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AI Video2024
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LTX-Video

Generates video from text or images via a DiT-based latent diffusion model: text-to-video, image-to-video, frame extension, and multi-keyframe conditioning in one model. A distilled 2B variant runs near real-time on one H100; 13B for higher quality.

Introduction

Open video generation has mostly forced a choice: cloud APIs you can't inspect, or local models too slow to iterate with. The bet here is that a DiT-based latent diffusion design can be efficient enough to make local, near-real-time synthesis practical on a single GPU — the distilled 2B variant generates faster than playback on an H100, turning video from a batch job into something you can actually iterate on.

What Sets It Apart
  • One model, many modes. Text-to-video, image-to-video, video extension, and multi-keyframe conditioning live in the same checkpoint, so you steer a shot with start/end frames instead of juggling task-specific models.
  • Distillation that's actually usable. A 2B distilled build chases speed, the 13B build chases quality, and FP8-quantized variants exist for tighter VRAM — meaning your hardware budget, not model availability, decides what you run.
  • A concrete envelope, not vibes. The 13B model runs at 1216x704 / 30 FPS by default and extends sequences toward ~60 seconds; native 4K, 50 FPS, and one-pass synchronized audio+video belong to the separate, newer LTX-2 line, not the 13B. These are stated targets you can plan a pipeline around.
Great Fit / Look Elsewhere

Great fit if you want open weights you can fine-tune and self-host, especially for image-to-video or keyframe-driven shots where control matters more than one-click polish. Look elsewhere if you need top-tier fidelity out of the box — it works best under 720x1280 and 257 frames, and quality degrades past that — or if you lack a recent high-VRAM GPU, since the distilled models visibly trade detail for their speed.

Information

  • Websitegithub.com
  • AuthorsLightricks
  • Published date2024/11/20

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