Flux Depth Pro
flux-depth-pro
Flux Depth Pro Model generates advanced depth estimations, enabling 3D effects and enhanced image realism.
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-depth-pro","version": "0.0.1","input": {"seed": null,"steps": "50","prompt": "your prompt here","guidance": "10","control_image": "your_file.image/jpeg","output_format": "jpg","safety_tolerance": "2","prompt_upsampling": false}})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: ~19 seconds
- Rate limit: 60 requests/minute
- Concurrent requests: 10 maximum
- Use long-polling to check prediction status until completion
Overview
Flux Depth Pro is designed for advanced image generation and manipulation tasks. It leverages control images, guidance settings, and upsampling capabilities to produce highly detailed and customized outputs. Its flexibility allows for both creative experimentation and precise outputs tailored to specific requirements.
Technical Specifications
Input Types: Supports a variety of input parameters including text prompts, control images, and detailed sliders for settings like guidance and steps.
Output Flexibility: Offers adjustable output formats and quality to suit diverse use cases.
Advanced Features: Safety tolerance ensures outputs are screened appropriately, while upsampling enhances fine details in generated images.
Key Considerations
Guidance vs. Creativity: High guidance values steer the output closer to the prompt, but may limit creative freedom. Lower values allow for more artistic interpretations.
Control Images: Using control images can help maintain consistency but may reduce the model’s creativity.
Steps: Increasing steps enhances image quality but can slow down processing time. Find a balance based on your priorities.
Safety Tolerance: Adjust this based on your content needs. Lower tolerance may flag outputs more strictly.
Legal Information
By using this model, you agree to:
- Black Forest Labs API agreement
- Black Forest Labs Terms of Service
Tips & Tricks
Seed Control: Use the same seed value for reproducibility. Experiment with different seeds to explore diverse outputs.
Steps and Guidance Tuning: Start with medium settings (e.g., steps: 20, guidance: 7) and adjust incrementally based on the results.
Control Image Usage: Use high-resolution and relevant control images to guide the model effectively. Blurry or unrelated images can negatively impact the result.
Prompt Structuring: Keep prompts clear and concise. Adding descriptors like “vivid,” “realistic,” or “high contrast” can significantly influence the outcome.
Safety Adjustments: Lower the safety tolerance for experimental work; increase it for professional or public-facing projects.
Capabilities
Visual Art Creation: Generate unique, artistic visuals based on detailed prompts.
Concept Visualization: Bring ideas to life for storyboards, prototypes, and creative projects.
Photo Enhancement and Editing: Use control images to refine and enhance photos with specific styles.
Creative Exploration: Experiment with different settings to produce imaginative and abstract results.
Professional Design: Tailor outputs for use in marketing, branding, and digital content creation.
What can I use for?
Image Generation: Create unique and detailed visuals based on textual prompts.
Style Transfer: Guide outputs using control images to adopt specific styles or structures.
Creative Projects: Experiment with abstract and imaginative ideas for artwork, design, and more.
Professional Applications: Use precise settings for tailored outputs in fields like marketing, design, and entertainment.
Things to be aware of
- Artistic Interpretations: Experiment with abstract prompts like “a surreal dreamscape with vibrant colors.”
- Photo Refinement: Input a control image to refine or enhance an existing photograph.
- Style Combinations: Blend multiple styles by referencing distinct control images and prompts.
- Concept Prototypes: Generate visual drafts for presentations, storyboards, or creative brainstorming.
- Guidance Exploration: Compare outputs at different guidance values (e.g., 5, 10, 20) to understand how this impacts results.
Limitations
Complex Prompts: Overly intricate prompts may lead to inconsistent results. Break them down into simpler components when needed.
Processing Time: High steps and upsampling can increase rendering time. Plan accordingly for large or complex projects.
Control Image Dependency: Reliance on control images may limit flexibility in output interpretation.
Output Format: JPG,PNG