LTX-2
AIOpen SourceFreeDiT-based audio‑video foundation model delivering synchronized high-fidelity video and audio with production-ready pipelines and LoRA trainer.
-(0 Reviews)
Free Available
Starting from Free
LTX-2
AIOpen SourceFree
DiT-based audio‑video foundation model delivering synchronized high-fidelity video and audio with production-ready pipelines and LoRA trainer.
-(0 Reviews)
Free Available
Starting from Free
About LTX-2
LTX-2 is a DiT-based audio‑video foundation model that generates synchronized audio and video in a single coherent process. It combines a video VAE, audio VAE and vocoder with transformer-based diffusion to produce high-fidelity outputs, multi-keyframe conditioning, and production-ready rendering pipelines. The project includes ltx-core (model & inference stack), ltx-pipelines (ready-to-run text→video, image→video, video→video and keyframe pipelines), and ltx-trainer (LoRA and full fine-tuning tools). Optimizations such as FP8 transformer support, gradient-estimation denoising, multi-stage (spatial + temporal) upscalers, and LoRA/IC‑LoRA training make it practical for research and production use cases that demand synchronized audiovisual outputs, long generations, and fine-grained creative control.
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Key Features
Synchronized Audio‑Video Generation: End-to-end model architecture that jointly generates coherent video frames and matching audio (including a vocoder) so motion, dialogue, ambience and music are time-aligned.
Production‑Ready Pipelines: ltx-pipelines provides high-level, CLI-capable pipelines (text-to-video, image-to-video, video-to-video, keyframe interpolation) that handle model loading, encoding/decoding, memory management and file I/O for production workflows.
LoRA & IC‑LoRA Trainer: ltx-trainer includes tools and configs for fast LoRA and IC‑LoRA fine-tuning (motion, style, likeness), enabling many useful adapters to be trained quickly and reproducibly.
Performance & Memory Optimizations: Supports FP8 transformers, gradient-estimation denoising to reduce steps, multi-stage two-stage pipelines (latents + spatial upscaler), and other memory optimizations for improved throughput and lower compute cost.
Multi‑Scale Temporal & Spatial Upscaling: Provides temporal upscalers and spatial upscalers in multi-stage pipelines to increase frame-rate (FPS) and resolution while balancing speed and visual quality.
Modular Core Components: ltx-core exposes schedulers, guiders, patchifiers, loaders and SingleGPUModelBuilder for custom inference stacks and research customization.
Text Encoding & Conditioning: Integrates Gemma text encoder and multi-keyframe conditioning and 3D camera logic for precise control over scene content, style, and temporal continuity.
Dataset & Training Utilities: Includes dataset preprocessing tools (resolution buckets, frame bucketing, sequence-length rules) and accelerate configs for distributed GPU training and large‑model fine-tuning.
Synchronized audio and video generation in a single model
Native 4K fidelity and up to 50 fps support
Generations up to 10 seconds with synchronized audio
DiT-based transformer architecture with separate video and audio VAEs and vocoder
Official Python packages: ltx-core (model & utilities), ltx-pipelines (inference pipelines), ltx-trainer (LoRA & fine-tuning)
Multiple pipeline types: text-to-video, image-to-video, video-to-video, keyframe interpolation
Two-stage and distilled pipelines with optional spatial upscaler and temporal upscaler
LoRA and IC-LoRA training support for fast adaptation and style/motion/likeness tuning
FP8 transformer support and gradient-estimation denoising (reduce steps while preserving quality)
Production-ready features: CLI, model loaders, file I/O, memory optimizations, and multi-GPU inference stack
Integration options: PyTorch codebase, Diffusers, ComfyUI workflows, Hugging Face model hosting, API providers
Use Cases
Producing short synchronized audio-video clips for creative content: generate up to multi-second clips with aligned dialogue, ambience and music for social media or cinematic previews.
Custom LoRA fine-tuning for style or likeness: train LoRA adapters to capture a specific motion style, actor likeness, or audio voice with limited compute and integrate them into inference pipelines.
Video-to-video editing and transformation: perform video-to-video translations, inpainting or interpolation using IC‑LoRAs and multi-stage pipelines for high-quality, temporally consistent results.
High-fidelity production rendering: generate high-resolution (4K-capable) outputs with temporal upscaling and spatial upscalers for polished, production-ready artifacts and post-processing.
Research and model development: experiment with diffusion schedulers, gradient-estimation loops, and FP8 transformer variants to test performance-quality trade-offs and novel conditioning strategies.
Building integrated APIs and services: deploy the inference pipelines and model loaders in server environments to provide programmatic access to audiovisual generation for apps and studios.
Generate high-fidelity synchronized audio-video clips from text prompts
Image-to-video and video-to-video transformations and style transfer
Create multi-keyframe animated sequences with precise frame-level control
Rapid LoRA/IC-LoRA fine-tuning to adapt style, motion, or voice likeness
Production pipelines for VFX, advertising, game cinematics, and content prototyping
Research and experimentation with multi-modal diffusion and temporal upscaling techniques
