StyleGAN-based CLIP-guided Image Shape Manipulation
Yuchen Qian Kohei Yamamoto Keiji Yanai
The University of Electro-Communications, Tokyo, Japan{qian-y,yamamoto-k,yanai}@mm.inf.uec.ac.jp

ABSTRACTIn this paper, we propose a text-guided image manipulation method
which focuses on editing shape attribute using text description. We
combine an image generation model, StyleGAN2, and image-text
matching model, CLIP, and we have achieved the goal of image
shape attribute manipulation by modifying the parameters of the
pretrained StyleGAN2 generator. Qualitative and quantitative evaluations are conducted to demonstrate the effectiveness of the proposed method.

KEYWORDS
GANs, text-guided image manipulation, image-text cross-modal
model, CLIP
ACM Reference Format:
Yuchen Qian Kohei Yamamoto Keiji Yanai. 2022. StyleGAN-based CLIPguided Image Shape Manipulation. In CBMI ’22: International Conference
on Content-based Multimedia Indexing, Sep 14–16, 2022, Graz, Austria. ACM,
New York, NY, USA, 5 pages. https://doi.org/10.1145/1122445.1122456

1

INTRODUCTION

Generative Adversarial Networks (GANs) [9] have brought revolutionary changes for image processing field, ranging from image
synthesis [6, 29], image editing [10, 18], and even some downstream
applications like classification and regression [27]. Recently, stylebased generative models [12, 13] boosted the ability of generating
realistic and diverse images. Latent spaces of StyleGAN often encode abundant and disentangled visual representations. The latent
spaces of pretrained StyleGAN model enable a wide variety ofimage synthesis and manipulations.
To fully utilize the expressive power of GANs, an intuitive andeasy-to-use interface for users is needed. Some image synthesis
and manipulation models [15, 17, 26] take natural language as
their interfaces. Some previous models [15, 16] limit their range ofmanipulations to the annotations that exist in the training dataset.
By using Contrastive Language-Image Pre-training (CLIP) [19]models, some recent methods [17, 26] break this limitation. With
the power of the latent space of pretrained StyleGAN, these models
enable a wide range of manipulations. However, most of theseworks mainly focus on controlling the appearance attributes, suchas colors and texture, of object on an image, compared to this,Permission to make digital or hard copies of all or part of this work for personal orclassroom use is granted without fee provided that copies are not made or distributedfor profit or commercial advantage and that copies bear this notice and the full citationon the first page. Copyrights for components of this work owned by others than ACM
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ACM ISBN 978-1-4503-XXXX-X/18/06. . . $15.00
https://doi.org/10.1145/1122445.1122456

Figure 1: Visual effects of manipulation achieved by ourmodel
editing the shape attributes, like the shape and size of the object
on an image, still remains a unsolved problem.
In this work, we focus on semantically editing the shape attributes of an image, especially the editing that bring influences toother parts of the image, such as changing the size of the wheelsof a car, which will make change not only on the wheels, but also
on other parts of this car. To do this, we combine the power of thestate-of-the-art generative model, StyleGAN, with the power of thestate-of-the-art visual-language model, CLIP. Different from exiting
CLIP-guided StyleGAN-based image editing methods that rely onfinding latent directions which change the target attributes in latent
spaces, our method manipulates images by finding directions in
GAN parameter space rather than latent spaces. In this work, we
use a shift to modify the parameters of the pretrained GAN model.
By changing GAN parameters, some shape attributes editing thatcannot be achieved in latent spaces can be achieved. Furthermore,
the shift achieved by a single optimization can be reused for thesame target attribute editing, which means a large number of images can be edited in a short time using the previously learned shift.
Figure 1 shows several examples of manipulation results using our
method. By extensive experiments, we demonstrate the effectiveness of our approach on a wide range of datasets. We also compare
our method to existing CLIP-guided StyleGAN-based image editing
methods and show that our methods have better performance on
shape attributes editing.
To sum up, our main contributions are as follows:• We propose a text-guided image manipulation model which
focuses on editing shape attributes.

CBMI ’22, Sep 14–16, 2022, Graz, Austria

Yuchen Qian, Kohei Yamamoto and Keiji Yanai• By shifting the parameters of pretrained StyleGAN2 generator rather than latent codes in the latent spaces, the proposed
method can edit the shape attribute and handle the ratiochanges due to the shape changes on one part.
• Experiments shows that our method can edit the shape attribute effectively. Compared to the previous methods which
modify the GAN latent spaces, our method has better editing
effects and image quality.

2

RELATED WORKS

Latent spaces of generative models often encode much disentangled
and interpretable visual representations. Early work [20] on GANs
observed the changes on generated images gradually by walking
in the latent space. In recent years, with the expressive powerof StyleGAN, many researches utilize StyleGAN latent spaces forsemantic image manipulation [2, 10, 21, 22, 24]. These methods canbe roughly divided into two groups: unsupervised and supervised.
Unsupervised methods [10, 22, 24] find meaningful latent directions
which make interpretable and distinguishable changes to the image.
However, found directions require manual check and annotation.
Some supervised methods [2, 21] uses pretrained attribute classifiers
to get the label for training and transformation. Thus their range ofediting is restricted to the annotations that existed in the training
dataset. Some recent methods [3, 17] use CLIP to supervise thetransformation, which break the limitation and enable a wide rangeof manipulations.
As for image manipulation using StyleGAN latent space, most of
the works find latent directions in the W or W+ latent spaces. Also,
some methods use other spaces for finding latent directions, such as
StyleSpace S [25], which is demonstrated to be more disentangled
compared to W or W+ latent spaces. The work of StyleCLIP also
proposes a method using this S space to get a better manipulation
effect. Furthermore, some works [4, 5] edit the image by modifying
the GAN parameter spaces rather than latent spaces. By exploring
the GAN parameter spaces, NaviGAN [5] achieves non-trivial visual
effects that cannot be produced in the latent space W or W+. Manyof these found the directions related to shape attribute manipulationin the GAN parameter space.
Natural language is often used as an interface for GAN-based
image generation and manipulation. Many of previous works [7,15, 16] for text-guided image manipulation need to train the models from scratch. Their ranges of manipulation are also limited tothe training dataset. In recent years, with the appearance of highperformance models that pretrained with large-scale dataset, someworks begin to use pretrained models for higher image quality andlower training cost. For the field of text-guided image manipulation,
Contrastive Language-Image Pre-training (CLIP) [19] model has
attracted much attention, which has achieved the state-of-the-art
performance on image-text matching. CLIP is pretrained with 400
million image-text pair data collected from the Internet and therepresentations that learned by CLIP are extremely powerful, which
can be used in a wide range of tasks from zero-shot image classification to image generation and manipulation. Some works [3, 17]
combine CLIP with pretrained image generation models like StyleGAN to enable wide-range high-quality image manipulation, which

Figure 2: Overview of the proposed model
mainly rely on learned representations in the latent spaces of StyleGAN. Paint by Word [3] lets user edit images with the given text
descriptions and masks which indicate the regions to be edited.
StyleCLIP [17] gives three approaches for CLIP-guided image manipulation. Two of them find latent directions in the latent space
𝑊 , and the last one finds latent directions in the latent space 𝑆. As
the other methods on image synthesis employing CLIP, StyleGANNADA [8], CLIP2StyleGAN [1] and DiffusionCLIP [14] have been
proposed so far.
In this work, for the purpose of image shape attribute editing,
we build our model based on the idea of NaviGAN, which edits
the image by modifying GAN parameter spaces rather than latent
spaces. We combine this with the powerful image-text matching
performance of CLIP to guide the editing with text descriptions.

3

APPROACH

We use a shift 𝑥 to shift the parameters of one layer of the pretrained
StyleGAN2 generator. The proposed model framework is shownin Figure 2. In this section, we first provide a short overview of
StyleGAN2 and CLIP, which are the two core components of ourmodel. Next, we describe the architecture and loss function of the
proposed model.

3.1

Preliminaries

StyleGAN and its updated version StyleGAN2 have become thestate-of-the-art image generators. By feeding different style vectors into each convolution layer of StyleGAN synthesis network,
StyleGAN generates high-quality high-resolution images.
StyleGAN has two main components: mapping network andsynthesis network. First, a vector 𝑧 which is sampled from Gaussian
distribution is turned into the latent vector 𝑤 in latent space W
by mapping network. Next, latent vector 𝑤 is fed into each layer ofthe synthesis network and the image is generated. Different layersof synthesis network control different parts of features. Thus, someworks achieve semantic control on generated images by modifying
latent code 𝑤 or 𝑧.
However, for image editing on shape attributes, previous work [5]indicated that some of the shape attributes editing cannot be achievedby modifying latent code 𝑤 in latent space W. Here, we aim toachieve image shape attributes editing by shifting the parametersof StyleGAN2 convolution layers. For editing real images using
pretrained StyleGAN2 model, GAN inversion module which mapsa real image into the latent space of GAN model is needed. In thiswork, we use encoder4editing (e4e) [23], which is designed forimage manipulation using the pre-trained StyleGAN model.
We use CLIP to provide guide for text-guided image manipulation. CLIP consists of an image encoder and a text encoder. CLIP
estimates semantic similarity between image and text by learning

StyleGAN-based CLIP-guided Image Shape Manipulation

CBMI ’22, Sep 14–16, 2022, Graz, Austriaa shared embedding space for image features and text features.
With the powerful image-text matching ability, CLIP can provide
effective feedback for image manipulation process.

3.2

Network Architecture

The inputs of the proposed model are an image 𝐼 and a text 𝑇 . The
text 𝑇 describes the target attribute in a short phrase such as “asmall-wheel car”. The output is an edited image 𝐼 ′ . The goal is totranslate the input image 𝐼 into the edited image 𝐼 ′ so that thetranslated image 𝐼 ′ matches the input text 𝑇 .
The shift 𝑥 is randomly initialized from a Gaussian distribution.
By optimizing 𝑥, the shifted generator outputs the image that semantically matches input text 𝑇 . The input image is first mapped
into the latent space W of StyleGAN2 generator by e4e. Then theshifted StyleGAN2 generator outputs the image 𝐼 ′ . 𝐼 ′ is sent to CLIP
with input text 𝑇 to estimate the image-text similarity of them. This
provides feedback for the optimization process of shift 𝑥.
For a single optimization, shift 𝑥 modifies one layer of the pretrained StyleGAN2 generator, obtaining the shift that edits a single
target attribute. For layer selection, we first choose a certain rangeof layers, observe the generation results of those layers, and thendecide the target layer. Only the shift 𝑥 is optimized, and the parameters of the rest parts are fixed.

3.3

of 𝜆2 is set to 0.02-0.03, and for fine-grained attribute editing likeeyes size, the value of 𝜆2 is set to 0.07-0.08.

Loss Function

We use CLIP matching loss 𝐿𝑐𝑙𝑖𝑝 and L2 regularization loss 𝐿2 to
optimize the shift 𝑥. 𝐷𝑐𝑙𝑖𝑝 is cosine similarity between image andtext features estimated by CLIP. This is the standard way among
the CLIP-based image translation such as StyleCLIP [17].
𝐿 = 𝜆1 𝐿𝑐𝑙𝑖𝑝 + 𝜆2 𝐿2= 𝜆1 𝐷𝑐𝑙𝑖𝑝 (𝐼 ′,𝑇 ) + 𝜆2 ||𝑥 || 2

(1)

To let the output image 𝐼 ′ semantically match the input text 𝑇 ,we use CLIP to estimate the image-text matching similarity of them.
In order to preserve irrelevant features of the original image while
editing target attribute, on top of satisfying the editing of the target
attribute, we use L2 regularization loss on 𝑥 to minimize the editing.
The value of 𝜆1 and 𝜆2 depends on the nature of target edit.

4

EXPERIMENTS

In this section, we describe the datasets and experimental settings
that used in our experiments. Then, we present the qualitative
results and quantitative results.

4.1

Figure 3: Some visual effects achieved by our model

Datasets and Implementation Details

We use the StyleGAN2 models pretrained on FFHQ [12], LSUNCar [28], LSUN-Horse [28] datasets. The GAN inversion model, e4e,
used in the experiment is pretrained on the corresponding dataset.
One single optimization takes one pair of input image and text that
describes the target attribute. It takes about 90 seconds to optimize
the shift 𝑥 in one 100-step optimization using a NVIDIA GeForceGTX 1080 Ti. For parameter values of loss function, we use 𝜆1 = 10
for our experiments, and the value of 𝜆2 is chosen to be the bestvalue in the range of 0.01-0.1 depending on the nature of target
editing. For overall attribute editing such as face width, the value

4.2

Qualitative Results and Analysis

We perform some qualitative analysis to show the performance ofimage shape attribute editing using our model. First, we present
several visual effects achieved by our method. Figure 3 shows fiveexamples. The shift achieved by optimization is multiplied withdifferent magnitude, generating the edited images. Different fromappearance attribute editing, shape attribute editing on one part ofobject may affect other parts, such as the editing on nose length inwhich the nose becomes longer, the position of the mouth also goes
downward. The proposed method can handle such ratio changes
on other parts and achieve the target feature editing. Note that thesign of the weights are sometimes reversed regarding the shapechange along the text description. On the other hand, some editing
such as the result shown in the third row may results in artifact
patterns, or editing of irrelevant attributes.
Next, we compare our method with the existing StyleGAN-based
CLIP-guided image manipulation method, StyleCLIP, on image
shape attribute editing. For StyleCLIP, we use all three methods
introduced in the work, two of which perform modification in latent
space W and one of which perform modification in latent space S.
Results are shown in Figure 4.
As can be seen, the two methods of StyleCLIP which modify inthe latent space 𝑊 can hardly achieve the goal of shape attribute
manipulations. Global directions, which modifies in the latent space
𝑆, can achieve some shape feature editing, but it may change theirrelevant style and texture. In the case of nose length editing, ourmethod can achieve the transformation of target attribute better
than StyleCLIP. For wheel size editing and horse belly size editing,the images generated by StyleCLIP sometimes change the styleand texture of original images. However, the proposed method also

CBMI ’22, Sep 14–16, 2022, Graz, Austria

Yuchen Qian, Kohei Yamamoto and Keiji Yanai

Figure 5: Examples of images generated by proposed method

Figure 6: Examples of images generated by StyleCLIP
Table 1: Results for qualitative evaluations

StyleCLIP

Figure 4: Comparisons of qualitative results with StyleCLIP
Wheel Size

Ours

sometimes makes changes to irrelevant features, such as changing
the shape of the mouth while editing the length of the nose.

4.3

GAN inversion

Quantitative Results and Analysis

We conduct quantitative evaluations to show the quality of edited
images achieved by our method. The metric we use for quantitativeevaluations is Fréchet Inception Distance (FID) [11].
First, in order to show that our method does not harm image
quality significantly, we estimate the FID score between the baseline
set and the evaluation set generated by our model. The baseline set
contains 3000 real images. For the evaluation set, we first performa single optimization for a target attribute to get a shift. Then theobtained shift is applied on the pretrained generator, and generated
3000 images by editing 3000 real images that used in the baselineset.
We do this experiment on two target manipulations: “wheel size”
and “cheek size”. For each target attribute, we multiply the shift
with different magnitudes to generate the evaluation sets under
different strengths. In this experiment, magnitudes ±3, ±5, ±10
are used. The example images that achieved by these magnitudes
are shown in Figure 5. Results are shown in Table 1. Since image
reconstructed by GAN inversion is not the same as the original
image, the FID values of the image set reconstructed by inverse
mapping are also included, and the shift magnitude is set to 0.
Next, we conduct quantitative evaluations on images edited by
StyleCLIP, and compare it with our model. As before, we measured
the FID values of 3000 real images of the baseline set and 3000
images manipulated by StyleCLIP. The baseline set uses the sameimages as previous, and the evaluation set is obtained by using the
global direction method of StyleCLIP. For the parameters of StyleCLIP, we select the parameters which can achieve similar visual
effect as the corresponding magnitudes of the proposed method.
The example images achieved by StyleCLIP are shown in Figure 6.

StyleCLIP
Cheek Size
Ours
GAN inversion

magnitude
-3
-5
-10
-3
-5
-10
0
-3
-5
-10
-3
-5
-10
0

FID
33.36
42.35
54.34
15.34
17.96
26.27
12.54
28.90
29.16
30.55
28.89
29.34
30.21
25.6

magnitude
+3
+5
+10
+3
+5
+10

FID
18.16
23.33
67.57
15.22
21.30
62.39

+3
+5
+10
+3
+5
+10

28.40
29.20
30.12
27.96
28.41
29.86

Table 1 shows the results of quantitative evaluations between
StyleCLIP and our method. The FID values of our proposed method
are better than StyleCLIP in the most cases. For wheel size, compared to StyleCLIP, our method preserves the texture of the wheels.
For cheek size, our method achieves similar results to StyleCLIP.
Overall, the results of the proposed model are proved to be better
than StyleCLIP in preserving the image quality.

5

CONCLUSION

In this work, we propose a text-guided image manipulation methodthat makes use of the pretrained StyleGAN2 and CLIP models. By
modifying the parameter of pretrained StyleGAN2 generator, theproposed model can achieve the goal of editing shape attribute. The
experimental results showed that, compared to the existing method
which modified the GAN latent spaces, our method was effectiveon image shape attribute editing. Also, our method can preserve
the image quality while editing target attribute.
As future works, we plan to select the layer in which the parameters are tuned automatically and to combine multiple layers withadaptive weights for more natural image translation. Acknowledgment This work was supported by JSPS KAKENHI Grant Numbers,
21H05812 and 22H00548, 22K19808.

StyleGAN-based CLIP-guided Image Shape Manipulation

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