The document discusses diffusion models, outlining both the forward and reverse diffusion processes that involve noise addition and removal respectively. It highlights advancements in denoising diffusion probabilistic models and their improved performance through optimized noise schedules and architectures. The content also includes experimental results showcasing the capabilities of super-resolution models and various studies conducted.

1. 2022 TPAMI

2.  What are Diffusion Models?
 Forward diffusion process
𝑥0 → 𝑥𝑇 逐步添加噪声 马尔科夫过程 扩散步长受 𝛽𝑡 ∈ 0,1 𝑡=1
𝑇
影响
𝑇 → ∞, 𝑋𝑇 → 𝑁(0, 𝐼)
𝛼𝑡 = 1 − 𝛽𝑡, 𝛼𝑡 = 𝑖=1
𝑇
𝛼𝑖

3.  What are Diffusion Models?
 Reverse diffusion process
𝑥𝑇 → 𝑥0 逐步去除噪声

4.  What are Diffusion Models?

5.  What are Diffusion Models?

6.  What are Diffusion Models?
 Loss Function

7.  What are Diffusion Models?
( Ho J, Jain A, Abbeel P. Denoising diffusion probabilistic models[J]. Advances in Neural Information
Processing Systems, 2020, 33: 6840-6851. )

8.  Improved Denoising Diffusion Probabilistic Models
( Nichol A Q, Dhariwal P. Improved denoising diffusion probabilistic models[C]//International Conference
on Machine Learning. PMLR, 2021: 8162-8171. )
Help diffusion models to obtain lower NLL
 Improving the Noise Schedule 𝜷𝒕 cosine-based variance schedule
( a small offset s to prevent βt from being too small near t = 0 )

9.  Improved Denoising Diffusion Probabilistic Models
( Nichol A Q, Dhariwal P. Improved denoising diffusion probabilistic models[C]//International Conference
on Machine Learning. PMLR, 2021: 8162-8171. )
 Learning 𝜽(𝒙𝒕, 𝒕)

10.  Conditional Denoising Diffusion Model
 Gaussian Diffusion Process
 Optimizing the Denoising Model

11.  Conditional Denoising Diffusion Model
 Inference via Iterative Refinement

12.  Conditional Denoising Diffusion Model
 Inference via Iterative Refinement

13.  SR3 Model Architecture

14.  Noise Schedules
( Chen N, Zhang Y, Zen H, et al. WaveGrad: Estimating gradients for waveform generation[J].
arXiv preprint arXiv:2009.00713, 2020. )
𝛾~𝑝(𝛾) 𝑝(𝛾) = 𝑡=1
𝑇 1
𝑇
𝑈(𝛾𝑡−1, 𝛾𝑡)
𝑡~𝑈({1,2, . . . , 𝑇}), 𝛾~𝑈(𝛾𝑡−1, 𝛾𝑡)

15.  Experiments
Super-resolution results (64×64 →
256×256) for SR3 and Regression on
ImageNet test images

16.  Experiments
Results of a SR3 model (64×64 →
512×512), trained on FFHQ, and
applied to images outside of the
training set

17.  Experiments

18.  Experiments
2AFC (2-alternative forced-choice)

19.  Cascaded High-Resolution Image Synthesis

20.  Ablation Studies

21. Thank you for your attention!