README.md

---
license: openrail++
tags:
  - mlx
  - apple-silicon
  - diffusion
  - stable-diffusion-xl
  - sdxl
  - text-to-image
base_model: stabilityai/stable-diffusion-xl-base-1.0
library_name: mlx-gen
pipeline_tag: text-to-image
---

# Stable Diffusion XL base 1.0 — MLX pre-quantized tiers

Pre-quantized, packed-load tiers of
[stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0)
for on-device Apple-Silicon inference with [SceneWorks / `mlx-gen`](https://github.com/SceneWorks/mlx-gen)
(the `sdxl` generator). Each tier is a **self-contained diffusers turnkey snapshot** (U-Net + both
CLIP text encoders + VAE + tokenizers + scheduler + `model_index.json`) that loads directly — no
in-app quantization pass, no dense transient.

Dual CLIP-L + OpenCLIP-bigG text encoders, real classifier-free guidance + negative prompt,
sdxl-family LoRA support. ~30 steps at guidance 7.0, native 1024×1024.

## Tiers

| dir      | precision | what's quantized |
|----------|-----------|------------------|
| `q4/` (default) | group-wise affine Q4, group size 64 | U-Net Linears + both CLIP encoders |
| `q8/`    | group-wise affine Q8, group size 64 | U-Net Linears + both CLIP encoders |
| `bf16/`  | dense (full-precision master) | nothing — verbatim source mirror |

The **VAE stays dense (f32)** in every tier (the SDXL VAE is int8/fp16-unstable). Convolutions,
GroupNorms, and the CLIP token/position embeddings also stay dense; only the true Linear projections
are packed. Quantization is byte-identical to `mlx-gen`'s load-time `nn.quantize` (bf16 cast, group
64).

## Usage

```rust
use mlx_gen::{LoadSpec, WeightsSource, Quant};
let spec = LoadSpec::new(WeightsSource::Dir("…/sdxl-base-mlx/q4".into())).with_quant(Quant::Q4);
let g = mlx_gen::load("sdxl", &spec)?;
```

## License

openrail++ (CreativeML Open RAIL++-M) — inherited from the source model
[stabilityai/stable-diffusion-xl-base-1.0](https://huggingface.co/stabilityai/stable-diffusion-xl-base-1.0). See `LICENSE`.