LTX-2/packages/ltx-pipelines/README.md

LTX-2 Pipelines

High-level pipeline implementations for generating audio-video content with Lightricks' LTX-2 model. This package provides ready-to-use pipelines for text-to-video, image-to-video, video-to-video, audio-to-video, keyframe interpolation, and retake tasks.

Pipelines are built using building blocks from ltx-core (schedulers, guiders, noisers, patchifiers) and handle the complete inference flow including model loading, encoding, decoding, and file I/O.

Key Features:

• 🎬 Multiple Pipeline Types: Text-to-video, image-to-video, video-to-video, audio-to-video, keyframe interpolation, and retake
• ⚡ Optimized Performance: Support for FP8 transformers, gradient estimation, and memory optimization
• 🎯 Production Ready: Two-stage pipelines for best quality output
• 🔧 LoRA Support: Easy integration with trained LoRA adapters
• 📦 Self-Contained: Handles model loading, encoding, decoding, and file I/O
• 🚀 CLI Support: All pipelines can be run as command-line scripts

Quick Start

# From the repository root
uv sync --frozen

# Run a pipeline (example: two-stage text-to-video)
python -m ltx_pipelines.ti2vid_two_stages \
    --checkpoint-path path/to/checkpoint.safetensors \
    --distilled-lora path/to/distilled_lora.safetensors 0.8 \
    --spatial-upsampler-path path/to/upsampler.safetensors \
    --gemma-root path/to/gemma \
    --prompt "A beautiful sunset over the ocean" \
    --output-path output.mp4

See Installation & Usage for full setup, CLI modules, and shared flags.

📚 Documentation

Topic	Description
Installation & Usage	Install, requirements, running pipelines from the CLI, common flags
Pipeline Selection Guide	Decision tree + feature comparison to pick the right pipeline
Available Pipelines	Full reference for all 11 pipelines
Conditioning Types	Image and video conditioning methods
Multimodal Guidance	CFG / STG / modality guidance parameters and tuning
Optimization Tips	FP8 quantization, torch.compile, gradient estimation
Multi-GPU Inference	Run a single generation across GPUs for latency (SP, TDP, distributed VAE, distributed Gemma)

🔗 Related Projects

• LTX-Core - Core model implementation and inference components (schedulers, guiders, noisers, patchifiers)
• LTX-Trainer - Training and fine-tuning tools