metadata
base_model:
- Lightricks/LTX-Video-0.9.5
library_name: diffusers
tags:
- ltx-video
- text-to-video
- candle
- rust
- gguf
- oxide-lab
- safetensors
language:
- en
license: other
pipeline_tag: text-to-video
LTX-Video in Rust (Candle)
This repository provides a high-performance, native Rust implementation of LTX-Video using the Candle ML framework.
Features
- 🦀 Native Rust: No Python dependency required for inference.
- 🚀 Performance: Optimized for NVIDIA GPUs with Flash Attention v2 and cuDNN.
- 💾 Memory Efficient: Supports GGUF quantization for T5-XXL text encoder and VAE tiling/slicing for generating HD videos on consumer GPUs.
- 🛠 Flexible: Easy to use CLI for video generation and library for custom integration.
Quick Start
Installation
Ensure you have Rust and the CUDA Toolkit installed, then:
git clone https://github.com/FerrisMind/candle-video
cd candle-video
cargo build --release --features flash-attn,cudnn
Video Generation
cargo run --example ltx-video --release -- \
--local-weights ./models/ltx-video \
--prompt "A serene mountain lake at sunset, photorealistic, 4k" \
--width 768 --height 512 --num-frames 97 \
--steps 30
Performance & Memory
| Resolution | Frames | VRAM (BF16) | VRAM (VAE Tiling) |
|---|---|---|---|
| 512x768 | 97 | ~8-13 GB | ~8-9 GB |
Note: Using GGUF T5 encoder saves an additional ~8-12GB of VRAM.
Credits
- Original Model: Lightricks/LTX-Video
- Framework: HuggingFace Candle
- T5 v1_1 XXl GGUF and Safetensors: city96/LTX-Video-gguf (for GGUF support patterns, T5 XXl GGUF and Safetensors)
For more details, visit the main GitHub Repository.