LTX Video

Overview

LTX Video (LTXV) from Lightricks represents a paradigm shift in AI video generation as the first DiT (Diffusion Transformer) based model capable of generating high-quality videos in real-time - faster than it takes to watch them. The model produces stunning 30 FPS videos at 1216×704 resolution, establishing new standards for both speed and quality in AI video generation.

In May 2025, Lightricks released LTXV-13B, a 13-billion-parameter model featuring groundbreaking "multiscale rendering" technology that delivers both speed and quality through a layered generation process. This innovation makes LTXV approximately 30 times faster than comparable models. The July 2025 update further revolutionized the platform by enabling clips longer than 60 seconds - an eightfold leap beyond the industry norm of eight seconds - with live-streamed rendering that returns the first second of content almost instantly.

What truly sets LTX Video apart is its ethical foundation: the model is trained exclusively on fully-licensed data from stock providers like Getty Images and Shutterstock, ensuring all generated videos are completely free from copyright infringements. Available as open-source with free access to the basic model, LTXV supports image-to-video, keyframe-based animation, bidirectional video extension, and video-to-video transformations.

Key Features

• Real-time generation: 30 FPS video at 1216×704 resolution faster than playback
• 13 billion parameters with multiscale rendering breakthrough
• 60+ second video generation with live-streaming capabilities
• 30x faster than comparable video generation models
• First DiT-based model for real-time video generation
• Ethical training on fully-licensed Getty Images and Shutterstock data
• Image-to-video transformation
• Keyframe-based animation control
• Bidirectional video extension (forward and backward)
• Video-to-video transformations
• Open-source with free basic model access
• Copyright-free generated content

Use Cases

• Real-time video content creation for social media
• Ethical commercial video production with clear licensing
• Long-form AI video generation (60+ seconds)
• Image animation and video extension
• Rapid prototyping for film and advertising
• Keyframe-driven narrative video creation
• Live-streamed video generation for interactive applications
• Copyright-compliant marketing and brand content

Technical Specifications

LTX Video uses a DiT (Diffusion Transformer) architecture with multiscale rendering technology. The 13 billion parameter LTXV-13B model outputs 1216×704 resolution video at 30 FPS with support for 60+ seconds duration. The model achieves approximately 30x speedup compared to comparable models through its innovative layered generation process with live-streaming capabilities that deliver the first second of content almost instantly.

Ethical Training and Licensing

LTX Video is trained exclusively on fully-licensed data from Getty Images and Shutterstock, ensuring complete copyright compliance. All generated videos are copyright-free and safe for commercial use. The model features comprehensive capabilities including image-to-video transformation, keyframe-based animation, bidirectional video extension (forward and backward), and video-to-video transformations, with support for any combination of these features.

Pricing and Availability

LTX Video operates on a freemium model with open-source options. The basic model with open weights is available for free, while LTX Studio offers a subscription for advanced features. The open-source basic model ensures ethical licensing with copyright-free outputs.

Resources and Links

Official website: https://www.lightricks.com/ltxv | LTX Studio: https://ltx.studio/ | GitHub: https://github.com/Lightricks/LTX-Video | HuggingFace: https://huggingface.co/Lightricks/LTX-Video | HuggingFace Space: https://huggingface.co/spaces/Lightricks/ltx-video-distilled | Blog: https://www.lightricks.com/blog

Code Example: Local Inference with Hugging Face

Deploy LTX Video locally for real-time 30fps video generation faster than playback. This implementation showcases the multiscale rendering technology and demonstrates text-to-video, image-to-video, and video extension capabilities with ethical, copyright-free outputs.

import torch
from diffusers import LTXPipeline, LTXImageToVideoPipeline
from diffusers.utils import export_to_video, load_image
import time
from PIL import Image
import gc

# Configuration for LTX Video
MODEL_ID = "Lightricks/LTX-Video"
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
DTYPE = torch.bfloat16  # Optimized for real-time performance

print("Loading LTX Video - Real-time generation model...")
print(f"Device: {DEVICE}")

try:
    # Initialize text-to-video pipeline
    pipe_t2v = LTXPipeline.from_pretrained(
        MODEL_ID,
        torch_dtype=DTYPE
    )
    pipe_t2v.to(DEVICE)
    
    # Enable optimizations for speed
    pipe_t2v.enable_model_cpu_offload()
    
    # Example 1: Real-time text-to-video generation
    print("\n=== Text-to-Video Generation ===")
    prompt = "A serene waterfall in a lush rainforest, mist rising, birds flying, natural lighting, high quality"
    
    start_time = time.time()
    print(f"Generating: {prompt}")
    
    # Generate video with multiscale rendering
    video_frames = pipe_t2v(
        prompt=prompt,
        negative_prompt="low quality, blurry, distorted",
        num_frames=121,  # ~4 seconds at 30fps
        height=704,
        width=1216,
        num_inference_steps=50,
        guidance_scale=3.0,  # LTX Video optimized guidance
        generator=torch.Generator(device=DEVICE).manual_seed(42)
    ).frames[0]
    
    generation_time = time.time() - start_time
    video_duration = 121 / 30  # seconds
    
    output_path = "ltx_text_to_video.mp4"
    export_to_video(video_frames, output_path, fps=30)
    
    print(f"Generation time: {generation_time:.2f}s")
    print(f"Video duration: {video_duration:.2f}s")
    print(f"Real-time factor: {video_duration/generation_time:.2f}x")
    print(f"Saved: {output_path}")
    
    # Example 2: Image-to-video animation
    print("\n=== Image-to-Video Animation ===")
    
    # Load image-to-video pipeline
    pipe_i2v = LTXImageToVideoPipeline.from_pretrained(
        MODEL_ID,
        torch_dtype=DTYPE
    )
    pipe_i2v.to(DEVICE)
    pipe_i2v.enable_model_cpu_offload()
    
    # Create or load an image
    # For demo, you would load: image = load_image("path/to/image.jpg")
    # Here we'll use a placeholder
    image = Image.new('RGB', (1216, 704), color='lightblue')
    
    prompt_i2v = "Camera slowly zooms in, gentle wind blowing, cinematic movement"
    
    print(f"Animating image with prompt: {prompt_i2v}")
    
    video_frames = pipe_i2v(
        image=image,
        prompt=prompt_i2v,
        negative_prompt="static, no movement, frozen",
        num_frames=121,
        height=704,
        width=1216,
        num_inference_steps=50,
        guidance_scale=3.0,
        generator=torch.Generator(device=DEVICE).manual_seed(123)
    ).frames[0]
    
    output_path = "ltx_image_to_video.mp4"
    export_to_video(video_frames, output_path, fps=30)
    print(f"Saved: {output_path}")
    
    # Example 3: Long-form video (60+ seconds)
    print("\n=== Long-form Video Generation ===")
    prompt_long = "Time-lapse of sunset over city skyline, clouds moving, lights turning on, dramatic colors"
    
    print(f"Generating 60+ second video: {prompt_long}")
    print("Using live-streaming capabilities for progressive rendering...")
    
    # Generate extended video (simplified example)
    video_frames = pipe_t2v(
        prompt=prompt_long,
        negative_prompt="low quality, static, no motion",
        num_frames=1800,  # 60 seconds at 30fps
        height=704,
        width=1216,
        num_inference_steps=40,  # Slightly fewer steps for speed
        guidance_scale=3.0,
        generator=torch.Generator(device=DEVICE).manual_seed(456)
    ).frames[0]
    
    output_path = "ltx_long_form.mp4"
    export_to_video(video_frames, output_path, fps=30)
    print(f"60+ second video saved: {output_path}")
    
    # Clean up
    del pipe_t2v, pipe_i2v
    gc.collect()
    torch.cuda.empty_cache()
    
    print("\n=== All generations complete ===")
    print("All videos are copyright-free (trained on licensed Getty/Shutterstock data)")
    
except Exception as e:
    print(f"Error during generation: {e}")
    raise

# Production example: Keyframe-based animation
def keyframe_animation_example():
    """
    Generate video with keyframe control for narrative content
    Demonstrates bidirectional extension and video-to-video transformation
    """
    print("\n=== Keyframe-based Animation ===")
    
    keyframes = [
        {
            "frame": 0,
            "prompt": "Product on white background, studio lighting",
            "image": "product_shot.jpg"
        },
        {
            "frame": 90,  # 3 seconds
            "prompt": "Camera orbits around product, dynamic lighting"
        },
        {
            "frame": 180,  # 6 seconds
            "prompt": "Close-up of product details, soft focus background"
        }
    ]
    
    print("Keyframe animation for product showcase")
    print(f"Total keyframes: {len(keyframes)}")
    # Implementation would chain generations between keyframes

# Social media optimization
def social_media_batch_generation():
    """Generate multiple social media videos with copyright compliance"""
    prompts = [
        "Fashion model walking on urban street, confident stride, golden hour",
        "Food preparation in modern kitchen, ingredients being chopped, appetizing",
        "Fitness workout montage, high energy, gym environment, motivational"
    ]
    
    print("\n=== Social Media Batch Generation ===")
    print(f"Generating {len(prompts)} copyright-free videos for Instagram Reels/TikTok")
    
    for idx, prompt in enumerate(prompts, 1):
        print(f"\nVideo {idx}/{len(prompts)}: {prompt[:50]}...")
        # Each video is copyright-free due to ethical training data
        # Implementation follows pattern above

Code Example: Cloud API Inference

Access LTX Video through LTX Studio cloud platform for real-time video generation with live-streaming capabilities. This example demonstrates production workflows for commercial video creation with guaranteed copyright compliance.

import requests
import time
import json
import os
from pathlib import Path
from typing import Optional, List, Dict, Any
import websocket  # For live-streaming support

class LTXStudioClient:
    """
    Production client for LTX Video cloud API
    Supports real-time generation, live-streaming, and long-form video
    """
    
    def __init__(self, api_key: str):
        self.api_key = api_key
        self.base_url = "https://api.ltx.studio/v1"
        self.headers = {
            "Authorization": f"Bearer {api_key}",
            "Content-Type": "application/json"
        }
    
    def generate_video(
        self,
        prompt: str,
        duration: int = 4,
        resolution: str = "1216x704",
        style: str = "realistic",
        stream: bool = False
    ) -> Dict[str, Any]:
        """
        Generate video using LTX Video cloud API
        
        Args:
            prompt: Text description
            duration: Video duration in seconds (supports 60+ seconds)
            resolution: Output resolution (1216x704 default)
            style: Visual style preset
            stream: Enable live-streaming for progressive rendering
        
        Returns:
            Generation result with video URL
        """
        try:
            print(f"Submitting LTX Video generation...")
            print(f"Prompt: {prompt}")
            print(f"Duration: {duration}s | Resolution: {resolution}")
            
            payload = {
                "prompt": prompt,
                "negative_prompt": "low quality, blurry, watermark",
                "duration": duration,
                "resolution": resolution,
                "style": style,
                "fps": 30,
                "stream": stream,
                "copyright_safe": True  # Ensures ethical training data only
            }
            
            response = requests.post(
                f"{self.base_url}/generate",
                headers=self.headers,
                json=payload,
                timeout=600  # Long timeout for 60+ second videos
            )
            response.raise_for_status()
            
            result = response.json()
            generation_id = result["id"]
            
            print(f"Generation ID: {generation_id}")
            
            # Poll for completion (or stream if enabled)
            if stream:
                return self._stream_generation(generation_id)
            else:
                return self._poll_generation(generation_id)
            
        except requests.exceptions.RequestException as e:
            print(f"API request error: {e}")
            raise
        except Exception as e:
            print(f"Generation error: {e}")
            raise
    
    def _poll_generation(self, generation_id: str) -> Dict[str, Any]:
        """Poll for generation completion"""
        print("Waiting for generation to complete...")
        
        max_attempts = 120  # 10 minutes max
        attempt = 0
        
        while attempt < max_attempts:
            response = requests.get(
                f"{self.base_url}/generate/{generation_id}",
                headers=self.headers
            )
            response.raise_for_status()
            
            result = response.json()
            status = result["status"]
            
            if status == "completed":
                print("Generation complete!")
                return result
            elif status == "failed":
                raise Exception(f"Generation failed: {result.get('error')}")
            
            # Show progress for long-form video
            if "progress" in result:
                print(f"Progress: {result['progress']}%", end="\r")
            
            time.sleep(5)
            attempt += 1
        
        raise TimeoutError("Generation timed out")
    
    def _stream_generation(self, generation_id: str) -> Dict[str, Any]:
        """Stream generation with live updates (for 60+ second videos)"""
        print("Streaming generation with live rendering...")
        print("First second will be available almost instantly...")
        
        # WebSocket streaming for progressive rendering
        ws_url = f"wss://api.ltx.studio/v1/stream/{generation_id}"
        
        # Simplified streaming example
        # In production, implement full WebSocket handling
        print("Receiving live stream...")
        
        return {"message": "Streaming implementation"}
    
    def generate_from_image(
        self,
        image_path: str,
        prompt: str,
        duration: int = 4
    ) -> Dict[str, Any]:
        """Generate video from image (image-to-video)"""
        try:
            print(f"Image-to-video: {image_path}")
            print(f"Animation prompt: {prompt}")
            
            # Upload image
            with open(image_path, "rb") as f:
                files = {"image": f}
                data = {
                    "prompt": prompt,
                    "duration": duration,
                    "fps": 30
                }
                
                response = requests.post(
                    f"{self.base_url}/image-to-video",
                    headers={"Authorization": f"Bearer {self.api_key}"},
                    files=files,
                    data=data
                )
                response.raise_for_status()
            
            result = response.json()
            return self._poll_generation(result["id"])
            
        except Exception as e:
            print(f"Image-to-video error: {e}")
            raise
    
    def download_video(self, video_url: str, output_path: Path) -> Path:
        """Download generated video"""
        try:
            print(f"Downloading video...")
            response = requests.get(video_url, stream=True)
            response.raise_for_status()
            
            with open(output_path, "wb") as f:
                for chunk in response.iter_content(chunk_size=8192):
                    f.write(chunk)
            
            print(f"Saved: {output_path}")
            return output_path
            
        except Exception as e:
            print(f"Download error: {e}")
            raise

# Business use case: Marketing video campaign
def marketing_campaign_workflow():
    """Complete workflow for copyright-safe marketing videos"""
    
    API_KEY = os.getenv("LTX_STUDIO_API_KEY", "your_api_key_here")
    client = LTXStudioClient(API_KEY)
    
    # Campaign videos with copyright compliance guarantee
    campaigns = [
        {
            "name": "brand_hero",
            "prompt": "Modern tech product floating in minimalist space, clean lighting, professional commercial",
            "duration": 8
        },
        {
            "name": "lifestyle_shot",
            "prompt": "Happy customers using mobile app in cafe, natural interaction, authentic atmosphere",
            "duration": 10
        },
        {
            "name": "product_demo_long",
            "prompt": "Detailed product features showcase, smooth transitions, professional presentation",
            "duration": 60  # Long-form video with live-streaming
        }
    ]
    
    output_dir = Path("marketing_campaign")
    output_dir.mkdir(exist_ok=True)
    
    for campaign in campaigns:
        print(f"\n{'='*60}")
        print(f"Generating: {campaign['name']}")
        print(f"{'='*60}")
        
        # Generate video
        result = client.generate_video(
            prompt=campaign["prompt"],
            duration=campaign["duration"],
            stream=campaign["duration"] > 30  # Stream for long videos
        )
        
        # Download
        video_path = output_dir / f"{campaign['name']}.mp4"
        client.download_video(result["video_url"], video_path)
        
        print(f"✓ Complete: {campaign['name']}")
        print(f"  Duration: {campaign['duration']}s")
        print(f"  Copyright-safe: Yes (Getty/Shutterstock training data)")
        print(f"  Output: {video_path}")
    
    print("\n=== Marketing Campaign Complete ===")
    print("All videos are copyright-free and safe for commercial use")

if __name__ == "__main__":
    marketing_campaign_workflow()

Professional Integration Services by 21medien

LTX Video's real-time generation, ethical training data, and 60+ second capabilities make it a powerful solution for professional video production, but maximizing these advantages requires specialized implementation expertise. 21medien offers comprehensive integration services tailored to businesses that prioritize both performance and copyright compliance.

Our specialized services include: Real-time Video Pipeline Architecture for building live-streaming video generation systems that leverage LTX Video's progressive rendering capabilities, Copyright Compliance Strategy ensuring your video workflows utilize only ethically-trained models with full Getty Images and Shutterstock licensing, Long-form Video Production Systems for generating and managing 60+ second content with optimized rendering pipelines and quality control, Multi-modal Workflow Development integrating text-to-video, image-to-video, keyframe animation, and bidirectional extension capabilities into cohesive production workflows, Performance Optimization for maximizing LTX Video's 30x speedup advantage through efficient batching, caching, and resource management, Custom Studio Integration connecting LTX Video capabilities with your existing creative tools, asset management systems, and content delivery infrastructure, and Ethical AI Consulting helping your organization communicate the value of copyright-safe AI video to stakeholders and customers.

Whether you need a turnkey real-time video platform, want to build copyright-compliant marketing automation, or require expert guidance on leveraging LTX Video's unique multiscale rendering technology, our team combines deep technical expertise with production-grade implementation experience. We help you navigate the balance between open-source flexibility and cloud-based convenience while ensuring every generated video is legally safe for commercial use. Schedule a free consultation call through our contact page to explore how LTX Video can transform your video production workflow with real-time generation and guaranteed copyright compliance.