README.md

---
license: other
license_name: ltx-2-community-license-agreement
license_link: https://github.com/Lightricks/LTX-2/blob/main/LICENSE
base_model:
- Lightricks/LTX-2.5
base_model_relation: quantized
pipeline_tag: text-to-video
library_name: diffusers
tags:
- video-generation
- text-to-video
- image-to-video
- audio-to-video
- quantized
- 4-bit
- int4
- svdquant
- ltx-2
- ltx-2.5
- quantfunc
---

# LTX-2.5-QuantFunc-4bit

**4-bit quantized [Lightricks LTX-2.5](https://huggingface.co/Lightricks/LTX-2.5) 22B distilled — pure 4-bit inference holds up to BF16 quality while cutting weight/VRAM footprint to roughly 1/3.** Quantized and loaded by the [QuantFunc](https://github.com/quantfunc) inference engine.

## Why pure 4-bit holds up

Compressing a 22B joint video/audio DiT down to **4-bit activations × 4-bit weights (pure a4w4)** runs into one main enemy: activation outliers, which blow past 4-bit's dynamic range and show up as blur and lost detail. QuantFunc adds two layers on top of the SVDQuant approach:

1. **A much stronger low-rank branch.** Algorithmic improvements let the low-rank branch absorb significantly more activation-outlier energy than standard SVDQuant — outliers get siphoned off before the main path is quantized to 4-bit, so the main path sees a much cleaner distribution.
2. **A smoother residual.** A combination of in-house techniques further smooths the outlier distribution in what's left after low-rank absorption, so the 4-bit grid covers the remaining distribution with far less error.

Net effect: **at pure a4w4 inference, output video (including audio) clarity, detail retention and motion coherence are on par with the BF16 baseline** — at roughly 1/3 the memory.

## Files

Quantizes the base checkpoint — [`ltx-2.5-22b-distilled-transformer-bf16.safetensors`](https://huggingface.co/Lightricks/LTX-2.5) from **Lightricks LTX-2.5 (22B, distilled variant)**:

| File | Format | Size | Minimum GPU |
|---|---|---|---|
| `ltx-2.5-22b-distilled-quantfunc-int4-r128.safetensors` | INT4 weights / INT4 activations (SVDQuant, rank-128) | ~14.06 GiB | NVIDIA SM120+ (Blackwell, e.g. RTX 50-series / RTX 6000D) |

## Produced & loadable ONLY by QuantFunc

This is **not** a standard diffusers/safetensors checkpoint. The file is produced by, and only loadable by, the **QuantFunc inference engine** (native C++/CUDA engine) or the **[ComfyUI-QuantFunc](https://github.com/quantfunc)** custom-node plugin for ComfyUI. It will not load in vanilla diffusers, ComfyUI's stock LTX loader, or any other inference stack. (This repo declares `library_name: diffusers` purely so Hugging Face counts real downloads of the flat `.safetensors` file below — see [HF's download-stats docs](https://huggingface.co/docs/hub/models-download-stats) — not because the file loads via the `diffusers` Python package.)

## Inference settings (required for correct output)

This is the **distilled** checkpoint, joint video+audio. It must be run with the distilled model's own schedule — a generic/default scheduler will produce degraded output:

- **Sigma schedule (8 steps, fixed):** `1.0, 0.99375, 0.9875, 0.98125, 0.975, 0.909375, 0.725, 0.421875, 0.0`
- **CFG:** `1` (no classifier-free guidance — this is a guidance-distilled model)
- **STG:** disabled (no Spatio-Temporal Guidance)

## Original / base model

- Base checkpoint: [`Lightricks/LTX-2.5`](https://huggingface.co/Lightricks/LTX-2.5) → `ltx-2.5-22b-distilled-transformer-bf16.safetensors` (~42 GB, bf16)
- Publisher: [Lightricks](https://huggingface.co/Lightricks)
- Upstream repo / inference code: [github.com/Lightricks/LTX-2](https://github.com/Lightricks/LTX-2)

This repository redistributes **derived (quantized) weights only**, produced from the base checkpoint above via the QuantFunc SVDQuant pipeline. No architecture or training changes were made — only post-training quantization (weight + activation, 4-bit, rank-128 SVD residual).

## License

Follows the original model's **LTX-2 Community License Agreement** ([license text](https://github.com/Lightricks/LTX-2/blob/main/LICENSE)). This repository distributes quantized weights only; copyright and licensing of the original model belong to Lightricks.

## Community

- 🌐 [QuantFunc website](https://www.quantfunc.com/)
- 🐙 [ComfyUI-QuantFunc on GitHub](https://github.com/QuantFunc/ComfyUI-QuantFunc)
- 🤗 [Hugging Face](https://huggingface.co/QuantFunc)  |  🤖 [ModelScope](https://www.modelscope.cn/profile/QuantFunc)
- 🎮 [Discord](https://discord.gg/jCp9TpFWcn)