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Advanced Video Generation with Transformers and Diffusion Models

Course Goal: To provide a comprehensive understanding of cutting-edge video generation techniques, focusing on Transformer-based architectures, diffusion models, and flow-based models, with a particular emphasis on the methods presented in the HunyuanVideo, CogVideoX, and Pyramidal Flow Matching papers. Students will gain practical experience in implementing and training these models using PyTorch.

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 CNNs, RNNs, and Transformers.
  • Experience with PyTorch and the Hugging Face Transformers library.
  • Familiarity with generative models (VAEs, GANs, Diffusion Models).

Course Duration: Approximately 8-10 weeks, with each module taking roughly 1 week.

Tools:

  • Python (>= 3.8)
  • PyTorch (latest stable version)
  • Hugging Face Transformers library
  • Hugging Face Datasets library
  • Hugging Face Accelerate library
  • Hugging Face Diffusers library
  • Jupyter Notebooks/Google Colab
  • Standard Python libraries (NumPy, Pandas, Matplotlib, etc.)
  • Potentially, Weights & Biases or TensorBoard for experiment tracking

Curriculum Draft:

Module 1: Recap and Foundations of Video Generation (Week 1)

  • Topic 1.1: Recap of Transformers and Generative Models:
    • Brief review of self-attention, encoder-decoder architectures.
    • Overview of VAEs, GANs, Autoregressive models, and Diffusion Models.
    • Limitations of existing approaches for video generation.
  • Topic 1.2: Challenges in Video Generation:
    • High dimensionality of video data.
    • Temporal consistency and coherence.
    • Computational cost and memory requirements.
    • Long-range dependencies and motion modeling.
  • Topic 1.3: Introduction to the Papers:
    • Overview of HunyuanVideo, CogVideoX, and Pyramidal Flow Matching.
    • Key contributions and innovations of each paper.
    • How these papers address the challenges of video generation.
  • Topic 1.4: Setting up the Development Environment for Video Generation:
    • Installing necessary libraries.
    • Configuring GPU usage for large-scale training.
    • Introduction to distributed training concepts.
  • Hands-on Exercises:
    • Review exercises on Transformers and Diffusion Models.
    • Setting up the development environment.
    • Exploring pre-trained video generation models (if available) for basic inference.

Module 2: Deep Dive into HunyuanVideo (Week 2 & 3)

  • Topic 2.1: HunyuanVideo Architecture - Part 1:
    • 3D Variational Autoencoder (3D VAE) for video compression.
      • Spatial and temporal compression.
      • Causal convolutions.
      • Training objectives and loss functions (L1, LPIPS, KL, GAN loss).
      • Context parallel implementation.
    • Implementing and training a 3D VAE in PyTorch.
  • Topic 2.2: HunyuanVideo Architecture - Part 2:
    • Expert Transformer with Adaptive LayerNorm.
    • 3D Full Attention mechanism.
    • Text-Video alignment strategies.
  • Topic 2.3: Progressive Training and Multi-Resolution Frame Packing:
    • Concept of progressive training.
    • Multi-resolution frame packing for efficient training.
    • Explicit Uniform Sampling.
  • Topic 2.4: Data Preprocessing for HunyuanVideo:
    • Data filtering techniques.
    • Video captioning and its importance.
    • Implementing data augmentation strategies.
  • Topic 2.5: Scaling Laws in Video Generation:
    • Understanding the relationship between model size, dataset size, and computational resources.
    • Discussion of the scaling experiments in the HunyuanVideo paper.
  • Hands-on Exercises:
    • Implementing the 3D VAE architecture in PyTorch.
    • Training a 3D VAE on a subset of video data.
    • Implementing the Expert Transformer with Adaptive LayerNorm.
    • Building the full HunyuanVideo architecture.
    • Setting up a progressive training pipeline.
    • Experiment with different data filtering techniques.

Module 3: Exploring CogVideoX (Week 4 & 5)

  • Topic 3.1: CogVideoX Architecture - Part 1:
    • Expert Transformer with Expert Adaptive LayerNorm.
    • Focusing on the unique aspects of CogVideoX's Expert Transformer design and its implications for video generation.
    • Comparison with the Expert Transformer in HunyuanVideo.
  • Topic 3.2: CogVideoX Architecture - Part 2:
    • 3D Causal VAE.
    • Differences and similarities between CogVideoX and HunyuanVideo's VAE approaches.
    • Discussing ablations and design choices in the paper regarding the VAE.
  • Topic 3.3: Progressive Training and Techniques in CogVideoX:
    • Multi-resolution frame pack and resolution progressive training.
    • Explicit Uniform Sampling for stable training.
    • Comparison with the progressive training strategies in HunyuanVideo.
  • Topic 3.4: CogVideoX's Approach to Long Video Generation:
    • How CogVideoX handles long-term consistency and dynamic plots.
    • Discussion of any specific techniques or architectural choices that address this challenge.
  • Hands-on Exercises:
    • Implementing key components of the CogVideoX architecture.
    • Comparing the 3D VAE implementations of CogVideoX and HunyuanVideo.
    • Experimenting with the progressive training techniques described in the CogVideoX paper.
    • Potentially, adapting the HunyuanVideo codebase to incorporate elements of CogVideoX's architecture.

Module 4: Pyramidal Flow Matching (Week 6 & 7)

  • Topic 4.1: Introduction to Flow-Based Models:
    • Review of normalizing flows.
    • Limitations of normalizing flows for high-dimensional data.
    • Introduction to the concept of flow matching.
  • Topic 4.2: Pyramidal Flow Matching - Core Concepts:
    • The idea of learning a continuous-time transformation (vector field).
    • Spatial and temporal pyramid representations.
    • Piecewise flow for each pyramid resolution.
    • Unified flow matching objective.
  • Topic 4.3: Mathematical Formulation of Pyramidal Flow Matching:
    • Detailed explanation of the training objective.
    • Connections to optimal transport.
  • Topic 4.4: Implementing Pyramidal Flow Matching:
    • Building a basic pyramidal flow matching model in PyTorch.
    • Implementing the unified flow matching objective.
  • Topic 4.5: Inference and Renoising in Pyramidal Flow Matching:
    • Handling jump points between pyramid stages.
    • Adding corrective Gaussian noise for continuity.
  • Topic 4.6: Temporal Pyramid for Efficient History Conditioning:
    • Using compressed, lower-resolution history for autoregressive generation.
    • Reducing token count and improving training efficiency.
  • Topic 4.7: Comparing Pyramidal Flow Matching with Diffusion Models:
    • Advantages and disadvantages of each approach.
    • Situations where one might be preferred over the other.
    • Potential for combining flow matching and diffusion techniques.
  • Hands-on Exercises:
    • Implementing a basic flow matching model in PyTorch.
    • Building a pyramidal flow matching model based on the paper's description.
    • Experimenting with generating data from a known distribution.
    • Implementing the temporal pyramid for history conditioning.
    • Comparing the performance of pyramidal flow matching with a diffusion model on a simple task.

Module 5: Advanced Topics and Applications (Week 8)

  • Topic 5.1: Scaling and Optimization Techniques:
    • Model parallelism and data parallelism for large-scale training.
    • Gradient checkpointing and other memory optimization techniques.
    • Mixed precision training.
    • Using the Hugging Face accelerate library for distributed training.
  • Topic 5.2: Advanced Video Editing and Control:
    • Prompt engineering for video generation.
    • Conditional generation with fine-grained control (e.g., using bounding boxes, sketches).
    • Video inpainting and outpainting.
  • Topic 5.3: Ethical Considerations and Societal Impact:
    • Bias in video generation models.
    • Responsible use of generative AI.
    • Potential for misuse (deepfakes, misinformation).
  • Topic 5.4: Deployment and Serving Video Generation Models:
    • Brief overview of model deployment strategies (e.g., using Flask, FastAPI, or cloud platforms).
    • Considerations for real-time video generation.
  • Hands-on Exercises:
    • Experimenting with different prompt engineering techniques.
    • Implementing conditional generation with simple controls.
    • Exploring model deployment options (optional).

Module 6: Project Work and Presentations (Week 9 & 10)

  • Topic 6.1: Project Definition and Guidance:
    • Students will work on individual or group projects applying the concepts learned throughout the course.
    • Project ideas will be provided, but students are encouraged to propose their own.
    • Guidance and mentorship will be provided by the instructor.
  • Topic 6.2: Project Development:
    • Students will dedicate time to developing their projects.
    • Regular check-ins and progress updates with the instructor.
  • Topic 6.3: Project Presentations:
    • Students will prepare and deliver presentations showcasing their projects.
    • Presentations should include a demonstration, explanation of the methodology, and discussion of results.
  • Project Ideas:
    • Implement and train a video generation model based on one of the three papers (HunyuanVideo, CogVideoX, or Pyramidal Flow Matching).
    • Improve an existing video generation model by incorporating techniques from one of the papers.
    • Develop a novel application based on video generation (e.g., video editing, animation, interactive storytelling).
    • Explore and compare different video generation architectures (e.g., Transformers vs. CNNs).
    • Investigate methods for improving the temporal consistency and coherence of generated videos.
    • Develop techniques for fine-grained control over video generation (e.g., using sketches, bounding boxes, or motion trajectories).
    • Train a model for a specific video domain (e.g., generating videos of human actions, natural landscapes, or animations).

Assessment:

  • Hands-on exercises throughout the modules.
  • Short quizzes to assess understanding of key concepts.
  • Mid-term project or assignment (e.g., implementing a specific component of one of the models).
  • Final project and presentation.
  • Class participation and engagement.

Key Pedagogical Considerations:

  • Code-First Approach: Emphasize practical implementation and experimentation alongside theoretical understanding.
  • Paper Reading and Discussion: Encourage students to read and critically analyze the three key papers.
  • Progressive Complexity: Gradually introduce more complex concepts and techniques, building upon the foundations established in earlier modules.
  • Focus on Key Innovations: Highlight the unique contributions of each paper and how they address the challenges of video generation.
  • Comparison and Contrast: Encourage students to compare and contrast the different approaches presented in the papers.
  • Real-World Applications: Connect the concepts to real-world applications and potential use cases.
  • Ethical Considerations: Discuss the ethical implications of video generation technology.
  • Community and Collaboration: Foster a collaborative learning environment through group projects, discussions, and peer feedback.