Text-to-Image
MLX
Safetensors
English
Chinese
moss_image
apple-silicon
moss-image
text-rendering
layout
quantized
8-bit precision
Instructions to use VisualInference/MOSS-Image-Base-MLX-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use VisualInference/MOSS-Image-Base-MLX-8bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download VisualInference/MOSS-Image-Base-MLX-8bit --local-dir MOSS-Image-Base-MLX-8bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
File size: 2,605 Bytes
f57d6b7 f06abf8 f57d6b7 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 | ---
license: apache-2.0
base_model: OpenMOSS-Team/MOSS-Image-Base
base_model_relation: quantized
library_name: mlx
pipeline_tag: text-to-image
language:
- en
- zh
tags:
- mlx
- apple-silicon
- moss-image
- text-to-image
- text-rendering
- layout
- quantized
- 8-bit
---
# MOSS-Image-Base for Apple Silicon (MLX, 8bit)
An MLX build of [OpenMOSS-Team/MOSS-Image-Base](https://huggingface.co/OpenMOSS-Team/MOSS-Image-Base), the pre-trained (not instruction-tuned) MOSS-Image checkpoint, for Macs with Apple Silicon. It uses the same quantization recipe as the verified [SFT 8bit tier](https://huggingface.co/VisualInference/MOSS-Image-SFT-MLX-8bit) and runs with [moss-image-mlx](https://github.com/felix-ab/moss-image-mlx).
Base is the checkpoint OpenMOSS rendered their official sample gallery with, and the one to use for LoRA work and for reproducing those samples. For general use, the [SFT tiers](https://huggingface.co/VisualInference/MOSS-Image-SFT-MLX-8bit) give more organised compositions.
- **Peak memory:** ~9.3–10.2 GB for 1–1.5 MP images.
- **Download:** 14.1 GB.
- **Official seeds reproduce official samples**, below.
## Official sample vs this tier, from the official seed and settings
| Official (OpenMOSS, CUDA, bf16) · This tier (MLX 8-bit, M4 Pro) |
|---|
|  |
|  |
| Sample | PSNR vs official | PSNR vs the port's own bf16 | Time | Peak |
|---|---|---|---|---|
| #013, 768×1360, 650-token prompt | 26.0 dB | 27.7 dB | 13.5 min | 10.2 GB |
| #009, 1024×1536, CFG 5 | 19.6 dB | 19.9 dB | 20.1 min | 9.3 GB |
With this model, bf16 rounding alone moves an image about 23 dB, so these are differences in fine detail, not composition. See [verification](https://github.com/felix-ab/moss-image-mlx#how-it-was-verified).
## Quickstart
```bash
pip install moss-image-mlx
moss-mlx --model VisualInference/MOSS-Image-Base-MLX-8bit --parameters parameters.json -o sample.png # an official example, exactly
moss-mlx "a misty harbor at dawn, tag style: best quality, highres" --model VisualInference/MOSS-Image-Base-MLX-8bit --guidance 4 -o harbor.png
```
**Prompting:** OpenMOSS recommends at least 50 steps (the default) and CFG 3–6 for Base, raising CFG for short prompts.
## License and attribution
Apache-2.0, the same as the original weights. See [NOTICE](NOTICE).
- **MOSS-Image:** © OpenMOSS.
- **Text encoder:** Qwen3.5-9B (Apache-2.0).
- **VAE:** FLUX.2 with the FLUX.2 small decoder by Black Forest Labs (Apache-2.0).
This is an independent conversion, not affiliated with or endorsed by OpenMOSS.
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