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flux-dev

Flux Dev

A 12 billion parameter rectified flow transformer capable of generating images from text descriptions

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Avg Run Time: 10.000s

Model Slug: flux-dev

Category: Text to Image

Input

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Output

Example Result

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Each execution costs $0.0250. With $1 you can run this model about 40 times.

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Get Prediction Result

Poll the prediction endpoint with the prediction ID until the result is ready. The API uses long-polling, so you'll need to repeatedly check until you receive a success status.

Table of Contents
Overview
Technical Specifications
Key Considerations
Tips & Tricks
Capabilities
What Can I Use It For?
Things to Be Aware Of
Limitations

Overview

FLUX.1 [dev] is a 12-billion-parameter rectified flow transformer developed by Black Forest Labs, designed to generate high-quality images from text descriptions. It serves as an open-weight model aimed at advancing scientific research and empowering artists to develop innovative workflows.

Technical Specifications

Model Architecture: FLUX.1 [dev] is a rectified flow transformer comprising 12 billion parameters, optimized for text-to-image generation tasks.

Training Methodology: The model was trained using guidance distillation, enhancing its efficiency while maintaining high output quality.

Key Considerations

License Restrictions: FLUX.1 [dev] is released under a non-commercial license, permitting use for personal, scientific, and certain commercial purposes as outlined in the license agreement.


Ethical Use: The model and its derivatives must not be used in ways that violate laws, exploit or harm minors, disseminate false information, or engage in activities that harass or bully individuals or groups.


Complete necessary data preprocessing steps: Ensure that input data is appropriately prepared before using the model.


Legal Information

By using this model, you agree to:

  • Black Forest Labs API agreement
  • Black Forest Labs Terms of Service

Tips & Tricks

Prompt Engineering: The quality of generated images is heavily influenced by the specificity and clarity of the input prompts. Experimenting with different prompting styles can yield better results.


Optimal parameter settings for training and inference: Adjust parameters to achieve the best results.

Capabilities

High-Quality Image Generation: FLUX.1 [dev] produces cutting-edge output quality, closely matching the performance of state-of-the-art models like FLUX.1 [pro].


Open Weights for Research: The availability of open weights encourages new scientific research and the development of innovative artistic workflows.


Training with Guidance Distillation: Improved efficiency through guided training methodologies.

What Can I Use It For?

Artistic Creation: Artists can leverage FLUX.1 [dev] to generate unique and high-quality images based on textual descriptions, enhancing creative workflows.


Research and Development: Researchers can utilize the model's open weights to explore advancements in AI-driven image generation and related fields.


Educational Purposes: Generate educational materials with ease.

Things to Be Aware Of

Experiment with Diverse Prompts: Test the model's versatility by inputting a wide range of text descriptions to observe the variety and quality of generated images.


Specific Examples: Experiment with diverse text descriptions to generate varied images.


Practical Use Cases: Test the model in real-world applications.


Parameter Tweaks: Optimize output by exploring different parameter settings.


Expected Output Examples: Preview the types of images the model can create.


Advanced Scenarios: Leverage advanced features for specialized tasks.


Creative Applications: Push the boundaries of art and creativity using the model.


Tool Integration: Combine the model with other tools for enhanced functionality.

Limitations

Prompt Sensitivity: The model's output quality and relevance are highly dependent on the input prompts, and it may not always generate images that perfectly match the descriptions.


Bias Amplification: As a statistical model, FLUX.1 [dev] may inadvertently amplify existing societal biases present in the training data.

Output Format: PNG, JPG, WEBP

Pricing Detail

This model runs at a cost of $0.025 per execution.

Pricing Type: Fixed

The cost remains the same regardless of which model you use or how long it runs. There are no variables affecting the price. It is a set, fixed amount per run, as the name suggests. This makes budgeting simple and predictable because you pay the same fee every time you execute the model.