(frame)
        except queue.Full:
            # Drop frame if queue is full
            pass

        # Process in background
        asyncio.create_task(process_frame_async(frame))

async def process_frame_async(frame):
    """Process single frame"""
    result = await pipeline.generate_async(
        frame,
        filter_style="cinematic",
        strength=0.7
    )

    # Output to stream
    await send_to_stream(result)

# Run with proper async loop
asyncio.run(process_stream())

What I Learned

Related Resources