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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](https://huggingface.co/OnomaAIResearch/Illustrious-XL-v2.0)
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.
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`](https://github.com/SceneWorks/SceneWorks/blob/main/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.
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