FLUX.1 – Open Source AI Image Generation Model launched by the founding team of Stable Diffusion | AI Toolset

What is FLUX.1

FLUX.1 is an open source launched by the founding team of Stable DiffusionAI image generation modelwith 12B parameters, is the largest literary graph model to date. Contains three variants: FLUX.1 (pro), FLUX.1 (dev) for open source non-commercial use, and FLUX.1 (schnell) for fast and efficient. FLUX.1 stands out with excellent image quality, highly realistic human anatomical performance and advanced tip word compliance capabilities, setting new industry standards for AI image generation.

Features of FLUX.1

• Large-scale parameters: With 12B (12 billion) parameters, it is one of the largest open source text-to-image models to date.
• Multimodal architecture: A hybrid architecture based on multimodal and parallel diffusion Transformer blocks, providing powerful image generation capabilities.
• High performance variants: Provides three model variants with different performance and uses, including Professional Edition (FLUX.1 (pro), Development Edition (FLUX.1 (dev)) and Quick Edition (FLUX.1 (schnell)).
• Image quality: Go beyond other popular models in terms of visual quality, prompt word compliance, size/aspect ratio variability, font and output diversity.
• Open source and accessibility: Some model variants such as FLUX.1 (dev) and FLUX.1 (schnell) are open source, easy to study and non-commercial applications.
• Technological innovation: The flow matching training method, rotation position embedding and parallel attention layer were introduced to improve model performance and hardware efficiency.

Technical principles of FLUX.1

• Multimodal architecture:FLUX.1 adopts a multimodal architecture, which means that the model can process and understand multiple types of data, such as text and images at the same time, thereby better capturing the correlation between different data.
• Parallel diffusion Transformer block: The model utilizes the parallel diffusion Transformer structure, an advanced neural network component that can efficiently process sequence data and enhance the model’s encoding and decoding capabilities of information.
• Flow matching training method: FLUX.1 improves the traditional diffusion model through the flow matching method. This method is a general technique for training generative models, which can simplify the training process and improve the generation quality of the model.
• Rotating position embed: The model introduces rotational position embedding technology, which is a special encoding method that can enhance the model’s ability to recognize different position features in the image, thereby improving the detailed performance of the image.
• Parallel attention layer: Through the parallel attention mechanism, the model is able to focus on multiple parts of the input sequence simultaneously, which helps capture long-distance dependencies and improves the accuracy of generated images.

Project address of FLUX.1

How to use FLUX.1

• Select the right model variant：
  • FLUX.1 (pro): Suitable for commercial applications that require top-level performance and need access through the API.
  • FLUX.1 (dev): Suitable for non-commercial use, it is an open source, guided distillation model available on HuggingFace.
  • FLUX.1 (schnell): Suitable for local development and personal use, it is the fastest model and is also available on HuggingFace.
• Setting up the environment：If it is a local deployment, you need to set up a Python environment and install the necessary dependency libraries.
• Install FLUX.1：You can clone the official GitHub repository to the local environment through Git and follow the guide to install the required Python packages.
• Using API：For FLUX.1 (pro), you need to register and get the API key to access the model.
• Writing code：Based on official documentation or sample code, write scripts to interact with the model and generate images.
• Generate an image：Using the interface provided by the model, enter a text prompt (prompt), and the model will generate an image based on the text.

Here is a simple usage example, assuming that you have set up your environment and installed the necessary dependencies:

# 克隆FLUX.1 GitHub仓库
git clone https://github.com/black-forest-labs/flux

# 进入仓库目录
cd flux

# 创建并激活Python虚拟环境
python3.10 -m venv .venv
source .venv/bin/activate

# 安装依赖
pip install -e '.(all)'

# 根据需要设置环境变量，例如指定模型权重路径
export FLUX_SCHNELL=path_to_flux_schnell_sft_file

# 使用提供的脚本进行图像生成
python -m flux --name 'FLUX.1 (schnell)' --loop

Application scenarios of FLUX.1

• Media and Entertainment: In movies, games, and animation production, FLUX.1 can be used to create realistic backgrounds, characters, and scenes.
• Art creation and design: Use FLUX.1 to generate high-quality images to assist artists and designers in realizing creative ideas quickly.
• Advertising and marketing: Generate attractive advertising images and marketing materials to improve publicity effectiveness.
• Education and research: In academic research, FLUX.1 can be used as a tool to explore new technologies and theories in image generation.
• Content creation: Provide unique images for social media, blogs and online content creation to increase the appeal of content.

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