StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasets
Axel Sauer

University of Tübingen
Germanya.sauer@uni-tuebingen.de

Katja Schwarz

University of Tübingen
Germany
katja.schwarz@uni-tuebingen.deAndreas Geiger

University of Tübingen
Germany
a.geiger@uni-tuebingen.de

Figure 1: Class-conditional samples generated by StyleGAN3 (left) and StyleGAN-XL (right) trained on ImageNet at resolution 2562 .

ABSTRACTKEYWORDS

Computer graphics has experienced a recent surge of data-centric
approaches for photorealistic and controllable content creation.
StyleGAN in particular sets new standards for generative modelingregarding image quality and controllability. However, StyleGAN’s
performance severely degrades on large unstructured datasets suchas ImageNet. StyleGAN was designed for controllability; hence,prior works suspect its restrictive design to be unsuitable for diverse datasets. In contrast, we find the main limiting factor to be thecurrent training strategy. Following the recently introduced Projected GAN paradigm, we leverage powerful neural network priors
and a progressive growing strategy to successfully train the latest
StyleGAN3 generator on ImageNet. Our final model, StyleGANXL, sets a new state-of-the-art on large-scale image synthesis andis the first to generate images at a resolution of 10242 at sucha dataset scale. We demonstrate that this model can invert andedit images beyond the narrow domain of portraits or specific object classes. Code, models, and supplementary videos can be foundat https://sites.google.com/view/stylegan-xl/.

Generative Adversarial Networks, Pretrained Models, Image Synthesis, Image Editing

CCS CONCEPTS• Computing methodologies → Learning latent representations; Image manipulation; Computer graphics; Neural networks.
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SIGGRAPH ’22 Conference Proceedings, August 7–11, 2022, Vancouver, BC, Canada© 2022 Copyright held by the owner/author(s). Publication rights licensed to ACM.
ACM ISBN 978-1-4503-9337-9/22/08. . . $15.00
https://doi.org/10.1145/3528233.3530738

ACM Reference Format:
Axel Sauer, Katja Schwarz, and Andreas Geiger, . 2022. StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasets. In Special Interest Group on Computer Graphics and Interactive Techniques Conference Proceedings (SIGGRAPH’22 Conference Proceedings), August 7–11, 2022, Vancouver, BC, Canada. ACM,
New York, NY, USA, 10 pages. https://doi.org/10.1145/3528233.3530738

1

INTRODUCTION

Computer graphics has long been concerned with generating photorealistic images at high resolution that allow for direct control
over semantic attributes. Until recently, the primary paradigm wasto create carefully designed 3D models which are then renderedusing realistic camera and illumination models. A parallel line ofresearch approaches the problem from a data-centric perspective. In
particular, probabilistic generative models [Goodfellow et al. 2014;
Song et al. 2021; van den Oord et al. 2017] have shifted the paradigm
from designing assets to designing training procedures and datasets.
Style-based GANs (StyleGANs) are a specific instance of these models, and they exhibit many desirable properties. They achieve highimage fidelity [Karras et al. 2019, 2020b], fine-grained semantic
control [Härkönen et al. 2020; Ling et al. 2021; Wu et al. 2021], andrecently alias-free generation enabling realistic animation [Karras
et al. 2021]. Moreover, they reach impressive photorealism on carefully curated datasets, especially of human faces. However, whentrained on large and unstructured datasets like ImageNet [Denget al. 2009], StyleGANs do not achieve satisfactory results yet. One
other problem plaguing data-centric methods, in general, is thatSIGGRAPH ’22 Conference Proceedings, August 7–11, 2022, Vancouver, BC, Canada

Sauer et al.

they become prohibitively more expensive when scaling to higher
resolutions as bigger models are required.
Initially, StyleGAN [Karras et al. 2019] was proposed to explicitly
disentangle factors of variations, allowing for better control andinterpolation quality. However, its architecture is more restrictive
than a standard generator network [Karras et al. 2018; Radford et al.
2016] which seems to come at a price when training on complex
and diverse datasets such as ImageNet. Previous attempts at scaling
StyleGAN and StyleGAN2 to ImageNet led to sub-par results [Grigoryev et al. 2022; Gwern 2020], giving reason to believe it might befundamentally limited for highly diverse datasets [Gwern 2020].
BigGAN [Brock et al. 2019] is the state-of-the-art GAN model
for image synthesis on ImageNet. The main factors for BigGANs
success are larger batch and model sizes. However, BigGAN has
not reached a similar standing as StyleGAN as its performancevaries significantly between training runs [Karras et al. 2020a]
and as it does not employ an intermediate latent space which isessential for GAN-based image editing [Abdal et al. 2021; Collins
et al. 2020; Patashnik et al. 2021; Wu et al. 2021]. Recently, BigGAN
has been superseded in performance by diffusion models [Dhariwal
and Nichol 2021]. Diffusion models achieve more diverse image
synthesis than GANs but are significantly slower during inference
and prior work on GAN-based editing is not directly applicable.
Following these arguments, successfully training StyleGAN on
ImageNet has several advantages over existing methods.
The previously failed attempts at scaling StyleGAN raise thequestion of whether architectural constraints fundamentally limit
style-based generators or if the missing piece is the right training
strategy. Recent work by [Sauer et al. 2021] introduced Projected
GANs which project generated and real samples into a fixed, pretrained feature space. Rephrasing the GAN setup this way leads tosignificant improvements in training stability, training time, anddata efficiency. Leveraging the benefits of Projected GAN training
might enable scaling StyleGAN to ImageNet. However, as observed
by [Sauer et al. 2021], the advantages of Projected GANs only partially extend to StyleGAN on the unimodal datasets they investigated. We study this issue and propose architectural changes toaddress it. We then design a progressive growing strategy tailoredto the latest StyleGAN3. These changes in conjunction with Projected GAN already allow surpassing prior attempts of training
StyleGAN on ImageNet. To further improve results, we analyze thepretrained feature network used for Projected GANs and find thatthe two standard neural architectures for computer vision, CNNs
and ViTs [Dosovitskiy et al. 2021], significantly improve performance when used jointly. Lastly, we leverage classifier guidance,a technique originally introduced for diffusion models to inject
additional class-information [Dhariwal and Nichol 2021].
Our contributions culminate in a new state-of-the-art on largescale image synthesis, pushing the performance beyond existing
GAN and diffusion models. We showcase inversion and editing for
ImageNet classes and find that Pivotal Tuning Inversion (PTI) [Roichet al. 2021], a powerful new inversion paradigm, combines well with
our model and even embeds out-of-domain images smoothly into
our learned latent space. Our efficient training strategy allows us
to triple the parameters of the standard StyleGAN3 while reachingprior state-of-the-art performance of diffusion models [Dhariwal

and Nichol 2021] in a fraction of their training time. It further enables us to be the first to demonstrate image synthesis on ImageNetscale at a resolution of 10242 pixels. We will open-source our codeand models upon publication.

2

BACKGROUND

We first introduce the main building blocks of our system: the StyleGAN3 generator [Karras et al. 2021] and Projected GAN’s [Sauer
et al. 2021] feature projectors and multi-scale discriminators.
StyleGAN.. This section describes style-based generators in general with a focus on the latest StyleGAN3 [Karras et al. 2021]. A
StyleGAN generator consists of a mapping network G𝑚 and asynthesis network G𝑠 . First, G𝑚 maps a normally distributed latent code z to a style code w. This style code w is then used formodulating the convolution kernels of G𝑠 to control the synthesis
process. The synthesis network G𝑠 of StyleGAN3 starts from aspatial map defined by Fourier features [Tancik et al. 2020; Xu et al.
2021]. This input then passes through 𝑁 layers of convolutions,non-linearities, and upsampling to generate an image. Each nonlinearity is wrapped by an upsampling and downsampling operation
to prevent aliasing. The low-pass filters used for these operations
are carefully designed to balance image quality, antialiasing, andtraining speed. Concretely, their cutoff and stopband frequencies
grow geometrically with network depth, the transition band halfwidths are as wide as possible within the limits of the layer sampling
rate, and only the last two layers are critically sampled, i.e., thefilter cutoff equals the bandlimit. The number of layers 𝑁 is 14,
independent of the final output resolution.
Style mixing and path length regularization are methods forregularizing style-based generators. In style mixing, an image is
generated by feeding sampled style codes w into different layersof G𝑠 independently. Path length regularization encourages that a
step of fixed size in latent space results in a corresponding fixedchange in pixel intensity of the generated image [Karras et al. 2020b].
This inductive bias leads to a smoother generator mapping andhas several advantages including fewer artifacts, more predictabletraining behavior, and better inversion.
Progressive growing was introduced by [Karras et al. 2018] forstable training at high resolutions but [Karras et al. 2020b] foundthat it can impair shift-equivariance. [Karras et al. 2021] observethat texture sticking artifacts are caused by a lack of equivarianceand carefully design StyleGAN3 to prevent texture sticking. Hence,in this paper, as we build on StyleGAN3, we can revisit the idea ofprogressive growing to improve convergence speed and synthesis
quality.
Projected GAN.. The original adversarial game between a generator G and a discriminator D can be extended by a set of feature
projectors {P𝑙 } [Sauer et al. 2021]. The projectors map real images
x and images generated by G to the discriminator’s input space.
The Projected GAN objective is formulated asÕmin max
Ex [log D𝑙 (P𝑙 (x))]
G {D𝑙 }𝑙 ∈L(1)+ Ez [log(1 − D𝑙 (P𝑙 (G(z))))]

StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasets

SIGGRAPH ’22 Conference Proceedings, August 7–11, 2022, Vancouver, BC, Canada

where {D𝑙 } is a set of independent discriminators operating on
different feature projections. The projectors consist of a pretrainedfeature network F, cross-channel mixing (CCM) and cross-scale
mixing (CSM) layers. The purpose of CCM and CSM is to prohibit
the discriminators from focusing on only a subset of its input feature
space which would result in mode collapse. Both modules employ
differentiable random projections that are not optimized during
GAN training. CCM mixes features across channels via random
1x1 convolutions, CSM mixes features across scales via residual
random 3x3 convolution blocks and bilinear upsampling. The output of CSM is a feature pyramid consisting of four feature maps atdifferent resolutions. Four discriminators operate independently onthese feature maps. Each discriminator uses a simple convolutionalarchitecture and spectral normalization [Miyato et al. 2018]. The
depth of the discriminator varies depending on its input resolution,i.e., a spatially larger feature map corresponds to a deeper discriminator. Other than spectral normalization, Projected GANs do not
use additional regularization such as gradient penalties [Mescheder
et al. 2018]. Lastly, [Sauer et al. 2021] apply differentiable dataaugmentation [Zhao et al. 2020] before F which improves Projected
GAN’s performance independent of the dataset size.
[Sauer et al. 2021] evaluate several combinations of F and G
and find an EfficientNet-Lite0 [Tan and Le 2019] and a FastGAN
generator [Liu et al. 2021] to work especially well. When using
a StyleGAN generator, they observe that the discriminators canquickly overpower the generator for suboptimal learning rates. The
authors suspect that the generator might adapt too slowly due toits design which modulates feature maps with styles learned by amapping network.

training the models on a single NVIDIA Tesla V100 takes 400 days
compared to the previously required 1914 V100-days. We refer toour model as StyleGAN-XL (Fig. 2).

3

SCALING STYLEGAN TO IMAGENET

As mentioned before, StyleGAN has several advantages over existing approaches that work well on ImageNet. But a naïve training
strategy does not yield state-of-the-art performance [Grigoryev
et al. 2022; Gwern 2020]. Our experiments confirm that even thelatest StyleGAN3 does not scale well, see Fig. 1. Particularly at highresolutions, the training becomes unstable. Therefore, our goal is totrain a StyleGAN3 generator on ImageNet successfully. Success isdefined in terms of sample quality primarily measured by inception
score (IS) [Salimans et al. 2016] and diversity measured by Fréchet
Inception Distance (FID) [Heusel et al. 2017]. Throughout this section, we gradually introduce changes to the StyleGAN3 baseline(Config-A) and track the improvements in Table 1. First, we modify the generator and its regularization losses, adapting the latent
space to work well with Projected GAN (Config-B) and for theclass-conditional setting (Config-C). We then revisit progressive
growing to improve training speed and performance (Config-D).
Next, we investigate the feature networks used for Projected GAN
training to find a well-suited configuration (Config-E). Lastly, we
propose classifier guidance for GANs to provide class information
via a pretrained classifier (Config-F). Our contributions enable us
to train a significantly larger model than previously possible while
requiring less computation than prior art. Our model is three times
larger in terms of depth and parameter count than a standard StyleGAN3. However, to match the prior state-of-the-art performanceof ADM [Dhariwal and Nichol 2021] at a resolution of 5122 pixels,

3.1

Adapting Regularization and Architectures

Training on a diverse and class-conditional dataset makes it necessary to introduce several adjustments to the standard StyleGAN configuration. We construct our generator architecture using layers of
StyleGAN3-T, the translational-equivariant configuration of StyleGAN3. In initial experiments, we found the rotational-equivariant
StyleGAN3-R to generate overly symmetric images on more complex datasets, resulting in kaleidoscope-like patterns.
Regularization. In GAN training, it is common to use regularization for both, the generator and the discriminator. Regularization
improves results on uni-modal datasets like FFHQ [Karras et al.
2019] or LSUN [Yu et al. 2015], whereas it can be detrimental on
multi-modal datasets [Brock et al. 2019; Gwern 2020]. Therefore, we
aim to avoid regularization when possible. [Karras et al. 2021] findstyle mixing to be unnecessary for the latest StyleGAN3; hence, we
also disable it. Path length regularization can lead to poor resultson complex datasets [Gwern 2020] and is, per default, disabled for
StyleGAN3 [Karras et al. 2021]. However, path length regularization is attractive as it enables high-quality inversion [Karras et al.
2020b]. We also observe unstable behavior and divergence whenusing path length regularization in practice. We found that thisproblem can be circumvented by only applying regularization after
the model has been sufficiently trained, i.e., after 200k images. For
the discriminator, following [Sauer et al. 2021], we use spectral
normalization without gradient penalties. In addition, we blur all
images with a Gaussian filter with 𝜎 = 2 pixels for the first 200𝑘
images. Discriminator blurring has been introduced in [Karras et al.
2021] for StyleGAN3-R. It prevents the discriminator from focusing
on high frequencies early on, which we found beneficial across all
settings we investigated.
Low-Dimensional Latent Space. As observed in [Sauer et al. 2021],
Projected GANs work better with FastGAN [Liu et al. 2021] thanwith StyleGAN. One main difference between these generators istheir latent space, StyleGAN’s latent space is comparatively highdimensional (FastGAN: R100 , BigGAN: R128 , StyleGAN: R512 ). Recent findings indicate that the intrinsic dimension of natural image
datasets is relatively low [Pope et al. 2021], ImageNet’s dimension
estimate is around 40. Accordingly, a latent code of size 512 is highly
redundant, making the mapping network’s task harder at the beginning of training. Consequently, the generator is slow to adapt andcannot benefit from Projected GAN’s speed up. We therefore reduce
StyleGAN’s latent code z to 64 and now observe stable training in
combination with Projected GAN, resulting in lower FID than thebaseline (Config-B). We keep the original dimension of the stylecode w ∈ R512 to not restrict the model capacity of the mapping
network G𝑚 .
Pretrained Class Embeddings. Conditioning the model on class
information is essential to control the sample class and improveoverall performance. A class-conditional variant of StyleGAN was
first proposed in [Karras et al. 2020a] for CIFAR10 [Krizhevsky

SIGGRAPH ’22 Conference Proceedings, August 7–11, 2022, Vancouver, BC, Canada

Sauer et al.

Figure 2: Training StyleGAN-XL. We feed a latent code z and class label c to the pretrained embedding and the mapping network G𝑚 togenerate style codes w. The codes modulate the convolutions of the synthesis network G𝑠 . During training, we gradually add layers to double theoutput resolution for each stage of the progressive growing schedule. We only train the latest layers while keeping the others fixed. G𝑚 is only
trained for the initial 162 stage and remains fixed for the higher-resolution stages. The synthesized image is upsampled when smaller than 2242
and passed through a CNN and a ViT and respective feature mixing blocks (CCM+CSM). At higher resolutions, the CNN receives the unalteredimage while the ViT receives a downsampled input to keep memory requirements low but still utilize its global feedback. Finally, we apply eight
independent discriminators on the resulting multi-scale feature maps. The image is also fed to classifier CLF for classifier guidance.
Table 1: Ablation Study on ImageNet 1282 . Left: Results for different configurations after training for 15 V100-days. Right: Comparing
combinations of different feature networks F. Beginning from the base configuration using an EfficientNet-lite0 (EffNet), we add a second F withvarying architecture type and pretraining objective (Class: Classification, Self: MoCo-v2 [Chen et al. 2020]).
Configuration
A
B
C
D
E
F

StyleGAN3+ Projected GAN & small z+ Pretrained embeddings+ Progressive growing+ ViT & CNN as F1,2
+ CLF guidance (StyleGAN-XL)

FID ↓

IS ↑

53.57
22.98
20.91
19.51
12.43
12.24

15.30
57.62
35.79
35.74
56.72
86.21

et al. 2009] where a one-hot encoded label is embedded into a 512dimensional vector and concatenated with z. For the discriminator,
class information is projected onto the last discriminator layer [Miyato and Koyama 2018]. We observe that Config-B tends to generate
similar samples per class resulting in high IS. To quantify mode coverage, we leverage the recall metric [Kynkäänniemi et al. 2019] andfind that Config-B achieves a low recall of 0.004. We hypothesizethat the class embeddings collapse when training with Projected
GAN. Therefore, to prevent this collapse, we aim to ease optimization of the embeddings via pretraining. We extract and spatiallypool the lowest resolution features of an Efficientnet-lite0 [Tan and
Le 2019] and calculate the mean per ImageNet class. The network
has a low channel count to keep the embedding dimension small,following the arguments of the previous section. The embedding
passes through a linear projection to match the size of z to avoid
an imbalance. Both G𝑚 and D𝑖 are conditioned on the embedding.
During GAN training, the embedding and the linear projection
are optimized to allow specialization. Using this configuration, we
observe that the model generates diverse samples per class, andrecall increases to 0.15 (Config-C). Note that for all configurationsin this ablation, we restrict the training time to 15 𝑉 -100 𝑑𝑎𝑦𝑠.
Hence, the absolute recall is markedly lower compared to the fully
trained models. Conditioning a GAN on pretrained features was
also recently investigated by [Casanova et al. 2021]. In contrast to

F1
EffNetEffNetEffNetEffNetModel
F2ResNet50
ResNet50
DeiT-M

Type
F1

F2

Objective
F1
F2

CNN
CNN
CNN
CNN

CNN
CNN
ViT

Class
Class
Class
Class

Class
Self
Class

FID ↓

IS ↑

19.51
16.16
18.53
12.43

35.74
49.13
38.26
56.72

our approach, [Casanova et al. 2021] condition on specific instances,instead of learning a general class embedding.

3.2

Reintroducing Progressive Growing

Progressively growing the output resolution of a GAN was introduced by [Karras et al. 2018] for fast and more stable training. The
original formulation adds layers during training to both G and D
and gradually fades in their contribution. However, in a later work,it was discarded [Karras et al. 2020b] as it can contribute to texture
sticking artifacts. Recent work by [Karras et al. 2021] finds that the
primary cause of these artifacts is aliasing, so they redesign eachlayer of StyleGAN to prevent it. This motivates us to reconsider
progressive growing with a carefully crafted strategy that aims tosuppress aliasing as best as possible. Training first on very low
resolutions, as small as 162 pixels, enables us to break down thedaunting task of training on high-resolution ImageNet into smaller
subtasks. This idea is in line with the latest work on diffusion models [Dhariwal and Nichol 2021; Ho et al. 2022; Nichol and Dhariwal
2021; Saharia et al. 2021]. They observe considerable improvements
in FID on ImageNet when using a two-stage model, i.e., stacking
an independent low-resolution model and an upsampling model togenerate the final image.
Commonly, GANs follow a rigid sampling rate progression, i.e.,
at each resolution, there is a fixed amount of layers followed by

StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasets

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an upsampling operation using fixed filter parameters. StyleGAN3
does not follow such a progression. Instead, the layer count is set to14, independent of the output resolution, and the filter parametersof up- and downsampling operations are carefully designed for
antialiasing under the given configuration. The last two layers are
critically sampled to generate high-frequency details. When adding
layers for the subsequent highest resolution, discarding the previously critically sampled layers is crucial as they would introduce
aliasing when used as intermediate layers [Karras et al. 2021, 2020b].
Furthermore, we adjust the filter parameters of the added layers
to adhere to the flexible layer specification of [Karras et al. 2021];
we refer to the supplementary for details. In contrast to [Karras
et al. 2018] we do not add layers to the discriminator. Instead, tofully utilize the pretrained feature network F, we upsample bothdata and synthesized images to F’s training resolution (2242 pixels)
when training on smaller images.
We start progressive growing at a resolution of 162 using 11 layers. Every time the resolution increases, we cut off 2 layers and add
7 new ones. Empirically, fewer layers result in worse performance;
adding more leads to increased overhead and diminishing returns.
For the final stage at 10242 , we add only 5 layers as the last two are
not discarded. This amounts to 39 layers at the maximum resolutionof 10242 . Instead of a fixed growing schedule, each stage is trained
until FID stops decreasing. We find it beneficial to use a large batch
size of 2048 on lower resolution (162 and 322 ), similar to [Brocket al. 2019]. On higher resolutions, smaller batch sizes suffice (642
to 2562 : 256, 5122 to 10242 : 128). Once new layers are added, thelower resolution layers remain fixed to prevent mode collapse.
In our ablation study, FID improves only slightly (Config-D)
compared to Config-C. However, the main advantage can be seen
at high resolutions, where progressive growing drastically reduces
training time. At resolution 5122 , we reach the prior state-of-theart (FID = 3.85) after 2 V100-days. This reduction is in contrast
to other methods such as ADM, where doubling the resolution
from 2562 to 5122 pixels corresponds to increasing training timefrom 393 to 1914 V100-days to find the best performing model1 . As
our aim is not to introduce texture sticking artifacts, we measure
𝐸𝑄-𝑇 , a metric for determining translation equivariance [Karras
et al. 2021], where higher is better. Config-C yields 𝐸𝑄-𝑇 = 55,
while Config-D attains 𝐸𝑄-𝑇 = 48. This only slight reduction inequivariance shows that Config-D restricts aliasing almost as well
as a configuration without growing. For context, architectures withaliasing yield 𝐸𝑄-𝑇 ∼ 15.

that an additional CNN leads to slightly lower FID. Combining
networks with different pretraining objectives does not offer benefits over using two classifier networks. However, combining an
EfficientNet with a ViT improves performance significantly. This
result corroborates recent results in neural architecture literature,
which find that supervised and self-supervised representations are
similar [Grigg et al. 2021], whereas ViTs and CNNs learn different
representations [Raghu et al. 2021]. Combining both architecturesappears to have complementary effects for Projected GANs. We
do not see significant improvements when adding more networks;
hence, Config-E uses the combination of EfficientNet [Tan and Le2019] and DeiT-base [Touvron et al. 2021].

3.3

Exploiting Multiple Feature Networks

An ablation study conducted in [Sauer et al. 2021] finds that most
pretrained feature networks F perform similarly in terms of FID
when used for Projected GAN training regardless of training data,
pretraining objective, or network architecture. However, the study
does not answer if combining several F is advantageous. Startingfrom the standard configuration, an EfficientNet-lite0, we add asecond F to inspect the influence of its pretraining objective (classification or self-supervision) and architecture (CNN or Vision Transformer (ViT) [Dosovitskiy et al. 2021]). The results in Table 1 show
1 Note that these settings are not directly comparable as the stem of our model ispretrained, but the values should give a general sense of the order of magnitude.

3.4

Classifier Guidance for GANs

[Dhariwal and Nichol 2021] introduced classifier guidance to injectclass information into diffusion models. Classifier guidance modifies
each diffusion step at time step 𝑡 by adding gradients of a pretrainedclassifier ∇x𝑡 log 𝑝𝜙 (c|𝑥𝑡 , 𝑡). The best results are obtained by applying guidance on class-conditional models and scaling the classifier
gradients by a constant 𝜆 > 1. This combination indicates that
our model may also profit from classifier guidance, even though it
already receives class information via embeddings.
We first pass the generated image x through a pretrained classifier CLF to predict the class label 𝑐𝑖 . We then add a cross-entropyÍloss L𝐶𝐸 = − 𝐶
𝑖=0 𝑐𝑖 log 𝐶𝐿𝐹 (𝑥𝑖 ) as an additional term to the generator loss and scale this term by a constant 𝜆. For the classifier,
we use DeiT-small [Touvron et al. 2021], which exhibits strongclassification performance while not adding much overhead to thetraining. Similar to [Dhariwal and Nichol 2021], we observe a significant improvement in IS, indicating an increase in sample quality(Config-F). We find 𝜆 = 8 to work well empirically. Classifier guidance only works well on higher resolutions (> 322 ); otherwise, it
leads to mode collapse. This is in contrast to [Dhariwal and Nichol2021] who exclusively guide their low-resolution model. The difference stems from how guidance is applied: we use it for modeltraining, whereas [Dhariwal and Nichol 2021] guide the sampling
process.

4

RESULTSIn this section, we first compare StyleGAN-XL to the state-of-theart approaches for image synthesis on ImageNet. We then evaluate
the inversion and editing capabilities of StyleGAN-XL. As describedabove, we scale our model to a resolution of 10242 pixels, which
no prior work has attempted so far on ImageNet. The resolutionof most images in ImageNet is lower. We therefore preprocess
the data with a super-resolution network [Liang et al. 2021], see
supplementary.

4.1

Image Synthesis

Both our work and [Dhariwal and Nichol 2021] use classifier networks to guide the generator. To ensure the models are not inadvertently optimizing for FID and IS, which also utilize a classifier
network, we propose random-FID (rFID). For rFID, we calculate
the Fréchet distance in the pool_3 layer of a randomly initialized
inception network [Szegedy et al. 2015]. The efficacy of random
features for evaluating generative models has been demonstrated

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Sauer et al.

in [Naeem et al. 2020]. Furthermore, we report sFID [Nash et al.
2021] to assess spatial structure. Lastly, sample fidelity and diversity
are evaluated via precision and recall [Kynkäänniemi et al. 2019].
In Table 2, we compare StyleGAN-XL to the currently strongest
GAN model (BigGAN-deep [Brock et al. 2019]) and diffusion models (CDM [Ho et al. 2022], ADM [Dhariwal and Nichol 2021]) on
ImageNet. The values for ADM are calculated with and without
additional methods (Upsampling U and Classifier Guidance G). For
StyleGAN2, we report numbers by [Grigoryev et al. 2022]. We findthat StyleGAN-XL substantially outperforms all baselines across all
resolutions in FID, sFID, rFID, and IS. An exception is recall, according to which StyleGAN-XL’s sample diversity lies between BigGAN
and ADM, making progress in closing the gap between these modeltypes. BigGAN’s sample quality is the best among all comparedapproaches, which comes at the price of significantly lower recall.
StyleGAN-XL allows for the truncation trick to increase sample
fidelity, i.e., we can interpolate a sampled style code 𝑤 with the¯ We observe that for StyleGAN-XL,
class-wise mean style code 𝑤.
truncation does not increase precision, indicating that developing
novel truncation methods for high-diversity GANs is an exciting research direction for future work. Interestingly, StyleGAN-XL attains
high diversity across all resolutions, which can be attributed to ourprogressive growing strategy. Furthermore, this strategy enables toscale to megapixel resolution successfully. Training at 10242 for asingle V100-day yields a noteworthy FID of 2.8. At this resolution,
we do not compare to baselines because of resource constraints asthey are prohibitively expensive to train. visualizes generated samples at increasing resolutions. Fig. 3 visualizes generated samples atincreasing resolutions. In the supplementary, we show additional
interpolations and qualitative comparisons to BigGAN and ADM.

47.5. For qualitative results, implementation details and additional
metrics, we refer to the supplementary.
Given the results above, it is also possible to further refine theobtained reconstructions. [Roich et al. 2021] recently introduced
pivotal tuning inversion (PTI). PTI uses an initial inverted style code
as a pivot point around which the generator is finetuned. Additional
regularization prevents altering the generator output far from thepivot. Combining PTI with StyleGAN-XL allows us to invert bothin-domain (ImageNet validation set) and out-of-domain images
almost precisely. At the same time, the generator output remains
perceptually smooth, see Fig. 4.

4.2

Inversion and Manipulation

GAN-editing methods first invert a given image into latent space,i.e., find a style code 𝑤 that reconstructs the image as faithful as
possible when passed through G𝑠 . Then, 𝑤 can be manipulated toachieve semantically meaningful edits [Goetschalckx et al. 2019;
Shen et al. 2020].
Inversion. Standard approaches for inverting G𝑠 use either latent
optimization [Abdal et al. 2019; Creswell and Bharath 2019; Karras
et al. 2020b] or an encoder [Alaluf et al. 2021; Perarnau et al. 2016;
Tov et al. 2021]. A common way to achieve low reconstruction
error is to use an extended definition of the latent space: W+.
For W+ a separate w is chosen for each layer of G𝑠 . However,as highlighted by [Tov et al. 2021; Zhu et al. 2020], this extended
definition achieves higher reconstruction quality in exchange forlower editability. Therefore, [Tov et al. 2021] carefully design an
encoder to maintain editability by mapping to regions of W+ thatare close to the original distribution of W. We follow [Karras et al.
2020b] and use the original latent space W. We find that StyleGANXL already achieves satisfactory inversion results using basic latent
optimization. For inversion on the ImageNet validation set at 5122 ,StyleGAN-XL yields PSNR = 13.5 on average, improving over
BigGAN at PSNR = 10.8. Besides better pixel-wise reconstruction,
StyleGAN-XL’s inversions are semantically closer to the target
images. We measure the FID between reconstructions and targets,
and StyleGAN-XL attains FID = 21.7 while BigGAN reaches FID =Image Manipulation. Given the inverted images, we can leverage GAN-based editing methods [Härkönen et al. 2020; Kocasari
et al. 2022; Shen and Zhou 2021; Spingarn et al. 2021; Voynov andBabenko 2020] to manipulate the style code w. In Fig. 5 (Left),we first invert a given source image via latent space optimization.
We can then apply a manipulation directions obtained by, e.g.,
GANspace [Härkönen et al. 2020]. Prior work [Jahanian et al. 2020]
also investigates in-plane translation. This operation can be directly
defined in the input grid of StyleGAN-XL. The input grid also allows
performing extrapolation, see Fig. 5 (Left).
An inherent property of StyleGAN is the ability of style mixing
by supplying the style codes of two samples to different layers of G𝑠 ,generating a hybrid image. This hybrid takes on different semantic
properties of both inputs. Style mixing is commonly employed for
instances of a single domain, i.e., combining two human portraits.
StyleGAN-XL inherits this ability and, to a certain extent, even generates out-of-domain combinations between different classes, akinto counterfactual images [Sauer and Geiger 2021]. This technique
works best for aligned samples, similar to StyleGAN’s originallyfavored setting, FFHQ. Curated examples are shown in Fig. 5 (Right).

5

LIMITATIONS AND FUTURE WORK

Our contributions allow StyleGAN to accomplish state-of-the-art
high-resolution image synthesis on ImageNet. Furthermore, applying it to big and small unimodal datasets is straightforward,
and we also achieve state-of-the-art performance on FFHQ and
Pokemon at resolution 10242 , see supplementary. Exploring new
editing methods and dataset generation [Chai et al. 2021; Li et al.
2022] using StyleGAN-XL are exciting future avenues. Furthermore,
future work may tackle an even larger megapixel dataset. However, a larger yet diverse dataset is not available so far. Current
large-scale, high-resolution datasets are of single object classes orcontain many similar images [Fregin et al. 2018; Perot et al. 2020;
Zhang et al. 2020]. In the supplementary, we discuss limitations of
the current model, which should be addressed in the future.

StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasets

SIGGRAPH ’22 Conference Proceedings, August 7–11, 2022, Vancouver, BC, Canada

Table 2: Image Synthesis on ImageNet. Empty cells indicate that the model was not available and the respective metric not evaluated in theoriginal work.
Model

FID ↓

sFID ↓

rFID ↓

IS ↑

Pr ↑

Rec ↑

Resolution 1282
BigGAN
CDM
ADM
ADM-G
StyleGAN-XLFID ↓sFID ↓

rFID ↓

IS ↑

Pr ↑

Rec ↑

49.20
6.95
4.88
10.94
3.94
2.30

7.36
158.70
6.02
6.14
4.02

75.24

0.87

0.28

125.7811.86
7.06

202.65158.70100.98215.84265.12

0.69
0.83
0.78

0.63
0.53
0.53

2.52

4.12

413.12260.140.76

0.51

Resolution 2562
6.02
3.52
5.91
2.97
1.81

7.18
128.80
5.09
5.09
3.82

6.09
13.29
3.80
1.82

145.83128.8093.31
141.37200.55

0.86

0.35

0.70
0.78
0.77

0.65
0.59
0.55

Resolution 5122
BigGANADM
ADM-G-U
StyleGAN-XL

ModelStyleGAN2BigGAN
CDM
ADM
ADM-G-U
StyleGAN-XL
Resolution 10242

8.43
23.24
3.85
2.41

8.13
10.19
5.86
4.06

312.00561.32210.8351.54

177.9058.06
221.72267.75

0.88
0.73
0.84
0.77

0.29
0.60
0.53
0.52

StyleGAN-XL

Figure 3: Samples at Different Resolutions Using the Same w. The samples are generated by the models obtained during progressive
growing. We upsample all images to 10242 using nearest-neighbor interpolation for visualization purposes. Zooming in is recommended.

Figure 4: Interpolations. StyleGAN-XL generates smooth interpolations between samples of different classes (Row 1 & Row 2). PTI allows
inverting to the latent space with low distortion (outermost image, Row 3 & Row 4), and consistently embeds out-of-domain inputs, such as theone on the bottom right.

SIGGRAPH ’22 Conference Proceedings, August 7–11, 2022, Vancouver, BC, Canada

Sauer et al.

Figure 5: Image Editing and Style Mixing. Left: First, a given image is inverted via PTI [Roich et al. 2021]. Right: Given two images, we canmix their styles. This methods works for samples of the same or similar classes, and to a certain extent, for distant classes. For this experiment, we
utilize random samples instead of inversions.

Figure 6: Image Manipulation via Language. Given a random sample, we manipulate the image by by following semantic directions inlatent space found by StyleMC [Kocasari et al. 2022]. The latent space directions from top to bottom are: "smile", "no stripes", and "big eyes".

StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasets

SIGGRAPH ’22 Conference Proceedings, August 7–11, 2022, Vancouver, BC, Canada

ACKNOWLEDGMENTS

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We acknowledge the financial support by the BMWi in the projectKI Delta Learning (project number 19A19013O). Andreas Geiger
was supported by the ERC Starting Grant LEGO-3D (850533). We
would like to thank Kashyap Chitta, Michael Niemeyer, and Božidar
Antić for proofreading. Lastly, we would like to thank Vanessa Sauer
for her general support.

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