license: creativeml-openrail-m
tags:
- mlx
- apple-silicon
- diffusion
- stable-diffusion-xl
- sdxl
- anime
- text-to-image
base_model: OnomaAIResearch/Illustrious-XL-v2.0
library_name: mlx-gen
pipeline_tag: text-to-image
Illustrious-XL v2.0 — MLX pre-quantized tiers
Pre-quantized, packed-load tiers of OnomaAIResearch/Illustrious-XL-v2.0
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.
Illustrious-XL v2.0 is the v2.0-STABLE snapshot — the last-annealing-phase checkpoint of a
cosine-annealing run, behaviourally distinct from (and more stable than) v1.0. It is architecturally
vanilla SDXL: dual CLIP-L + OpenCLIP-bigG, real CFG + negative prompt, eps prediction, VAE scaling
factor 0.13025, full sdxl-family LoRA support. Danbooru-tag prompting, ~30 steps at guidance 7.0.
Resolution note
Unlike v1.0, v2.0 tends to duplicate the subject in wide frames — a 1girl, solo prompt can
render two characters once the frame gets wide (measured: it duplicates at 1344×768 and 1536×1536,
while tall and square frames stay clean). Prefer square or tall framing; the SceneWorks catalog
omits the widest aspect buckets for this model.
Provenance
Upstream ships a single-file LDM checkpoint (Illustrious-XL-v2.0.safetensors) that the MLX
sdxl loader cannot read. These tiers were produced offline with
scripts/build_sdxl_turnkey.py.
The conversion also normalizes two v2.0 quirks: a stray position_ids buffer (dropped) and a BF16
VAE (kept dense at F32/F16 per tier). Component configs are the canonical SDXL descriptors.
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. 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).
License
CreativeML OpenRAIL-M, per the upstream model card. Commercial use OK, ungated; behavioral-use restrictions apply. NOTE this differs from v1.0's SDXL (OpenRAIL++) license.