Flux 1.1 Pro
flux-1.1-pro
Faster, better FLUX Pro. Text-to-image model with excellent image quality, prompt adherence, and output diversity.
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-1-1-pro","version": "0.0.1","input": {"prompt_upsampling": false,"seed": 0,"width": 256,"height": 256,"prompt": "your prompt here","aspect_ratio": "1:1","output_format": "jpg","output_quality": 80,"safety_tolerance": 2,"image_prompt": "your_file.jpeg"}})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: ~8 seconds
- Rate limit: 60 requests/minute
- Concurrent requests: 10 maximum
- Use long-polling to check prediction status until completion
Overview
FLUX 1.1 Pro is Black Forest Labs' most advanced and efficient text-to-image model, providing superior speed and image quality while maintaining excellent prompt adherence and output diversity.
Technical Specifications
Model Architecture: FLUX1.1 [pro] is built on a state-of-the-art hybrid architecture, combining multimodal capabilities and parallel diffusion transformer blocks, ensuring exceptional performance. The model operates with 12 billion parameters, offering unparalleled depth and nuance.
Training Innovations: The model employs a cutting-edge training approach called flow matching. This methodology generalizes traditional diffusion processes, enabling FLUX1.1 [pro] to generate images with superior detail, coherence, and creativity.
Performance Metrics: Achieving industry-leading benchmarks, FLUX1.1 [pro] has earned the highest Elo score on the Artificial Analysis image evaluation platform, reflecting its dominance in speed, image quality, and prompt alignment.
Enhanced Speed: FLUX1.1 [pro] is optimized for rapid image generation, delivering outputs six times faster than its predecessor. This improvement makes it suitable for both real-time applications and high-volume batch processing.
Output Quality: The model supports ultra high-resolution image generation, producing visuals up to 2K resolution while maintaining excellent adherence to user prompts and fine details.
Key Considerations
Ethical Use: Ensure that generated images comply with ethical guidelines and do not infringe upon copyright or promote harmful content.
Licensing: By using FLUX1.1 [pro], users agree to the Black Forest Labs API agreement and Terms of Service.
Six times faster generation than its predecessor FLUX.1 Pro
Based on a hybrid architecture of multimodal and parallel diffusion transformer blocks
Scaled to 12B parameters
Built on flow matching, a general method for training generative models
Incorporates rotary positional embeddings and parallel attention layers
Legal Information
By using this model, you agree to:
- Black Forest Labs API agreement
- Black Forest Labs Terms of Service
Tips & Tricks
Prompt Engineering: Experiment with varying prompt structures to achieve diverse and high-quality outputs.
Batch Processing: Leverage the model's efficiency to process multiple image generations in parallel, enhancing workflow productivity.
Use simple one-word prompts for more natural-looking results
Try using 'IMG' followed by four numbers and 'CR2' to simulate Canon camera files
Add file extensions to prompts for more realistic outputs
Avoid complex prompts that may result in overly perfect images
Capabilities
Rapid Generation: Delivers images six times faster than previous versions, enhancing real-time application feasibility.
High-Resolution Outputs: Supports ultra high-resolution image generation up to 2K, maintaining prompt fidelity.
Improved hardware efficiency
Superior text prompt compliance
Enhanced output diversity
What can I use for?
Digital Art Creation: Generate high-quality images for artistic projects, concept art, and visual storytelling.
Content Generation: Produce engaging visuals for marketing materials, social media, and multimedia presentations.
Prototyping and Design: Utilize rapid image generation for product design mockups, architectural visualizations, and creative brainstorming.
Professional photography generation
Concept art development
Stock photo alternatives
Things to be aware of
Style Exploration: Experiment with prompts that specify different artistic styles, such as "a surrealist painting of a cityscape at dawn."
Scene Composition: Create complex scenes by detailing elements and their interactions, like "a futuristic marketplace bustling with diverse alien species."
Abstract Concepts: Test the model's interpretative capabilities with abstract prompts, such as "the essence of tranquility depicted in shades of blue."
Generate realistic photos using the 'IMG_XXXX_CR2' format
Create professional product photography with simple one-word prompts
Generate images with varying levels of prompt complexity
Try portrait photography with natural lighting and poses
Experiment with landscape photography in different conditions
Create realistic stock photo alternatives
Generate concept art with different artistic styles
Test the model's capabilities with architectural visualization
Compare results between simple and complex prompts
Limitations
Input Constraints: While the model excels with well-crafted text prompts, ambiguous or poorly defined prompts may lead to less satisfactory results.
Output Variability: Despite improved prompt adherence, some generated images may not fully capture complex or highly detailed scene descriptions.
Resource Requirements: High-resolution image generation may demand substantial computational resources, potentially impacting performance on lower-end hardware.
Some complex prompts may result in overly polished images
Performance may vary based on prompt complexity
Output Format: PNG, JPG
