Instructions to use mlx-community/HiDream-O1-Image-Dev-mlx-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/HiDream-O1-Image-Dev-mlx-bf16 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir HiDream-O1-Image-Dev-mlx-bf16 mlx-community/HiDream-O1-Image-Dev-mlx-bf16
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
README: variants table + cross-link to Q6/Q8 sibling repos
Browse files
README.md
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@@ -71,16 +71,21 @@ All generated by the included generator script on a 64 GB Mac Studio. Click any
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More: [`sample_outputs/hero/`](sample_outputs/hero/).
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## Install
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More: [`sample_outputs/hero/`](sample_outputs/hero/).
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## Variants
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| Variant | Repo | Backbone size | RAM (1024) | Quality |
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| **BF16** (this repo) | `Mrbizarro/HiDream-O1-Image-Dev-mlx-bf16` | 17.5 GB | 16 GB | ✅ Clean across all trained dims |
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| Q8 | [`Mrbizarro/HiDream-O1-Image-Dev-mlx-q8`](https://huggingface.co/Mrbizarro/HiDream-O1-Image-Dev-mlx-q8) | 10 GB | 11.5 GB | ⚠ Clean at square dims, grid at non-square |
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| Q6 | [`Mrbizarro/HiDream-O1-Image-Dev-mlx-q6`](https://huggingface.co/Mrbizarro/HiDream-O1-Image-Dev-mlx-q6) | 8 GB | 8.5 GB | ⚠ Clean at square dims, grid at non-square |
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**Q4 was tested and rejected** — brightness collapses, every image ships dark.
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### Why BF16 is the safe default
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Per-group dequantization rounding (Q6/Q8) compounds across the 36 decoder layers and shows as a visible 32-pixel grid in flat regions (skies, walls, water), specifically at **non-square trained dimensions** like 1440×2560 or 3104×1312. BF16 matches the upstream's `torch_dtype=torch.float32 + autocast(bfloat16)` precision and is the only quant clean across all trained dimensions.
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If your workflow is square-only (1024×1024, 2048×2048) and you're RAM-constrained, **Q6 is half the size and 2× faster** — no quality loss at those dims. Use Q6 on a 16 GB Mac, BF16 on 32 GB+.
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## Install
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