Deploying Diffusion Models at Scale
Chapter 1: Scaling Challenges and Architectures
Computational Requirements of Diffusion Models
Latency and Throughput Considerations
Architectural Patterns for Generative AI Deployment
Synchronous vs. Asynchronous Processing
MLOps Principles for Diffusion Models
Chapter 2: Optimizing Diffusion Models for Inference
Inference Bottlenecks in Diffusion Processes
Model Quantization Techniques (INT8, FP16)
Knowledge Distillation for Diffusion Models
Sampler Optimization Strategies
Hardware Acceleration (GPUs, TPUs)
Compiler Optimization (TensorRT, OpenVINO)
Benchmarking Inference Performance
Hands-on Practical: Optimizing a Diffusion Model
Chapter 3: Infrastructure for Scalable Deployment
Containerizing Diffusion Models with Docker
GPU Resource Management in Containers
Orchestration with Kubernetes
Managing GPU Nodes in Kubernetes
Autoscaling Strategies for Inference Workloads
Serverless GPU Inference Options
Storage Considerations for Models and Data
Hands-on Practical: Deploying on Kubernetes
Chapter 4: Building Scalable Inference APIs
API Design Patterns for Generative Models
Handling Long-Running Generation Tasks
Request Batching Techniques
Implementing Request Queues
Rate Limiting and Throttling
Authentication and Authorization
API Versioning Strategies
Hands-on Practical: Building an Inference API
Chapter 5: Monitoring and Maintaining Deployed Models
Essential Metrics for Diffusion Model Deployment
Setting up Logging and Tracing
Monitoring Tools and Platforms
Detecting Performance Regressions
Monitoring Generation Quality
Cost Monitoring and Alerting
Model Retraining and Update Strategies
Hands-on Practical: Setting up Monitoring
Chapter 6: Advanced Deployment Techniques
Multi-Region and Global Deployment Strategies
Canary Releases and A/B Testing Models
Advanced Cost Optimization Strategies
Handling GPU Failures and Spot Instance Interruptions
Optimizing Data Transfer Costs
Cold Starts in Serverless and Container Environments
Load Balancing Strategies for Stateful/Long Tasks
Inference Bottlenecks in Diffusion Processes
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- Denoising Diffusion Probabilistic Models, Jonathan Ho, Ajay Jain, and Pieter Abbeel, 2020 Advances in Neural Information Processing Systems (NeurIPS) DOI: 10.48550/arXiv.2006.11239 - Foundational paper introducing the Denoising Diffusion Probabilistic Models, outlining the iterative refinement process that forms the basis of diffusion model inference.
- Denoising Diffusion Implicit Models, Jiaming Song, Chenlin Meng, and Stefano Ermon, 2020 International Conference on Learning Representations (ICLR) DOI: 10.48550/arXiv.2010.02502 - Introduces Denoising Diffusion Implicit Models (DDIMs), offering a faster and more efficient sampling strategy compared to DDPMs, directly addressing the bottleneck of numerous sampling steps.
- U-Net: Convolutional Networks for Biomedical Image Segmentation, Olaf Ronneberger, Philipp Fischer, and Thomas Brox, 2015 Medical Image Computing and Computer-Assisted Intervention (MICCAI), Vol. 9351 (Springer, Cham) DOI: 10.1007/978-3-319-24574-4_28 - Presents the U-Net architecture, which is the core neural network repeatedly evaluated in diffusion models and a primary source of computational cost during inference.
- High-Resolution Image Synthesis with Latent Diffusion Models, Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, Björn Ommer, 2022 Conference on Computer Vision and Pattern Recognition (CVPR) DOI: 10.48550/arXiv.2112.10752 - Introduces Latent Diffusion Models (LDMs) like Stable Diffusion, which operate in a compressed latent space to enable high-resolution image generation, highlighting the computational scale of modern diffusion models.
- Progressive Distillation for Fast Sampling of Diffusion Models, Tim Salimans and Jonathan Ho, 2022 International Conference on Learning Representations (ICLR) DOI: 10.48550/arXiv.2202.00512 - Proposes a distillation method to enable diffusion models to produce high-quality samples with significantly fewer sampling steps, directly addressing the bottleneck of repetitive U-Net evaluations.