Image Generation

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.

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:

image	caption
images/001.jpg	a red sports car in cyberpunk style
images/002.jpg	a blue sedan in cyberpunk style
images/003.jpg	a motorcycle in cyberpunk style

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}")

# Create fine-tune
curl -X POST https://hub.oxen.ai/api/repos/YOUR_NAMESPACE/YOUR_REPO/fine_tunes \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "resource": "main/images.parquet",
    "base_model": "black-forest-labs/FLUX.1-dev",
    "script_type": "image_generation",
    "training_params": {
      "image_column": "image",
      "caption_column": "caption",
      "steps": 2000
    }
  }'

# Start training (use the ID from the response)
curl -X POST https://hub.oxen.ai/api/repos/YOUR_NAMESPACE/YOUR_REPO/fine_tunes/FINE_TUNE_ID/actions/run \
  -H "Authorization: Bearer YOUR_API_KEY"

Key Parameters

Only these fields are required to start:

Parameter	Description	Example
`image_column`	Name of your image column	`"image"`, `"file"`, `"path"`
`caption_column`	Name of your caption/prompt column	`"caption"`, `"prompt"`, `"description"`
`steps`	Number of training steps (1000-3000 typical)	`2000`

All other parameters use sensible defaults.

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.

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)

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']}")

Next Steps

Advanced parameters - Learning rate, LoRA, sampling settings
Deploy your model - Generate images with your fine-tuned model
Parameter guide - Understanding training parameters

Common Issues

Images not loading

Ensure image paths are relative to your repository root, or use full URLs. Check that images are committed to your Oxen repository.

Out of memory error

Reduce batch_size to 1. Image models require significant GPU memory.

Results don't match my style

Try training for more steps (3000-5000) or increase your dataset size. Ensure captions clearly describe the unique aspects of your style.

Training taking too long

Start with 1000 steps for testing. FLUX models take 1-2 hours on GPU for 2000 steps.

Fine-Tuning API

API Reference

fine_tunes

evaluations

Image Generation

Overview

Your Data

Minimal Example

Key Parameters

Supported Models

Data Requirements

Monitor Progress

Next Steps

Common Issues

​Overview

​Your Data

​Minimal Example

​Key Parameters

​Supported Models

​Data Requirements

​Monitor Progress

​Next Steps

​Common Issues

Overview

Your Data

Minimal Example

Key Parameters

Supported Models

Data Requirements

Monitor Progress

Next Steps

Common Issues