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
for on-device Apple-Silicon inference with 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:
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.