Why Variety Matters: Diversity in your dataset is crucial because LoRAs learn patterns from your training data. If your dataset contains too many similar images, the LoRA may "bake in" repetitive characteristics that become difficult to override during generation. For example:

Too many images with the same pose → LoRA always generates that pose
Repetitive facial expressions → Limited emotional range in outputs
Consistent lighting/backgrounds → LoRA struggles with different environments
Similar angles → Poor performance when requesting different viewpoints

A varied dataset ensures your LoRA remains flexible and responsive to different prompts rather than being locked into learned patterns.

Dataset Requirements:

30-100 images at 1024x1024 resolution (optimal for Flux)
More images can improve results, provided they maintain variety
Place all images in a single folder

Alternative: Synthetic Dataset Generation If you have a single character design, you can use the Image to Character LoRA Dataset workflow with Flux Kontext to generate a diverse training set automatically.

For this process, you may bring your own character design or create a new one using the Text to Image workflow.

⚠️ Important: When using AI-generated images, manually review and remove any images with:

Deformed anatomy
Nonsensical backgrounds
Visual artifacts or errors

These flaws can be learned and reproduced by your LoRA if left in the training data.

Step 2: Caption Your Dataset

Captions are text descriptions that accompany each training image, teaching the LoRA what it's looking at. These descriptions help the model understand which visual elements correspond to your character versus background elements, clothing, poses, and other scene details. Quality captions are essential because they guide how the LoRA learns to respond to prompts - poorly described images can lead to confused outputs or unwanted associations being learned alongside your character.

Captioning can be done manually by creating text files and writing descriptions of each image, making sure that the file names match (image_01.png, image_01.txt), repeat for each image in your dataset.

Alternatively, you can generate captions automatically with the Detailed Auto Caption workflow:

Input your dataset folder path
Set your desired output path
The workflow will automatically:
- Resize images to 1024x1024 (or appropriate aspect ratio)
- Generate detailed captions for each image
- Save both processed images and matching caption files

This prepares your dataset for training with minimal manual work required.

Step 3: Training Your LoRA

Time to train! Use the ComfyUI Flux LoRA Trainer workflow:

Setup Requirements:

Input your captioned dataset folder path (in all three workflow inputs)
Set your trigger word in all designated nodes
Create a sample prompt for testing
Name your LoRA

Training Process:

Click generate and wait 1.5-3 hours (depending on dataset size)
The training will produce 4 checkpoint benchmarks:
- 750 steps
- 1500 steps
- 2250 steps
- 3000 steps (final)

Step 4: Test and Compare Results

Congratulations! Your LoRA is trained. Now determine which checkpoint works best using the Flux Character LoRA Test and Compare workflow:

Load all 4 LoRA checkpoints (750, 1500, 2250, 3000 steps)
Apply your trigger word
Use the provided example prompts or create your own (trigger word must be included)
Generate a comparison grid of 16 images

Evaluation Tips:

Look for the sweet spot between under-trained and over-trained results
Under-trained LoRAs may lack character accuracy
Over-trained LoRAs may be too rigid or produce artifacts
Compare results using identical prompts, seeds, and settings

Choose the checkpoint that best balances character accuracy with generation flexibility!

LoRA Training Workflows

floyoofficial

566

Animation

Filmmaking

Flux

Game Development

LoRA

Text to Image

Test and compare multiple epochs of a character LoRA side by side with preset prompts When training a LoRA, you'll usually have a few checkpoints throughout the process to test. This workflow lets you load up to 4 LoRAs to test side by side, making it easier to determine which one is right for you! Key Inputs: LoRA Loaders: Load each LoRA epoch for the same character in up to 4 groups. Groups Bypasser: Enable/disable groups as needed. If you only have 2 epochs to test, disable the back 2 groups! Triggerword: Simply add the trigger word for your LoRA and it will auto-fill in the default prompts. Leave blank if you're using your own custom prompts that include the trigger word. LoRA Testing Prompts: Default prompts work well to get an idea of how your character will look in different situations, but feel free to replace them with your own prompts (max 4).

Flux Character LoRA Test and Compare

pixelworld_ai

709

Captioning

Flux

LORA Training

Generate high-quality captions for LoRA training and automatically resize images to SDXL/Flux-compatible resolution.

Detailed Auto Caption

Generate high-quality captions for LoRA training and automatically resize images to SDXL/Flux-compatible resolution.

floyoofficial

1.2k

Flux

LORA Training

Created by @Kijai on Github, please support the original creator!

ComfyUI Flux LoRA Trainer

Created by @Kijai on Github, please support the original creator!

Flux Kontext - Single Image to Character LoRA

pixelworld_ai

1.1k

Dataset

Flux

Kontext

LoRa

Training

Generate a 60-Image LoRA Dataset from a Single Character Image

Flux Kontext - Single Image to Character LoRA

Generate a 60-Image LoRA Dataset from a Single Character Image

Table of Contents

Author

This is an official guide by the Floyo team and we’re pulling up our sleeves every single day to research the latest techniques in the AI Art space.