FLUX Dev LoRA

flux-dev-lora

FLUX.1-Dev LoRA is an AI model designed for precise image generation and fine-tuning.

Partner Model

A100 80GB

Fast Inference

REST API

Try in Console API Docs Examples

Model Information

Response Time~15 sec

StatusActive

Version

0.0.1

Updated11 days ago

API Reference

View Full Documentation

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 requests
import time

API_KEY = "YOUR_API_KEY"  # Replace with your API key
HEADERS = {
    "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-dev-lora",
            "version": "0.0.1",
            "input": {
  "seed": null,
  "image": "your image here",
  "prompt": "your prompt here",
  "hf_lora": "your hf lora here",
  "lora_scale": "0.8",
  "num_outputs": "1",
  "aspect_ratio": "1:1",
  "output_format": "webp",
  "guidance_scale": "3.5",
  "output_quality": "80",
  "prompt_strength": "0.8",
  "num_inference_steps": "28",
  "disable_safety_checker": "true"
}
        }
    )
    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 result
        elif 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 prediction
    prediction_id = create_prediction()
    print(f"Prediction created: {prediction_id}")
    
    # Get result
    result = 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: ~15 seconds
Rate limit: 60 requests/minute
Concurrent requests: 10 maximum
Use long-polling to check prediction status until completion

Overview

The Flux Dev LoRA Model is a robust and customizable model designed for generating high-quality image outputs through fine-tuning and LoRA (Low-Rank Adaptation) techniques. This model excels in producing tailored results by leveraging user-defined inputs such as prompt strength, guidance scale, and aspect ratios. The primary goal of this model is to provide users with creative control while maintaining efficiency and flexibility in various use cases.

Technical Specifications

LoRA Integration: Efficient parameter adaptation for targeted output refinement.
Multi-Aspect Ratio Support: Offers dynamic image scaling for a variety of visual outputs.
Guidance Mechanism: Combines user-driven prompts with model-determined enhancements for balanced outputs.
Output Optimization: Adjustable output quality to cater to specific project needs, from drafts to high-resolution production.

Core Functionalities for Flux Dev LoRA

Flexible prompt input for dynamic creativity.
Support for multiple image aspect ratios.
Fine-grained control over generation quality, inference steps, and LoRA scaling.

Key Considerations

Prompt Strength:

Use lower values (e.g., 0.2–0.4) for subtle prompt influence.
Higher values (e.g., 0.8–1.0) prioritize strict adherence to the prompt but may reduce creativity.

Inference Steps:

Lower values (10–20) result in faster outputs but may lack detail.
Higher values (30–50) provide refined outputs at the cost of longer processing times.

Output Quality:

Values between 70–90 strike a balance between file size and visual fidelity.
Use 90–100 for publication-ready results.

LoRA Scale:

Use 0.5–0.7 for moderate impact.
Values closer to 1.0 prioritize LoRA weights but may overpower other parameters.

Tips & Tricks

Input Optimization for Flux Dev LoRA for Best Results

Prompt:
- Keep prompts concise and contextually relevant.
- Avoid conflicting details in the prompt to ensure clarity in the output.
Aspect Ratio:
- For social media visuals, use 4:5 or 5:4.
- For cinematic content, opt for 21:9.
Prompt Strength:
- Use lower strengths when combining multiple inputs like images and prompts.
- Increase strength when the text prompt is the primary guide.
Guidance Scale:
- Use 3–5 for balanced outputs.
- Increase to 7–9 for precise adherence to detailed prompts.
LoRA Scale:
- Use 0.4–0.6 for subtle adaptations.
- Maximize to 1.0 for scenarios requiring significant LoRA weight.
Num Inference Steps:
- Use 15–25 for drafts.
- Set to 40–50 for high-detail outputs.
Output Quality:
- Set to 80–90 for general usage.
- Maximize for high-resolution requirements or publication.
Disable Safety Checker:
- Only disable if absolutely necessary and ensure content aligns with usage policies.

Capabilities

Generate visually striking outputs with minimal effort with Flux Dev LoRA.

Fine-tune results for specific creative or professional needs.

High flexibility through user-adjustable parameters.

What can I use for?

Creative Projects: Generate artwork, illustrations, or concept designs.

Content Creation: Produce high-quality visuals for marketing or social media.

Experimentation: Explore LoRA’s adaptability for unique use cases.

Things to be aware of

Experiment with different aspect ratios to explore various framing styles.

Use the same seed value with slight variations in prompt strength to compare outputs.

Adjust inference steps and guidance scale to find the optimal balance between speed and quality.

Test with and without LoRA scaling to observe its impact on outputs.

Limitations

Aspect Ratio Support: While versatile, some ratios may require manual cropping for non-standard sizes.

Processing Time: High inference steps or quality settings can increase generation time.

Output Consistency: Randomized seed values can lead to variations between runs.

Output Format:WEBP,PNG,JPG

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