Developed by a team of former Stability AI machine learning engineers called Black Forest Labs, the Flux family of models quickly gained popularity because Flux’s model weights were open sourced, enabling the community to fine-tune them similarly to Stability AI’s Stable Diffusion family of models.
Flux models also have superior out-of-the-box performance in terms of image quality and prompt adherence than other current models, competing with industry-leading closed-source models like Midjourney and DALL-E.
Enterprise businesses are starting to explore Flux models because they offer the same value that Stable Diffusion did: the ability to train your IP into a custom model while owning the model and model weights.
The Three Versions of Flux.1
The Flux.1 models come in three different versions, each tailored to different audiences and use cases: Schnell, Dev, and Pro.
Schnell Version
The Schnell version of the Flux.1 model is the fully open-source edition, freely available for anyone to use and modify. Designed for rapid deployment and ease of use, it’s an excellent choice for teams that need a reliable model with minimal setup.
Schnell excels in prompt adherence and produces high-quality images with minimal configuration, making it suitable for a wide range of general-purpose applications.
However, it’s important to note that Schnell is a step-distilled version of the more powerful Flux models, meaning it doesn’t offer the same level of output quality as the Dev and Pro versions. Because of these limitations, Schnell’s utility for enterprises is fairly limited, especially in use cases that require a high level of control and quality.
Dev Version
The Dev version of the Flux.1 models is most similar to SDXL in that it provides a model file with model weights that enterprises can finetune, control, and run in a secure environment separate from any external APIs or other organizations’ models. Unlike the Schnell version, the Dev model retains the full power and flexibility of Flux.1, making it the version that enterprises are likely to care about the most. Unlike the Schnell version and SDXL, the Dev model requires a commercial license, which we’ll explain below.
Pro Version
The Pro version of the Flux.1 models is Black Forest Labs’ most advanced model, but it comes with a significant limitation: it is only accessible through an API. This means that while the Pro model may produce higher quality outputs out-of-the-box, it cannot be fine-tuned or trained using enterprises’ IP. Essentially, it functions like other closed-source products such as ChatGPT or DALL·E, where you access a dedicated version of the model via API.
This setup is ideal for teams that need a straightforward solution for tasks like text-to-image or image-to-image generation. The newest version of the Pro model performs better than other closed source models on the market. However, for those looking to fully customize or control the model, the Pro version may not be the best fit.
Our recommendation: Focus on the Dev Version for Enterprise Teams
For most enterprise teams, the Dev version of the Flux.1 models is likely to be the most attractive option. Similar to an SDXL model, the Dev version allows teams to control the model weights, fine-tune it for their specific use cases, and maintain it securely, separate from other models.
The ability to fine-tune and optimize the model ensures that it can meet the highest standards of performance and accuracy, making it a valuable asset for teams looking to integrate AI into their operations at a deeper level.
The Pro version may make sense for businesses looking for API-based image generation.
Given these considerations, this guide will primarily focus on deploying the Dev version, as it represents the best balance of flexibility, control, and performance for enterprise teams.
Preparing for Deployment
Before deploying the Flux.1 Dev model, it’s important to clearly define your specific use case, as this will shape your deployment strategy.
You have two primary options for deployment: deploying in-house (and self-hosting) or partnering with an expert team and software provider to handle the entire deployment lifecycle.
Each has its own set of advantages and considerations. The right choice depends on your enterprise’s resources, goals, and long-term needs.
Deploying Flux Models In-House
When choosing to deploy the Flux.1 Dev model in-house, your team will manage the complete lifecycle of your AI solution. This includes everything from selecting and configuring the hosting environment to handling scaling, maintenance, and updates for both the infrastructure and the application running the model.
Cloud Hosting Considerations for Flux Model Deployment
If your team decides to self-host in the cloud, you’ll need to choose a cloud hosting solution and manage the entire deployment process yourself. This includes setting up virtual machines, managing storage, and ensuring network security.
You’ll need to select a cloud provider that best suits your needs—options like Amazon AWS, Google Cloud Platform, or Microsoft Azure are popular. Beyond deployment, your team will be responsible for ongoing maintenance, updates, and scaling, which can be resource-intensive.
Local Hosting Considerations for Flux Model Deployment
For organizations that require local hosting, the Flux.1 model can be deployed directly onto workstations. This setup can reduce reliance on cloud resources, but comes with hardware and maintenance challenges. Running Flux.1 locally requires top-tier hardware (such as machines with at least 24GB of VRAM). Your team will also be fully responsible for regular maintenance, security updates, and performance tuning.
Choosing the Right Application for Self-Hosting
To get the most value out of the Flux.1 model, simply hosting it won’t suffice. You’ll need a platform capable of running complex workflows and delivering the tools necessary for enterprise-level AI image generation. While there are several open-source options available, they come with their own pros and cons, especially regarding long-term stability.
Overview of Application Options
- ComfyUI: ComfyUI is a highly customizable platform with a vast marketplace of nodes and workflows. However, since these nodes and workflows are maintained by community members, stability can be an issue. Enterprises should be prepared for the possibility of nodes breaking or becoming outdated and unsupported, which could require additional internal support to manage, especially if they are incorporated into high value production workflows.
- Invoke: For teams seeking a turnkey solution, Invoke offers a fully integrated platform with built-in support for Flux.1 models, project management tools, and collaboration features. Its control canvas provides an intuitive interface for interacting with models, while the workflow builder lets teams create custom workflows using core nodes maintained by Invoke’s team, ensuring stability. This makes Invoke an excellent choice for enterprises looking for a powerful, ready-to-deploy solution with minimal setup effort.
- A1111 (Automatic1111): A1111 was an early open source AI image generator and has a lot of community users, so you’ll find helpful community-created documentation and tutorials. However, it’s important to note that the project isn’t backed by a full-time development team or paid contributors. This makes the platform’s future development uncertain, which could pose long-term risks for enterprise teams that need stability and continuous updates.
Partnering with a Technology Provider to Deploy Flux AI Models
For many enterprises, partnering with a technology provider that offers both end-to-end deployment support and a robust platform to train, manage, and utilize AI models offers the most efficient path to success.
Rather than simply providing consulting support or simply providing the technology, a true partner supplies the technology and the expertise to manage everything—from model deployment and training to an application layer with the creation tools to make your team successful.
This approach ensures your organization stays ahead of rapid AI advancements, with a partner who can guide you through evolving technologies and help you continuously leverage AI to drive business outcomes at scale.
Key Advantages of Partnering with a Full-Service Provider
• Simplicity and Reliability: A technology partner handles the technical complexities of maintaining and optimizing your AI solution, ensuring it remains secure, up-to-date, and scalable. This allows your team to focus on maximizing the business value of AI, without the operational burden.
• Integrated Platform and Customization: A technology partner not only deploys your AI solution but also provides an enterprise-grade platform that supports multi-user management, advanced security, and seamless integration into your existing workflows. This level of customization and support ensures the technology fits your specific needs.
• Reduced Operational Complexity: While in-house deployment can appear cost-effective, the hidden costs of managing and scaling infrastructure can quickly add up. A comprehensive partner minimizes this complexity, ensuring smooth operations and scalable growth without unexpected challenges.
Choosing Your Deployment Path
When preparing to deploy the Flux.1 Dev model, the choice between self-hosting and partnering with a comprehensive provider depends on your organization’s needs, resources, and goals. For most enterprises, working with a full-service technology partner offers the best balance of simplicity, scalability, and reduced operational burden. However, for specialized use cases—such as high-security VFX studios where on-premise solutions are required—self-hosting may still be the preferred option.
Our recommendation: We may be biased, but for most enterprises, partnering with a technology provider that offers both the platform and the services needed to manage Flux models at scale is the best approach. This ensures your AI solution delivers value efficiently, with minimal operational overhead.
Invoke was Black Forest Lab’s first official partner in offering full service end-to-end deployment of Flux models for enterprise businesses. Learn more.
Understanding and Obtaining a License for Flux.1 Models
When deploying the Flux.1 models, it’s important to understand the licensing requirements associated with each version, as this will impact how and where the models can be used, particularly in commercial environments.
Schnell Model License
The Schnell version of the Flux.1 model is released under the Apache 2.0 license. This license is highly permissive, allowing users to freely use, modify, and distribute the model with few restrictions. The key benefits of the Apache 2.0 license include:
• Commercial Use: Users can utilize the Schnell model for commercial purposes without needing to obtain additional licenses.
• Modification and Distribution: The license allows users to modify the model and distribute the modified versions, as long as they include a copy of the original license and note any changes that have been made.
This makes the Schnell model a great starting point for those who want to experiment with Flux.1 models without worrying about licensing costs or restrictions. However, for most enterprise use cases, where higher performance and more advanced features are required, the Dev and Pro models are more relevant.
Dev Model License
The Dev version of the Flux.1 model requires a commercial license from Black Forest Labs for any commercial use. This means that to legally use the Dev model in a production environment, especially for generating revenue, you need to obtain a license.
Obtaining a Commercial License
• Direct from Black Forest Labs: Enterprises can request a commercial license directly from Black Forest Labs. Black Forest Labs has provided a contact form on their website for requesting additional information.
• Through a Service Provider: Alternatively, you can obtain the commercial license through a service provider like Invoke. Invoke has a strategic partnership with Black Forest Labs, allowing them to provide a commercial license to customers.
Ensuring Long-Term Success
Given the pace of innovation in this space, managing and maintaining a cutting-edge AI deployment is guaranteed to quickly become a full-time job.
This is why many enterprise teams are turning to providers, like Invoke, to not only handle the technical aspects of deploying and maintaining the base model, but they also stay on top of the latest developments in the ecosystem, ensuring that the application that runs your models remains at the forefront of AI capabilities.
All images for this post were provided by Black Forest Labs.
