---
license: apache-2.0
tags:
- ltx-2.3
- int8
- comfyui
- quantization
- ampere
---

# LTX-2.3 INT8 Quantized Models

I have quantized various **LTX-2.3 models and text encoders into INT8** to be used in **ComfyUI**.

Make sure you have **INT8 nodes installed**, as ComfyUI does **not natively support INT8 as of Q1 2026**.

These INT8 models are designed to **speed up workflows on Ampere (RTX 30XX) GPUs**, which are relatively inefficient with **FP8 or FP4 quantization**.

---

## Demo Video

<video width="480" controls preload="metadata">
  <source src="https://huggingface.co/Winnougan/LTX-2.3-INT8/resolve/main/LTX_2.3_i2v_00006_.mp4" type="video/mp4">
  Your browser does not support the video tag.
</video>

---

## Usage

Example loading with Transformers:

```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("Winnougan/LTX-2.3-INT8")
model = AutoModelForSeq2SeqLM.from_pretrained("Winnougan/LTX-2.3-INT8")
```

These models are primarily intended for **ComfyUI workflows using INT8-compatible nodes**.

---

## Notes

- Quantization performed for **performance on Ampere GPUs (RTX 30XX series)**.
- INT8 offers a **good balance between speed, VRAM usage, and quality**.
- Ensure your ComfyUI installation supports **INT8 tensor loading**.

---

## Disclaimer

These are **quantized versions of the original LTX-2.3 models**.  
All credit for the original models goes to their respective authors.

Quantization may introduce **minor differences in output quality compared to FP16 models**.