Instructions to use mlx-community/ltx-2.5-mlx-q8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/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 mlx-community/ltx-2.5-mlx-q8
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
LTX-2.5 β int8 text encoder (MLX)
A complete LTX-2.5 component tree whose only difference from
mlx-community/ltx-2.5-mlx is the Gemma-4 text
encoder, quantized to int8 (13.6 GB vs 23.8 GB). Everything else is byte-identical to the base
repo and is duplicated here only so this tree resolves standalone.
β οΈ This repo does NOT contain a quantized DiT. The transformer is a separate axis: see
mlx-community/ltx-2.5-mlx-ditq8. The two
are independent β you can mix an int8 encoder with a bf16 or int8 DiT.
Why it exists β it is load-bearing on small machines, not a nicety
With the DiT streamed, LTX-2.5's peak memory is set by a geometry-independent ~24.8 GB floor that turns out to be the bf16 encoder (measured: a 256Γ256Γ9 clip and a 512Γ288Γ121 clip peak within 0.01 GB of each other β a decode-bound peak would scale with geometry; this does not). That floor is over the budget of both 24 GB and 32 GB machines.
Swapping in this int8 encoder takes the whole-run peak to 14.6 GB / 15.4 GB respectively. Neither lever works alone: resident is DiT-bound, so the encoder swap is invisible without streaming, and streaming alone leaves the encoder floor.
Recipe
int8, group 64, with embed_tokens deliberately EXCLUDED (kept bf16). Hidden state 00 is
embedTokens(ids) Β· embedScale β one of the 49 states the connector consumes directly, and the input
to all 48 layers. Quantizing it moves that state on its own; state 00 == 1.000000 exactly is the
fingerprint that it stayed bf16.
Fidelity
Connector output at valid token positions β the number that decides a text-encoder quant β reads 0.999820 against a 0.999879 bf16 floor.
β οΈ Read that metric, not a whole-array cosine. With a short prompt most of the 1024 positions are learned registers, identical across every encoder arm, which dilutes a whole-array figure 3β30Γ: a deliberately-poisoned arm scored 0.994316 whole-array but 0.829329 on its real tokens.
β οΈ The sample MOVES. Output is not bit-identical to bf16 (mean β0.1421/Ο 0.4253 vs β0.1274/0.4222) β a different but equally valid video, because changing the encoder changes the conditioning and therefore the trajectory. Judged acceptable in a blind 4-pair perceptual A/B across prompts including a close-up face, at both 704Γ512 and a clamped 512Γ288 one-stage geometry. Comparing the arms with SSIM/PSNR would be a category error.
Licence
LTX-2 Community License β see LICENSE.md and the acceptable-use policy snapshot. Derivative of
Lightricks LTX-2.5, carrying its terms including the Attachment A revenue gate and non-compete.
Redistribution permitted under Β§3 with those terms propagated.
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Quantized
Model tree for mlx-community/ltx-2.5-mlx-q8
Base model
Lightricks/LTX-2.5