FLUX.1-Fill-dev-Training

This repository contains the code for training FLUX.1-Fill-dev, a powerful image inpainting model. This varient is specifically trained for high-quality background changing and generation in images. This model excels at:

• Seamless background replacement for portraits, product photos, and landscapes
• Context-aware background generation that matches the foreground subject
• Realistic background transitions without edge artifacts or color inconsistencies
• Diverse background styles including solid colors, gradients, natural scenes, and interior environments

The training approach focuses on maintaining foreground subject integrity while completely transforming backgrounds, making it ideal for:

• Portrait photography enhancement
• E-commerce product image standardization
• Real estate virtual staging
• Social media content creation

Unlike traditional image matting or background removal tools, FLUX.1-Fill-dev uses advanced diffusion techniques to generate photorealistic backgrounds that complement the subject, ensuring natural lighting, perspective, and shadows.

Setup and Installation

Dependencies

# Clone the repository
git clone https://github.com/your-username/FLUX.1-Fill-dev-Training.git
cd FLUX.1-Fill-dev-Training

# Install dependencies
pip install accelerate transformers diffusers wandb

Environment Setup

Make sure you have access to the following:

• A GPU with 80GB VRAM (H100 recommended)
• Required datasets: "raresense/BGData" and "raresense/BGData_Validation" available on Hugging Face

Training Instructions

Configuration

The main training script train_model.sh contains key variables that you can modify:

# Main data columns
export SOURCE_COLUMN="ghost_images"  # Column containing source images
export TARGET_COLUMN="target"        # Column containing target images
export MASK_COLUMN="binary_mask"     # Column containing binary masks
export CAPTION_COLUMN="prompt"       # Column containing text prompts

# Model and dataset configuration
export MODEL_NAME="black-forest-labs/FLUX.1-Fill-dev"
export TRAIN_DATASET_NAME="raresense/BGData"
export TEST_DATASET_NAME="raresense/BGData_Validation"
export OUTPUT_DIR="trained-flux-inpaint"

Running Training

To start training:

# Make sure the script is executable
chmod +x train_model.sh

# Run the training script
./train_model.sh

Resume Training

To resume training from a checkpoint, uncomment the following line in train_model.sh:

# --resume_from_checkpoint="latest"  \

Training Results

Below are examples of the model's performance before and after fine-tuning.

Example 1: Complete Body Mask

Source Image	Mask	Generated Output	Prompt
			A model posing in front of a grand marble staircase inside a luxury mansion, opulent lighting, chandeliers glistening, cinematic ambiance, ultra-realistic, Vogue editorial, 8K

Example 2: Product Photography

Source Image	Mask	Generated Output	Prompt
			Placed on the polished wooden desk

Example 3: Garment Mask Only

Source Image	Mask	Generated Output	Prompt
			A lady wearing red turtleneck posing in front of a massive graffiti-covered wall , high fashion, streetwear shoot, moody lighting, ultra-HD, cinematic

Model Inference

After training, you can use the model for inference:

from diffusers import FluxFillPipeline
import torch

# Load the fine-tuned model
pipeline = FluxFillPipeline.from_pretrained(
    "path/to/trained-flux-inpaint",
    torch_dtype=torch.float16
).to("cuda")

# Run inference
output = pipeline(
    prompt="your text prompt here",
    image=source_image,
    mask_image=mask_image,
    num_inference_steps=28,
    guidance_scale=30
).images[0]

# Save the result
output.save("output.png")

Monitoring

This training script uses Weights & Biases for monitoring. Make sure you're logged in to your W&B account:

wandb login

You can track the training progress, visualize validation images, and monitor loss in the W&B dashboard.