Developing a Text-to-Image Generation Model with Diffusion Models
Objective
Build a text-to-image generation system using Diffusion Models, specifically focusing on implementing a model similar to Stable Diffusion. This project involves training a generative model that can create high-quality images from textual descriptions. You will gain hands-on experience with state-of-the-art generative models, understanding the intricacies of Diffusion Models and how they surpass traditional GANs in generating realistic images.
Learning Outcomes
By completing this project, you will:
- Understand Diffusion Models and their role in generative modeling.
- Implement a text-to-image generation pipeline using advanced architectures.
- Gain experience with large-scale model training, including handling substantial computational requirements.
- Explore optimization techniques specific to Diffusion Models.
- Evaluate generative models using appropriate metrics and human evaluations.
- Stay abreast of the latest advancements in generative AI technologies.
Prerequisites and Theoretical Foundations
1. Advanced Python Programming
- Deep Learning Frameworks: Proficiency with PyTorch.
- Efficient Coding Practices: Writing optimized code for high-performance computing.
- Parallel Computing: Understanding of GPU acceleration and distributed training.
2. Mathematics and Machine Learning Foundations
- Probability and Statistics: Understanding stochastic processes.
- Optimization Techniques: Familiarity with gradient descent, learning rate scheduling.
- Deep Learning Concepts:
- Transformers: Attention mechanisms.
- Autoencoders: Variational Autoencoders (VAEs).
- Generative Models: GANs, Normalizing Flows.
3. Understanding of Diffusion Models
- Concepts:
- Forward and reverse diffusion processes.
- Denoising autoencoders.
- Key Papers:
- “Denoising Diffusion Probabilistic Models” by Ho et al.
- “Diffusion Models Beat GANs on Image Synthesis” by Dhariwal and Nichol.
4. Experience with Natural Language Processing
- Text Embeddings: Understanding of tokenization and embedding techniques.
- Transformer Models: Familiarity with BERT, GPT architectures.
Tools Required
- Programming Language: Python 3.8+
- Libraries and Frameworks:
- PyTorch: Deep learning framework (
pip install torch>=1.13.0
) - PyTorch Lightning: For easier model training (
pip install pytorch-lightning>=1.9.0
) - Transformers: Hugging Face Transformers (
pip install transformers>=4.26.0
) - Datasets: For data handling (
pip install datasets>=2.10.0
) - Accelerate: For distributed training (
pip install accelerate>=0.17.0
) - OpenAI CLIP: For text-image embeddings (
pip install git+https://github.com/openai/CLIP.git
)
- PyTorch: Deep learning framework (
- Hardware:
- Minimum:
- GPU: Single NVIDIA RTX 3060 (12GB VRAM)
- RAM: 16GB system memory
- Storage: 10GB SSD
- CUDA: Version 11.6+
- Recommended:
- GPU: NVIDIA RTX 3080/4080 (16GB VRAM)
- RAM: 32GB system memory
- Storage: 50GB SSD
- CUDA: Version 11.6+
- Minimum:
- Datasets:
- Oxford Flowers-102: Access via Hugging Face Datasets
- Size: ~330MB
- 8,189 images with captions
- Alternative: Pokemon BLIP Captions
- Size: ~150MB
- Access via
lambdalabs/pokemon-blip-captions
- Oxford Flowers-102: Access via Hugging Face Datasets
Project Structure
text_to_image_diffusion/
│
├── data/
│ └── captions_images_dataset/
│ ├── images/
│ └── captions.txt
│
├── src/
│ ├── dataset.py
│ ├── model.py
│ ├── train.py
│ ├── sample.py
│ └── utils.py
│
└── notebooks/
└── exploration.ipynb
Steps and Tasks
1. Data Preparation
Tasks:
- Choose a Dataset:
- Oxford Flowers: Contains flowers with detailed descriptions
- Pokemon Dataset: Smaller dataset with clear image-text pairs
- Download and Preprocess Data:
- Ensure images are resized and normalized.
- Tokenize and encode text descriptions.
Implementation:
# Example of data loading with Oxford Flowers
from datasets import load_dataset
dataset = load_dataset("nelorth/oxford-flowers", split="train")
# Preprocessing
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained('bert-base-unclosed')
def preprocess(examples):
examples['input_ids'] = tokenizer(examples['caption'], truncation=True, padding='max_length')['input_ids']
return examples
dataset = dataset.map(preprocess)
2. Understanding and Implementing Diffusion Models
Tasks:
- Study Diffusion Model Architecture:
- Understand forward and reverse diffusion processes.
- Implement the Denoising Process:
- Build the neural network that predicts noise.
- Integrate Text Conditioning:
- Use text embeddings to condition the image generation.
Implementation:
import torch.nn as nn
class UNetModel(nn.Module):
def __init__(self, text_embedding_dim):
super(UNetModel, self).__init__()
# Define the layers of the UNet model
# Integrate text embeddings at appropriate layers
def forward(self, x, t, text_embeddings):
# x: noised image
# t: timestep
# text_embeddings: encoded text
# Implement the forward pass
return denoised_image
3. Setting Up the Training Pipeline
Tasks:
- Define the Noise Schedule:
- Set up beta schedules for forward diffusion.
- Implement Loss Functions:
- Use simplified loss functions as per DDPM.
- Configure Training Loop:
- Handle data loading, model saving, and logging.
Implementation:
import torch
# Noise schedule
betas = torch.linspace(1e-4, 0.02, 1000)
# Training loop
for epoch in range(num_epochs):
for batch in dataloader:
images = batch['image']
captions = batch['caption']
# Add noise
t = torch.randint(0, 1000, (images.size(0),))
noisy_images = add_noise(images, t, betas)
# Forward pass
loss = compute_loss(model, noisy_images, t, captions)
# Backpropagation
optimizer.zero_grad()
loss.backward()
optimizer.step()
4. Text Embedding with CLIP
Tasks:
- Use Pre-trained CLIP Model:
- Extract text embeddings for conditioning.
- Integrate CLIP Embeddings into the Model:
- Modify the UNet model to accept text embeddings.
Implementation:
import clip
# Load CLIP model
device = "cuda" if torch.cuda.is_available() else "cpu"
clip_model, preprocess = clip.load("ViT-B/32", device=device)
# Get text embeddings
def get_text_embeddings(captions):
text_tokens = clip.tokenize(captions).to(device)
with torch.no_grad():
text_embeddings = clip_model.encode_text(text_tokens)
return text_embeddings
5. Sampling and Image Generation
Tasks:
- Implement the Reverse Diffusion Process:
- Generate images from pure noise using the trained model.
- Develop Sampling Techniques:
- Use guidance techniques to improve image quality.
Implementation:
def sample_images(model, text_embeddings, num_steps=1000):
# Start from random noise
x = torch.randn((batch_size, 3, image_size, image_size)).to(device)
for t in reversed(range(num_steps)):
# Predict noise
x = denoise_step(model, x, t, text_embeddings)
return x
6. Evaluation and Fine-Tuning
Tasks:
- Evaluate Generated Images:
- Use metrics like FID (Fréchet Inception Distance).
- Perform human evaluations for quality and relevance.
- Fine-Tune Model Parameters:
- Adjust hyperparameters based on evaluation results.
Implementation:
from pytorch_fid import fid_score
# Calculate FID score
fid = fid_score.calculate_fid_given_paths([real_images_path, generated_images_path], batch_size, device)
print(f"FID Score: {fid}")
7. Optimization and Scaling
Tasks:
- Optimize Training Performance:
- Use mixed-precision training with AMP.
- Implement gradient checkpointing.
- Scale Up Training:
- Utilize multiple GPUs or distributed training.
- Experiment with Larger Models:
- Increase model depth or width for better performance.
Implementation:
# Mixed-precision training
scaler = torch.cuda.amp.GradScaler()
with torch.cuda.amp.autocast():
loss = compute_loss(model, noisy_images, t, captions)
# Backpropagation with scaler
scaler.scale(loss).backward()
scaler.step(optimizer)
scaler.update()
8. Documentation and Reporting
Tasks:
- Document the Model Architecture and Training Process:
- Provide clear explanations of choices made.
- Visualize Results:
- Create a gallery of generated images.
- Prepare a Project Report or Presentation:
- Summarize objectives, methods, results, and conclusions.
Further Enhancements
- Implement Classifier-Free Guidance:
- Improve image-text alignment and quality.
- Explore Latent Diffusion Models:
- Reduce computational requirements by operating in latent space.
- Integrate with User Interfaces:
- Build a web app to generate images from user input texts.
- Experiment with Different Architectures:
- Try other backbone models like ViT or ResNet.