Illustrious-XL v2.0 tiered MLX turnkey (q4/q8/bf16) — sc-10612

.gitattributes

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README.md

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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.

bf16/model_index.json

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+{
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+  "_diffusers_version": "0.30.0.dev0",
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+  "image_encoder": [
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+    "CLIPTextModelWithProjection"
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+    "CLIPTokenizer"
+  ],
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+    "diffusers",
+    "UNet2DConditionModel"
+  ],
+  "vae": [
+    "diffusers",
+    "AutoencoderKL"
+  ]
+}

bf16/scheduler/scheduler_config.json

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+  "trained_betas": null
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+  "pad_token_id": 1,
+  "projection_dim": 768,
+  "torch_dtype": "float32",
+  "transformers_version": "4.42.3",
+  "vocab_size": 49408
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