NFT Collections Generator - Vincenzo Imperati

A comprehensive deep learning toolkit for generating unique NFT collections using advanced Generative Adversarial Networks (GANs). This project combines multiple GAN architectures to create high-quality, diverse digital artwork suitable for NFT collections.

🎯 Overview#

This project implements a multi-stage pipeline for NFT generation that leverages three different GAN architectures:

DCGAN (Deep Convolutional GAN) - Base image generation at 64x64 resolution
SRGAN (Super-Resolution GAN) - Upscaling to 256x256 with enhanced details
DCGAN-2D - Dual-discriminator architecture for style blending between collections

The pipeline produces high-quality, unique digital artworks by combining generative modeling with image enhancement techniques.

🏗️ Architecture#

Core Components#

1. DCGAN Generator#

Input: 100-dimensional noise vector
Architecture: 5-layer transposed convolutional network
Output: 64x64x3 RGB images
Features:
- Batch normalization for stable training
- ReLU activations with Tanh output
- Progressive upsampling from 4x4 to 64x64

2. DCGAN Discriminator#

Input: 64x64x3 RGB images
Architecture: 5-layer convolutional network
Features:
- LeakyReLU activations
- Batch normalization (except first layer)
- Binary classification (real/fake)

3. SRGAN Super-Resolution#

Purpose: Enhance 64x64 images to 256x256
Generator: ResNet-based with sub-pixel convolution
Loss Function: Combination of:
- Adversarial loss
- Perceptual loss (VGG-based)
- Mean squared error
- Total variation loss

4. DCGAN-2D (Dual Collection Blending)#

Innovation: Two discriminators for style mixing
Purpose: Generate art that blends characteristics from two different collections
Training: Alternating optimization with weighted loss functions

📁 Project Structure#

NFT-Collections-Generator/
├── models/
│   ├── DCGAN/
│   │   └── DCGAN.ipynb          # Base GAN implementation
│   ├── SRGAN/
│   │   └── SRGAN.ipynb          # Super-resolution enhancement
│   └── DCGAN-2D/
│       └── DCGAN-2D.ipynb       # Dual-collection blending
├── generateNFT.ipynb            # Complete pipeline execution
├── README.md
└── images/                      # Training data directory
    ├── real/
    │   ├── 64/
    │   │   ├── Collection1/
    │   │   └── Collection2/
    │   └── 128/
    │       └── Collection2/
    │           └── EAPES/

🚀 Getting Started#

Prerequisites#

# Core dependencies
pip install torch torchvision
pip install numpy matplotlib opencv-python
pip install tqdm ipywidgets
pip install Pillow

Training Pipeline#

1. Prepare Your Dataset#

# Organize images in the following structure:
images/real/64/YourCollection/
└── [your training images here]

2. Train Base DCGAN#

# In models/DCGAN/DCGAN.ipynb
modelDCGAN = DCGAN(
    dataroot='../../images/real/64/YourCollection',
    logfolder='output_folder',
    num_epochs=50,
    batch_size=128,
    image_size=64
)
img_list, G_losses, D_losses = modelDCGAN.train()

3. Train SRGAN for Super-Resolution#

# In models/SRGAN/SRGAN.ipynb
# Prepare high-resolution dataset at 128x128
train_set = TrainDatasetFromFolder(
    '../../images/real/128/YourCollection',
    crop_size=88,
    upscale_factor=4
)

4. Generate NFTs#

# In generateNFT.ipynb - Complete pipeline
# 1. Generate base image with DCGAN
# 2. Apply noise reduction
# 3. Upscale with SRGAN
# 4. Final enhancement

Generation Parameters#

Parameter	Description	Default	Range
`batch_size`	Training batch size	128	32-256
`num_epochs`	Training epochs	50	10-200
`lr`	Learning rate	0.0002	0.0001-0.01
`nz`	Noise dimension	100	50-200
`ngf/ndf`	Feature map size	64	32-128

🎨 Advanced Features#

Multi-Collection Blending (DCGAN-2D)#

Create unique art by blending styles from two different collections:

modelDCGAN_2D = DCGAN_2D(
    dataroot1='../../images/real/64/Collection1',
    dataroot2='../../images/real/64/Collection2',
    weight1=0.5,  # Balance between collections
    weight2=0.5
)

Quality Enhancement Pipeline#

The complete generation process includes:

Base Generation: DCGAN creates 64x64 initial artwork
Noise Reduction: OpenCV denoising for cleaner images
Super-Resolution: SRGAN upscales to 256x256 with detail enhancement
Final Polish: Additional denoising and formatting

Loss Function Innovation#

DCGAN-2D Discriminator Fusion:

# Multiple fusion strategies implemented:
output = torch.min(output1, output2)    # Conservative approach
# output = torch.max(output1, output2)  # Aggressive approach  
# output = (output1 + output2) / 2      # Balanced approach

📊 Training Monitoring#

Loss Tracking#

Generator and Discriminator losses automatically logged
Visual progress saved at regular intervals
Training curves plotted for analysis

Quality Metrics#

Real vs. Fake image comparisons
Progressive generation examples
Loss convergence analysis

🔧 Customization#

Model Architecture#

Easily modify network architectures by adjusting:

Layer depths and feature map sizes
Activation functions and normalization
Skip connections and residual blocks

Training Strategy#

Adaptive learning rates
Loss weighting for different objectives
Custom data augmentation pipelines

💡 Use Cases#

NFT Collections: Generate thousands of unique digital artworks
Art Style Transfer: Blend characteristics from different art styles
Data Augmentation: Expand training datasets for other ML projects
Creative Exploration: Experiment with AI-generated art concepts

🚨 Important Notes#

Hardware Requirements#

GPU: CUDA-compatible GPU recommended (8GB+ VRAM)
RAM: 16GB+ system memory for large batch sizes
Storage: Sufficient space for datasets and generated outputs

Training Tips#

Start with smaller datasets to validate pipeline
Monitor discriminator/generator balance during training
Experiment with different loss weightings for style control
Use checkpointing for long training runs

🛠️ Troubleshooting#

Common Issues#

Mode Collapse:

Reduce discriminator learning rate
Increase generator training frequency
Add noise to discriminator inputs

Training Instability:

Lower learning rates for both networks
Implement gradient clipping
Use spectral normalization

Memory Issues:

Reduce batch size
Use gradient accumulation
Enable mixed precision training

📈 Performance Optimization#

Mixed Precision: Use torch.cuda.amp for faster training
Data Loading: Optimize num_workers for your system
Batch Size: Balance between memory usage and training stability
Model Pruning: Remove unnecessary parameters for inference

🤝 Contributing#

Contributions welcome! Areas for improvement:

New GAN architectures (StyleGAN, Progressive GAN)
Advanced loss functions
Better evaluation metrics
Web interface for easy generation