raullenchai/mflux

选用指南

AI 根据 README 整理 · 2026/10/19 01:06:23

适用场景

适用于在 Mac 上本地生成图像、进行图像编辑、放大、深度估计等任务。

查看 README 依据

- Quantization and local model loading - LoRA support (multi-LoRA, scales, library lookup), including LyCORIS LoKr on FLUX.1 and FLUX.2 - Metadata export + reuse, plus prompt file support - Float16 compute for Apple M1-family GPUs on FLUX.2, FLUX 3 Image Klein, Z-Image and Qwen-Image 2.1 (`--compute-precision float16`, see the [common README](src/mflux/models/common/README.md#float16-compute-apple-m1-family)) **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 for FLUX.1, Union for Z-Image and Qwen-Image-2.1, depth for Krea 2), 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]

使用方式

通过命令行工具使用，例如 mflux-generate-z-image-turbo，也提供 Python API。

查看 README 依据

### 💿 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 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 ``` 

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. <details> <summary>Python API</summary> Create a standalone `generate.py` script with inline `uv` dependencies: ```python #!/usr/bin/env -S uv run --script # /// script # requires-python = ">=3.10" # dependencies = [ # "mflux", # ] # /// from mflux.models.z_image import ZImageTurbo model = ZImageTurbo(quantize=8)

部署要求

需要安装 uv，并支持 Python 3.10 或更高版本。

查看 README 依据

### 💿 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 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 ``` 

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. <details> <summary>Python API</summary> Create a standalone `generate.py` script with inline `uv` dependencies: ```python #!/usr/bin/env -S uv run --script # /// script # requires-python = ">=3.10" # dependencies = [ # "mflux", # ] # /// from mflux.models.z_image import ZImageTurbo model = ZImageTurbo(quantize=8)