Instructions to use SceneWorks/realvisxl-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use SceneWorks/realvisxl-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir realvisxl-mlx SceneWorks/realvisxl-mlx
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
| license: openrail++ | |
| tags: | |
| - mlx | |
| - apple-silicon | |
| - diffusion | |
| - stable-diffusion-xl | |
| - sdxl | |
| - text-to-image | |
| base_model: SG161222/RealVisXL_V5.0 | |
| library_name: mlx-gen | |
| pipeline_tag: text-to-image | |
| # RealVisXL V5.0 β MLX pre-quantized tiers | |
| Pre-quantized, packed-load tiers of [SG161222/RealVisXL_V5.0](https://huggingface.co/SG161222/RealVisXL_V5.0) | |
| 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 β 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 1024Γ1024. | |
| ## 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 (full-precision master) | nothing β verbatim source mirror | | |
| The **VAE stays dense (f32)** in every tier β 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. | |
| ## Usage | |
| ```rust | |
| use mlx_gen::{LoadSpec, WeightsSource, Quant}; | |
| let spec = LoadSpec::new(WeightsSource::Dir("β¦/realvisxl-mlx/q4".into())).with_quant(Quant::Q4); | |
| let g = mlx_gen::load("sdxl", &spec)?; | |
| ``` | |
| ## License | |
| openrail++ β inherited from the source model | |
| [SG161222/RealVisXL_V5.0](https://huggingface.co/SG161222/RealVisXL_V5.0). See `LICENSE`. | |