---
license: other
pipeline_tag: text-to-video
base_model:
- Lightricks/LTX-Video
tags:
- coreai
- core-ml
- apple-silicon
- text-to-video
- video-generation
- ltx-video
---

# LTX-Video 2B (distilled) → Core AI — the zoo's first VIDEO model

[Lightricks/LTX-Video](https://github.com/Lightricks/LTX-Video), config
`ltxv-2b-0.9.6-distilled` — **text → video** via an 8-step distilled flow-matching DiT. All three
neural nets run as Core AI `.aimodel` bundles; only the FlowMatch sampler loop runs on host.

**This repo holds the Core AI bundles** (each `.aimodel` is a directory). Conversion + runner +
Mac app are in the [coreai-model-zoo](https://github.com/john-rocky/coreai-model-zoo)
(`conversion/ltxvideo/`, `apps/CoreAIVideo/`).

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## Use it

▶️ **Run it (source)** — [`apps/CoreAIVideo`](https://github.com/john-rocky/coreai-model-zoo/tree/main/apps/CoreAIVideo),
the zoo app that ships this model (text → video on Mac: T5 + 8-step flow-matching DiT + causal video VAE, host FlowMatch sampler; build & run steps in its README).

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## Sample

512×768 · 49 frames · 8 steps · **~14 s on a Mac GPU** (Apple silicon). Prompt: *"A clear glass of
water on a wooden table, slow motion droplet falling into it creating ripples, cinematic."*

<video controls autoplay loop muted src="https://huggingface.co/mlboydaisuke/LTX-Video-2B-CoreAI/resolve/main/sample.mp4"></video>

## Bundles

| net | shape (demo 512×768×49f) | dtype | bundle |
|---|---|---|---|
| T5-XXL text encoder | ids(1,256)+mask(1,256) → (1,256,4096) | bf16 8.9 G | `t5_bf16.aimodel` |
| DiT denoiser (one step) | latent(1,N,128)+grid(1,3,N)+text(1,256,4096)+mask(1,256)+t(1,1) → (1,N,128) | fp16 3.6 G | `dit_fp16.aimodel` |
| Causal video VAE decoder | latent(1,128,lf,lh,lw)+t(1,) → pixels(1,3,F,H,W) | fp16 1.0 G | `vae_fp16.aimodel` |

`N = lf·lh·lw`, `lf=(F-1)//8+1`, `lh=H/32`, `lw=W/32` (VAE 32× spatial / 8× temporal). DiT + VAE are
**fixed-shape** (here 512×768×49f → N=2688) — re-convert per target resolution; **T5 is
resolution-independent** (seq 256), so `t5_bf16` is reused. At `guidance_scale=1` (distilled) CFG is
off and `stg_scale=0`, so the DiT runs batch-1, single-conditioning.

## Numerics

Per-net **converted-vs-eager cosine = 1.000000** (T5, DiT, VAE). The DiT also reproduces torch on
**every one of the 8 real sampler steps** (cos 1.000000, max|Δ| ~1e-3). End-to-end pixel cosine vs a
reference is ~0.93 — but that is **stochastic-sampler variance, not error**: two torch runs (MPS vs
CPU, same seed) are *also* cos 0.9325. Gate by per-step cos + visual.

**T5 must be bf16 or fp32** — the encoder overflows in fp16 (washed-out video); bf16 has fp32's
exponent range at half the size. DiT + VAE are fp16-clean. Ship set **13.5 G** (vs 27 G fp32).

## Run it

The 3 bundles run on `coreai.runtime` (load with an explicit `SpecializationOptions.default()` for
GPU — `AIModel.load(path, None)` trips an MPSGraph error; keep the `AIModel` refs alive). The host
reuses LTX's real FlowMatch sampler / patchify / `indices_grid` / decode-noise. See
`conversion/ltxvideo/_run_coreai.py` (CLI) and `apps/CoreAIVideo/` (a SwiftUI Mac app: type a
prompt → video, ~14 s).

## On-device note

iPhone is a stretch: T5-XXL is 4.76 B params and the DiT's video-latent attention working set grows
with frame count. **Mac is the shipped path.**