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A Style-Based Generator Architecture for Generative Adversarial Networks

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A Style-Based Generator Architecture for Generative Adversarial Networks presented by Hyunwook Lee

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2022. 06. 03. A Style-Based Generator Architecture for Generative Adversarial Networks Tero Karras, Samuli Laine, Timo Aila CVPR 2019 Hyunwook Lee
Contents • Overview • Preliminaries • Disentangled Representation • Various Normalization in Deep Learning Domain • StyleGAN • Disentanglement of StyleGAN • AdaIN in the StyleGAN • Applications of StyleGAN • Case: Music Generation • Conclusion
3 Overview: What is the StyleGAN? (a) Traditional generator and (b) StyleGAN generator One of the most famous GANs for image synthesis Automatic, unsupervised separation of high-level attributes Control image synthesis inspired by style transfer Scale-specific mixing and interpolation Learnable Operation AdaIN
4 Overview: Examples with StyleGAN • StyleGAN enables scale-specific styling • Different styles in each layer only affect to the corresponding scale of style • Coarse – pose, general hair style, face, eyeglasses • Middle – small facial features, hair style, eyes • Fine – color scheme and microstructure • How they achieve these styling?  AdaIN from Style Transfer & Disentangled Representation!
5 Preliminaries: Disentangled Representation Entangled and disentangled representation One of the unsupervised representation learning in generative learning Control image synthesis inspired by style transfer Automatic, unsupervised separation of high-level attributes Scale-specific mixing and interpolation
6 Preliminaries: Disentangled Representation Training of the GANs in traditional way  Latent z will be a kind of feature vector (i.e., representation) Train & Test • Change of the latent z in arbitrary dimension causes changes of two or more features  these features are entangled! • Degrades interpretability and controllability of generation process  each latent dimension should correspond to one “independent feature”
7 Preliminaries: Disentangled Representation • Based on manifold hypothesis • real-world high-dimensional data lie on low- dimensional manifolds embedded within the high- dimensional space • Unit Gaussian is not enough to represents image manifolds • Images can badly reconstructed • A latent space that consists of linear subspaces, each of which controls one factor of variation • Reading materials: • InfoGAN, β-VAE, Spatial CBN, LAPGAN,…
8 Various Normalization in Deep Learning • Commonly utilized in most of the deep learning models • Main idea: normalize layer input  guarantee all the layers have same / similar input distribution mean/std among minibatch mean/std among channels mean/std in minibatch mean/std in minibatch
9 Instance Normalization in Style Transfer • Convolutional feature statistics of DNN can capture the style of images • Recent work reveals that channel-wise mean/variance are effective for style transfer  Instance Normalization can be seen as one of the style normalization!
10 Adaptive Instance Normalization • Given context image x and style image y, the style can be obtained by: • Normalize x (remove style of the context)  denormalize x with style of y
11 Style-based Generator (StyleGAN) How can they achieve Disentangled Representation? How can they design a generator as a style transfer? Why do they need noise input for each layer? (a) Traditional generator and (b) StyleGAN generator Learnable Operation AdaIN
12 StyleGAN: Disentangled Representation • Latent space disentanglement is crucial part for both style transfer and generative model • Hard to achieved by direct mapping (b in lower figure) • StyleGAN generates disentangled intermediate latent space 𝒲 • Not a fixed distribution, but learned mapping • Spatially invariant, modified by affined transformation A • Generate images from disentangled representation is much easier than that from entangled representation  mapping network surely trained to generate disentangled representation
13 StyleGAN: AdaIN as Styling Methods • By affined transformation A, the vector w be the style y = (ys, yb) • ys is style deviation and yb is style mean • Step-by-Step • Input x is normalized as Instance Normalization • Effectively localize the styles • Denormalized by ys and yb • To guarantee ys is standard deviation (i.e., positive value), actual multiplier is ys + 1 • Forward to next layer • Note: scale-specific styling is only possible when we can separate each network output gradually
14 StyleGAN: Style Mixing • Encouraging the styles to localize by Style Mixing in training • Simply, • try to run two different latent code z1, z2 • Mix corresponding intermediate latent w1, w2 at a randomly selected point in 𝑔 • preventing the network from assuming that adjacent styles are correlated  more localized, scale-specific modification!
15 StyleGAN: Style Mixing
16 Style Mixing in Coarse Level (42 - 82)
17 Style Mixing in Middle Level (162 - 322) • Bring smaller scale face features, hair style, eye open / close,…
18 Style Mixing in Fine Level (642 – 10242) • Mainly bring color scheme and microstructures • Doesn’t change coarse / middle styles
19 StyleGAN: Stochastic Variation • Traditional GANs achieves stochastic variation by… • generating spatially-varying pseudorandom numbers • Consumes network capacity • Not always successful • In StyleGAN… • Introduce random noise in layer-level • Hypothesis: there is pressure to introduce new content as soon as possible at any point • Fake discriminator • The easiest way: introducing new random noise for each layers  variation with random noises
20 StyleGAN: Stochastic Variation • The main areas of stochastic variation is • the hair • Silhouettes • parts of background  The noise doesn’t affect to global aspects!
21 StyleGAN: Water Droplet –like Artifacts
22 Advances of StyleGAN: StyleGAN2
23 Advances of StyleGAN: StyleGAN2 Phase artifacts in StyleGAN Examples of unnatural images w/ StyleGAN • StyleGAN (left) has texture sticking problem due to the progressive growing • Each Image in different scale generated by corresponding generator, independently • Adopting ResNet architecture to solve problem • Note: not perfectly solved – it’ll be discussed in StyleGAN3 Examples of natural images w/ StyleGAN2
24 Advances of StyleGAN: StyleGAN3 • StyleGAN2 (left) has not perfectly solved texture sticking problem • (left) Averaged images w/ small changes of latent should blur the central image • (left) But StyleGAN2 have stick to the same pixel coordinates • asdf
25 Advances of StyleGAN: StyleGAN3
26 Applications of StyleGAN: Image Domain • InterFaceGAN • Extract linear editing directions through attribute-level supervision • StyleFlow • First to present editing that is stable to be composed • Normalizing flows and attribute-level supervision • DyStyle • Addresses compositional editing directly • Accurate, elaborate, and diverse editing • StyleCLIP • Free textual editing w/ visual-linguistic pretrained model • Pose with Style • Human pose supervision to edit body poses and clothing • StyleMapGAN • Localized editing by augmenting StyleGAN’s architecture w/ spatially adaptive modulation
27 Applications of StyleGAN: StyleMapGAN
28 Applications of StyleGAN: StyleMapGAN • Localized editing by augmenting StyleGAN’s architecture w/ spatially adaptive modulation • Localied editing conducted with
29 Music Generation: Recent Works w/wo GANs • Style-Conditioned Music Generation • Style transfer-like methods in music generation w/ LSTM-based GANs • Making style codebook that decides overall style of the music • Symbolic Music Generation with Transformer-GANs • Compound Word Transformer: Learning to Compose Full-Song Music over Dynamic Directed Hypergraphs • Transformer-based music generation model
30 Music Generation: Why is the StyleGAN hard to utilized? • Main “scale-specific controllability” of the StyleGAN comes from the stacked CNN w/ various size  To utilize StyleGAN, it should be separable • Music composition should be infinitely extended  cannot utilize CNNs in temporal dimension • Separation of the musical components (e.g., Motive – Phrase – Period)  Hard to modeled like CNNs (intuitive separation of the components are hard)  Each of them shares overall flow  separation causes incoherence music • Separation of the Midi components (e.g., bar – beat - …)  Using CNNs to combine them can cause information loss (e.g., structured tokens) • Too many additional features to consider • StyleGAN and Image processing  no other input or consideration except image • Music has a bunch of extra features like instrument
31 Conclusion • “Scale-specific controllability” of the StyleGAN comes from the stacked CNN w/ various size  To utilize StyleGAN, target domain output should be separable (e.g., 4x4  8x8  16x16  32x32  …  1024x1024) • Maybe utilized in GUI design, but it will be more like “Conditioned image synthesis regardless of the structure” • If we utilize StyleGAN in GUI design, we should defense… • why do we ignore the structures? • Isn’t this design a combination of existing designs?
Thank you

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A Style-Based Generator Architecture for Generative Adversarial Networks

  • 1. 2022. 06. 03. A Style-Based Generator Architecture for Generative Adversarial Networks Tero Karras, Samuli Laine, Timo Aila CVPR 2019 Hyunwook Lee
  • 2. Contents • Overview • Preliminaries • Disentangled Representation • Various Normalization in Deep Learning Domain • StyleGAN • Disentanglement of StyleGAN • AdaIN in the StyleGAN • Applications of StyleGAN • Case: Music Generation • Conclusion
  • 3. 3 Overview: What is the StyleGAN? (a) Traditional generator and (b) StyleGAN generator One of the most famous GANs for image synthesis Automatic, unsupervised separation of high-level attributes Control image synthesis inspired by style transfer Scale-specific mixing and interpolation Learnable Operation AdaIN
  • 4. 4 Overview: Examples with StyleGAN • StyleGAN enables scale-specific styling • Different styles in each layer only affect to the corresponding scale of style • Coarse – pose, general hair style, face, eyeglasses • Middle – small facial features, hair style, eyes • Fine – color scheme and microstructure • How they achieve these styling?  AdaIN from Style Transfer & Disentangled Representation!
  • 5. 5 Preliminaries: Disentangled Representation Entangled and disentangled representation One of the unsupervised representation learning in generative learning Control image synthesis inspired by style transfer Automatic, unsupervised separation of high-level attributes Scale-specific mixing and interpolation
  • 6. 6 Preliminaries: Disentangled Representation Training of the GANs in traditional way  Latent z will be a kind of feature vector (i.e., representation) Train & Test • Change of the latent z in arbitrary dimension causes changes of two or more features  these features are entangled! • Degrades interpretability and controllability of generation process  each latent dimension should correspond to one “independent feature”
  • 7. 7 Preliminaries: Disentangled Representation • Based on manifold hypothesis • real-world high-dimensional data lie on low- dimensional manifolds embedded within the high- dimensional space • Unit Gaussian is not enough to represents image manifolds • Images can badly reconstructed • A latent space that consists of linear subspaces, each of which controls one factor of variation • Reading materials: • InfoGAN, β-VAE, Spatial CBN, LAPGAN,…
  • 8. 8 Various Normalization in Deep Learning • Commonly utilized in most of the deep learning models • Main idea: normalize layer input  guarantee all the layers have same / similar input distribution mean/std among minibatch mean/std among channels mean/std in minibatch mean/std in minibatch
  • 9. 9 Instance Normalization in Style Transfer • Convolutional feature statistics of DNN can capture the style of images • Recent work reveals that channel-wise mean/variance are effective for style transfer  Instance Normalization can be seen as one of the style normalization!
  • 10. 10 Adaptive Instance Normalization • Given context image x and style image y, the style can be obtained by: • Normalize x (remove style of the context)  denormalize x with style of y
  • 11. 11 Style-based Generator (StyleGAN) How can they achieve Disentangled Representation? How can they design a generator as a style transfer? Why do they need noise input for each layer? (a) Traditional generator and (b) StyleGAN generator Learnable Operation AdaIN
  • 12. 12 StyleGAN: Disentangled Representation • Latent space disentanglement is crucial part for both style transfer and generative model • Hard to achieved by direct mapping (b in lower figure) • StyleGAN generates disentangled intermediate latent space 𝒲 • Not a fixed distribution, but learned mapping • Spatially invariant, modified by affined transformation A • Generate images from disentangled representation is much easier than that from entangled representation  mapping network surely trained to generate disentangled representation
  • 13. 13 StyleGAN: AdaIN as Styling Methods • By affined transformation A, the vector w be the style y = (ys, yb) • ys is style deviation and yb is style mean • Step-by-Step • Input x is normalized as Instance Normalization • Effectively localize the styles • Denormalized by ys and yb • To guarantee ys is standard deviation (i.e., positive value), actual multiplier is ys + 1 • Forward to next layer • Note: scale-specific styling is only possible when we can separate each network output gradually
  • 14. 14 StyleGAN: Style Mixing • Encouraging the styles to localize by Style Mixing in training • Simply, • try to run two different latent code z1, z2 • Mix corresponding intermediate latent w1, w2 at a randomly selected point in 𝑔 • preventing the network from assuming that adjacent styles are correlated  more localized, scale-specific modification!
  • 16. 16 Style Mixing in Coarse Level (42 - 82)
  • 17. 17 Style Mixing in Middle Level (162 - 322) • Bring smaller scale face features, hair style, eye open / close,…
  • 18. 18 Style Mixing in Fine Level (642 – 10242) • Mainly bring color scheme and microstructures • Doesn’t change coarse / middle styles
  • 19. 19 StyleGAN: Stochastic Variation • Traditional GANs achieves stochastic variation by… • generating spatially-varying pseudorandom numbers • Consumes network capacity • Not always successful • In StyleGAN… • Introduce random noise in layer-level • Hypothesis: there is pressure to introduce new content as soon as possible at any point • Fake discriminator • The easiest way: introducing new random noise for each layers  variation with random noises
  • 20. 20 StyleGAN: Stochastic Variation • The main areas of stochastic variation is • the hair • Silhouettes • parts of background  The noise doesn’t affect to global aspects!
  • 21. 21 StyleGAN: Water Droplet –like Artifacts
  • 23. 23 Advances of StyleGAN: StyleGAN2 Phase artifacts in StyleGAN Examples of unnatural images w/ StyleGAN • StyleGAN (left) has texture sticking problem due to the progressive growing • Each Image in different scale generated by corresponding generator, independently • Adopting ResNet architecture to solve problem • Note: not perfectly solved – it’ll be discussed in StyleGAN3 Examples of natural images w/ StyleGAN2
  • 24. 24 Advances of StyleGAN: StyleGAN3 • StyleGAN2 (left) has not perfectly solved texture sticking problem • (left) Averaged images w/ small changes of latent should blur the central image • (left) But StyleGAN2 have stick to the same pixel coordinates • asdf
  • 26. 26 Applications of StyleGAN: Image Domain • InterFaceGAN • Extract linear editing directions through attribute-level supervision • StyleFlow • First to present editing that is stable to be composed • Normalizing flows and attribute-level supervision • DyStyle • Addresses compositional editing directly • Accurate, elaborate, and diverse editing • StyleCLIP • Free textual editing w/ visual-linguistic pretrained model • Pose with Style • Human pose supervision to edit body poses and clothing • StyleMapGAN • Localized editing by augmenting StyleGAN’s architecture w/ spatially adaptive modulation
  • 28. 28 Applications of StyleGAN: StyleMapGAN • Localized editing by augmenting StyleGAN’s architecture w/ spatially adaptive modulation • Localied editing conducted with
  • 29. 29 Music Generation: Recent Works w/wo GANs • Style-Conditioned Music Generation • Style transfer-like methods in music generation w/ LSTM-based GANs • Making style codebook that decides overall style of the music • Symbolic Music Generation with Transformer-GANs • Compound Word Transformer: Learning to Compose Full-Song Music over Dynamic Directed Hypergraphs • Transformer-based music generation model
  • 30. 30 Music Generation: Why is the StyleGAN hard to utilized? • Main “scale-specific controllability” of the StyleGAN comes from the stacked CNN w/ various size  To utilize StyleGAN, it should be separable • Music composition should be infinitely extended  cannot utilize CNNs in temporal dimension • Separation of the musical components (e.g., Motive – Phrase – Period)  Hard to modeled like CNNs (intuitive separation of the components are hard)  Each of them shares overall flow  separation causes incoherence music • Separation of the Midi components (e.g., bar – beat - …)  Using CNNs to combine them can cause information loss (e.g., structured tokens) • Too many additional features to consider • StyleGAN and Image processing  no other input or consideration except image • Music has a bunch of extra features like instrument
  • 31. 31 Conclusion • “Scale-specific controllability” of the StyleGAN comes from the stacked CNN w/ various size  To utilize StyleGAN, target domain output should be separable (e.g., 4x4  8x8  16x16  32x32  …  1024x1024) • Maybe utilized in GUI design, but it will be more like “Conditioned image synthesis regardless of the structure” • If we utilize StyleGAN in GUI design, we should defense… • why do we ignore the structures? • Isn’t this design a combination of existing designs?

Editor's Notes

  1. Batch Norm  가장 기본적인 normalization technique, batch의 평균 / 분산이 전체 데이터셋을 대표한다는 가정하에 실행. inference와 training시의 실행 방식이 다름 Layer Norm  입력 scale에 robust, 가중치의 scale / shifting에 robust Instance Norm  각 채널별 / 배치별로 mean / std normalization, inferenc단에서도 동일하게 이용가능, 명암 대비 등을 normalize할 수 있음 Group Norm  2018년 Kaiming He가 발표, Layer Norm과 Instance Norm의 절충안
  2. Bring High-level aspects (i.e., pose, general hair style, face shape, and eye glasses)
  3. Normalization으로 인해 발생하는 smooth하지 못한 mapping임 64 by 64 image부터 나타나며, 모든 feature map에 발생함  AdaIN이 결국 channel간의 연관성을 박살을 내기때문 또한, normalization이 입력에 의존하기때문에, 입력에 아주 큰 spike가 있다면 다른 곳에서 세부 조정이 쉬워지기때문에
  4. Bias 및 noise가 normalize 전에 적용된다면 상대적인 영향력이 style magnitude에 반비례하게 됨.  noise와 bias가 style과 correlate하게 됨  따라서, normalization 이후에 noise 및 bias를 적용함 Mean 빼는 부분을 없앰 + data를 기반으로 한 normalization을 없앰 AdaIN을 weight의 norm / denorm으로 변경 (bias가 없는 convolution의 성질을 생각해본다면 간단하게 가능함.)
  5. Average value를 다루는 것으로 EMA 추가. Upsample을 통해 Filtering 진행 Upsampling에는 (a)에 있는 Filter를 이용  continuous, infinite spatial domain 가정
  6. Note: scale-specific styling is only possible when we can separate each network output gradually