FLUX-LORA
FLUX.1-Dev LoRA is text to image AI model designed for precise image generation and fine-tuning.
Official Partner
Avg Run Time: 15.000s
Model Slug: flux-hf-lora
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Input
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Output
Example Result
Preview and download your result.

API & SDK
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.
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.
Readme
Overview
The FLUX HF 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 HF 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 HF 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 HF LoRA.
Fine-tune results for specific creative or professional needs.
High flexibility through user-adjustable parameters.
What Can I Use It 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
Pricing
Pricing Detail
This model runs at a cost of $0.001540 per second.
The average execution time is 15 seconds, but this may vary depending on your input data.
The average cost per run is $0.023100
Pricing Type: Execution Time
Cost Per Second means the total cost is calculated based on how long the model runs. Instead of paying a fixed fee per run, you are charged for every second the model is actively processing. This pricing method provides flexibility, especially for models with variable execution times, because you only pay for the actual time used.
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