Metadata-Version: 2.3
Name: abstractvision-mflux
Version: 0.17.5.post1
Summary: MLX native implementations of state-of-the-art generative image models.
Keywords: flux,ai,ml,transformers,mlx,huggingface,apple-silicon,diffusers,qwen,qwen-image,seedvr2,z-image
Author: Filip Strand
Author-email: Filip Strand <strand.filip@gmail.com>
License: MIT License
         
         Copyright (c) 2026 Filip Strand
         
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Classifier: Intended Audience :: Developers
Classifier: Operating System :: MacOS
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Classifier: Programming Language :: Python :: 3.14
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Maintainer: AbstractVision
Requires-Python: >=3.10
Project-URL: homepage, https://github.com/lpalbou/mflux
Project-URL: upstream, https://github.com/filipstrand/mflux
Provides-Extra: dev
Description-Content-Type: text/markdown

![image](src/mflux/assets/logo.jpg)

[![AbstractVision MFLUX](https://img.shields.io/pypi/v/abstractvision-mflux?label=AbstractVision%20MFLUX&logo=pypi&logoColor=white)](https://pypi.org/project/abstractvision-mflux/)
[![MLX](https://img.shields.io/pypi/v/mlx?label=MLX&logo=pypi&logoColor=white)](https://pypi.org/project/mlx/)
[![CI](https://github.com/lpalbou/mflux/actions/workflows/tests.yml/badge.svg)](https://github.com/lpalbou/mflux/actions/workflows/tests.yml)

### About

Run the latest state-of-the-art generative image models locally on your Mac in native MLX!

### Table of contents

- [💡 Philosophy](#-philosophy)
- [💿 Installation](#-installation)
- [🎨 Models](#-models)
- [✨ Features](#-features)
- [🌱 Related projects](#related-projects)
- [🙏 Acknowledgements](#-acknowledgements)
- [⚖️ License](#%EF%B8%8F-license)

---

### 💡 Philosophy

MFLUX is a line-by-line MLX port of several state-of-the-art generative image models from the [Huggingface Diffusers](https://github.com/huggingface/diffusers) and [Huggingface Transformers](https://github.com/huggingface/transformers) libraries. All models are implemented from scratch in MLX, using only tokenizers from the [Huggingface Transformers](https://github.com/huggingface/transformers) library. MFLUX is purposefully kept minimal and explicit, [@karpathy](https://gist.github.com/awni/a67d16d50f0f492d94a10418e0592bde?permalink_comment_id=5153531#gistcomment-5153531) style.

---

### 💿 Installation
If you haven't already, [install `uv`](https://github.com/astral-sh/uv?tab=readme-ov-file#installation), then run:

```sh
uv tool install --upgrade abstractvision-mflux
```

This AbstractVision fork is published on PyPI as `abstractvision-mflux`, while the installed Python module and CLI commands remain `mflux`.

After installation, the following command shows all available MFLUX CLI commands: 

```sh
uv tool list 
```

To generate your first image using, for example, the z-image-turbo model, run

```
mflux-generate-z-image-turbo \
  --prompt "A puffin standing on a cliff" \
  --width 1280 \
  --height 500 \
  --seed 42 \
  --steps 9 \
  -q 8
```

![Puffin](src/mflux/assets/puffin.png)

The first time you run this, the model will automatically download which can take some time. See the [model section](#-models) for the different options and features, and the [common README](src/mflux/models/common/README.md) for shared CLI patterns and examples.

Python API

Create a standalone `generate.py` script with inline `uv` dependencies:

```python
#!/usr/bin/env -S uv run --script
# /// script
# requires-python = ">=3.10"
# dependencies = [
#   "abstractvision-mflux",
# ]
# ///
from mflux.models.z_image import ZImageTurbo

model = ZImageTurbo(quantize=8)
image = model.generate_image(
    prompt="A puffin standing on a cliff",
    seed=42,
    num_inference_steps=9,
    width=1280,
    height=500,
)
image.save("puffin.png")
```

Run it with:

```sh
uv run generate.py
```

For more Python API inspiration, look at the [CLI entry points](src/mflux/models/z_image/cli/z_image_turbo_generate.py) for the respective models.

⚠️ Troubleshooting: hf_transfer error

If you encounter a `ValueError: Fast download using 'hf_transfer' is enabled (HF_HUB_ENABLE_HF_TRANSFER=1) but 'hf_transfer' package is not available`, you can install MFLUX with the `hf_transfer` package included:

```sh
uv tool install --upgrade abstractvision-mflux --with hf_transfer
```

This will enable faster model downloads from Hugging Face.

DGX / NVIDIA (uv tool install)

```sh
uv tool install --python 3.13 abstractvision-mflux
```


---

### 🎨 Models

MFLUX supports the following model families. They have different strengths and weaknesses; see each model’s README for full usage details.

| Model | Release date | Size | Type | Training | Description |
| --- | --- | --- | --- | --- | --- |
|[Z-Image](src/mflux/models/z_image/README.md) | Nov 2025 | 6B | Distilled & Base | Yes | Fast, small, very good quality and realism. |
|[FLUX.2](src/mflux/models/flux2/README.md) | Jan 2026 | 4B & 9B | Distilled & Base | Yes | Fastest + smallest with very good qaility and edit capabilities. |
|[FIBO](src/mflux/models/fibo/README.md) | Oct 2025+ | 8B | Distilled & Base | No | Very good JSON-based prompt understanding. Has edit capabilities. |
|[SeedVR2](src/mflux/models/seedvr2/README.md) | Jun 2025 | 3B & 7B | — | No | Best upscaling model. |
|[Qwen Image](src/mflux/models/qwen/README.md) | Aug 2025+ | 20B | Base | No | Large model (slower); strong prompt understanding and world knowledge. Has edit capabilities |
|[Depth Pro](src/mflux/models/depth_pro/README.md) | Oct 2024 | — | — | No | Very fast and accurate depth estimation model from Apple. |
|[FLUX.1](src/mflux/models/flux/README.md) | Aug 2024 | 12B | Distilled & Base | No (legacy) | Legacy option with decent quality. Has edit capabilities with 'Kontext' model and upscaling support via ControlNet |

---

### ✨ Features

**General**
- Quantization and local model loading
- LoRA support (multi-LoRA, scales, library lookup)
- Metadata export + reuse, plus prompt file support

**Model-specific highlights**
- Text-to-image and image-to-image generation.
- LoRA finetuning
- In-context editing, multi-image editing, and virtual try-on
- ControlNet (Canny), depth conditioning, fill/inpainting, and Redux
- Upscaling (SeedVR2 and Flux ControlNet)
- Depth map extraction and FIBO prompt tooling (VLM inspire/refine)

See the [common README](src/mflux/models/common/README.md) for detailed usage and examples, and use the model section above to browse specific models and capabilities.

> [!NOTE]
> As MFLUX supports a wide variety of CLI tools and options, the easiest way to navigate the CLI in 2026 is to use a coding agent (like [Cursor](https://cursor.com), [Claude Code](https://www.anthropic.com/claude-code), or similar). Ask questions like: “Can you help me generate an image using z-image?”

---

### 🌱 Related projects

- [MindCraft Studio](https://themindstudio.cc/mindcraft#models) — macOS app built on mflux by [@shaoju](https://github.com/shaoju)
- [Mflux-ComfyUI](https://github.com/raysers/Mflux-ComfyUI) by [@raysers](https://github.com/raysers)
- [MFLUX-WEBUI](https://github.com/CharafChnioune/MFLUX-WEBUI) by [@CharafChnioune](https://github.com/CharafChnioune)
- [mflux-fasthtml](https://github.com/anthonywu/mflux-fasthtml) by [@anthonywu](https://github.com/anthonywu)
- [mflux-streamlit](https://github.com/elitexp/mflux-streamlit) by [@elitexp](https://github.com/elitexp)

---

### 🙏 Acknowledgements

MFLUX would not be possible without the great work of:

- The MLX Team for [MLX](https://github.com/ml-explore/mlx) and [MLX examples](https://github.com/ml-explore/mlx-examples)
- Black Forest Labs for the [FLUX project](https://github.com/black-forest-labs/flux)
- Bria for the [FIBO project](https://huggingface.co/briaai/FIBO)
- Tongyi Lab for the [Z-Image project](https://tongyi-mai.github.io/Z-Image-blog/)
- Qwen Team for the [Qwen Image project](https://qwen.ai/blog?id=a6f483777144685d33cd3d2af95136fcbeb57652&from=research.research-list)
- ByteDance, @numz and @adrientoupet for the [SeedVR2 project](https://github.com/numz/ComfyUI-SeedVR2_VideoUpscaler)
- Hugging Face for the [Diffusers library implementations](https://github.com/huggingface/diffusers) 
- Depth Pro authors for the [Depth Pro model](https://github.com/apple/ml-depth-pro?tab=readme-ov-file#citation)
- The MLX community and all [contributors and testers](https://github.com/filipstrand/mflux/graphs/contributors)

---

### ⚖️ License

This project is licensed under the [MIT License](LICENSE).
