Instructions to use MrMofer/ltx-2.5-mlx-q8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use MrMofer/ltx-2.5-mlx-q8 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir ltx-2.5-mlx-q8 MrMofer/ltx-2.5-mlx-q8
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Which exact ltx-2-mlx revision supports this LTX 2.5 checkpoint with --low-ram?
Hey, thanks for sharing this conversion! I’m hoping to try it on a 24 GB Mac using --low-ram.
Which exact ltx-2-mlx commit or branch did you test it with? The current public main still appears to be LTX 2.3-oriented, so I want to make sure I’m using the right runtime.
Also, does the bundled Gemma 4 encoder load automatically, or is an extra flag needed?
Thanks!
Thanks! You’re correct that upstream main is still LTX-2.3-oriented.
I tested this conversion with the community LTX-2.5 runtime on:
- Branch:
ltx25 - Commit:
57952288076766abe27dda3a774b2c24f7346977 - Runtime: https://github.com/MrMoferFRAN/ltx-2-mlx/tree/ltx25
Please use mlx-lm >= 0.31.3. The bundled Gemma 4 encoder loads automatically from text_encoder/; no --gemma flag or separate download is required.
For a 24 GB Mac, start with --distilled --low-ram and a modest resolution/frame count. The LTX-2.5 distilled pipeline includes the Euler ancestral sampler internally for stage 1; it is not a separate CLI mode.