An Undergraduate Thesis Project on Ultrasound Image Analysis with DDPM, DDPM-variance, and Latent Diffusion Models

This project implements and compares three generations of diffusion probabilistic models for medical image synthesis and data augmentation: Standard DDPM, DDPM with learned variance prediction, and Latent Diffusion Models (LDM). The primary objective is to evaluate the effectiveness of synthetic data augmentation for improving classification performance on a 4-class udder ultrasound image dataset. Comprehensive experiments demonstrate that data augmentation with diffusion models significantly enhances downstream classifier accuracy across multiple backbone architectures. Additionally, we explore mixed-data augmentation strategies that combine samples from different generative models to leverage complementary strengths.

1. Introduction
2. Background and Related Work
3. Methodology
4. Experimental Setup
5. Results and Analysis
   • 5.6 Mixed-Data Augmentation Experiments
6. Project Structure
7. Usage Instructions
8. Citation

Medical imaging datasets, particularly in specialized domains like veterinary ultrasound, often suffer from limited sample sizes and class imbalances. This scarcity poses significant challenges for training robust deep learning models. Traditional data augmentation techniques (flipping, rotation, color jittering) provide limited diversity and may not capture the complex anatomical variations present in medical images.

This project addresses these limitations by exploring diffusion-based data augmentation (DiffDA) for udder ultrasound images. We implement and compare three state-of-the-art diffusion models:

1. Model 1 (DDPM): Standard Denoising Diffusion Probabilistic Model [1]
2. Model 2 (DDPM-variance): Improved DDPM with learned variance prediction and perceptual loss [2]
3. Model 3 (LDM): Latent Diffusion Model operating in compressed latent space [3]

The generated synthetic images are used to augment training data for downstream classification tasks, with systematic evaluation across four classifier architectures: ResNet-18, Swin-T, ViT-Tiny, and ConvNeXt-Tiny.

Key Contributions:

• Implementation of three diffusion model variants for medical image synthesis
• Comprehensive evaluation of diffusion-based data augmentation for ultrasound classification
• Analysis of the relationship between generative quality metrics (FID, LPIPS) and downstream classification performance
• Mixed-data augmentation experiments exploring complementary effects of combining LDM and DDPM-variance generated samples
• Open-source codebase for reproducible research in medical image augmentation

DDPMs [1] are generative models that learn data distributions by reversing a gradual noising process. The forward process adds Gaussian noise over T timesteps:

$$ q(\mathbf{x}_t|\mathbf{x}_{t-1}) = \mathcal{N}(\mathbf{x}_t; \sqrt{1-\beta_t}\mathbf{x}_{t-1}, \beta_t\mathbf{I}) $$

The reverse process is learned by a neural network:

$$ p_\theta(\mathbf{x}_{t-1}|\mathbf{x}_t) = \mathcal{N}(\mathbf{x}_{t-1}; \mu_\theta(\mathbf{x}_t, t), \sigma_t^2\mathbf{I}) $$

Training uses a simplified objective predicting the added noise:

$$ \mathcal{L}_{\text{simple}} = \mathbb{E}_{t, \mathbf{x}_0, \boldsymbol{\epsilon}} \left[ | \boldsymbol{\epsilon} - \boldsymbol{\epsilon}_\theta(\mathbf{x}_t, t) |^2 \right] $$

Nichol & Dhariwal [2] extended DDPMs with several improvements:

• Learned variance prediction: Network outputs both noise and variance parameters
• Cosine noise schedule: More gradual noise addition than linear scheduling
• Improved architecture: U-Net with attention at multiple resolutions

These improvements yield better log-likelihoods and enable sampling with fewer steps while maintaining quality.

LDMs [3] operate in a compressed latent space using a pretrained autoencoder, dramatically reducing computational requirements:

$$ \mathcal{E}(\mathbf{x}) = \mathbf{z} \quad \text{(Encoder)}, \quad \mathcal{D}(\mathbf{z}) = \tilde{\mathbf{x}} \quad \text{(Decoder)} $$

The diffusion process occurs in the latent space $\mathbf{z} \in \mathbb{R}^{h \times w \times c}$, where $h \times w$ is 64× smaller than the original image. This enables training high-resolution diffusion models with limited computational resources.

Recent studies [4,5] have explored diffusion models for data augmentation (DiffDA). Key findings include:

• Traditional generative metrics (FID, IS) do not strongly correlate with downstream classification performance
• Classification accuracy is the gold standard for evaluating DiffDA effectiveness
• Diffusion models can generate diverse, high-quality medical images that improve classifier robustness

• Backbone: UNet2DModel from HuggingFace Diffusers
• Conditioning: Class labels via num_class_embeds parameter
• Loss: Charbonnier loss for robust denoising
• Training: DDPMScheduler with β-schedule="scaled_linear"
• Key Features: EMA weights, weighted sampling for class imbalance

• Backbone: UNet2DConditionModel with cross-attention conditioning
• Output Channels: 6 (3 for noise prediction, 3 for variance prediction)
• Conditioning: Learned LabelEmbedding with 256-dim embeddings, 8 tokens
• Loss Components:
  • MSE loss for noise prediction
  • Perceptual loss (VGG16-based) for feature preservation
  • Laplacian edge loss for anatomical detail preservation
• Key Features: Learned variance, classifier-free guidance (CFG), edge-aware training

• VAE: Pretrained AutoencoderKL (stabilityai/sd-vae-ft-mse)
• Latent Space: 4 channels, 64×64 resolution (scale_factor=0.18215)
• Diffusion: In latent space with DPMSolverMultistepScheduler
• Conditioning: Cross-attention with 512-dim label embeddings
• Loss: Combined noise prediction and perceptual loss
• Key Features: Latent space efficiency, DPM++ solver, CFG scaling

All models implement class-conditional generation via label embeddings:

# DDPM-variance: Cross-attention conditioning
class LabelEmbedding(nn.Module):
    def __init__(self, num_classes=5, embedding_dim=256, num_tokens=8):
        self.embedding = nn.Embedding(num_classes, embedding_dim)
        self.mlp = nn.Sequential(
            nn.Linear(embedding_dim, embedding_dim * 2),
            nn.SiLU(),
            nn.Linear(embedding_dim * 2, embedding_dim * num_tokens)
        )
        
# LDM: Similar architecture with 512-dim embeddings

Conditioning is implemented with classifier-free guidance (CFG) during sampling, enabling trade-off between fidelity and diversity.

The complete augmentation workflow:

1. Model Training: Train diffusion model on original dataset
2. Image Generation: Generate synthetic images to reach target count (5000/class)
3. Dataset Composition: Combine original and synthetic images
4. Classifier Training: Train downstream classifiers on augmented datasets
5. Evaluation: Compare performance against baseline (original data only)

• Fréchet Inception Distance (FID): Measures distribution similarity
• Inception Score (IS): Measures diversity and recognizability
• LPIPS Diversity: Learned Perceptual Image Patch Similarity within/between classes

• Balanced Accuracy: Accounts for class imbalance
• F1-Score: Harmonic mean of precision and recall
• AUC-ROC: Area under receiver operating characteristic curve
• Confusion Matrices: Per-class performance visualization

The project uses a udder ultrasound image dataset with 4 classes representing different anatomical or pathological conditions. The original dataset structure:

datasets/
├── train/
│   ├── 1/    # Class 1 images
│   ├── 2/    # Class 2 images  
│   ├── 3/    # Class 3 images
│   └── 4/    # Class 4 images
└── test/     # Test set (not used for diffusion model training)

Key Statistics:

• Original training images: Variable count per class (class-imbalanced)
• Target augmentation: 5000 images per class
• Image resolution: 256×256 pixels (DDPM/DDPM-variance) / 512×512 pixels (LDM)
• Preprocessing: Resize, random horizontal flip, color jitter, normalization to [-1, 1]

• Batch Size: 6 (DDPM), 13 (DDPM-variance), 1 (LDM) - limited by GPU memory
• Epochs: 120 (DDPM), 50 (DDPM-variance), 100 (LDM)
• Learning Rate: 5e-6 (DDPM), 5e-5 (DDPM-variance), 5e-6 (LDM) with cosine warmup
• Mixed Precision: FP16 for memory efficiency
• EMA Decay: 0.9999 for stable training
• Class Imbalance Handling: WeightedRandomSampler with inverse class frequency weights

• Backbones: ResNet-18, Swin-T, ViT-Tiny, ConvNeXt-Tiny (pretrained on ImageNet)
• Advanced Techniques:
  • Mixup (α=0.05) for regularization
  • Label smoothing (ε=0.05) for calibration
  • RandAugment (N=2, M=9) for robustness
  • Dropout (p=0.2) for preventing overfitting
• Optimization: SGD with momentum (0.9), weight decay (1e-4)
• Scheduling: CosineAnnealingWarmRestarts with T_0=10, T_mult=2
• Early Stopping: Patience=12 epochs based on validation loss

Four augmentation scales were tested to study the effect of synthetic data quantity:

1. Generative Quality Assessment:

   • Compute FID between real and synthetic images (compute_fid.py)
   • Calculate LPIPS diversity scores within and between classes (LPIPS.py)
   • Visual inspection of generated samples

2. Classification Performance:

   • 5-fold cross-validation for reliable metrics
   • Balanced accuracy as primary metric (handles class imbalance)
   • Statistical significance testing between baseline and augmented results
   • Confusion matrix analysis for per-class performance

3. Ablation Studies:

   • Effect of different augmentation quantities (500, 1000, 2000, 5000)
   • Comparison across three diffusion model architectures
   • Impact of advanced training techniques (mixup, label smoothing, etc.)

• DDPM (Model 1): 71.65 (IS: 4.69 ± 0.06) - Evaluated on ddpm_augmented_v1/train
• DDPM-variance (Model 2): [Value not recorded in current log file]
• LDM (Model 3): 54.20 (IS: 5.28 ± 0.09) - Evaluated on ldm_augmented_v2/train

Lower FID indicates better distribution matching with real images. LDM achieves the best FID score (54.20), suggesting better generative quality, though DDPM-variance shows superior downstream classification performance.

• DDPM (Model 1): 0.659 overall diversity score (range: 0.583-0.664 per class)
• LDM (Model 3): 0.649 overall diversity score (range: 0.574-0.645 per class)
• DDPM-variance (Model 2): [Value not recorded in current log file]

LPIPS measures perceptual diversity - higher scores indicate more diverse generated images. Both DDPM and LDM show good diversity (>0.64), with DDPM showing slightly higher overall diversity.

The key finding: All diffusion models improved downstream classification accuracy, with DDPM-variance (Model 2) achieving the best results.

Key Observations:

1. ConvNeXt-Tiny achieves the highest absolute accuracy (95.05%) and largest improvement (+24.16%) with DDPM-variance augmentation
2. All models exceed 90% accuracy with DDPM-variance augmentation, demonstrating the superior effectiveness of this model for data augmentation
3. Swin-T shows remarkable improvement (+23.62%), reaching 94.51% accuracy
4. Even ViT-Tiny, which had the highest baseline accuracy, still improves by +13.53% to reach 90.11%

Note: These results represent the performance with DDPM-variance (Model 2) augmentation, which significantly outperforms the standard DDPM (Model 1) results shown in earlier experiments.

Figure: Confusion matrix for ResNet-18 trained on DDPM-variance (Model 2) augmented data. Shows excellent per-class accuracy with minimal confusion between classes.

Figure: Performance comparison of ResNet-18 on baseline vs. DDPM-variance augmented datasets. Demonstrates dramatic accuracy improvement from 68.99% to 92.31% (+23.32%).

Figure: Confusion matrix for ConvNeXt-Tiny trained on DDPM-variance augmented data. Shows near-perfect classification across all 4 classes with minimal errors.

Figure: ConvNeXt-Tiny performance comparison showing dramatic improvement from 70.89% to 95.05% (+24.16%) - the highest overall accuracy achieved in this study.

Figure: Comprehensive bar chart comparing all four classifiers' performance on baseline vs. DDPM-variance augmented datasets. All models achieve over 90% accuracy, with ConvNeXt-Tiny reaching 95.05% - demonstrating the exceptional effectiveness of DDPM-variance for medical image data augmentation.

Additional detailed confusion matrices and comparison charts for Swin-T and ViT-Tiny trained on DDPM-variance augmented data are available in the Final_Analysis_Report_ddpm_variance_V2/ folder.

The table below shows the performance improvement comparison across all three diffusion models for ResNet-18:

Analysis: Both DDPM-variance (Model 2) and LDM (Model 3) show dramatically better performance than standard DDPM (Model 1), with improvements around +23.3% vs +14.55%. While LDM shows slightly better ResNet-18 performance (+23.42% vs +23.32%), DDPM-variance achieves the highest overall accuracy with ConvNeXt-Tiny (95.05%) and best balance across all metrics (see Section 5.3).

Complete results for all models:

• Model 1 (DDPM): Final_Analysis_Report_4_2/
• Model 2 (DDPM-variance): Final_Analysis_Report_ddpm_variance_V2/
• Model 3 (LDM): Final_Analysis_Report_vdm_2/

Analysis from extra_images5000_added_ddpm_vdm_variance/DDPM_VARIACNE_LDM_Data_Augmentation_Comparison_Summary.csv:

DDPM-variance (Model 2) emerges as the optimal choice for this application, balancing quality, speed, and downstream performance.

Figure: Comprehensive bar chart comparing all three diffusion models (DDPM, DDPM-variance, LDM) across multiple metrics including FID scores, generation speed, memory usage, and downstream classification improvement. DDPM-variance shows the best balance of quality and efficiency.

Detailed numerical comparison metrics are available in extra_images5000_added_ddpm_vdm_variance/DDPM_VARIACNE_LDM_Data_Augmentation_Comparison_Summary.csv.

Experiments with 500, 1000, 2000, and 5000 synthetic images per class reveal:

• Diminishing returns: Largest gains from 500→1000, smaller from 2000→5000
• Threshold effect: ~2000 images/class appears sufficient for most classifiers
• Class-dependent benefits: Minority classes benefit more from augmentation

The project includes comprehensive analysis reports for each diffusion model in separate folders:

• Final_Analysis_Report_4_2/: Complete analysis for DDPM (Model 1) augmentation results
• Final_Analysis_Report_ddpm_variance_V2/: Complete analysis for DDPM-variance (Model 2) augmentation results
• Final_Analysis_Report_vdm_2/: Complete analysis for LDM (Model 3) augmentation results

Each folder contains confusion matrices, performance comparison charts, and detailed metrics for all four classifier backbones trained on that model's augmented dataset.

• DDPM-variance produces sharper anatomical details due to edge-aware loss and perceptual regularization
• LDM generates globally coherent structures but may lack fine details due to latent space compression
• All models maintain class-specific characteristics (important for medical validity)

The label_test/ directory contains comprehensive visualizations of label embeddings and cross-attention mechanisms used in Models 2 & 3 (DDPM-variance and LDM). These visualizations demonstrate the effectiveness of the cross-attention conditioning mechanism:

Figure: 2D PCA projection of label embeddings showing clear separation of classes 0-4 in embedding space. Class 0 is distinctly separated, while classes 3 and 4 form a close cluster.

Figure: t-SNE visualization of label embeddings (seed 1). The non-linear dimensionality reduction reveals natural clustering patterns.

Figure: t-SNE visualization of label embeddings (seed 2). Consistent clustering across different random initializations validates embedding quality.

Figure: Mean t-SNE visualization showing stable embedding structure across multiple runs.

Figure: 5×5 cosine similarity matrix heatmap. Diagonal = 1.0 (self-similarity), off-diagonal values show semantic relationships between classes.

Figure: Euclidean distance matrix between class embeddings, quantifying separation in embedding space.

Figure: Statistical distribution of attention token norms, validating proper initialization and training of cross-attention parameters.

These visualizations confirm that the learned label embeddings form a semantically meaningful space where similar classes are closer together, enabling effective cross-attention conditioning during image generation. The consistent clustering patterns across multiple visualization techniques (PCA, t-SNE) and the structured similarity/distance matrices demonstrate that the models have learned meaningful class representations.

Recognizing the complementary strengths of different diffusion models, we conducted mixed-data augmentation experiments to investigate whether combining samples from multiple generative models could yield superior classification performance. The experimental design addresses:

• Single-model limitations: LDM offers fast sampling and high visual fidelity but limited diversity; DDPM-Variance provides excellent diversity but slower sampling and small-sample instability
• Complementary effects: By mixing samples from both models, we aim to leverage LDM's global coherence and DDPM-Variance's edge preservation capabilities

Three mixed-data groups were created with different LDM-to-DDPM-Variance ratios (where a = LDM proportion):

Methodology:

1. Start with DDPM-Variance augmented set (5000 images/class) as baseline
2. Incrementally replace samples with LDM-generated images at specified ratios
3. Maintain consistent base data subsets for fair comparison

Results from mixed_data_Comparison_Summary.csv show mixed-data augmentation outperforms single-model approaches:

1. Best Overall Performance: ConvNeXt-Tiny with a=0.8 achieves 97.80% accuracy - the highest across all experiments in this study
2. Model-Specific Preferences:
   • ConvNeXt-Tiny benefits from LDM-dominant mixes (97.80% at a=0.8)
   • Swin-T and ViT-Tiny perform best with DDPM-Variance-dominant mixes (95.60% and 92.86% at a=0.2)
   • ResNet-18 shows balanced improvement across mixes
3. Complementary Benefits: Mixed data consistently outperforms single-model augmentation, validating the hypothesis that LDM and DDPM-Variance have complementary strengths
4. Practical Implications: For medical image augmentation, optimal mixing ratios depend on the target classifier architecture

Figure: Comprehensive bar chart comparing all augmentation strategies including mixed-data experiments. Shows performance advantages of mixed-data augmentation over single-model approaches.

Figure: Multi-series line chart showing classification accuracy as a function of LDM mixing ratio (Alpha). Demonstrates how different classifier architectures respond to varying proportions of LDM and DDPM-Variance samples, with ConvNeXt-Tiny achieving peak performance at α=0.8 (80% LDM + 20% DDPM-Var).

The project follows a well-organized directory structure designed for reproducibility and clarity:

ddpm/
├── Core Model Training Scripts
│   ├── train.py                 # Model 1: Standard DDPM training
│   ├── train_ddpm_variance.py   # Model 2: DDPM with learned variance
│   └── train_ldm.py             # Model 3: Latent Diffusion Model
│
├── Data Augmentation Generators
│   ├── add_p.py                 # Generate images using DDPM (Model 1)
│   ├── ddpm_variance_add.py     # Generate images using DDPM-variance (Model 2)
│   └── vdm_add.py               # Generate images using LDM (Model 3)
│
├── Evaluation & Metrics
│   ├── FID.py                   # FID calculation for diffusion models
│   ├── compute_fid.py           # FID/IS for augmented datasets
│   ├── LPIPS.py                 # LPIPS diversity score calculation
│   └── all_experiments_log.json # Log of all experiment results
│
├── Classification Experiments
│   ├── compare_4_model.py       # Train 4 classifiers with advanced techniques
│   ├── jieguo.py                # Generate analysis reports and visualizations
│   ├── comparison.py            # Multi-experiment comparison
│   └── Classification_Experiments/ # All experiment outputs
│       ├── Augmented_Dataset_4_2/           # Best results with Model 1 augmentation
│       │   ├── models/                      # Trained classifier weights
│       │   └── results/                     # Performance metrics
│       ├── Augmented_Dataset_ddpm_variance_V2/ # Best results with Model 2 augmentation
│       ├── Augmented_Dataset_vdm_2/         # Best results with Model 3 augmentation
│       ├── Final_Analysis_Report_4_2/       # Final analysis for Model 1
│       ├── Final_Analysis_Report_ddpm_variance_V2/ # Final analysis for Model 2
│       ├── Final_Analysis_Report_vdm_2/     # Final analysis for Model 3
│       ├── label_test/                      # Label embedding visualizations
│       ├── mixed_data/                       # Mixed-data experiment results (LDM + DDPM-Var)
│       └── extra_images5000_added_ddpm_vdm_variance/ # Final comparison results
│
├── Datasets
│   ├── datasets/                # Original dataset (train/test split)
│   ├── Base_datasets/           # Original base datasets
│   ├── Base_datasets_augmented/ # DDPM-augmented datasets (500, 1000, 2000, 5000/class)
│   ├── ddpm_augmented_v1/       # Model 1 augmented dataset (5000/class)
│   ├── ddpm_variance_augmented_v2/ # Model 2 augmented dataset (5000/class)
│   └── ldm_augmented_v2/        # Model 3 augmented dataset (5000/class)
│
├── Model Checkpoints
│   ├── ddpm-udder-results*/     # Model 1 training checkpoints
│   ├── ddpm_variance_*/         # Model 2 training checkpoints
│   └── ldm_udder_v*/            # Model 3 training checkpoints
│
└── Utility Scripts
    ├── split.py                 # Dataset splitting utility
    ├── test_*.py                # Various testing scripts
    └── prompt.txt               # Project notes and prompts

• Base_datasets_augmented/: Traditional DDPM augmentation to 500 images/class
• Base_datasets_augmented_2/: Augmentation to 1000 images/class
• Base_datasets_augmented_3/: Augmentation to 2000 images/class
• Base_datasets_augmented_4/: Augmentation to 5000 images/class
• Purpose: Validate that augmentation to 5000 images improves all 4 classifiers

• ddpm_variance_v*/: Intermediate weights for Model 2 training
• ddpm_udder_v*/: Intermediate weights for Model 1 training
• ldm_udder_v*/: Intermediate weights for Model 3 training
• Note: Highest numbered version (e.g., ddpm_variance_22) contains the final trained model

• Classification_Experiments/Augmented_Dataset_4_2/: Best classifier results with Model 1 augmentation
• Classification_Experiments/Augmented_Dataset_ddpm_variance_V2/: Best results with Model 2 augmentation
• Classification_Experiments/Augmented_Dataset_vdm_2/: Best results with Model 3 augmentation
• Classification_Experiments/Final_Analysis_Report_*/: Complete analysis reports for each model

• Classification_Experiments/label_test/: Visualization of attention mechanisms and label embeddings in Models 2 & 3
• Classification_Experiments/mixed_data/: Mixed-data experiment results (LDM + DDPM-Var combinations)
• Classification_Experiments/extra_images5000_added_ddpm_vdm_variance/: Final experimental results comparison

# Clone the repository
git clone [repository_url]
cd ddpm

# Create and activate conda environment (recommended)
conda create -n ddpm python=3.9
conda activate ddpm

# Install PyTorch (CUDA 11.8 example)
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

# Install other dependencies
pip install diffusers accelerate transformers datasets
pip install lpips torchmetrics matplotlib scikit-learn
pip install timm  # for classifier backbones

python train.py

Configuration: Edit Config class in train.py for data paths, batch size, etc.

python train_ddpm_variance.py

Configuration: Modify Config class in train_ddpm_variance.py. Key parameters: use_variance_prediction=True, cross_attention_dim=256, cfg=5.

python train_ldm.py

Note: Requires pretrained VAE from stabilityai/sd-vae-ft-mse. Set vae_model path in config.

After training, generate synthetic images to reach 5000 images per class:

python add_p.py

Configure: Set target_count=5000, model_path to trained checkpoint in add_p.py.

python ddpm_variance_add.py

Configure: Update model_path and output_dir in ddpm_variance_add.py.

python vdm_add.py

Configure: Set model_path, vae_path, and output_dir in vdm_add.py.

# Compute FID and Inception Score
python compute_fid.py

# Calculate LPIPS diversity scores
python LPIPS.py

Configuration: Update REAL_DIR and FAKE_DIR in compute_fid.py to point to real and generated datasets.

# Train all 4 classifiers on augmented dataset
python compare_4_model.py

# Generate comprehensive analysis reports
python jieguo.py

# Compare multiple experiments
python comparison.py

Configuration: Modify dataset paths and training parameters in compare_4_model.py.

To reproduce the key experiments from this study:

1. Baseline Augmentation (Model 1):

   # Use checkpoints from ddpm-udder-results6/ or ddpm-udder-results7/
   python add_p.py  # Set model_path accordingly

2. DDPM-variance Augmentation (Model 2):

   # Use checkpoints from ddpm_variance_22/
   python ddpm_variance_add.py

3. LDM Augmentation (Model 3):

   # Use checkpoints from ldm_udder_v22/
   python vdm_add.py

• Label Embeddings: python test_label.py generates t-SNE and cosine similarity plots
• Generated Samples: test_ddpm_variance.py, test_ldm.py produce sample images
• Attention Maps: Check label_test/ directory for cross-attention visualizations

If you use this codebase or build upon this work, please cite:

@article{your_thesis_2026,
  title={RESEARCH ON DIFFUSION-BASED IMAGE GENERATION ALGORITHMS FOR BOVINE UDDER MASTITIS},
  author={Junze Ye},
  journal={Undergraduate Thesis},
  year={2026},
  publisher={NJAU}
}

[1] Ho, J., Jain, A., & Abbeel, P. (2020). Denoising Diffusion Probabilistic Models. Advances in Neural Information Processing Systems.

[2] Nichol, A. Q., & Dhariwal, P. (2021). Improved Denoising Diffusion Probabilistic Models. International Conference on Machine Learning.

[3] Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022). High-Resolution Image Synthesis with Latent Diffusion Models. IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[4] Groh, M., et al. (2023). Evaluating the Performance of StyleGAN2-ADA on Medical Images. International Conference on Medical Image Computing and Computer-Assisted Intervention.

[5] Diffusion Models for Data Augmentation Survey (2023). arXiv preprint arXiv:2308.12453.

This work was conducted as part of an undergraduate thesis project at [NJAU]. Special thanks to my supervisor, Professor Zhai, for guidance and support throughout this project. Also thanks to the open-source community for the Diffusers library that made this research possible.

This project is available for academic research purposes. For commercial use, please contact the author.

Last Updated: April 2026
Project Status: Completed Research