---
license: other
license_name: ltx-2-community-license-agreement
license_link: LICENSE.md
base_model: Lightricks/LTX-2.5
tags:
- mlx
- ltx-video
- ltx-2.5
- text-to-video
- image-to-video
- audio-video
pipeline_tag: text-to-video
---

# LTX-2.5 — MLX conversion (bf16, split components)

MLX-format conversion of [Lightricks LTX-2.5](https://huggingface.co/Lightricks/LTX-2.5)
(joint audio+video DiT, 22B) for Apple Silicon, in the per-component split layout consumed by
[`ltx-2-mlx`](https://github.com/xocialize/ltx-2-mlx) (branch `ltx-2.5`).

> **Status (2026-08-13): the port is COMPLETE on both consumers.** Python-MLX
> ([`ltx-2-mlx`](https://github.com/xocialize/ltx-2-mlx), branch `ltx-2.5`) and Swift-MLX
> ([`ltx-2-mlx-swift`](https://github.com/xocialize/ltx-2-mlx-swift)) both generate end to end,
> including the DFR pipeline with temporal rounds. Parity-gated per component against the
> PyTorch reference: text encoder 49 states (mean cosine 0.999985), DiT forward, sampler,
> keyframe slots, and the DFR canvas geometry bit-exactly.
>
> Still a **research port** — it is not a supported product, and see *Memory* below before you
> plan a run.

## ⚠️ License — read before use

These weights are **Derivatives of LTX-2.5** and are distributed under the
**LTX-2.x Community License Agreement** (license date 2026-08-11). A complete copy ships in
this repo as [`LICENSE.md`](LICENSE.md), and the Acceptable Use Policy it incorporates by
reference is snapshotted here as
[`ltx-acceptable-use-policy-snapshot-2026-08-12.pdf`](ltx-acceptable-use-policy-snapshot-2026-08-12.pdf)
(the version in effect at your time of use governs — check
[Lightricks' current AUP](https://static.lightricks.com/legal/ltx-acceptable-use-policy.pdf)).

**Transfer notice (Agreement §3.5).** Your use of these weights is subject to the LTX-2.x
Community License Agreement. If you (aggregated across entities under common control) have
annual revenues of **US $10,000,000 or more**, you are a "Commercial Entity" under the
Agreement and **must obtain a paid license from Lightricks before any use** other than the
Agreement's non-commercial-purpose carve-outs (testing, evaluation, non-commercial R&D in
non-production environments). Sub-threshold commercial and production use is royalty-free
under the Agreement's terms.

Further obligations that travel with these weights include (not exhaustive — read the
license): machine-generated content disclosure (Attachment A §5), no removal or circumvention
of any transparency/provenance features (§6), EU AI Act / CA AI Transparency Act
responsibilities for providers/deployers (§6), and the Attachment A acceptable-use terms.

## Modification notice (Agreement §3.3)

Every tensor file here is **modified from the original Lightricks release**: re-serialized to
MLX conventions (channels-last conv layouts, component-split files, renamed keys per the
`ltx-2-mlx` dialect, fused projections split). No weights were trained, fine-tuned, or
numerically altered beyond layout/serialization transforms. Conversion tooling:
[`scripts/convert_ltx25.py`](https://github.com/xocialize/ltx-2-mlx/blob/ltx-2.5/scripts/convert_ltx25.py).

## Components

| File | Contents | Params |
|---|---|---|
| `transformer-distilled.safetensors` | distilled joint-AV DiT (fixed 8-step, CFG=1) | 22B |
| `transformer-dev.safetensors` | dev (full) joint-AV DiT | 22B |
| `gemma4-12b-ltx-v1/` | Lightricks-tuned Gemma-4-unified text encoder, HF layout (loads via `mlx-lm`) | 12B |
| `connector.safetensors` | text-embeddings connectors + aggregate projections | — |
| `vae_encoder.safetensors` / `vae_decoder.safetensors` | conv video VAE (byte-identical to LTX-2.3's) | 726M |
| `vae_diffusion_decoder.safetensors` | DiffVAE 1-step x0 video decoder (NA attention) | 417M |
| `audio_vae.safetensors` / `vocoder.safetensors` | audio VAE + BigVGAN v2 + BWE (byte-identical to 2.3's) | 182M |
| `spatial_upscaler_x2_v1_1.safetensors` | ×2 spatial latent upscaler (byte-identical to 2.3's) | 498M |
| `temporal_upscaler_x2_v1_0.safetensors` | ×2 temporal latent upscaler (byte-identical to 2.3's) | 131M |
| `duration_head.safetensors` | prompt→duration predictor (fused MHA split to q/k/v) | 1.9M |
| `config.json` / `embedded_config.json` | pipeline + transformer configs | — |

## Conversion receipts

- Components LTX-2.5 re-ships byte-identical to LTX-2.3 (conv VAE, audio stack, both
  upscalers) were converted from the 2.5 sources and verified **bit-identical** to the
  established `mlx-forge` 2.3 conversions — validating this converter's conv/layout/rename
  handling against independent tooling.
- DiT template enforced at conversion: 4091 tensors per variant = the 2.3 key template
  − 96 video-FF biases (`ff_bias: false`) + `keyframes_abs_pos_embedding`.
- Text-encoder parity vs the PyTorch reference (transformers `Gemma4Unified`): tokenization
  identical; all 49 tapped hidden states ≥ 0.9997 cosine; pre-connector projections ≥ 0.99996.
- The embedded LTX-2.x license text present in upstream file metadata is preserved.

## Memory — read this before planning a run

Measured on Apple Silicon (128 GB unified), 448×320×9 frames, bf16, via the Swift consumer.
Your numbers will differ with resolution and frame count; the *structure* is what transfers.

| | peak |
|---|---|
| default (text encoder co-resident with the DiT) | **62.40 GB** |
| with the DiT evicted around the encode phase | **40.66 GB** |

**The text encoder is the surprise.** `gemma4-12b-ltx-v1/` is an unquantized **bf16 12B —
23.8 GB on disk, ~24.4 GB resident**. LTX-2.3 used a 4-bit Gemma-3 (~7 GB), so anything you
carry over from a 2.3 setup will badly under-estimate 2.5. The DiT itself is a 37.98 GB
resident floor at bf16.

Two levers, and they stack:

1. **Evict the DiT around the phases that don't need it.** −21.74 GB (−34.8%) with output
   **bit-identical** and wall-clock within run-to-run noise. This is a scheduling change, not
   a quality trade — it is on by default in the Swift consumer.
2. **Quantize the text encoder to int8** (group 64, keeping `embed_tokens` in bf16): encoder
   24.42 → 14.20 GB, end-to-end 62.40 → 52.18 GB. We measured this as **numerically faithful**
   (valid-token cosine 0.999820 against a 0.999879 bf16 floor) and **perceptually neutral** in a
   blinded 6-pair operator A/B (3 ties, 2–1, all "very close").
   ⚠️ **int4 was REJECTED** — 0.996728 on the same metric, consistent with an independent
   in-fleet measurement of a different frozen encoder. Note that several third-party MLX packs
   ship this encoder at 4-bit with no published quality data.
   **No quantized sibling is published here**; the recipe is in the consuming repo.

⚠️ **If you quantize it yourself:** the default `mlx_lm.convert -q` also quantizes
`embed_tokens`, which is hidden state 00 *and* the input to all 48 layers — exclude it. The
`mixed_*` recipes are worse: with tied embeddings they put the embedding table at 3 bits.

## Usage

```bash
git clone -b ltx-2.5 https://github.com/xocialize/ltx-2-mlx
cd ltx-2-mlx && uv sync
uv run ltx-2-mlx generate \
  --model mlx-community/ltx-2.5-mlx \
  --distilled \
  --prompt "a red fox standing in deep snow, closeup wildlife photography, golden hour" \
  -H 512 -W 768 -f 121 --frame-rate 24 -o fox.mp4
```

**Multishot (LTX-2.5 generated keyframe slots)** — `--num-generated-keyframes N` places N
invented keyframes at evenly spaced interior positions. Note what this does and does not do:
it relaxes the effective temporal compression at those positions (each slot costs a full
latent frame of tokens to buy one pixel frame). In our measurement it did **not**, on its own,
turn a shot-listed prompt into multiple cut shots — that is the DFR pipeline's job.

**DFR (diffusion fidelity rendering)** is implemented in both consumers. Its *spatial*
detailing pass was preferred by an operator on 4 of 4 matched pairs for roughly ×1.2 time.
Its *temporal rounds* are a parity/quality feature, **not** a speed one: they cost ×2.767
(1 round) / ×3.542 (2 rounds), and generating the same deliverable natively at the target
frame rate was both ~2.8× cheaper and preferred. Use rounds when you want the temporal
density, not to save time.

### Swift

```swift
// https://github.com/xocialize/ltx-2-mlx-swift — MLX-Swift consumer.
// 2.5 is detected from the CHECKPOINT (the in-dir gemma4-12b-ltx-v1/), never a path name,
// so a renamed or relocated copy still resolves correctly.
let pipeline = try await LTX2Pipeline.load(ltxDir: modelDir, gemmaDir: gemma4Dir)
let out = try await pipeline.t2vTwoStage(prompt: prompt, height: 320, width: 448,
                                         numFrames: 25, fps: 24, seed: 4242)
```

## Provenance

Converted from [`Lightricks/LTX-2.5`](https://huggingface.co/Lightricks/LTX-2.5) (comfy split
pack; connectors sourced from the transformer-file bundle, which is what the reference
runtime loads) and [`Lightricks/LTX-2.5-Diffusers`](https://huggingface.co/Lightricks/LTX-2.5-Diffusers)
(diffusion decoder). All credit for the models to Lightricks — see the
[LTX-2 reference implementation](https://github.com/Lightricks/LTX-2) and the
[LTX-2.5 announcement](https://huggingface.co/Lightricks/LTX-2.5).