---
license: openrail++
tags:
  - mlx
  - apple-silicon
  - diffusion
  - stable-diffusion-xl
  - sdxl
  - anime
  - text-to-image
base_model: OnomaAIResearch/Illustrious-XL-v1.0
library_name: mlx-gen
pipeline_tag: text-to-image
---

# Illustrious-XL v1.0 — MLX pre-quantized tiers

Pre-quantized, packed-load tiers of [OnomaAIResearch/Illustrious-XL-v1.0](https://huggingface.co/OnomaAIResearch/Illustrious-XL-v1.0)
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.

Illustrious-XL is a Danbooru-tag anime SDXL finetune (OnomaAI). It is architecturally vanilla SDXL:
dual CLIP-L + OpenCLIP-bigG text encoders, real classifier-free guidance + negative prompt, eps
prediction, VAE scaling factor 0.13025, and full sdxl-family LoRA support. ~30 steps at guidance 7.0,
native 1024×1024, and it handles wide frames up to 1536×1536.

## Provenance

Upstream ships a **single-file LDM checkpoint** (`Illustrious-XL-v1.0.safetensors`), which the MLX
`sdxl` loader cannot read. These tiers were produced offline from that checkpoint with
[`scripts/build_sdxl_turnkey.py`](https://github.com/SceneWorks/SceneWorks/blob/main/scripts/build_sdxl_turnkey.py):
`StableDiffusionXLPipeline.from_single_file` → diffusers component tree → per-tier quantization. The
component configs are the canonical SDXL descriptors (adopted verbatim from a known-good SDXL
turnkey after an architecture-key match), not `from_single_file`'s output.

## 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 (f16 source mirror) | nothing |

The **VAE stays dense in every tier** — the SDXL VAE is int8/fp16-unstable, so it is never
quantized. Convolutions, GroupNorms, and the CLIP token/position embeddings also stay dense (gather
lookups and convs, not matmuls); only the true Linear projections are packed. Quantization is
byte-identical to `mlx-gen`'s load-time `nn.quantize` (bf16 cast, group 64).

## License

SDXL license — CreativeML Open RAIL++-M, per the upstream model card. Commercial use OK, ungated;
behavioral-use restrictions apply.