Instructions to use FrankDase/LTX-2.5-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use FrankDase/LTX-2.5-MLX with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir LTX-2.5-MLX FrankDase/LTX-2.5-MLX
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
LTX-2.5-MLX
Unofficial Apple Silicon MLX conversion of the official
Lightricks/LTX-2.5 distilled
checkpoint.
This repository contains model weights only. It is intended for local inference on Apple Silicon with the MLX port of LTX. The checkpoint was converted from the official BF16 release; the distilled transformer and Gemma 4 text encoder are stored in 8-bit MLX format, while the remaining components retain BF16 precision.
Included components
- LTX 2.5 distilled video/audio transformer
- Gemma 4 LTX text encoder and modality connectors
- Video encoder and decoder VAE
- Audio VAE and vocoder
- Spatial and temporal latent upscalers
- LTX 2.5 model configuration
The optional prompt rewriter is intentionally not included. Prompt enhancement
uses a separate instruction-tuned multimodal model such as
FrankDase/Gemma-4-E2B-it-MLX.
Capabilities
- Text-to-video
- Image-to-video with an optional start frame
- Image-to-video with an optional end frame
- Interpolation between start and end frames
- Audio-to-video
- Combined image and audio conditioning
- Synchronized video and audio generation
- Distilled two-stage generation with latent upscaling
Support for individual modes depends on the runner and pipeline version used.
Requirements
- Apple Silicon Mac
- Native ARM64 Python environment
- MLX 0.31 or newer
- A compatible LTX 2.5 MLX runtime
The model was tested locally on a Mac with an M4 processor and 64 GB unified memory. Lower-memory systems may require reduced resolution, frame count, and a low-RAM execution mode.
Basic example
With a compatible ltx-2-mlx installation:
caffeinate -dimsu /usr/bin/arch -arm64 \
ltx-2-mlx generate \
--model "/path/to/LTX-2.5-MLX" \
--distilled \
--low-ram \
--prompt "A red fox walking through fresh snow, cinematic natural light" \
--height 320 \
--width 448 \
--frames 97 \
--frame-rate 24 \
--seed 42 \
--output "output.mp4"
LTX 2.5 expects frame counts in the form 8n + 1, for example 9, 97, or 121
frames. Distilled inference uses its dedicated few-step schedule; guidance
settings intended for the full development checkpoint should not be applied.
Prompting
LTX 2.5 works best with a detailed, continuous description covering the scene, subject motion, lighting, camera behavior, and sound. Short prompts can be expanded before generation with the separate Gemma 4 prompt enhancer linked above. For image-to-video, the reference image and text prompt should both be provided to the multimodal prompt enhancer.
Model provenance
- Original model:
Lightricks/LTX-2.5 - MLX runtime:
MrMoferFRAN/ltx-2-mlx - Conversion format: MLX Safetensors
- Transformer: distilled, 8-bit
- Text encoder: Gemma 4 LTX encoder, 8-bit
This is a format conversion, not a fine-tune. No training data was added and the underlying model behavior remains attributable to the original LTX 2.5 release.
License
The converted weights remain subject to the
LTX-2.x Community License.
Review the original license before using, redistributing, or deploying this
model. In particular, eligibility and commercial-use conditions depend on the
terms defined by Lightricks.
This repository is not affiliated with or endorsed by Lightricks.
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