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Automated PR - 2026-03-30

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2026-03-30 17:59:34 +00:00
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[![Website](https://img.shields.io/badge/Website-LTX-181717?logo=google-chrome)](https://ltx.io)
[![Model](https://img.shields.io/badge/HuggingFace-Model-orange?logo=huggingface)](https://huggingface.co/Lightricks/LTX-2.3)
[![Demo](https://img.shields.io/badge/Demo-Try%20Now-brightgreen?logo=vercel)](https://app.ltx.studio/ltx-2-playground/i2v)
[![Demo](https://img.shields.io/badge/Demo-Try%20Now-brightgreen?logo=data:image/png;base64,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)](https://console.ltx.video/playground)
[![Paper](https://img.shields.io/badge/Paper-PDF-EC1C24?logo=adobeacrobatreader&logoColor=white)](https://arxiv.org/abs/2601.03233)
[![Discord](https://img.shields.io/badge/Join-Discord-5865F2?logo=discord)](https://discord.gg/ltxplatform)
@@ -72,7 +72,7 @@ Download the following models from the [LTX-2.3 HuggingFace repository](https://
### ⚡ Optimization Tips
* **Use DistilledPipeline** - Fastest inference with only 8 predefined sigmas (8 steps stage 1, 4 steps stage 2)
* **Enable FP8 quantization** - Enables lower memory footprint: `--quantization fp8-cast` (CLI) or `quantization=QuantizationPolicy.fp8_cast()` (Python). For Hopper GPUs with TensorRT-LLM, use `--quantization fp8-scaled-mm` for FP8 scaled matrix multiplication.
* **Enable FP8 quantization** - Enables lower memory footprint: `--quantization fp8-cast` (CLI) or `quantization=QuantizationPolicy.fp8_cast()` (Python). Fp8-cast should be used with bf16 checkpoints, it shall downcast them on the fly. For Hopper GPUs with TensorRT-LLM, use `--quantization fp8-scaled-mm` for FP8 scaled matrix multiplication. Fp8-scaled-mm should be used with fp8 checkpoints.
* **Install attention optimizations** - Use xFormers (`uv sync --extra xformers`) or [Flash Attention 3](https://github.com/Dao-AILab/flash-attention) for Hopper GPUs
* **Use gradient estimation** - Reduce inference steps from 40 to 20-30 while maintaining quality (see [pipeline documentation](packages/ltx-pipelines/README.md#denoising-loop-optimization))
* **Skip memory cleanup** - If you have sufficient VRAM, disable automatic memory cleanup between stages for faster processing