Phase 58 Planning: Diffusion Models & Score-Based Generative AI

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Phase 58: Diffusion Models & Score-Based Generative AI

Overview

Phase 58 implements diffusion models and score-based generative architectures — the foundation of modern generative AI systems including Stable Diffusion, DALL-E 2, and Imagen. These models learn to reverse a gradual noising process, generating high-quality samples through iterative denoising.

Key References

• Ho et al. (2020) — Denoising Diffusion Probabilistic Models (DDPM): foundational framework for discrete-time diffusion with learned reverse process
• Song et al. (2021) — Score-Based Generative Modeling through SDEs: unified continuous-time framework connecting score matching and diffusion
• Nichol & Dhariwal (2021) — Improved DDPM: cosine noise schedule, learned variance, importance sampling for training
• Song et al. (2021) — Denoising Diffusion Implicit Models (DDIM): deterministic sampling, accelerated generation, interpolation in latent space
• Rombach et al. (2022) — Latent Diffusion Models / Stable Diffusion: diffusion in compressed latent space via VAE, cross-attention conditioning
• Ramesh et al. (2022) — DALL-E 2 (unCLIP): CLIP-guided hierarchical diffusion with prior and decoder
• Saharia et al. (2022) — Imagen: cascaded pixel-space diffusion with T5-XXL text encoder, dynamic thresholding
• Karras et al. (2022) — EDM: optimal noise schedule design, preconditioning, deterministic ODE sampling
• Peebles & Xie (2023) — DiT (Diffusion Transformers): replacing UNet with transformer backbone, scalable architecture
• Lipman et al. (2023) — Flow Matching: simulation-free training of continuous normalizing flows, optimal transport paths
• Ho & Salimans (2022) — Classifier-Free Guidance: trading off diversity for fidelity without external classifiers
• Dhariwal & Nichol (2021) — ADM (Diffusion Models Beat GANs): architecture improvements, classifier guidance

Sub-phases

Sub-phase	Component	Focus
58.1	NoiseScheduler	Forward diffusion process, variance schedules (linear, cosine, learned), continuous-time noise levels, SNR-based timestep sampling
58.2	ScoreEstimator	Score function learning, UNet/DiT denoiser architectures, time conditioning, cross-attention for conditioning, ε/v/x₀ prediction
58.3	SamplingEngine	DDPM/DDIM/DPM-Solver sampling, classifier-free guidance, ancestral sampling, deterministic vs stochastic paths
58.4	LatentDiffusionEncoder	VAE latent space encoding, perceptual compression, CLIP conditioning, multi-modal conditioning pipelines
58.5	DiffusionOrchestrator	End-to-end generation pipeline, training coordination, inference optimization, cascaded diffusion, flow matching

Architecture Integration

NoiseScheduler ──► ScoreEstimator ──► SamplingEngine
                        │                    │
              LatentDiffusionEncoder ─────────┘
                        │
              DiffusionOrchestrator (coordinates all)

Dependencies

• Phase 46 (Self-Supervised Learning) — representation learning for conditioning
• Phase 56 (Interpretability) — attention visualization for diffusion models
• Phase 57 (MoE) — expert routing for multi-task generation