Instructions to use SceneWorks/sd3.5-large-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Diffusers
How to use SceneWorks/sd3.5-large-mlx with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("SceneWorks/sd3.5-large-mlx", torch_dtype=torch.bfloat16, device_map="cuda") prompt = "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k" image = pipe(prompt).images[0] - MLX
How to use SceneWorks/sd3.5-large-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir sd3.5-large-mlx SceneWorks/sd3.5-large-mlx
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Draw Things
- DiffusionBee
File size: 1,727 Bytes
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license: other
license_name: stabilityai-ai-community
license_link: LICENSE.md
base_model: stabilityai/stable-diffusion-3.5-large
pipeline_tag: text-to-image
tags:
- text-to-image
- stable-diffusion
- stable-diffusion-3
- mlx
- sceneworks
---
# Stable Diffusion 3.5 Large — SceneWorks MLX quant matrix
Pre-built, turnkey MLX tiers of **Stability AI Stable Diffusion 3.5 Large** (8B MMDiT) for
[SceneWorks](https://github.com/SceneWorks/SceneWorks), hosted as separate downloadable artifacts so
the app loads a tier directly with **no on-device conversion peak** (sc-8513, epic 8506).
Each subdirectory is a complete `from_snapshot`-loadable diffusers tree — packed/dense
`transformer/` plus the dense triple text encoder (CLIP-L + CLIP-G + T5-XXL), tokenizers, and 16-ch
VAE:
| Tier | `transformer/` precision | Use |
| --- | --- | --- |
| `q4/` | Q4 (asymmetric group-affine, group-64) — **default** | lowest memory |
| `q8/` | Q8 (asymmetric group-affine, group-64) | near-lossless |
| `bf16/`| dense bf16 (the source backbone, no quantization) | highest fidelity, high-memory Macs |
Only the MMDiT transformer is quantized; the text encoders and VAE are reused dense in every tier.
The packed weights auto-detect their quantization on load.
## License & attribution
This model is a derivative of **Stable Diffusion 3.5 Large** and is distributed under the
**[Stability AI Community License](LICENSE.md)** (free for research, non-commercial, and commercial
use by organizations with under \$1M in annual revenue; see the license for terms).
> This Stability AI Model is licensed under the Stability AI Community License, Copyright © Stability
> AI Ltd. All Rights Reserved.
**Powered by Stability AI.**
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