Pre-quantized, packed-load tiers of SG161222/RealVisXL_V5.0 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.

RealVisXL V5.0 is a photoreal SDXL finetune (openrail++, commercial-OK, ungated). Same architecture as SDXL base: dual CLIP-L + OpenCLIP-bigG text encoders, real classifier-free guidance + negative prompt, sdxl-family LoRA support. ~30 steps at guidance 7.0, native 1024×1024.

The VAE stays dense (f32) 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), so a tier loaded here matches an in-app-quantized dense checkpoint bit-for-bit.

openrail++ — inherited from the source model SG161222/RealVisXL_V5.0. See LICENSE.