Train a Custom Style (LoRA)

Train a custom style using your own images to generate AI art with consistent visual aesthetics.

Train a Custom Style (LoRA)

Train a custom style using your own images to generate AI art with consistent visual aesthetics.

Overview

LoRA (Low-Rank Adaptation) is a fine-tuning technique that teaches an AI model a new visual style using a small set of example images. It works by injecting new “style weights” into an existing model, making training both fast and efficient. This guide walks through the complete workflow:

Prepare Training Images

Curate and upload high-quality images that represent your desired style

Submit Training Job

Send a POST request to /styles/train with your image URLs and configuration

Monitor Progress

Track your training job status using the returned job_id

Generate with Your Style

Apply your trained style to image generation

Prepare Training Images

Curating Your Dataset

The quality of your training images directly impacts results. Different training types have different requirements:

Type	Use Case	Tips
Style	Artistic styles, visual aesthetics	Consistent style across varied subjects
Character	Personal likeness, consistent characters	Varied poses, expressions, lighting
Object	Specific items, products	Multiple angles, consistent object

How Many Images?

Quality matters far more than quantity. A small set of excellent images will outperform a large set of mediocre ones.

Dataset Size	Guidance
5 images	Minimum viable. Can work for simple, consistent styles
10-30 images	Recommended. Best balance of quality and coverage
50+ images	Diminishing returns unless style has high variation

Quality > Quantity15 high-quality images will produce better results than 50 low-quality ones. Prioritize:

High resolution (1024x1024 minimum)
Consistent style across all images
No watermarks, text overlays, or compression artifacts
Varied subjects while maintaining style coherence

Example Datasets

Character training: Photos of a person with varied poses, expressions, and lighting conditions. Avoid including other people in the images.
Style training: A collection of artwork in a consistent style. For example, The Metropolitan Museum of Art Ukiyo-E Dataset provides Japanese woodblock prints ideal for training an artistic style.

Upload Images

Before training, upload your images to get hosted URLs. Use the /assets endpoint:

import requests
import os
from dotenv import load_dotenv
import mimetypes

load_dotenv()

API_BASE = "https://api.krea.ai"
API_TOKEN = os.getenv("API_TOKEN")

image_dir = "training_images"
uploaded_urls = []

for filename in os.listdir(image_dir):
    if filename.lower().endswith(('.jpg', '.jpeg', '.png', '.webp')):
        filepath = os.path.join(image_dir, filename)
        mime_type, _ = mimetypes.guess_type(filepath)

        with open(filepath, 'rb') as f:
            response = requests.post(
                f"{API_BASE}/assets",
                headers={"Authorization": f"Bearer {API_TOKEN}"},
                files={"file": (filename, f, mime_type)},
                data={"description": f"Training image: {filename}"}
            )

        if response.ok:
            data = response.json()
            uploaded_urls.append(data["image_url"])
            print(f"Uploaded: {filename}")
        else:
            print(f"Failed: {filename}")

print(f"\nUploaded {len(uploaded_urls)} images")

Save the returned image_url values—you’ll pass these to the training endpoint.

Train Your Style

Basic Training Example

Submit your image URLs to start training:

import requests
import os
from dotenv import load_dotenv

load_dotenv()

API_BASE = "https://api.krea.ai"
API_TOKEN = os.getenv("API_TOKEN")

# Training URLs from the upload step
urls = [
    "https://krea.ai/assets/img1.jpg",
    "https://krea.ai/assets/img2.jpg",
    "https://krea.ai/assets/img3.jpg",
    # ... more images
]

response = requests.post(
    f"{API_BASE}/styles/train",
    headers={
        "Authorization": f"Bearer {API_TOKEN}",
        "Content-Type": "application/json"
    },
    json={
        "name": "Ukiyo-E Style",
        "urls": urls,
        "model": "flux_dev",
        "type": "Style",
        "max_train_steps": 500
    }
)

response.raise_for_status()
job = response.json()
print(f"Training started! Job ID: {job['job_id']}")

Training Types

The type parameter sets intelligent defaults optimized for your use case:

Type	Best For
`Style`	Artistic styles, visual aesthetics
`Character`	Personal likeness, consistent characters
`Object`	Specific items, products
`Default`	Generic training

Parameters

Required Parameters

name

string

required

A descriptive name for your custom style.Example: "Ukiyo-E Style", "Product Photos"

urls

array

required

Array of image URLs to train on. Include more images for better results.

Optional Parameters

model

string

default:"flux_dev"

Base model for training:Image models:

flux_dev - High quality, versatile
flux_schnell - BFL’s realtime model
qwen - Alibaba’s model
wan22 - Image generation only

Video models:

wan - Alibaba’s video model

type

string

default:"Default"

Training category: Style, Object, Character, or Default

trigger_word

string

Custom word to activate this style in prompts. When not specified, uses the style name.

Choose unique trigger words that won’t appear in typical prompts. Use underscores for multi-word triggers: ukiyo_style

Advanced Parameters

learning_rate

number

Controls training intensity. Higher values train faster but may overfit.Recommended range: 0.0001 - 0.001

max_train_steps

integer

Maximum training iterations. Range: 1-2000

batch_size

integer

Images processed simultaneously. Larger batches = faster training but more memory.

Tuning Advanced Parameters

Start with defaults set by the type field—they work well for most cases. Only adjust these if you’re seeing specific issues:

Learning Rate

Controls how aggressively the model adapts to your training images.

Value	When to Use
0.0001 (lower)	Overfitting issues, complex styles, small datasets
0.0003 (default)	Most use cases
0.0005-0.001 (higher)	Faster training

Signs you need to adjust:

Outputs look identical to training images → lower the rate
Style influence is weak after training → raise the rate slightly

Training Steps

How long the model trains on your images.

Dataset Size	Recommended Steps
5-10 images	300-500 steps
15-30 images	500-800 steps
50+ images	800-1500 steps

Signs you need to adjust:

Outputs are too rigid, ignoring prompts → reduce steps
Style influence is weak → increase steps
Generated images look exactly like training data → reduce steps (overfitting)

Iterative TuningIf your first training attempt doesn’t produce the results you want:

Start by adjusting max_train_steps (most common fix)
Then try learning_rate if steps alone don’t help

Response Format

{
  "job_id": "550e8400-e29b-41d4-a716-446655440000",
  "status": "queued",
  "created_at": "2024-01-15T10:30:00Z"
}

Monitor Training Progress

Training typically takes 5-15 minutes. Poll the Jobs API to check status:

import requests
import time
import os
from dotenv import load_dotenv

load_dotenv()

API_BASE = "https://api.krea.ai"
API_TOKEN = os.getenv("API_TOKEN")
job_id = "your-job-id"

while True:
    response = requests.get(
        f"{API_BASE}/jobs/{job_id}",
        headers={"Authorization": f"Bearer {API_TOKEN}"}
    )
    job = response.json()
    status = job["status"]

    print(f"Status: {status}")

    if status == "completed":
        style_id = job["result"]["style_id"]
        print(f"Training complete! Style ID: {style_id}")
        break
    elif status in ["failed", "cancelled"]:
        print(f"Training {status}")
        break

    time.sleep(30)

Job Status Values

Training jobs progress through these states:

queued - Waiting in queue
processing - Active training
completed - Training finished successfully
failed - Training encountered an error
cancelled - Job manually cancelled

Use Your Trained Style

Once training completes, apply your style to image generation using the styles parameter:

import requests
import time
import os
from dotenv import load_dotenv

load_dotenv()

API_BASE = "https://api.krea.ai"
API_TOKEN = os.getenv("API_TOKEN")
STYLE_ID = "w29t6pvy0"

response = requests.post(
    f"{API_BASE}/generate/image/bfl/flux-1-dev",
    headers={
        "Authorization": f"Bearer {API_TOKEN}",
        "Content-Type": "application/json"
    },
    json={
        "prompt": "An abstract, colorful, surreal composition of symmetry and balance. Swirling colors, imagery of trees and coalescing patterns converge. The lantern of light and death. It is as if the world was at once dark, and yet again lit.",
        "width": 1024,
        "height": 1024,
        "steps": 20,
        "styles": [
            {
                "id": STYLE_ID,
                "strength": 0.95
            }
        ]
    }
)

job = response.json()
job_id = job["job_id"]
print(f"Generation started! Job ID: {job_id}")

# Poll for completion
while True:
    check = requests.get(
        f"{API_BASE}/jobs/{job_id}",
        headers={"Authorization": f"Bearer {API_TOKEN}"}
    )
    status_data = check.json()

    if status_data["status"] == "completed":
        image_url = status_data["result"]["urls"][0]
        print(f"Image ready: {image_url}")
        break
    elif status_data["status"] == "failed":
        print("Generation failed")
        break

    time.sleep(2)

Style Strength

The strength parameter (0.0-1.0) controls how strongly your style is applied:

Strength	Effect
0.5-0.7	Subtle influence, maintains prompt flexibility
0.8-0.9	Strong style application, recommended starting point
0.95-1.0	Maximum style adherence, may reduce prompt responsiveness

Start with 0.8 strength and adjust based on results. Lower values give more creative freedom; higher values enforce stricter style adherence.

Combining Multiple Styles

Apply multiple styles by adding them to the styles array:

"styles": [
    {"id": "style-id-1", "strength": 0.6},
    {"id": "style-id-2", "strength": 0.4}
]

Best Practices

Image Selection

Use as many high-quality images as you have for optimal results
Ensure consistent style across all training images
Include variety in subjects while maintaining style coherence
Avoid watermarks, text overlays, or artifacts
Use images at least 1024x1024 resolution

Training Configuration

Start with default parameters using the type field
For styles: 500-1000 steps is usually sufficient
Lower learning rates (0.0001-0.0003) prevent overfitting
Increase steps if style isn’t strong enough
Decrease steps if output is too rigid

Trigger Words

Use the same trigger word if you plan on combining multiple styles
Trigger words are automatically injected into the prompt if you include the style
Avoid common words that appear in typical prompts
Use underscores for multi-word triggers: my_custom_style

Getting Started

Core Concepts

Code Examples

Tutorials

Train a Custom Style

Train a Custom Style (LoRA)

Train a Custom Style (LoRA)

Overview

Prepare Training Images

Curating Your Dataset

How Many Images?

Upload Images

Train Your Style

Basic Training Example

Training Types

Parameters

Required Parameters

Optional Parameters

Tuning Advanced Parameters

Response Format

Monitor Training Progress

Use Your Trained Style

Style Strength

Combining Multiple Styles

Best Practices

Getting Started

Core Concepts

Code Examples

Tutorials

Train a Custom Style (LoRA)

Train a Custom Style (LoRA)

​Overview

​Prepare Training Images

​Curating Your Dataset

​How Many Images?

​Upload Images

​Train Your Style

​Basic Training Example

​Training Types

​Parameters

​Required Parameters

​Optional Parameters

​Tuning Advanced Parameters

​Response Format

​Monitor Training Progress

​Use Your Trained Style

​Style Strength

​Combining Multiple Styles

​Best Practices

Overview

Prepare Training Images

Curating Your Dataset

How Many Images?

Upload Images

Train Your Style

Basic Training Example

Training Types

Parameters

Required Parameters

Optional Parameters

Tuning Advanced Parameters

Response Format

Monitor Training Progress

Use Your Trained Style

Style Strength

Combining Multiple Styles

Best Practices