LTX 2.3

LTX 2.3 - Open-source AI video generator with cinematic 4K quality

Launched on Mar 20, 2026

Struggling to produce high-quality videos at scale without a massive budget? LTX 2.3 is a 22B-parameter open-source AI video model that turns text, images, and audio into stunning 4K videos at 50 FPS. It supports text-to-video, image-to-video, audio-driven sync, and native 9:16 portrait output. 18× faster than WAN 2.2 on H100, it empowers creators and developers to generate cinematic content in minutes.

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What Is LTX 2.3? The Open-Source AI Video Generator Built for Creators Who Mean Business

Picture this: you're a content marketer with a product launch in two days, a filmmaker storyboarding your next short, or an indie game developer who needs a cinematic trailer — yesterday. Traditional video production means booking studios, coordinating crews, and burning through budgets that most teams simply don't have. Even with conventional AI tools, you're often wrestling with slow render times, clunky workflows, and output quality that falls short of professional standards.

That's exactly the gap LTX 2.3 was designed to close. Developed by Lightricks, the Israeli creative AI company behind some of the most widely used creative apps in the world, LTX 2.3 is a 22-billion-parameter open-source AI video generation model built on the DiT (Diffusion Transformer) architecture. It's not just another text-to-video wrapper — it's a full multimodal production engine that accepts text, images, audio, and existing video as inputs, then outputs cinema-grade results at up to 4K resolution and 50 frames per second.

What sets LTX 2.3 apart in an increasingly crowded market? Speed, for one: on an H100 GPU, LTX 2.3 runs 18 times faster than WAN 2.2, meaning the time between creative idea and finished clip shrinks from hours to minutes. And because the model weights are freely available on Hugging Face — with commercial use permitted for individuals and organizations earning under $10M annually — the barrier to professional-quality AI video has never been lower.

The academic foundation is equally solid. The underlying technology is documented in the research paper "LTX-2: Efficient Joint Audio-Visual Foundation Model" (arXiv:2601.03233), and the open-source community has responded enthusiastically: 5,000+ GitHub stars and 750+ forks speak to both the technical credibility and the active developer ecosystem growing around this model. Thousands of filmmakers, marketers, and developers use LTX 2.3 daily — on the cloud platform at ltx23.app and through self-hosted deployments alike.

LTX 2.3 at a Glance
  • 22B DiT architecture — one of the largest open-source video generation models available today
  • True multimodal input — generate from text, image, audio, or existing video in one unified pipeline
  • Native 9:16 portrait video — trained on real portrait data, not cropped from landscape
  • Up to 4K @ 50 fps — broadcast-grade output for professional and commercial use
  • Fully open-source & commercially licensed — free for individuals and businesses under $10M annual revenue

The Features That Actually Move the Needle

LTX 2.3 ships with six core capabilities, and each one is designed to solve a specific production bottleneck rather than just check a feature-list box. Here's what you can actually do with them.

Text-to-Video Generation lets you describe any scene in natural language — up to 2,000 characters — and watch the 22B DiT engine translate it into fluid motion, accurate lighting, and physically plausible animation. Whether you're visualizing a product concept or roughing out a cinematic sequence, the model handles the complexity so you don't have to.

Image-to-Video Conversion is where static assets come alive. Upload a product photo, an app mockup, or a concept illustration, and LTX 2.3 generates natural camera movement and realistic animation with noticeably fewer freeze-frame artifacts than earlier-generation models. You can use it to turn a single product image into a polished demo reel in minutes.

Audio-to-Video Synchronization goes beyond background music. Feed in an audio track and the model generates visuals that match — with lip sync for spoken content, beat-aligned motion for music, and spatial audio cues that inform the visual composition. This makes it genuinely useful for music visualization, dubbed advertising, and localized content at scale.

Native Portrait Video (9:16) is a feature that sounds simple but matters enormously if you're producing for TikTok, Instagram Reels, or YouTube Shorts. Unlike models that crop a landscape frame, LTX 2.3 was trained on real portrait-orientation data and outputs natively at 1080×1920 — which means better composition, no missing edges, and content that actually looks like it was made for mobile.

4K @ 50fps Professional Output covers resolutions from 1080p through 1440p to full 4K, with frame rate options at 24, 25, 48, and 50 fps. For broadcast, pre-visualization, or any deliverable where quality is non-negotiable, this is the spec sheet you need.

Multi-Style Engine handles anime, cinematic, and photorealistic content within a single model. As Emma Zhang, one of LTX 2.3's regular users, puts it: "Multi-style engine handles anime, cinematic, and photorealistic content — no need to switch tools." That consolidation alone saves meaningful time in any multi-format production workflow.

  • Open-source and commercially free for individuals and businesses under $10M revenue — no licensing anxiety
  • True multimodal pipeline — text, image, audio, and video inputs all in one model
  • 18× faster than WAN 2.2 on H100 GPUs, dramatically reducing iteration time
  • Native 9:16 portrait output trained on real vertical data, not post-cropped
  • Broadcast-grade specs: up to 4K resolution at 50 fps
  • Active developer ecosystem with ComfyUI integration, Python SDK, and GGUF quantization
  • Local deployment requires serious hardware: NVIDIA GPU with 32GB+ VRAM recommended — not suitable for most consumer machines
  • Maximum clip length of 20 seconds per generation; longer sequences require stitching multiple clips
  • Diffusers library support is still incoming — some integrations are not yet fully available

Who Gets the Most Out of LTX 2.3?

LTX 2.3 is a versatile tool, but it's not one-size-fits-all. Here are the five user types who consistently see the biggest impact.

If you're a social media content creator, the volume problem is real — TikTok, Reels, and Shorts algorithms reward consistency, but producing fresh vertical video every day is exhausting. LTX 2.3's native 9:16 mode means you can batch-generate a week of content in an afternoon, and its prompt-based workflow makes A/B testing visual concepts as easy as editing a sentence. What used to take days now takes hours.

When your team runs marketing or e-commerce, product video at catalog scale is the challenge. Hiring a studio for every SKU isn't viable; stock footage never quite fits your brand. With LTX 2.3, you can upload a reference image, maintain visual consistency across your product line, and produce demo-quality video at a fraction of traditional production costs. Rachel Kim, who manages video production for an e-commerce brand, notes: "We produce product videos at catalog scale for a fraction of what traditional studios charge."

For film pre-production teams, the cost of changing your mind after a shoot day is enormous. LTX 2.3 lets you test camera angles, lighting setups, and visual effects before a single frame is captured on set. Ryan Nakamura, a filmmaker who integrated LTX 2.3 into his pre-production workflow, reports that it "cut our production costs in half while doubling our total creative output volume." Pitchable pre-viz sequences become a morning's work, not a week's.

Indie game developers face a different version of the same problem: cinematic trailers and in-engine cutscenes require animation cycles that take weeks to produce. LTX 2.3 can generate high-quality game trailer content and cutscene visuals directly from text prompts or concept art, compressing that production timeline significantly while maintaining the visual quality that players expect.

UX designers and product managers often need to show rather than tell — but polished app demo videos traditionally require screen recording, editing, and professional post-production. LTX 2.3's image-to-video mode changes that equation: upload your app mockup, add a prompt describing the user flow, and within minutes you have a walkthrough demo ready for stakeholder presentations or app store listings. As Aisha Patel describes it: "Image-to-video mode animates mockups into polished walkthrough demos in minutes."

💡 Cloud or Local? You Don't Have to Choose

If you want to start generating immediately without any hardware setup, visit ltx23.app and create a free account — all rendering happens in the cloud, no GPU required. If you need full control, custom LoRA fine-tuning, or on-premise deployment, head to Hugging Face to download the open-source model weights and run LTX 2.3 on your own infrastructure. LTX 2.3 genuinely supports both paths.


Getting Started: From Zero to Your First AI Video

Whether you want to be generating in the next five minutes or you're planning a self-hosted deployment, here's how both paths work.

The Cloud Path — No Setup Required

The fastest way to experience LTX 2.3 is through ltx23.app:

  1. Create a free account at ltx23.app — new users receive complimentary credits to start generating immediately.
  2. Choose your generation mode: Text-to-Video, Image-to-Video, or Audio-to-Video, depending on what you're starting with.
  3. Define your input: Write a text prompt up to 2,000 characters, or upload your reference image or audio file.
  4. Configure your output: Set clip length (4–20 seconds), aspect ratio (16:9, 9:16, 1:1, or 4:3), resolution, and frame rate.
  5. Generate and download: Click generate, and your high-resolution AI video is ready to download — no local GPU, no installation, no waiting for render queues.

The entire process from prompt to downloadable file typically takes a few minutes, and all compute happens in the cloud.

The Developer Path — Self-Hosted and Customizable

For teams that need full control, LTX 2.3's open-source architecture supports a complete local deployment workflow:

  • Prerequisites: Python ≥ 3.12, CUDA > 12.7, NVIDIA GPU with 32GB+ VRAM (recommended), 32GB RAM, 60GB storage (Windows)
  • Download your checkpoint from Hugging Face: choose from the full bf16 model (ltx-2.3-22b-dev), the efficient 8-step distilled version (ltx-2.3-22b-distilled), or the LoRA variant. Spatial and temporal upscalers are also available separately.
  • Integrate via ComfyUI using native nodes already available through ComfyUI Manager, or use the Python library directly in your own pipelines. Diffusers library support is on the roadmap.
  • Fine-tune with LoRA to adapt the model to your brand's visual identity or a specific style.

You can also explore the model's API capabilities through the LTX API Playground at console.ltx.video/playground/ before committing to a full integration.

💡 Running on a Consumer GPU? Use the Quantized Models

If your GPU has less than 32GB of VRAM, don't write off local deployment just yet. The GGUF and FP8 quantized versions of LTX 2.3 — including the ltx-2.3-22b-distilled-lora-384 checkpoint — significantly reduce VRAM requirements while preserving most of the generation quality. Check the technical documentation at docs.ltx.video for recommended settings based on your hardware configuration.


Which Plan Is Right for Your Team?

LTX 2.3 follows a straightforward two-track pricing philosophy: a cloud subscription for teams who want managed infrastructure and instant access, and a free open-source path for developers and organizations comfortable with self-hosting.

On the cloud side, all paid plans include access to every generation mode (Text-to-Video, Image-to-Video, AI Image Generation), Motion Control, up to 4K resolution, generation privacy protection, priority queue access, commercial usage rights, and the ability to cancel at any time. Annual billing saves you 30% compared to monthly.

Plan Monthly Annual (Save 30%) Annual Credits Cost per 100 Credits Best For
Starter $19.9/mo $13.9/mo ($166.8/yr) 14,400 credits $1.16 Individual creators and small teams exploring AI video
Premium $39.9/mo $27.9/mo ($334.8/yr) 33,600 credits $1.00 Growing marketing teams with consistent production needs
Advanced $99.9/mo $69.9/mo ($838.8/yr) 120,000 credits $0.70 High-volume professional teams — fastest generation speed and expert team support

We'd suggest starting with Starter if you're evaluating whether AI video fits your workflow, Premium once you have a regular production cadence, and Advanced when volume and turnaround time are business-critical.

If you'd rather not commit to a subscription, new users receive free credits on registration — enough to run real tests before you make any decision.

For developers and smaller organizations, the open-source route is entirely free: download the model weights from Hugging Face and use LTX 2.3 commercially with no license fees, provided your annual revenue is under $10 million. Larger organizations with higher revenue should reach out about a commercial license.


Frequently Asked Questions

What is LTX 2.3, and how is it different from other AI video tools?

LTX 2.3 is a 22-billion-parameter open-source AI video generation model developed by Lightricks, built on the DiT (Diffusion Transformer) architecture. What makes it genuinely different is the combination of scale, speed, and openness: it's 18× faster than WAN 2.2 on H100 GPUs, supports true multimodal inputs (text, image, audio, and video in one pipeline), and is fully open-source with commercial use permitted for most creators and businesses. Most competing tools are closed-source, API-only, or lack native audio-visual synchronization.

Do I need a local GPU to use ltx23.app? What are the hardware requirements for self-hosting?

No GPU is required to use ltx23.app — all rendering runs in the cloud, so any device with a modern browser works. If you want to run LTX 2.3 locally, the recommended setup is an NVIDIA GPU with 32GB+ VRAM, 32GB of system RAM, 60GB of storage, Python ≥ 3.12, and CUDA > 12.7. For lower-spec hardware, GGUF and FP8 quantized checkpoints are available to reduce VRAM requirements.

What video specs does LTX 2.3 support?

LTX 2.3 supports the following output specifications: Resolutions — 1080p, 1440p, and 4K. Frame rates — 24, 25, 48, and 50 fps. Aspect ratios — 16:9 (landscape), 9:16 (portrait), 1:1 (square), and 4:3. Clip length — 4 to 20 seconds per generation. Native portrait output is 1080×1920, trained on real vertical data rather than cropped from landscape frames.

Can I use LTX 2.3-generated videos for commercial purposes?

Yes, absolutely. Videos generated through ltx23.app come with full commercial rights — no watermarks and no royalties. For the open-source model, the license permits commercial use free of charge for individuals and organizations with annual revenue under $10 million. If your organization exceeds that threshold, you'll need to purchase a commercial license. Full details are in the terms of service at ltx23.app/terms-of-service.

How does LTX 2.3 compare to Sora 2, Veo 3.1, and Kling 3.0?

Each model has its strengths, but LTX 2.3 holds meaningful advantages in specific areas. Compared to Sora 2, LTX 2.3 is open-source, accessible without a waitlist, and includes native audio-visual synchronization. Against Veo 3.1, LTX 2.3 matches 4K @ 50fps output quality while offering full open-source access and LoRA fine-tuning support. Relative to Kling 3.0, LTX 2.3 offers a broader resolution range, native portrait format, and downloadable model weights for self-hosted deployment.

How are credits consumed, and do unused credits expire?

Credits are consumed per generation based on factors like resolution, length, and frame rate — higher-quality outputs use more credits. For specific credit consumption rates per generation type, the pricing page at ltx23.app/pricing has the most current details. Regarding expiration, we recommend checking the current terms at ltx23.app/terms-of-service, as credit policies can be updated. If you have questions about your specific plan, the support team is reachable at support@ltx23.app.

What developer integration options are available (ComfyUI, Python, API)?

LTX 2.3 supports several integration paths: ComfyUI via native nodes available through ComfyUI Manager — the most accessible option for visual workflow builders. Python library for programmatic integration, requiring Python ≥ 3.12 and CUDA > 12.7. API Playground at console.ltx.video/playground/ for testing API capabilities before full integration. Diffusers library support is on the roadmap but not yet fully released. The model also supports custom LoRA fine-tuning, and technical documentation is available at docs.ltx.video.

How is my generated content kept private?

LTX 2.3 on ltx23.app applies several layers of protection: encryption in transit for all data transfers, access controls, and logging for security auditing. Generation privacy protection is included in all paid plans. Lightricks does not sell your personal data and shares it only with necessary service providers. The full privacy policy — updated October 24, 2025 — is available at ltx23.app/privacy-policy.

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