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LTX-2.3-mlx-fp16
gajesh/LTX-2.3-mlx-fp16
LTX-2.3-mlx-fp16 is a text-to-video model from gajesh. Use it when you need video from a text prompt. It is set up for mlx. The card lists the license as other.
MLX-optimized weights for Lightricks/LTX-2.3 video generation model, converted for Apple Silicon.
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.safetensors70 GB · 100%
At a glance
- Task
- Text-to-Video
- Library
- mlx
- License
- other
- Access
- Public
- Created
- Mar 30, 2026
- Updated
- Mar 30, 2026
- SHA
- b58b3365
Base models
Model card
From the Hugging Face model README
LTX-2.3 22B Distilled (MLX, float16)
MLX-optimized weights for Lightricks/LTX-2.3 video generation model, converted for Apple Silicon.
Model Details
- Model: LTX-2.3 22B Distilled (joint audio-video diffusion transformer)
- Format: MLX safetensors (float16)
- Total Size: ~66GB (transformer: 39GB, text encoder: 22GB, VAE: 1.7GB, upsampler: 1GB)
- Minimum RAM: 64GB Apple Silicon Mac
- Original: Lightricks/LTX-2.3
Also available in 4-bit quantized (35GB, fits 32GB Macs).
Architecture
| Component | Parameters | Size (fp16) |
|---|---|---|
| DiT Transformer (48 layers) | 22B | 39 GB |
| Gemma 3 12B (text encoder) | 12B | 22 GB |
| Video VAE Decoder | ~500M | 777 MB |
| Spatial Upsampler | ~500M | 950 MB |
| Audio VAE + Vocoder | ~500M | 348 MB |
Benchmarks (M4 Max 128GB)
Head-to-head comparison on the same machine, same config (576x1024, 121 frames, 5s video):
| PyTorch MPS (BF16) | MLX FP16 (Video+Audio) | MLX FP16 (Video-only) | |
|---|---|---|---|
| Stage 1 (8 steps, half-res) | 66.7s | 67.1s | 51.2s |
| Stage 2 (3 steps, full-res) | 157.9s | 139.2s | 101.3s |
| Total denoising | 264.0s | 206.2s | 152.5s |
| Speedup | 1.0x | 1.3x | 1.7x |
| Peak memory | >60 GB | 46.8 GB | 34.2 GB |
Additional configs:
| Config | Resolution | Frames | Time | Memory |
|---|---|---|---|---|
| FP16 video-only | 512x512 | 49 (2s) | 35.1s | 34.2 GB |
| FP16 video-only | 320x512 | 25 (1s) | 10.4s | 31.3 GB |
Key Advantages
- 1.7x faster than PyTorch MPS in video-only mode
- 42% less memory (34 GB vs 60+ GB) -- runs on 64GB Macs where PyTorch can't
- Fused Metal attention via
mx.fast.scaled_dot_product_attention - Unified memory -- no CPU/GPU transfer overhead
Usage
pip install mlx-ltx
from mlx_ltx.pipeline import DistilledPipeline, save_video
pipeline = DistilledPipeline("path/to/mlx-weights")
video = pipeline(
prompt="A golden retriever playing piano in a concert hall",
height=576, width=1024, num_frames=121,
seed=42,
)
save_video(video, "output.mp4", fps=24.0)
Conversion
Converted from the original PyTorch checkpoint using mlx-ltx:
mlx-ltx-convert \
--checkpoint /path/to/ltx-2.3-22b-distilled.safetensors \
--gemma-root /path/to/gemma-3-12b-it/ \
--output-dir ./mlx-weights/
License
This model conversion follows the LTX-2 Community License.
Acknowledgments
- Lightricks for the original LTX-2.3 model
- MLX by Apple for the framework
- Acelogic/LTX-2-MLX for reference implementation patterns
- Task
- Text-to-Video
- Library
- mlx
- License
- other
- Languages
- en
- Created
- Mar 30, 2026
- Updated
- Mar 30, 2026