Illustrious-XL v1.0 tiered MLX turnkey (q4/q8/bf16) — sc-10611

.gitattributes

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README.md

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+---
+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.

bf16/model_index.json

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

bf16/scheduler/scheduler_config.json

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+  "_diffusers_version": "0.30.0.dev0",
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+  "trained_betas": null
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+  "num_hidden_layers": 12,
+  "pad_token_id": 1,
+  "projection_dim": 768,
+  "torch_dtype": "float32",
+  "transformers_version": "4.42.3",
+  "vocab_size": 49408
+}

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