Instructions to use mlx-community/HiDream-O1-Image-Dev-mlx-q6 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-q6 with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir HiDream-O1-Image-Dev-mlx-q6 mlx-community/HiDream-O1-Image-Dev-mlx-q6
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
HiDream-O1-Image-Dev — MLX Q6 (Apple Silicon)
Ported by Mrbizarro · MIT licensed · published to mlx-community
🎛️ Run it one-click in Phosphene
Phosphene is a free local generative-video panel for Apple Silicon. HiDream is wired into its Image Studio. Install Pinokio, then in Pinokio install Phosphene. Note: Phosphene's HiDream integration uses BF16 by default since edit requires BF16 — this Q6 repo is for text-to-image-only workflows on RAM-constrained machines.
A 6-bit quantized MLX port of HiDream-ai/HiDream-O1-Image-Dev.
⚠ Q6 does NOT support edit / multi-ref. Per-group dequantization noise compounds against reference-image features in attention and produces degenerate output. For edit / multi-reference workflows use the BF16 sibling repo instead.
Sibling repos
- 🟢 BF16 (full precision) — 17.5 GB, ~16 GB RAM, clean across all dimensions. Use this when in doubt.
- 🟡 Q6 (this repo) — 8 GB, ~8.5 GB RAM, fast. Best at square 2048×2048 or 1024×1024. Visible 32-pixel patch grid in flat regions at non-square dims.
- 🟡 Q8 — 10 GB, ~11.5 GB RAM, same artifact behaviour as Q6 at non-square dims.
When to use Q6
- ✅ Square 1024×1024 or 2048×2048 — clean output, half the time of BF16
- ✅ RAM-constrained — fits 16 GB Macs alongside other apps
- ❌ Non-square dims (1440×2560, 3104×1312, etc) — visible 32-pixel patch grid in skies, walls, water → use BF16
What's in this repo
model.safetensors— Q6 quantized backbone (8 GB)extras/custom_heads.safetensors— diffusion-side heads (75 MB, BF16)config.json(withquantization: {bits: 6, group_size: 64}so mlx-vlm wrapsLinear → QuantizedLinearcorrectly)- Tokenizer + processor configs
Code
The inference scripts are not in this repo — they live in the BF16 sibling repo under scripts/hidream_o1/. Clone that for code, this for weights only.
Quick start
# Get the code
hf download mlx-community/HiDream-O1-Image-Dev-mlx-bf16 --local-dir hidream-o1-mlx \
--include "scripts/*" --include "*.md" --include "*.txt" --include "*.gitattributes"
cd hidream-o1-mlx
uv venv --python 3.11 && uv pip install -r requirements.txt
# Get the Q6 weights
hf download mlx-community/HiDream-O1-Image-Dev-mlx-q6 --local-dir mlx_models/hidream-o1-dev-q6
# Run (square dims only for clean output)
.venv/bin/python scripts/hidream_o1/generate_hidream_o1_mlx.py \
--model-path mlx_models/hidream-o1-dev-q6 \
--prompt "your prompt here" \
--width 2048 --height 2048 \
--output out.png
Performance
| Resolution | Per step | Total (28 steps) | Peak RAM | Quality |
|---|---|---|---|---|
| 1024×1024 | 1.30 s | 36 s | 8.5 GB | ✅ clean |
| 2048×2048 | 5.51 s | 154 s | 9 GB | ✅ clean |
| 1440×2560 (non-square) | 4.50 s | 127 s | 8.5 GB | ⚠ patch grid visible |
License
MIT — see the BF16 repo for the full LICENSE file and acknowledgements.
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Quantized
Model tree for mlx-community/HiDream-O1-Image-Dev-mlx-q6
Base model
HiDream-ai/HiDream-O1-Image-Dev