[!IMPORTANT] Naming notice (2026-04-10). The "HLWQ" technique used in this model is being rebranded to HLWQ (Hadamard-Lloyd Weight Quantization). The change is only the name; the algorithm and the weights in this repository are unchanged.

The rebrand resolves a name collision with an unrelated, earlier KV cache quantization method also named HLWQ (Han et al., arXiv:2502.02617, 2025). HLWQ addresses weight quantization with a deterministic Walsh-Hadamard rotation and Lloyd-Max scalar codebook; Han et al.'s HLWQ addresses KV cache quantization with a random polar rotation. The two methods are technically distinct.

Existing loaders that load this repository by ID continue to work without changes. Future model uploads will use the HLWQ name.

Reference paper for this technique: arXiv:2603.29078 (v2 in preparation; v1 still uses the old name).

LTX-2.3 (22B) — HLWQ Q5 (Bit-Packed)

Download Size

Compression

Quick Start

Architecture

• 22B parameters — largest video model we've quantized
• 48 transformer blocks, hidden=4096, MLP=16384
• Joint audio-video generation — synchronized audio + video
• head_dim=64 (Hadamard-compatible)
• 5,947 tensors total (BF16)
• Spatial + temporal upscalers included

Files

LTX-2.3-22B-HLWQ-Q5/
├── setup.py                          # One-command setup
├── generate_ltx.py                   # Easy generation wrapper
├── polarquant/
│   ├── codes/
│   │   ├── chunk_00_codes.safetensors (1.5 GB)
│   │   ├── chunk_01_codes.safetensors (1.5 GB)
│   │   ├── chunk_02_codes.safetensors (1.5 GB)
│   │   └── chunk_03_codes.safetensors (0.1 GB)
│   └── bf16/
│       └── ltx23_bf16.safetensors    (9.1 GB)
└── upscalers/
    ├── ltx-2.3-spatial-upscaler-x2-1.1.safetensors  (1.0 GB)
    └── ltx-2.3-temporal-upscaler-x2-1.0.safetensors (0.3 GB)

Hardware

Links

• HLWQ Paper
• GitHub (HLWQ)
• Original LTX-2.3
• LTX-2 Paper
• LTX GitHub

Citation