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Image LoRA Trainer

A CLI tool to train LoRA adapters for text-to-image models using a folder of images with intelligent content-aware cropping.

Stick figure sorting images next to a barrel

How It Works

flowchart TD
    Start(["📁 Input Images"]) --> Load["Load Images"]
    Load --> Check{"Crop Focus - Specified?"}

    Check -->|Yes| YOLO["🔍 YOLO11 Detection"]
    Check -->|No| Center["Center Crop"]

    YOLO --> Found{"Target - Found?"}
    Found -->|Yes| Crop["Smart Crop + Padding"]
    Found -->|No| Skip["⏭️ Skip Image"]

    Center --> Resize["Resize to target resolution"]
    Crop --> Resize

    Resize --> Train["🎯 LoRA Training (Diffusers + PEFT)"]
    Skip --> Log

    Train --> Save["💾 Save LoRA weights"]
    Save --> Log["📊 Generate training_log.json"]

    Log --> End(["✅ Trained LoRA + Logs"])

    style Start fill:#E8F4F8,stroke:#2C5F7C,stroke-width:3px,color:#1a1a1a
    style Load fill:#FFF4E6,stroke:#8B6914,stroke-width:2px,color:#1a1a1a
    style Check fill:#F0E6FF,stroke:#6B46C1,stroke-width:2px,color:#1a1a1a
    style YOLO fill:#E6F7FF,stroke:#1E5A8E,stroke-width:2px,color:#1a1a1a
    style Center fill:#FFF0F5,stroke:#8B4789,stroke-width:2px,color:#1a1a1a
    style Found fill:#F0E6FF,stroke:#6B46C1,stroke-width:2px,color:#1a1a1a
    style Crop fill:#E6FFE6,stroke:#2D5F2D,stroke-width:2px,color:#1a1a1a
    style Skip fill:#FFE6E6,stroke:#8B2E2E,stroke-width:2px,color:#1a1a1a
    style Resize fill:#FFF4E6,stroke:#8B6914,stroke-width:2px,color:#1a1a1a
    style Train fill:#E6F7FF,stroke:#1E5A8E,stroke-width:2px,color:#1a1a1a
    style Save fill:#E6FFE6,stroke:#2D5F2D,stroke-width:2px,color:#1a1a1a
    style Log fill:#FFF4E6,stroke:#8B6914,stroke-width:2px,color:#1a1a1a
    style End fill:#E8F4F8,stroke:#2C5F7C,stroke-width:3px,color:#1a1a1a
Loading

Features

  • Content-Aware Cropping: Uses YOLO11 segmentation to automatically detect and crop to specific objects from the COCO dataset (faces, people, animals, etc.)
  • Smart Filtering: Automatically skips images that don't contain the target feature
  • Training Logs: Generates detailed JSON logs of processed, skipped, and failed images
  • LoRA/QLoRA Training: Full training pipeline using diffusers and peft with optional quantization
  • Multiple Model Support: Works with Stable Diffusion 1.5, SDXL, and Z-Image-Turbo (fast 8-step DiT model)
  • Visual Verification: Includes a generation script to test your trained LoRA

Installation

Requires Python 3.13+ and uv for dependency management.

# Clone the repository
git clone git@github.com:paazmaya/image-lora-trainer.git
cd image-lora-trainer

# Install dependencies
uv sync

GPU Setup (Required)

This tool requires a CUDA-capable GPU. Training on CPU is impractically slow for diffusion models.

  1. Verify you have a CUDA-capable NVIDIA GPU

  2. Install CUDA 13 drivers from NVIDIA's website

  3. Install PyTorch with CUDA support:

    uv pip uninstall torch torchvision -y
uv pip install torch torchvision --index-url https://download.pytorch.org/whl/cu130

    Its about 1.73 GB to download.

  4. Verify GPU is detected:

    uv run python -c "import torch; print('CUDA available:', torch.cuda.is_available()); print('GPU:', torch.cuda.get_device_name(0) if torch.cuda.is_available() else 'No GPU')"

    You should see something like:

    CUDA available: True
GPU: NVIDIA GeForce RTX 4070 Ti

    If you see CUDA available: False, the CPU-only version of PyTorch is installed. Follow step 3 above.

Quick Start

1. Prepare Your Images

Place your training images in a folder:

my_images/
├── photo1.jpg
├── photo2.png
└── photo3.jpg

2. Train a LoRA

Basic training:

uv run python src/main.py --input-dir my_images --base-model runwayml/stable-diffusion-v1-5

With content-aware cropping (only trains on images with faces):

uv run python src/main.py \
  --input-dir my_images \
  --base-model runwayml/stable-diffusion-v1-5 \
  --crop-focus person \
  --resolution 512 \
  --steps 1000

With QLoRA (4-bit quantization for lower memory usage):

uv run python src/main.py \
  --input-dir my_images \
  --base-model stabilityai/stable-diffusion-xl-base-1.0 \
  --use-qlora \
  --resolution 1024

Training Z-Image-Turbo LoRA

Z-Image-Turbo is a fast 6B parameter diffusion transformer that produces high-quality images in just 8 steps.

Basic Z-Image training:

uv run python src/main.py train-zimage \
  --input-dir my_images \
  --instance-prompt "a photo of sks person"

With 8-bit quantization (for lower VRAM usage, ~12GB instead of ~24GB):

uv run python src/main.py train-zimage \
  --input-dir my_images \
  --instance-prompt "a photo of sks person" \
  --use-8bit \
  --steps 500

With all options:

uv run python src/main.py train-zimage \
  --input-dir my_images \
  --instance-prompt "a photo of sks karate practitioner" \
  --crop-focus person \
  --use-8bit \
  --lr 1e-5 \
  --lora-rank 16 \
  --steps 1000

With locally available model:

Learning rate: 1e-5 (lower than SD/SDXL) LoRA rank: 16 (can go higher for more capacity) Steps: 500-1500 for characters/styles

Note: Z-Image-Turbo uses a training adapter by default to prevent the distillation from breaking during training. This is recommended for short training runs (styles, concepts, characters).

3. Check Training Results

After training, check the training_log.json in your output directory:

{
  "base_folder": "/absolute/path/to/my_images",
  "trained": ["image1.png", "image2.png"],
  "skipped": ["image3.png"],
  "failed": []
}

4. Generate Images with Your LoRA

For Stable Diffusion:

uv run python src/generate.py sd \
  --base-model runwayml/stable-diffusion-v1-5 \
  --lora-path stable-diffusion-v1-5_my_images \
  --prompt "a photo of a sks person" \
  --output result.png

For Z-Image-Turbo:

uv run python src/generate.py zimage \
  --lora-path zimage-turbo_my_images \
  --prompt "a photo of sks person, professional studio lighting" \
  --output result.png

Important: Use the same trigger word ("sks" in this example) that you specified in --instance-prompt during training.

More generation examples:

# Portrait with different styling
uv run python src/generate.py sd --lora-path <path> --prompt "portrait of sks person, oil painting"

# Different context
uv run python src/generate.py sd --lora-path <path> --prompt "sks person in a futuristic city"

CLI Options

Training SD/SDXL (src/main.py train)

Option Description Default
--input-dir Path to training images Required
--output-dir Output directory for LoRA Current directory
--base-model Hugging Face model ID or local path runwayml/stable-diffusion-v1-5
--resolution Training image resolution 512
--crop-focus Object to focus on (e.g., "person", "face", "dog") None (center crop)
--use-qlora Enable 4-bit quantization False
--instance-prompt Training prompt with trigger word "a photo of a sks person"
--steps Number of training steps 1000
--epochs Number of epochs (overrides steps) None

Training Z-Image-Turbo (src/main.py train-zimage)

Option Description Default
--input-dir Path to training images Required
--output-dir Output directory for LoRA Current directory
--base-model Z-Image model ID Tongyi-MAI/Z-Image-Turbo
--resolution Training image resolution 1024
--crop-focus Object to focus on None (center crop)
--use-8bit Enable 8-bit quantization False
--no-training-adapter Disable de-distillation adapter False (adapter enabled)
--instance-prompt Training prompt with trigger word "a photo of a sks person"
--steps Number of training steps 1000
--lr Learning rate 1e-5
--lora-rank LoRA rank 16
--lora-alpha LoRA alpha 16
--save-steps Save checkpoint every N steps 500

About --instance-prompt: The instance prompt contains a trigger word (like "sks") that the model learns to associate with your training images. This trigger word is what you'll use later when generating images with the LoRA.

  • Use a unique, uncommon token (e.g., "sks", "xyz", "abc123")
  • Include the class name (e.g., "person", "dog", "style")
  • Example: "a photo of sks person" → Use "sks person" in generation prompts

Generation (src/generate.py sd / src/generate.py zimage)

SD Generation Options:

Option Description Default
--base-model Base model ID or path runwayml/stable-diffusion-v1-5
--lora-path Path to trained LoRA Required
--prompt Generation prompt Required
--output Output filename output.png
--steps Inference steps 30

Z-Image Generation Options:

Option Description Default
--base-model Z-Image model ID Tongyi-MAI/Z-Image-Turbo
--lora-path Path to trained LoRA Required
--prompt Generation prompt Required
--output Output filename output.png
--width Image width 1024
--height Image height 1024
--steps Inference steps 8
--seed Random seed None (random)
--lora-scale LoRA weight scale 1.0

Content-Aware Cropping

When you specify --crop-focus, the tool uses YOLO11 to detect objects in your images:

  • Supported objects: Any object in the COCO dataset (person, dog, cat, car, etc.)
  • Behavior: Images without the target object are automatically skipped
  • Fallback: If no focus is specified, images are center-cropped

Example crop focuses:

  • person - Crops to people
  • face - Crops to faces (use "person" for full body)
  • dog, cat - Crops to animals
  • car, truck - Crops to vehicles

Utility Scripts

Model Precision Conversion (scripts/convert_floats.py)

Convert diffusers models or safetensor files to reduced precision formats:

# Convert a model directory to bfloat16 (default)
uv run python scripts/convert_floats.py --input H:/my-model
# Output: H:/my-model-bf16

# Convert to float8 e4m3fn (higher precision, good for inference)
uv run python scripts/convert_floats.py --input H:/my-model --dtype e4m3fn
# Output: H:/my-model-e4m3fn

# Convert to float8 e5m2 (wider range)
uv run python scripts/convert_floats.py --input H:/my-model --dtype e5m2
# Output: H:/my-model-e5m2

# Convert a single safetensors file
uv run python scripts/convert_floats.py --input H:/models/model.safetensors
# Output: H:/models/model-bf16.safetensors

Supported formats:

  • bf16 - bfloat16 (16-bit, ~50% size reduction from fp32)
  • e4m3fn - float8 with 4-bit exponent, 3-bit mantissa (higher precision)
  • e5m2 - float8 with 5-bit exponent, 2-bit mantissa (wider dynamic range)

Development

Running Tests

uv run pytest tests/

Linting and Formatting

uv run ruff check --fix
uv run ruff format

Output Structure

After training, your output directory will contain:

stable-diffusion-v1-5_my_images/
├── adapter_config.json       # LoRA configuration
├── adapter_model.safetensors # LoRA weights
├── training_log.json         # Processing log
├── logs/                     # Training logs
└── processed_images/         # Preprocessed images

License

MIT

Acknowledgments

About

A CLI tool to train LoRA adapters for text-to-image models using a folder of images with intelligent content-aware cropping

Topics

pony text-to-image diffusion-models stable-diffusion lora-training z-image-turbo illustrious

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