Text-to-Video
Diffusers
LTX2VideoPipeline
custom_ltx2
video-generation
image-to-video
audio-to-video
quantized
4-bit precision
int4
svdquant
ltx-2
ltx-2.5
quantfunc
Instructions to use QuantFunc/LTX-2.5-QuantFunc-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use QuantFunc/LTX-2.5-QuantFunc-4bit with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("QuantFunc/LTX-2.5-QuantFunc-4bit", dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Download config.json from QuantFunc/LTX-2.5-QuantFunc-4bit: direct link, hf CLI and curl.
- Browser
- Download file 424 Bytes
-
https://huggingface.co/QuantFunc/LTX-2.5-QuantFunc-4bit/resolve/main/config.json
- Command line
-
hf download hf://QuantFunc/LTX-2.5-QuantFunc-4bit/config.json
-
curl -L -o config.json https://huggingface.co/QuantFunc/LTX-2.5-QuantFunc-4bit/resolve/main/config.json
424 Bytes
| { | |
| "model_type": "custom_ltx2", | |
| "architectures": [ | |
| "LTX2VideoPipeline" | |
| ], | |
| "name_or_path": "QuantFunc/LTX-2.5-QuantFunc-4bit", | |
| "task": "text-to-video", | |
| "framework": "quantfunc", | |
| "license": "other", | |
| "description": "Pre-quantized LTX-2.5 22B distilled video+audio model by QuantFunc", | |
| "_comment": "This is a minimal config to enable download stats on HF Hub. Not used for loading." | |
| } | |