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Overview

Fine-tune image generation models to create images in your specific style, brand, or artistic direction. Perfect for branded content, character design, or artistic styles.

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Your Data

Your training data should have:
  • Image column - Paths or URLs to your training images
  • Caption column - Text descriptions of each image
Example data in images.parquet:
imagecaption
images/001.jpga red sports car in cyberpunk style
images/002.jpga blue sedan in cyberpunk style
images/003.jpga motorcycle in cyberpunk style

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Minimal Example

import requests

url = "https://hub.oxen.ai/api/repos/YOUR_NAMESPACE/YOUR_REPO/fine_tunes"
headers = {
    "Authorization": "Bearer YOUR_API_KEY",
    "Content-Type": "application/json"
}

# Create fine-tune
data = {
    "resource": "main/images.parquet",
    "base_model": "black-forest-labs/FLUX.1-dev",
    "script_type": "image_generation",
    "training_params": {
        "image_column": "image",         # Your image column name
        "caption_column": "caption",     # Your caption column name
        "steps": 2000
    }
}

response = requests.post(url, headers=headers, json=data)
fine_tune_id = response.json()["fine_tune"]["id"]

# Start training
run_url = f"{url}/{fine_tune_id}/actions/run"
requests.post(run_url, headers=headers)

print(f"Fine-tune started: {fine_tune_id}")

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Key Parameters

Only these fields are required to start:
ParameterDescriptionExample
image_columnName of your image column"image", "file", "path"
caption_columnName of your caption/prompt column"caption", "prompt", "description"
stepsNumber of training steps (1000-3000 typical)2000
All other parameters use sensible defaults.

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Supported Models

Popular choices for image generation:
  • black-forest-labs/FLUX.1-dev - High quality, state-of-the-art
  • black-forest-labs/FLUX.2-dev - Latest version, even better quality
  • Qwen/Qwen-Image - Fast, good for quick iterations
See the full model list for all available options.

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Data Requirements

For best results:
  • Quantity: 10-50 images minimum, 100-500 images ideal
  • Quality: High resolution, consistent style
  • Captions: Descriptive prompts that explain what makes your images unique
  • Consistency: Images should share common elements (style, subject, theme)

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Monitor Progress

Image models generate sample outputs during training. Check them to see progress: 
status_url = f"https://hub.oxen.ai/api/repos/YOUR_NAMESPACE/YOUR_REPO/fine_tunes/{fine_tune_id}"
response = requests.get(status_url, headers=headers)
fine_tune = response.json()["fine_tune"]

print(f"Status: {fine_tune['status']}")
print(f"Current step: {fine_tune.get('current_step', 0)}")

# Sample images are generated every 200 steps by default
if "sample_outputs" in fine_tune:
    print(f"Sample images: {fine_tune['sample_outputs']}")

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Next Steps

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Common Issues

Ensure image paths are relative to your repository root, or use full URLs. Check that images are committed to your Oxen repository.
Reduce batch_size to 1. Image models require significant GPU memory.
Try training for more steps (3000-5000) or increase your dataset size. Ensure captions clearly describe the unique aspects of your style.
Start with 1000 steps for testing. FLUX models take 1-2 hours on GPU for 2000 steps.