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vanch007/LTX-2.5-mlx
LTX-2.5-mlx is a image-to-video model from vanch007. Use it for the image-to-video task on the model card, and read the license before you ship it in a product. It is set up for mlx. The card lists the license as other.
This repository contains a local Apple-Silicon MLX conversion of the gated Lightricks/LTX-2.5 checkpoint. It is a derived conversion for MLX; it is not an official Lightricks release. Review and comply with the origin…
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Updated Aug 13, 2026
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From the Hugging Face model README
This repository contains a local Apple-Silicon MLX conversion of the gated Lightricks/LTX-2.5 checkpoint. It is a derived conversion for MLX; it is not an official Lightricks release. Review and comply with the original model license and usage policy before redistributing or using the files.
gemma4-q4/: Gemma4 Unified, affine 4-bit, group size 64, layer-sharded.transformer-q8/: 48-block LTX-2.5 AV Transformer, affine 8-bit, group size
64 on eligible Transformer Linear layers; non-block parameters remain BF16.The Transformer index and Gemma index are included. Conversion manifests record the source revision, tensor inventory, quantization policy, and completed shards. No original BF16 Transformer payload is included.
Clone the companion runtime from
vanch007/ltx-2.5-mlx, download this
model repository, and either place the files in the runtime's expected layout
or point the local assembly/generation wrapper at the downloaded directories.
The runtime supports one-stage and two-stage T2AV, repeatable I2V image
anchors, and generated-keyframe conditioning.
The conversion was validated locally on an M3 Max/128 GB Mac:
These checks do not claim full-pipeline pixel or waveform equivalence with the official PyTorch implementation. The full 42 GiB BF16 Transformer source sweep was intentionally skipped; only the converted Q8 artifacts are published.
See the runtime repository's conversion scripts and the pinned source manifest.
The source model is gated and requires Hugging Face access approval. The local
conversion used the LTX-2.5 source revision recorded in each
conversion-manifest.json.