FLUX HF LoRA
flux-hf-lora
FLUX.1-Dev LoRA is developed by Black Forest Lab an AI model designed for precise image generation and fine-tuning.
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-hf-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 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: ~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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