[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-72601":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":11,"languages":10,"totalLinesOfCode":10,"stars":12,"forks":13,"watchers":14,"openIssues":15,"contributorsCount":16,"subscribersCount":16,"size":16,"stars1d":17,"stars7d":18,"stars30d":19,"stars90d":16,"forks30d":16,"starsTrendScore":20,"compositeScore":21,"rankGlobal":10,"rankLanguage":10,"license":22,"archived":23,"fork":23,"defaultBranch":24,"hasWiki":23,"hasPages":23,"topics":25,"createdAt":10,"pushedAt":10,"updatedAt":39,"readmeContent":40,"aiSummary":41,"trendingCount":16,"starSnapshotCount":16,"syncStatus":42,"lastSyncTime":43,"discoverSource":44},72601,"mflux","filipstrand\u002Fmflux","filipstrand","MLX native implementations of state-of-the-art generative image models","",null,"Python",2135,153,22,80,0,14,38,79,42,28.56,"MIT License",false,"main",[26,27,28,29,30,31,32,33,34,35,36,37,38],"ai","apple-silicon","diffusers","fibo","flux","huggingface","ml","mlx","qwen","qwen-image","seedvr2","transformers","z-image","2026-06-12 02:03:05","![image](src\u002Fmflux\u002Fassets\u002Flogo.jpg)\n\n[![MFLUX](https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fv\u002Fmflux?label=MFLUX&logo=pypi&logoColor=white)](https:\u002F\u002Fpypi.org\u002Fproject\u002Fmflux\u002F)\n[![MLX](https:\u002F\u002Fimg.shields.io\u002Fpypi\u002Fv\u002Fmlx?label=MLX&logo=pypi&logoColor=white)](https:\u002F\u002Fpypi.org\u002Fproject\u002Fmlx\u002F)\n[![CI](https:\u002F\u002Fgithub.com\u002Ffilipstrand\u002Fmflux\u002Factions\u002Fworkflows\u002Ftests.yml\u002Fbadge.svg)](https:\u002F\u002Fgithub.com\u002Ffilipstrand\u002Fmflux\u002Factions\u002Fworkflows\u002Ftests.yml)\n\n### About\n\nRun the latest state-of-the-art generative image models locally on your Mac in native MLX!\n\n### Table of contents\n\n- [💡 Philosophy](#-philosophy)\n- [💿 Installation](#-installation)\n- [🎨 Models](#-models)\n- [✨ Features](#-features)\n- [🌱 Related projects](#related-projects)\n- [🙏 Acknowledgements](#-acknowledgements)\n- [⚖️ License](#%EF%B8%8F-license)\n\n---\n\n### 💡 Philosophy\n\nMFLUX is a line-by-line MLX port of several state-of-the-art generative image models from the [Huggingface Diffusers](https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Fdiffusers) and [Huggingface Transformers](https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Ftransformers) libraries. All models are implemented from scratch in MLX, using only tokenizers from the [Huggingface Transformers](https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Ftransformers) library. MFLUX is purposefully kept minimal and explicit, [@karpathy](https:\u002F\u002Fgist.github.com\u002Fawni\u002Fa67d16d50f0f492d94a10418e0592bde?permalink_comment_id=5153531#gistcomment-5153531) style.\n\n---\n\n### 💿 Installation\nIf you haven't already, [install `uv`](https:\u002F\u002Fgithub.com\u002Fastral-sh\u002Fuv?tab=readme-ov-file#installation), then run:\n\n```sh\nuv tool install --upgrade mflux\n```\n\nAfter installation, the following command shows all available MFLUX CLI commands: \n\n```sh\nuv tool list \n```\n\nTo generate your first image using, for example, the z-image-turbo model, run\n\n```\nmflux-generate-z-image-turbo \\\n  --prompt \"A puffin standing on a cliff\" \\\n  --width 1280 \\\n  --height 500 \\\n  --seed 42 \\\n  --steps 9 \\\n  -q 8\n```\n\n![Puffin](src\u002Fmflux\u002Fassets\u002Fpuffin.png)\n\nThe 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\u002Fmflux\u002Fmodels\u002Fcommon\u002FREADME.md) for shared CLI patterns and examples.\n\n\u003Cdetails>\n\u003Csummary>Python API\u003C\u002Fsummary>\n\nCreate a standalone `generate.py` script with inline `uv` dependencies:\n\n```python\n#!\u002Fusr\u002Fbin\u002Fenv -S uv run --script\n# \u002F\u002F\u002F script\n# requires-python = \">=3.10\"\n# dependencies = [\n#   \"mflux\",\n# ]\n# \u002F\u002F\u002F\nfrom mflux.models.z_image import ZImageTurbo\n\nmodel = ZImageTurbo(quantize=8)\nimage = model.generate_image(\n    prompt=\"A puffin standing on a cliff\",\n    seed=42,\n    num_inference_steps=9,\n    width=1280,\n    height=500,\n)\nimage.save(\"puffin.png\")\n```\n\nRun it with:\n\n```sh\nuv run generate.py\n```\n\nFor more Python API inspiration, look at the [CLI entry points](src\u002Fmflux\u002Fmodels\u002Fz_image\u002Fcli\u002Fz_image_turbo_generate.py) for the respective models.\n\u003C\u002Fdetails>\n\n\u003Cdetails>\n\u003Csummary>⚠️ Troubleshooting: hf_transfer error\u003C\u002Fsummary>\n\nIf 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:\n\n```sh\nuv tool install --upgrade mflux --with hf_transfer\n```\n\nThis will enable faster model downloads from Hugging Face.\n\n\u003C\u002Fdetails>\n\n\u003Cdetails>\n\u003Csummary>DGX \u002F NVIDIA (uv tool install)\u003C\u002Fsummary>\n\n```sh\nuv tool install --python 3.13 mflux\n```\n\u003C\u002Fdetails>\n\n---\n\n### 🎨 Models\n\nMFLUX supports the following model families. They have different strengths and weaknesses; see each model’s README for full usage details.\n\n| Model | Release date | Size | Type | Training | Description |\n| --- | --- | --- | --- | --- | --- |\n|[Z-Image](src\u002Fmflux\u002Fmodels\u002Fz_image\u002FREADME.md) | Nov 2025 | 6B | Distilled & Base | Yes | Fast, small, very good quality and realism. |\n|[FLUX.2](src\u002Fmflux\u002Fmodels\u002Fflux2\u002FREADME.md) | Jan 2026 | 4B & 9B | Distilled & Base | Yes | Fastest + smallest with very good qaility and edit capabilities. |\n|[FIBO](src\u002Fmflux\u002Fmodels\u002Ffibo\u002FREADME.md) | Oct 2025+ | 8B | Distilled & Base | No | Very good JSON-based prompt understanding. Has edit capabilities. |\n|[SeedVR2](src\u002Fmflux\u002Fmodels\u002Fseedvr2\u002FREADME.md) | Jun 2025 | 3B & 7B | — | No | Best upscaling model. |\n|[Qwen Image](src\u002Fmflux\u002Fmodels\u002Fqwen\u002FREADME.md) | Aug 2025+ | 20B | Base | No | Large model (slower); strong prompt understanding and world knowledge. Has edit capabilities |\n|[Depth Pro](src\u002Fmflux\u002Fmodels\u002Fdepth_pro\u002FREADME.md) | Oct 2024 | — | — | No | Very fast and accurate depth estimation model from Apple. |\n|[FLUX.1](src\u002Fmflux\u002Fmodels\u002Fflux\u002FREADME.md) | Aug 2024 | 12B | Distilled & Base | No (legacy) | Legacy option with decent quality. Has edit capabilities with 'Kontext' model and upscaling support via ControlNet |\n\n---\n\n### ✨ Features\n\n**General**\n- Quantization and local model loading\n- LoRA support (multi-LoRA, scales, library lookup)\n- Metadata export + reuse, plus prompt file support\n\n**Model-specific highlights**\n- Text-to-image and image-to-image generation.\n- LoRA finetuning\n- In-context editing, multi-image editing, and virtual try-on\n- ControlNet (Canny), depth conditioning, fill\u002Finpainting, and Redux\n- Upscaling (SeedVR2 and Flux ControlNet)\n- Depth map extraction and FIBO prompt tooling (VLM inspire\u002Frefine)\n\nSee the [common README](src\u002Fmflux\u002Fmodels\u002Fcommon\u002FREADME.md) for detailed usage and examples, and use the model section above to browse specific models and capabilities.\n\n> [!NOTE]\n> 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:\u002F\u002Fcursor.com), [Claude Code](https:\u002F\u002Fwww.anthropic.com\u002Fclaude-code), or similar). Ask questions like: “Can you help me generate an image using z-image?”\n\n\n---\n\n\u003Ca id=\"related-projects\">\u003C\u002Fa>\n\n### 🌱 Related projects\n\n- [MindCraft Studio](https:\u002F\u002Fthemindstudio.cc\u002Fmindcraft#models) — macOS app built on mflux by [@shaoju](https:\u002F\u002Fgithub.com\u002Fshaoju)\n- [Mflux-ComfyUI](https:\u002F\u002Fgithub.com\u002Fraysers\u002FMflux-ComfyUI) by [@raysers](https:\u002F\u002Fgithub.com\u002Fraysers)\n- [MFLUX-WEBUI](https:\u002F\u002Fgithub.com\u002FCharafChnioune\u002FMFLUX-WEBUI) by [@CharafChnioune](https:\u002F\u002Fgithub.com\u002FCharafChnioune)\n- [mflux-fasthtml](https:\u002F\u002Fgithub.com\u002Fanthonywu\u002Fmflux-fasthtml) by [@anthonywu](https:\u002F\u002Fgithub.com\u002Fanthonywu)\n- [mflux-streamlit](https:\u002F\u002Fgithub.com\u002Felitexp\u002Fmflux-streamlit) by [@elitexp](https:\u002F\u002Fgithub.com\u002Felitexp)\n\n---\n\n### 🙏 Acknowledgements\n\nMFLUX would not be possible without the great work of:\n\n- The MLX Team for [MLX](https:\u002F\u002Fgithub.com\u002Fml-explore\u002Fmlx) and [MLX examples](https:\u002F\u002Fgithub.com\u002Fml-explore\u002Fmlx-examples)\n- Black Forest Labs for the [FLUX project](https:\u002F\u002Fgithub.com\u002Fblack-forest-labs\u002Fflux)\n- Bria for the [FIBO project](https:\u002F\u002Fhuggingface.co\u002Fbriaai\u002FFIBO)\n- Tongyi Lab for the [Z-Image project](https:\u002F\u002Ftongyi-mai.github.io\u002FZ-Image-blog\u002F)\n- Qwen Team for the [Qwen Image project](https:\u002F\u002Fqwen.ai\u002Fblog?id=a6f483777144685d33cd3d2af95136fcbeb57652&from=research.research-list)\n- ByteDance, @numz and @adrientoupet for the [SeedVR2 project](https:\u002F\u002Fgithub.com\u002Fnumz\u002FComfyUI-SeedVR2_VideoUpscaler)\n- Hugging Face for the [Diffusers library implementations](https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Fdiffusers) \n- Depth Pro authors for the [Depth Pro model](https:\u002F\u002Fgithub.com\u002Fapple\u002Fml-depth-pro?tab=readme-ov-file#citation)\n- The MLX community and all [contributors and testers](https:\u002F\u002Fgithub.com\u002Ffilipstrand\u002Fmflux\u002Fgraphs\u002Fcontributors)\n\n---\n\n### ⚖️ License\n\nThis project is licensed under the [MIT License](LICENSE).\n","mflux 是一个基于 MLX 的本地实现，用于在 Mac 上运行最新的生成式图像模型。项目核心功能包括支持多种先进的生成式图像模型，并且这些模型都是从 Huggingface Diffusers 和 Transformers 库中移植而来，使用了 MLX 重写以实现高性能的本地运行。技术特点上，mflux 保持了极简和明确的设计风格，仅依赖于 Huggingface Transformers 提供的 tokenizer。此工具非常适合需要在苹果硅芯片设备上进行图像生成任务的研究人员或开发者使用，无论是通过命令行接口还是 Python API 都能轻松调用。",2,"2026-06-11 03:42:44","high_star"]