Flux Schnell
flux-schnell
Achieve lightning-fast results with the simplicity and power of Flux Schnell.
Model Information
Input
Configure model parameters
Output
View generated results
Result
Preview, share or download your results with a single click.
Prerequisites
- Create an API Key from the Eachlabs Console
- Install the required dependencies for your chosen language (e.g., requests for Python)
API Integration Steps
1. Create a Prediction
Send a POST request to create a new prediction. This will return a prediction ID that you'll use to check the result. The request should include your model inputs and API key.
import requestsimport timeAPI_KEY = "YOUR_API_KEY" # Replace with your API keyHEADERS = {"X-API-Key": API_KEY,"Content-Type": "application/json"}def create_prediction():response = requests.post("https://api.eachlabs.ai/v1/prediction/",headers=HEADERS,json={"model": "flux-schnell","version": "0.0.1","input": {"megapixels": "1","num_outputs": 1,"prompt": "your prompt here","aspect_ratio": "1:1","seed": 0,"output_format": "webp","output_quality": 80,"disable_safety_checker": false,"go_fast": false,"num_inference_steps": 4}})prediction = response.json()if prediction["status"] != "success":raise Exception(f"Prediction failed: {prediction}")return prediction["predictionID"]
2. 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.
def get_prediction(prediction_id):while True:result = requests.get(f"https://api.eachlabs.ai/v1/prediction/{prediction_id}",headers=HEADERS).json()if result["status"] == "success":return resultelif result["status"] == "error":raise Exception(f"Prediction failed: {result}")time.sleep(1) # Wait before polling again
3. Complete Example
Here's a complete example that puts it all together, including error handling and result processing. This shows how to create a prediction and wait for the result in a production environment.
try:# Create predictionprediction_id = create_prediction()print(f"Prediction created: {prediction_id}")# Get resultresult = get_prediction(prediction_id)print(f"Output URL: {result['output']}")print(f"Processing time: {result['metrics']['predict_time']}s")except Exception as e:print(f"Error: {e}")
Additional Information
- The API uses a two-step process: create prediction and poll for results
- Response time: -
- Rate limit: 60 requests/minute
- Concurrent requests: 10 maximum
- Use long-polling to check prediction status until completion
Overview
FLUX Schnell is a high-performance AI model designed for rapid data processing and analysis. It is optimized for handling large datasets efficiently while delivering accurate and actionable insights. This model is particularly suited for applications in data analytics, real-time monitoring, and decision-making systems, providing a balance between speed and precision.
Technical Specifications
Model Architecture: FLUX.1 [schnell] is a rectified flow transformer comprising 12 billion parameters, optimized for text-to-image generation tasks.
Training Methodology: The model was trained using latent adversarial diffusion distillation, enhancing its efficiency while maintaining high output quality.
Inference Speed: Capable of generating high-quality images in only 1 to 4 inference steps, making it one of the fastest models in its class.
Model Architecture:
- Built on a modular framework optimized for high-speed computation and scalability.
- Features a multi-threaded design for concurrent task execution.
Performance:
- Capable of processing datasets containing millions of entries within seconds.
- Designed for low latency in real-time applications..
Key Considerations
Compliance with License: Ensure all usage adheres to the Apache 2.0 terms.
Ethical Use:
Avoid harmful applications such as disinformation or harassment.
Respect privacy and intellectual property in generated content.
Bias Awareness:
Always review generated content for cultural or contextual appropriateness.
Legal Information
By using this model, you agree to:
- Black Forest Labs API agreement
- Black Forest Labs Terms of Service
Tips & Tricks
Optimize Prompt Structure: Use clear, descriptive phrases with specific details.
- Instead of “a mountain,” try “a snow-capped mountain under a starry night sky with glowing auroras.”
Optimal Parameter Settings for Training and Inference: Adjust parameters to achieve the best results.
Capabilities
High-Speed Generation: Produces high-quality images in under a few seconds.
Detailed Visuals: Captures intricate details, making it suitable for artistic and professional applications.
Flexible Outputs: Supports various creative styles, from realistic to abstract.
Fast Image Generation: Optimized for speed without compromising quality.
Versatile Applications: Suitable for various text-to-image tasks.
Efficient Resource Utilization: Designed for use in resource-constrained environments.
What can I use for?
Artwork Creation: Generate concept art, story illustrations, or custom designs.
Prototyping: Quickly visualize ideas for apps, websites, or product designs.
Marketing Materials: Create visually appealing content for ads, social media, or presentations.
Educational Content: Use for visual aids in teaching or academic research.
Creative Experimentation: Explore imaginative ideas by tweaking prompts.
Dynamic Content Creation: Generate visuals for real-time or on-demand scenarios.
Research and Development: Utilize the model for studies requiring fast results.
Things to be aware of
Creative Prompts: Test with fun ideas like:
- “A futuristic cityscape glowing with neon lights at night.”
- “A cozy wooden cabin by a snowy lake during sunrise.”
Scenario Variations: Modify scenes by changing time of day, weather, or lighting.
Art Styles: Experiment with styles like “minimalist,” “surreal,” or “realistic.”
Batch Processing: Generate multiple images from similar prompts to explore variations.
Specific Examples: Experiment with diverse text prompts to generate a wide range of images.
Parameter Adjustments: Fine-tune settings to balance speed and quality.
Creative Applications: Leverage the model for innovative and experimental projects.
Limitations
Prompt Dependency: Outputs are only as good as the prompts provided. Vague or ambiguous prompts may result in less relevant images.
Training Biases: Images might unintentionally reflect biases from the model’s training data.
Output Consistency: While fast, results may vary slightly across multiple runs with similar prompts.
Output Format: PNG, JPG, WEBP
