Course Goal: To provide learners with an in-depth understanding and practical mastery of building, training, and deploying state-of-the-art text-to-image models, with a specific focus on Rectified Flow Transformers as detailed in the Stability AI SD3 paper. This course will emphasize the theoretical underpinnings, practical implementation using PyTorch, and advanced techniques for high-resolution image generation.
Prerequisites:
- Successful completion of "Modern AI Development: From Transformers to Generative Models" or equivalent knowledge.
- Strong proficiency in Python and Object-Oriented Programming.
- Solid understanding of deep learning concepts, including:
- Neural Networks (CNNs, Transformers)
- Backpropagation and Optimization
- Loss functions
- Regularization
- Experience with PyTorch and the Hugging Face ecosystem (Transformers, Datasets, Accelerate, Diffusers).
- Familiarity with generative models (VAEs, Diffusion Models).
- Comfortable working with research papers and implementing algorithms from them.
Course Duration: 10-12 weeks (flexible, depending on the depth of coverage and project scope)
Tools:
- Python (>= 3.8)
- PyTorch (latest stable version)
- Hugging Face Libraries:
- Transformers
- Datasets
- Accelerate
- Diffusers
- Jupyter Notebooks/Google Colab
- Standard Python libraries (NumPy, Pandas, Matplotlib, etc.)
- Weights & Biases (wandb) or similar for experiment tracking (optional but highly recommended)
- Cloud GPU resources (e.g., Google Colab Pro, AWS, Paperspace)
Curriculum Draft:
Module 1: Recap and Introduction to the SD3 Paper (Week 1)
- Topic 1.1: Review of Core Concepts:
- Transformers (Self-Attention, Multi-Head Attention, Encoder-Decoder)
- Generative Models (Diffusion Models: Forward/Reverse Process, Noise Schedules)
- Text Encoders (CLIP, T5)
- Latent Space Representations
- Topic 1.2: Introduction to Rectified Flows:
- Limitations of traditional diffusion models
- The concept of Rectified Flows: Straightening probability paths
- Advantages of Rectified Flows: Improved sampling efficiency
- Topic 1.3: Overview of the SD3 Paper:
- Key contributions and innovations
- Architecture: MM-DiT (Multi-Modal Diffusion Transformer)
- Improved noise samplers for Rectified Flows
- Scaling laws and performance benchmarks
- Topic 1.4: Setting up the Advanced Development Environment:
- Configuring cloud GPU resources
- Installing necessary libraries (latest versions)
- Setting up experiment tracking (wandb)
- Hands-on Exercises:
- Review exercises on Transformers and Diffusion Models in PyTorch.
- Setting up the development environment and running a basic example.
- Experiment with simple coding exercises on rectifying flow using Hugging Face Diffusers
Module 2: Deep Dive into Rectified Flows (Week 2)
- Topic 2.1: Mathematical Foundations of Rectified Flows:
- Ordinary Differential Equations (ODEs) and probability flow
- Derivation of the Rectified Flow objective
- Connections to optimal transport
- Topic 2.2: Implementing Rectified Flows in PyTorch:
- Building a basic Rectified Flow model from scratch
- Understanding the training objective and loss function
- Implementing different noise samplers from the paper
- Topic 2.3: Comparing Rectified Flows with Diffusion Models:
- Empirical evaluation of sampling efficiency and sample quality
- Analyzing the trajectories of Rectified Flows vs. Diffusion Models
- Topic 2.4: Advanced Rectified Flow Techniques:
- Reflow: Iterative refinement for improved sample quality
- Conditional Rectified Flows
- Hands-on Exercises:
- Implementing and training a Rectified Flow model on a simple dataset.
- Comparing the performance of different noise samplers.
- Implementing reflow and evaluating its impact on sample quality.
Module 3: The Multi-Modal Diffusion Transformer (MM-DiT) (Week 3)
- Topic 3.1: Limitations of Existing Architectures:
- UNets vs. Transformers for diffusion models
- Challenges in handling multi-modal inputs (text and images)
- Topic 3.2: Understanding the MM-DiT Architecture:
- Separate streams for text and image tokens
- Bi-directional information flow between modalities
- Modulation mechanisms
- Topic 3.3: Implementing MM-DiT in PyTorch:
- Building the MM-DiT blocks (attention, MLP)
- Handling text and image inputs
- Implementing the modulation layers
- Topic 3.4: Comparing MM-DiT with other Architectures:
- Benchmarking against UViT and DiT on text-to-image tasks
- Hands-on Exercises:
- Implementing the MM-DiT architecture in PyTorch.
- Training a small MM-DiT model on a text-to-image dataset.
- Comparing MM-DiT performance with UViT or DiT.
Module 4: Training Strategies and Techniques from the SD3 Paper (Week 4)
- Topic 4.1: Improved Noise Samplers:
- Logit-Normal Sampling
- Mode Sampling with Heavy Tails
- Analyzing the impact of different samplers on training
- Topic 4.2: QK Normalization:
- Understanding the attention-logit growth instability
- Implementing QK normalization for stable training
- Empirical evaluation of QK normalization
- Topic 4.3: Positional Encodings for Varying Aspect Ratios:
- Limitations of standard positional encodings
- Implementing flexible positional encodings for different resolutions
- Topic 4.4: Timestep Shifting at Higher Resolutions:
- Rationale and implementation of timestep shifting
- Evaluating the impact on sample quality
- Hands-on Exercises:
- Implementing the improved noise samplers and integrating them into the training loop.
- Implementing and testing QK normalization.
- Experimenting with different positional encodings and timestep shifting.
Module 5: Scaling Laws and High-Resolution Training (Week 5)
- Topic 5.1: Understanding Scaling Laws:
- The relationship between model size, data, and compute
- Predicting model performance based on scaling trends
- Topic 5.2: Techniques for Scaling Up Training:
- Data parallelism and model parallelism
- Gradient accumulation
- Mixed precision training (bf16)
- Topic 5.3: High-Resolution Training Strategies:
- Progressive growing
- Latent space training
- Fine-tuning on higher resolutions
- Topic 5.4: Analyzing the Scaling Experiments in the SD3 Paper:
- Replicating the scaling experiments (if feasible)
- Interpreting the results and drawing conclusions
- Hands-on Exercises:
- Scaling up the training of an MM-DiT model using Hugging Face Accelerate.
- Experimenting with different high-resolution training techniques.
- Analyzing the relationship between model size, data, and performance.
Module 6: Text Encoders and Prompt Engineering (Week 6)
- Topic 6.1: The Role of Text Encoders in Text-to-Image Models:
- Impact of different text encoders (CLIP, T5) on performance
- Understanding the strengths and weaknesses of each encoder
- Topic 6.2: Advanced Text Encoding Techniques:
- Using multiple text encoders
- Fine-tuning text encoders for specific tasks
- Topic 6.3: Prompt Engineering for Text-to-Image Models:
- Crafting effective prompts for high-quality image generation
- Techniques for controlling image style, composition, and content
- Prompt optimization strategies
- Topic 6.4: Analyzing the Impact of T5 in the SD3 Paper:
- Understanding the trade-offs between using T5 and CLIP
- Experimenting with different text encoder combinations
- Hands-on Exercises:
- Experimenting with different text encoders (CLIP, T5) and combinations.
- Fine-tuning a text encoder for a specific text-to-image task.
- Developing and testing different prompt engineering techniques.
Module 7: Advanced Training Techniques (Week 7)
- Topic 7.1: Direct Preference Optimization (DPO):
- Adapting DPO for text-to-image models
- Implementing DPO for fine-tuning
- Evaluating the impact of DPO on human preference
- Topic 7.2: Finetuning for Instruction-based Image Editing:
- Concatenating input and target latents
- Finetuning on image editing datasets (e.g., InstructPix2Pix)
- Evaluating the performance on various editing tasks
- Topic 7.3: Exploring Other Advanced Techniques:
- Classifier-free guidance
- Negative prompting
- Attention masking
- Hands-on Exercises:
- Implementing DPO and fine-tuning a model with preference data.
- Finetuning a model for instruction-based image editing.
- Experimenting with other advanced training techniques.
Module 8: Evaluation and Benchmarking (Week 8)
- Topic 8.1: Quantitative Evaluation Metrics:
- FID, CLIP Score, Inception Score
- Limitations of automated metrics
- Topic 8.2: Human Evaluation:
- Designing human evaluation studies
- Collecting and analyzing human preference data
- Comparing model performance based on human ratings
- Topic 8.3: Benchmarking against State-of-the-Art Models:
- Comparing performance with models like DALL-E 3, Imagen, and previous Stable Diffusion versions
- Using benchmarks like PartiPrompts and GenEval
- Topic 8.4: Comprehensive Evaluation Strategies:
- Combining quantitative and qualitative evaluations
- Analyzing model strengths and weaknesses
- Hands-on Exercises:
- Evaluating trained models using different metrics.
- Designing and conducting a small-scale human evaluation study.
- Benchmarking against other text-to-image models.
Module 9: Deployment and Serving (Week 9)
- Topic 9.1: Model Optimization for Inference:
- Quantization
- Knowledge distillation
- Model pruning
- Topic 9.2: Serving Text-to-Image Models:
- Using frameworks like Flask or FastAPI
- Building a simple web application for text-to-image generation
- Topic 9.3: Deployment on Cloud Platforms:
- Deploying models on AWS, Google Cloud, or other platforms
- Considerations for scalability and cost
- Topic 9.4: Ethical Considerations in Deployment:
- Bias and fairness in text-to-image models
- Responsible use and deployment practices
- Hands-on Exercises:
- Optimizing a trained model for inference.
- Building a web application for text-to-image generation.
- Deploying a model on a cloud platform (optional).
Module 10: Project and Future Directions (Week 10-12)
- Topic 10.1: Project Definition and Planning:
- Developing project ideas based on learned concepts
- Defining project scope and deliverables
- Planning project timelines and milestones
- Topic 10.2: Project Work and Mentorship:
- Dedicated time for project development
- Regular check-ins and guidance from the instructor
- Topic 10.3: Project Presentations and Review:
- Presenting project results and findings
- Peer review and feedback
- Topic 10.4: Future Directions in Text-to-Image Research:
- Emerging trends and research areas
- Potential advancements and applications
- Resources for continued learning
- Project Ideas:
- Building a specialized text-to-image model for a specific domain (e.g., fashion, art, design).
- Developing a novel image editing application based on learned techniques.
- Exploring and implementing new architectures or training strategies.
- Conducting an in-depth analysis of different text-to-image models and their biases.
- Creating a tool for generating synthetic data for specific tasks.
- Investigating the security vulnerabilities of text-to-image models.
Assessment:
- Weekly Quizzes: To assess understanding of theoretical concepts.
- Hands-on Exercises: Graded for correctness and completion.
- Mid-term Project/Assignment: Implementing a core component of the SD3 architecture (e.g., MM-DiT or Rectified Flow training).
- Final Project: A substantial project demonstrating mastery of the course material, potentially involving research, implementation, and evaluation.
- Class Participation: Active engagement in discussions and Q&A sessions.
Key Pedagogical Considerations:
- Strong Emphasis on Implementation: The course will heavily focus on implementing the concepts from the paper in PyTorch.
- Research-Oriented: Learners will be encouraged to read and understand research papers, fostering a deeper understanding of the field.
- Hands-on Project: The final project will be a significant component, allowing learners to apply their knowledge creatively.
- Community and Collaboration: Fostering a collaborative learning environment through discussions and peer feedback.
- Ethical Awareness: Addressing the ethical implications of text-to-image technology throughout the course.