My ultimate Flux LoRA training guide
This material was created to help artists, creatives, developers, and curious minds master the process of creating LoRA models using Flux, one of the most promising and revolutionary technologies in the field of generative artificial intelligence.
Whether you're a beginner or someone already familiar with model training, this guide has been designed to be straightforward, practical, and full of insights that will optimize your results.
What is a LoRA?
LoRA, or Low-Rank Adaptation, is a fine-tuning technique that allows AI models to be trained quickly and efficiently. Instead of training an entire model from scratch, LoRA adapts pre-existing models, using much less computational resources and time. It has become extremely popular because:
Reduces training costs: Less GPU, more accessible.
Is flexible: Can be used to train very specific concepts, such as an artist's style, character appearance, or object details.
Is shareable: LoRA models are compact files that can be used and combined with other base models.
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What is Flux?
Flux is a new AI model architecture that goes beyond Stable Diffusion's capabilities, offering greater fidelity, quality, and creative control. It enables the generation of images with impressive precision, capturing details and nuances that were previously difficult to achieve with other technologies.
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Why Train a LoRA in Flux?
Flux stands out for its ease of training and quality of results. It excels in:
Precise Replication
Whether capturing the essence of a person, character, artistic style, or product, Flux delivers unmatched fidelity.
Creative Enhancement
Introduces new controls and possibilities, enabling faster and richer workflows.
Versatility
Perfect for businesses, artists, and agencies looking to create mass visual content while maintaining consistency and customization.
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Guide Structure
This guide is divided into two main parts:
Essential Fundamentals
Dataset construction.
Caption creation.
Training configurations.
Detailed explanation of key parameters such as learning rate, steps, and epochs.
Practical Applications by LoRA Type
Characters: How to replicate individuals or characters with fidelity.
Artistic Styles: Capturing the essence of an existing style or creating something new.
Objects and Products: Training models for specific products, with commercial application.
Training Configuration
Learn to adjust training parameters.
Training refinement tips.
Each section includes detailed examples, best practices, and tips to avoid common mistakes.
With this, we're ready to dive into the fundamental concepts that will serve as the foundation for training LoRAs in Flux.
Understanding the Fundamentals
Training a LoRA in Flux is like teaching a new skill to an AI. For this, we need three main ingredients: datasets, captions, and the right parameters. In this section, we'll demystify what these elements are and why they're crucial for your model's success.
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Dataset
The dataset is the collection of images that serves as the basis for LoRA training. It needs to be carefully selected and prepared to ensure the model learns concepts clearly and accurately.
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Key Characteristics of a Well-Crafted Dataset:
Consistency: Ensure all images represent the same concept or style, avoiding drastic changes.
Quality: Use sharp, well-lit, high-resolution images (minimum 512x512, ideally 1024x1024).
Controlled Variety: Introduce variations in angle, pose, lighting, and setting without compromising concept uniformity.
Organization: Structure images in folders and name files logically to facilitate training and future adjustments.
Ideal Size: A number between 15 to 50 images is sufficient for most LoRAs, depending on concept complexity.
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Captions
Captions are descriptions associated with each image in the dataset. They guide the model during training, providing contextual information about image content.
Captions work as a bridge between images and model learning. They help the model identify key elements that should be learned, ensuring the LoRA captures desired characteristics.
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Training Configurations
Training configurations define how the model will process the dataset and learn from it. These configurations include parameters such as resolution, learning rate, number of steps and epochs, among others.
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Main Training Parameters:
Learning Rate
Defines the model's learning pace. Too high a rate can cause instability, while too low a rate can prolong training.
Steps and Epochs
Steps refer to the total number of iterations during training.
Epochs indicate how many times the model will process all dataset images.
In Flux, steps are usually more important than epochs.
Batch Size
Number of images processed simultaneously in each iteration. Larger batches require more memory but can speed up training.
Resolution
Higher resolutions capture more details but require more processing power. Standard is 512x512, but 1024x1024 is ideal for high quality.
Snapshot Interval
Defines how frequently the model will save intermediate versions during training. Useful for identifying the optimal learning point.
Trigger Word
Word or phrase used to activate the LoRA in prompts. Should be unique and easily identifiable.
With these fundamentals, you'll have a solid foundation for creating and training high-quality LoRAs in Flux. In the following chapters, we'll explore how to apply these concepts to different types of LoRAs, providing specific guidance for characters, styles, and objects.
Character LoRA
Training a character LoRA is a delicate process that requires attention to dataset quality and consistency. The idea is to faithfully capture unique characteristics of an individual, such as appearance, expressions, and posture, ensuring the model reproduces these elements accurately in different scenarios and prompts.
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Image Selection and Preparation
Ideal Image Quantity:
Minimum: 15 images.
Recommended: 25 to 60 high-quality images.
More images can be useful for complex characters, but avoid extensive or inconsistent datasets.
Character Focus:
Images should highlight face, body, and unique details like hairstyles, characteristic clothes, or facial marks.
Bad Example: Photos with exaggerated accessories or other elements that divert attention from the character.
Good Example: Images with centered character, simple clothes, and no distractions.
Controlled Variety:
Include different angles (frontal, side, and three-quarters).
Vary lighting and settings while maintaining character consistency.
Example Distribution:
6 full-body photos.
10 waist-up photos.
10 face close-ups.
Avoid Confusion:
Don't include flashy accessories like large hats, sunglasses, or extreme makeup that alter character perception.
Don't use photos with other characters, even in the background.
Style and Appearance Consistency:
If the character has short black hair, all images should reflect this.
Avoid sudden changes like different hair colors, cuts, or weight.
Avoid Low-Quality Images:
Blurry, dark, or pixelated photos can harm training.
Highlight Natural Expressions:
Use images that capture typical character emotions and expressions.
Avoid: Exaggerated expressions or caricatured poses.
Visual Effects and Backgrounds:
Simple backgrounds help maintain focus on the character.
Use tools to remove complex backgrounds if necessary.
Diversity Control:
Ensure images don't have recurring elements that could bias the model, like the same background or lighting in all photos.
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Dataset Example
Character: Redhead Woman with Freckles
Quantity: 25 images.
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Distribution:
6 full-body photos (standing, from different angles).
10 waist-up photos (relaxed poses in varied environments).
9 close-ups (with natural expressions and soft lighting).
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General Description:
Distinctive features: Red hair, facial freckles, green eyes.
Clothing style: Light, casual dresses.
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Captions:
Full Body Photo:
”full body photo of [trigger] woman standing confidently, long red hair with visible freckles, wearing a light green summer dress, in a sunny meadow with wildflowers, soft natural lighting”
Waist-Up Photo:
”medium shot photo of [trigger] woman sitting at a wooden table, long red hair and visible freckles, wearing a casual white blouse, in a cozy cafe, warm ambient lighting”
Close-Up:
”close-up photo of [trigger] woman with long red hair, visible freckles, and green eyes, slightly smiling, wearing a soft beige sweater, against a blurred golden sunset background, soft natural lighting”
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Common Mistakes When Training Character LoRAs
Images with Too Many Distractive Details:
Elaborate settings can divert focus from the character.
Solution: Use images with neutral backgrounds or remove unnecessary elements.
Drastic Appearance Changes:
Photos showing the character with drastically different haircuts or clothes can confuse the model.
Solution: Maintain style consistency.
Lack of Controlled Diversity:
A dataset with very similar images (same pose or setting) can result in a rigid model.
Solution: Vary angles, lighting, and expressions.
With these practices, you'll be ready to create a solid and well-structured dataset for character LoRAs, maximizing your training potential in the Flux model.
Style LoRA
Training a style LoRA is a powerful technique for capturing artistic characteristics of an aesthetic, art movement, or specific author. The goal is to ensure the model faithfully reproduces traits like brushstrokes, color palette, and characteristic compositions, maintaining creative flexibility in different scenarios.
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Image Selection and Preparation
Ideal Image Quantity:
Minimum: 20 images.
Recommended: 30 to 100 high-quality images.
Avoid using excessive images that don't add style variation.
Style Focus:
Images should be consistent, exclusively representing the style in question.
Bad Example: Works of varied styles mixed in the same dataset.
Good Example: A cohesive set of impressionist paintings from different artists.
Controlled Variety:
Include different types of composition (landscapes, portraits, still life) to increase flexibility.
Maintain consistency in style's striking features.
Avoid Confusion:
Don't use images with watermarks, logos, or elements that don't reflect the style.
Remove details that might divert training, like signatures or text in images.
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Captions
Simple and Cohesive Structure:
Style LoRA captions should be consistent, focusing exclusively on style description.
Examples:
painting in the style of [trigger][trigger] style[trigger]landscape in the style of [trigger]
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Common Mistakes When Training Style LoRAs
Style Mixing:
Using images of different styles in the same dataset can generate inconsistent results.
Solution: Ensure all images represent the same artistic style.
Excess Context in Captions:
Adding unnecessary information in captions can confuse the model.
Solution: Use only simple and direct captions.
Low-Quality Images:
Pixelated or blurry photos harm training.
Solution: Use high-resolution images, preferably above 1024x1024 pixels.
With these guidelines, you'll be able to create robust style LoRAs capable of faithfully reproducing artistic style characteristics in the Flux model. Let's now explore training LoRAs for objects and products.
Object/Product LoRA
Training an object or product LoRA is useful for capturing the unique appearance of specific items like vehicles, clothes, technology, or accessories. The goal is to ensure the model reproduces these items consistently and in detail.
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Image Selection and Preparation
Ideal Image Quantity:
Minimum: 20 images.
Recommended: 30 to 100 high-quality images.
Object Focus:
Images should highlight the object from different angles, settings, and lighting.
Bad Example: Photos with multiple objects in the same frame.
Good Example: Clean images with centered object.
Controlled Variety:
Include images from different angles (top, front, side, and back).
Maintain consistency in object details.
Avoid Confusion:
Don't use images with elements that might divert focus, like secondary objects or complex backgrounds.
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Captions
Standard Structure:
[camera angle] [medium] of [trigger] object/product [description] [local] [light]
Examples:
Frontal photo of [trigger] car, metallic finish, in a neon-lit cityscape, under soft artificial lighting
close-up photo of [trigger] ceramic coffee mug with a matte black finish, on a wooden table, morning sunlight streaming in
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Common Mistakes When Training Object/Product LoRAs
Insufficient Object Focus:
Using images with backgrounds or distractive elements can harm training.
Solution: Center the object and remove unnecessary elements.
Insufficient Angle Variety:
Only one object perspective can limit the model.
Solution: Include images from multiple angles and distances.
Low-Quality Images:
Blurry or pixelated photos can generate unsatisfactory results.
Solution: Use high-resolution images (minimum 1024x1024).
Training Configurations
Training configurations are the backbone of a successful LoRA model. They control how Flux processes your dataset, learns from it, and adjusts its internal parameters to capture desired characteristics.
In this section, we'll explore the main training parameters, explaining their functions and how to adjust them to achieve optimal results. The goal is to provide the foundation for you to not only follow standard configurations but also have confidence to experiment with them and adjust them according to model behavior and dataset nature.
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Main Parameters
Learning Rate:
Default Value:
1e-4Recommended Range:
5e-5to2e-4.Why: A lower value (
5e-5) helps avoid overfitting in small or specific datasets, while higher values (2e-4) accelerate learning in larger datasets.When to Adjust:
Increase: Larger or more varied dataset.
Reduce: Small or very specific dataset.
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Steps:
Default Value: 1000 steps.
Recommended Range: 2000 to 5000 steps.
Why: Fewer steps reduce the risk of overfitting, while more steps increase accuracy in varied datasets.
For style LoRAs, fewer steps (500-700) are usually sufficient to capture the pattern without overfitting.
For characters or products with many details, more steps (2000-3000) may be necessary to ensure accuracy.
When to Adjust:
Increase: For complex styles or objects.
Reduce: For simple or very specific concepts.
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Batch Size:
Default Value: 4.
Recommended Range: 2 to 24.
Why: Larger batches increase training stability on more powerful GPUs, while smaller batches reduce memory demand.
GPUs like RTX 3090 (24GB) handle larger batches well (
16+).GPUs with less VRAM (8-12GB) may need to reduce to
2-8.Generally, 4 is a great default to balance quality and efficiency.
When to Adjust:
Increase: If your GPU supports more memory (VRAM).
Reduce: If facing memory issues or smaller datasets.
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Resolution:
Minimum Value:
512x512.Recommended:
1024x1024.Why: Higher resolutions capture more details but require more memory.
Although
1024x1024is recommended for maximum quality, training with512x512is more efficient for hardware with less VRAM.Hybrid resolutions (for example, using bucketing with
512x512,768x768, and1024x1024images) can improve result consistency.
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Trigger Word:
Recommended: A coded word
Example: GALAX1W4TCH = Galaxy Watch
Using a coded word like "GALAX1W4TCH" works well to avoid interference with other pre-trained tokens in the base model.
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Save Every N Steps/Epochs:
Recommended: 100-400 steps or 1 Epoch (Iteration where the model processes the entire dataset)
Why: Ensures regular checkpoints to compare intermediate results.
When to Adjust: Saving at smaller intervals (
100) helps refine the model, while larger intervals (400) save time and storage space.Saving every x steps or each complete epoch is ideal to ensure you are going to use the best model.
It's especially important in long training sessions to avoid progress loss if something goes wrong.
Training Refinement Tips
After configuring and starting training, model refinement is essential to achieve consistent and flexible results. Here are some practical tips for evaluating and adjusting your training:
Test Intermediate Checkpoints:
Save checkpoints at regular intervals (100-250 steps) and test each with sample prompts.
Evaluate model fidelity to the original concept and its ability to generate creative variations.
Adjust LoRA Strength:
Experiment with different weights for the LoRA (between
0.6and1.2) in prompts. This helps identify the ideal balance between LoRA and base model.
Compare Visual Results:
Generate side-by-side images using different Epochs and analyze which offers the best result for your purpose.
Use tools like ComfyUI to facilitate visual comparisons.
Review Dataset:
If the model shows inconsistencies, review your dataset to eliminate images that might be introducing noise or confusion.
Trigger Word Evaluation:
Test prompts with and without the trigger word. If the model generates satisfactory results without the trigger, consider revising training to strengthen association.
With this guide in hand, you have the tools and knowledge needed to explore training LoRAs in Flux, a technology that's redefining the boundaries of creativity. Whether your goal is to replicate iconic characters, create new artistic styles, or capture the essence of unique products, Flux offers endless possibilities to transform ideas into reality. The secret to success lies in combining a well-structured dataset, precise captions, and optimized configurations. Experiment, learn from results, and continue evolving your techniques. The creative AI revolution is just beginning, and you're now part of it.
