An open-source implementation for training LoRA (Low-Rank Adaptation) layers for Qwen/Qwen-Image, Qwen/Qwen-Image-Edit, and FLUX.1-dev models by FlyMy.AI.

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🔗 Useful Links:

• 🌐 Official Website
• 📚 Documentation
• 💬 Discord Community
• 🤗 Pre-trained Qwen LoRA Model
• 🤗 Pre-trained FLUX LoRA Model
• 🚀 Train Your Own FLUX LoRA
• 🐦 X (Twitter)
• 💼 LinkedIn
• 📺 YouTube
• 📸 Instagram

• LoRA-based fine-tuning for efficient training
• Support for Qwen-Image, Qwen-Image-Edit, and FLUX.1-dev models
• Compatible with Hugging Face diffusers
• Easy configuration via YAML
• Control-based image editing with LoRA
• Open-source implementation for LoRA training
• Full training support for Qwen-Image
• High-quality portrait and character training for FLUX

16.10.2025

• ✅ Added FLUX.1-dev LoRA training support
• ✅ Added pre-trained FLUX LoRA model example

02.09.2025

• ✅ Added full training for Qwen-Image and Qwen-Image-Edit

20.08.2025

• ✅ Added Qwen-Image-Edit LoRA trainer support

09.08.2025

• ✅ Add pipeline for train for < 24GiB GPU

08.08.2025

• ✅ Added comprehensive dataset preparation instructions
• ✅ Added dataset validation script (utils/validate_dataset.py)
• ✅ Freeze model weights during training

🚧 Under Development: We are actively working on improving the code and adding test coverage. The project is in the refinement stage but ready for use.

📋 Development Plans:

• ✅ Basic code is working
• ✅ Training functionality implemented
• ✅ FLUX.1-dev support added
• 🔄 Performance optimization in progress
• 🔜 Test coverage coming soon

---

Requirements:

• Python 3.10

1. Clone the repository and navigate into it:

   git clone https://github.com/FlyMyAI/flymyai-lora-trainer
   cd flymyai-lora-trainer
   

2. Install required packages:

   pip install -r requirements.txt
   

3. Install the latest diffusers from GitHub:

   pip install git+https://github.com/huggingface/diffusers
   

4. Download pre-trained LoRA weights (optional):

   # Qwen LoRA weights
   git clone https://huggingface.co/flymy-ai/qwen-image-realism-lora
   
   # FLUX LoRA weights
   git clone https://huggingface.co/flymy-ai/flux-dev-anne-hathaway-lora
   
   # Or download specific files
   wget https://huggingface.co/flymy-ai/qwen-image-realism-lora/resolve/main/flymy_realism.safetensors
   wget https://huggingface.co/flymy-ai/flux-dev-anne-hathaway-lora/resolve/main/pytorch_lora_weights.safetensors
   

---

The training data should follow the same format for both Qwen and FLUX models, where each image has a corresponding text file with the same name:

dataset/
├── img1.png
├── img1.txt
├── img2.jpg
├── img2.txt
├── img3.png
├── img3.txt
└── ...

For control-based image editing, the dataset should be organized with separate directories for target images/captions and control images:

dataset/
├── images/           # Target images and their captions
│   ├── image_001.jpg
│   ├── image_001.txt
│   ├── image_002.jpg
│   ├── image_002.txt
│   └── ...
└── control/          # Control images
    ├── image_001.jpg
    ├── image_002.jpg
    └── ...

1. Images: Support common formats (PNG, JPG, JPEG, WEBP)
2. Text files: Plain text files containing image descriptions
3. File naming: Each image must have a corresponding text file with the same base name

my_training_data/
├── portrait_001.png
├── portrait_001.txt
├── landscape_042.jpg
├── landscape_042.txt
├── abstract_design.png
├── abstract_design.txt
└── style_reference.jpg
└── style_reference.txt

For FLUX character training (portrait_001.txt):

ohwx woman, professional headshot, studio lighting, elegant pose, looking at camera

For Qwen landscape training (landscape_042.txt):

Mountain landscape at sunset, dramatic clouds, golden hour lighting, wide angle view

For FLUX portrait training (abstract_design.txt):

ohwx woman, modern portrait style, soft lighting, artistic composition

1. Image Quality: Use high-resolution images (recommended 1024x1024 or higher)
2. Description Quality: Write detailed, accurate descriptions of your images
3. Consistency: Maintain consistent style and quality across your dataset
4. Dataset Size: For good results, use at least 10-50 image-text pairs
5. Trigger Words:
   • For FLUX character training: Use "ohwx woman" or "ohwx man" as trigger words
   • For Qwen training: No specific trigger words required
6. Auto-generate descriptions: You can generate image descriptions automatically using Florence-2

You can verify your data structure using the included validation utility:

python utils/validate_dataset.py --path path/to/your/dataset

This will check that:

• Each image has a corresponding text file
• All files follow the correct naming convention
• Report any missing files or inconsistencies

---

To begin training with your configuration file (e.g., train_lora_4090.yaml), run:

accelerate launch train_4090.py --config ./train_configs/train_lora_4090.yaml

To begin training with your configuration file (e.g., train_lora.yaml), run:

accelerate launch train.py --config ./train_configs/train_lora.yaml

Make sure train_lora.yaml is correctly set up with paths to your dataset, model, output directory, and other parameters.

To begin training with your configuration file (e.g., train_full_qwen_image.yaml), run:

accelerate launch train_full_qwen_image.py --config ./train_configs/train_full_qwen_image.yaml

Make sure train_full_qwen_image.yaml is correctly set up with paths to your dataset, model, output directory, and other parameters. The proposed method was tested on an NVIDIA H200 GPU environment.

After training, you can load your trained model from the checkpoint directory for inference.

Simple Example:

from diffusers import QwenImagePipeline, QwenImageTransformer2DModel, AutoencoderKLQwenImage
import torch
from omegaconf import OmegaConf
import os

def load_trained_model(checkpoint_path):
    """Load trained model from checkpoint"""
    print(f"Loading trained model from: {checkpoint_path}")
    
    # Load config to get original model path
    config_path = os.path.join(checkpoint_path, "config.yaml")
    config = OmegaConf.load(config_path)
    original_model_path = config.pretrained_model_name_or_path
    
    # Load trained transformer
    transformer_path = os.path.join(checkpoint_path, "transformer")
    transformer = QwenImageTransformer2DModel.from_pretrained(
        transformer_path,
        torch_dtype=torch.bfloat16,
        low_cpu_mem_usage=True
    )
    transformer.to("cuda")
    transformer.eval()
    
    # Load VAE from original model
    vae = AutoencoderKLQwenImage.from_pretrained(
        original_model_path,
        subfolder="vae",
        torch_dtype=torch.bfloat16
    )
    vae.to("cuda")
    vae.eval()
    
    # Create pipeline
    pipe = QwenImagePipeline.from_pretrained(
        original_model_path,
        transformer=transformer,
        vae=vae,
        torch_dtype=torch.bfloat16
    )
    pipe.to("cuda")
    
    print("Model loaded successfully!")
    return pipe

# Usage
checkpoint_path = "/path/to/your/checkpoint"
pipe = load_trained_model(checkpoint_path)

# Generate image
prompt = "A beautiful landscape with mountains and lake"
image = pipe(
    prompt=prompt,
    width=768,
    height=768,
    num_inference_steps=30,
    true_cfg_scale=5,
    generator=torch.Generator(device="cuda").manual_seed(42)
)

# Save result
output_image = image.images[0]
output_image.save("generated_image.png")

Complete Example Script:

python inference_trained_model_gpu_optimized.py

Checkpoint Structure:

The trained model is saved in the following structure:

checkpoint/
├── config.yaml          # Training configuration
└── transformer/         # Trained transformer weights
    ├── config.json
    ├── diffusion_pytorch_model.safetensors.index.json
    └── diffusion_pytorch_model-00001-of-00005.safetensors
    └── ... (multiple shard files)

For control-based image editing training, use the specialized training script:

accelerate launch train_qwen_edit_lora.py --config ./train_configs/train_lora_qwen_edit.yaml

The configuration file train_lora_qwen_edit.yaml should include:

• img_dir: Path to target images and captions directory (e.g., ./extracted_dataset/train/images)
• control_dir: Path to control images directory (e.g., ./extracted_dataset/train/control)
• Other standard LoRA training parameters

To begin training with your configuration file (e.g., train_full_qwen_edit.yaml), run:

accelerate launch train_full_qwen_edit.py --config ./train_configs/train_full_qwen_edit.yaml

---

from diffusers import DiffusionPipeline
import torch

model_name = "Qwen/Qwen-Image"

# Load the pipeline
if torch.cuda.is_available():
    torch_dtype = torch.bfloat16
    device = "cuda"
else:
    torch_dtype = torch.float32
    device = "cpu"

pipe = DiffusionPipeline.from_pretrained(model_name, torch_dtype=torch_dtype)
pipe = pipe.to(device)

from diffusers import QwenImageEditPipeline
import torch
from PIL import Image

# Load the pipeline
pipeline = QwenImageEditPipeline.from_pretrained("Qwen/Qwen-Image-Edit")
pipeline.to(torch.bfloat16)
pipeline.to("cuda")

For Qwen-Image:

# Load LoRA weights
pipe.load_lora_weights('flymy-ai/qwen-image-realism-lora', adapter_name="lora")

For Qwen-Image-Edit:

# Load trained LoRA weights
pipeline.load_lora_weights("/path/to/your/trained/lora/pytorch_lora_weights.safetensors")

You can find LoRA weights here

No trigger word required

prompt = '''Super Realism portrait of a teenager woman of African descent, serene calmness, arms crossed, illuminated by dramatic studio lighting, sunlit park in the background, adorned with delicate jewelry, three-quarter view, sun-kissed skin with natural imperfections, loose shoulder-length curls, slightly squinting eyes, environmental street portrait with text "FLYMY AI" on t-shirt.'''
negative_prompt =  " "
image = pipe(
    prompt=prompt,
    negative_prompt=negative_prompt,
    width=1024,
    height=1024,
    num_inference_steps=50,
    true_cfg_scale=5,
    generator=torch.Generator(device="cuda").manual_seed(346346)
)

# Display the image (in Jupyter or save to file)
image.show()
# or
image.save("output.png")

# Load input image
image = Image.open("/path/to/your/input/image.jpg").convert("RGB")

# Define editing prompt
prompt = "Make a shot in the same scene of the person moving further away from the camera, keeping the camera steady to maintain focus on the central subject, gradually zooming out to capture more of the surrounding environment as the figure becomes less detailed in the distance."

# Generate edited image
inputs = {
    "image": image,
    "prompt": prompt,
    "generator": torch.manual_seed(0),
    "true_cfg_scale": 4.0,
    "negative_prompt": " ",
    "num_inference_steps": 50,
}

with torch.inference_mode():
    output = pipeline(**inputs)
    output_image = output.images[0]
    output_image.save("edited_image.png")

Input Image:

Prompt: "Make a shot in the same scene of the left hand securing the edge of the cutting board while the right hand tilts it, causing the chopped tomatoes to slide off into the pan, camera angle shifts slightly to the left to center more on the pan."

Output without LoRA:

Output with LoRA:

---

FLUX.1-dev is a powerful text-to-image model that excels at generating high-quality portraits and character images. Our LoRA training implementation allows you to fine-tune FLUX for specific characters or styles.

To begin FLUX LoRA training with your configuration file, run:

accelerate launch train_flux_lora.py --config ./train_configs/train_flux_config.yaml

Make sure train_flux_config.yaml is correctly set up with paths to your dataset, model, output directory, and other parameters.

from diffusers import DiffusionPipeline
import torch

model_name = "black-forest-labs/FLUX.1-dev"

# Load the pipeline
if torch.cuda.is_available():
    torch_dtype = torch.bfloat16
    device = "cuda"
else:
    torch_dtype = torch.float32
    device = "cpu"

pipe = DiffusionPipeline.from_pretrained(model_name, torch_dtype=torch_dtype)
pipe = pipe.to(device)

# Load LoRA weights
pipe.load_lora_weights('flymy-ai/flux-dev-anne-hathaway-lora', adapter_name="lora")

You can find our pre-trained FLUX LoRA weights here

Trigger word required: "ohwx woman"

prompt = '''Portrait of ohwx woman, professional headshot, studio lighting, elegant pose, looking at camera, soft shadows, high quality, detailed facial features, cinematic lighting, 85mm lens, shallow depth of field'''
negative_prompt = "blurry, low quality, distorted, bad anatomy"
image = pipe(
    prompt=prompt,
    negative_prompt=negative_prompt,
    width=1024,
    height=1024,
    num_inference_steps=30,
    guidance_scale=3.5,
    generator=torch.Generator(device="cuda").manual_seed(346346)
)

# Display the image (in Jupyter or save to file)
image.images[0].show()
# or
image.images[0].save("output.png")

Below are examples of images generated with our FLUX Anne Hathaway LoRA model:

Prompt: "ohwx woman portrait selfie"

Prompt: "ohwx woman perfectly symmetrical young female face close-up, presented with double exposure overlay blending nature textures like leaves and water"

Prompt: "ohwx woman Macro photography style close-up of female face with light makeup, focused on eyes and lips, illuminated by golden hour sunlight for warm tones"

Prompt: "Close-up of ohwx woman in brown knitted turtleneck sweater. Sitting with big black and white panda, hugging it, looking at camera"

Want to train your own FLUX LoRA model? Use our online training platform:

🚀 Train Your Own FLUX LoRA on FlyMy.AI

Features:

• ✅ Easy-to-use web interface
• ✅ No local GPU required
• ✅ Optimized training pipeline
• ✅ Fast training times
• ✅ Professional results

---

We provide ready-to-use ComfyUI workflows that work with both our Qwen and FLUX trained LoRA models. Follow these steps to set up and use the workflows:

1. Download the latest ComfyUI:

   • Visit the ComfyUI GitHub repository
   • Clone or download the latest version

2. Install ComfyUI:

   • Follow the installation instructions from the ComfyUI repository
   • Make sure all dependencies are properly installed

3. Download model weights:

   For Qwen-Image:

   • Go to Qwen-Image ComfyUI weights
   • Download all the model files

   For FLUX.1-dev:

   • Go to FLUX.1-dev model
   • Download all the model files

4. Place model weights in ComfyUI:

   • Copy the downloaded model files to the appropriate folders in ComfyUI/models/
   • Follow the folder structure as specified in the model repositories

5. Download our pre-trained LoRA weights:

   • Qwen LoRA: flymy-ai/qwen-image-realism-lora
   • FLUX LoRA: flymy-ai/flux-dev-anne-hathaway-lora
   • Download the LoRA .safetensors files

6. Place LoRA weights in ComfyUI:

   • Copy the LoRA files to ComfyUI/models/loras/

7. Load the workflow:

   • Open ComfyUI in your browser
   • For Qwen: Load qwen_image_lora_example.json
   • For FLUX: Load flux_anne_hathaway_lora_example.json
   • The workflows are pre-configured to work with our LoRA models

• ✅ Pre-configured for Qwen-Image + LoRA inference
• ✅ Pre-configured for FLUX.1-dev + LoRA inference
• ✅ Optimized settings for best quality output
• ✅ Easy prompt and parameter adjustment
• ✅ Compatible with all our trained LoRA models

The ComfyUI workflows provide a user-friendly interface for generating images with our trained LoRA models without needing to write Python code.

---

If you have questions or suggestions, join our community:

• 🌐 FlyMy.AI
• 💬 Discord Community
• 🐦 Follow us on X
• 💼 Connect on LinkedIn
• 📧 Support

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