# FLDM-VTON: Faithful Latent Diffusion Model for Virtual Try-on

Chenhui Wang1 Tao Chen1 Zhihao Chen1 Zhizhong Huang2
Taoran Jiang3 Qi Wang3 Hongming Shan1
1 Institute of Science and Technology for Brain-inspired Intelligence, Fudan
University
2 School of Computer Science, Fudan University
3 Suzhou Xiangji Technology Service Co., Ltd.
<EMAIL_ADDRESS><EMAIL_ADDRESS>Corresponding author

###### Abstract

Despite their impressive generative performance, latent diffusion model-based
virtual try-on (VTON) methods lack faithfulness to crucial details of the
clothes, such as style, pattern, and text. To alleviate these issues caused by
the diffusion stochastic nature and latent supervision, we propose a novel
Faithful Latent Diffusion Model for VTON, termed FLDM-VTON. FLDM-VTON improves
the conventional latent diffusion process in three major aspects. First, we
propose incorporating warped clothes as both the starting point and local
condition, supplying the model with faithful clothes priors. Second, we
introduce a novel clothes flattening network to constrain generated try-on
images, providing clothes-consistent faithful supervision. Third, we devise a
clothes-posterior sampling for faithful inference, further enhancing the model
performance over conventional clothes-agnostic Gaussian sampling. Extensive
experimental results on the benchmark VITON-HD and Dress Code datasets
demonstrate that our FLDM-VTON outperforms state-of-the-art baselines and is
able to generate photo-realistic try-on images with faithful clothing details.

Figure 1:  Comparison between state-of-the-art baselines and our FLDM-VTON on
the VITON-HD dataset.

##  1 Introduction

Image-based virtual try-on (VTON) aims to transfer a piece of in-shop flat
clothes onto one person’s body while preserving the details of both the human
and the clothes, such as style, pattern, and text. In the past decade, VTON
has attracted considerable attention [?; ?; ?; ?; ?; ?; ?], and with the rapid
advances of generative artificial intelligence [?; ?] it has the great
potential to improve users’ shopping experience by bridging the gap between
users and online shopping.

Prior methods for VTON highly rely on generative adversarial networks (GANs)
[?; ?; ?] to synthesize try-on images. Typically, they first use thin plate
spline (TPS)-based [?; ?; ?] or appearance flow-based [?; ?] algorithms to
warp the flat clothes to the person’s body, and then use GAN to further refine
the previously generated try-on images. Nevertheless, due to the mode collapse
issue [?], GAN-based methods fail to synthesize photo-realistic try-on images
and accurately capture intricate clothing details, often leading to obvious
flaws on the generated results as shown in Figure 1.

Recently, the diffusion model has shown remarkable generative capabilities
across various tasks, such as image inpainting, image editing, and even
segmentation [?; ?; ?; ?]. Compared with GAN, the diffusion model offers more
stable training and direct likelihood estimation. However, directly applying
the diffusion model to high-resolution VTON is infeasible due to limited
computational resources. Therefore, current diffusion-based VTON methods [?;
?] are built upon the latent diffusion model (LDM) [?] that performs diffusion
process in a latent space. Although showing effectiveness in generating
realistic try-on images, they often produce _unfaithful_ clothing details with
respect to the original flat clothes.

We identify the _stochastic nature_ and _latent supervision_ of LDM as the key
limiting factors for the faithfulness. On one hand, the diffusion stochastic
nature poses a challenge in preserving clothing details, as indicated by the
initial Gaussian noise introduced at the sampling process and the added
Gaussian noise at each time-step. On the other hand, the latent supervision
falls short in providing image-level supervision for fine clothing details.
Thus, generating highly faithful clothing details with respect to the original
flat clothes using the diffusion model remains a significant challenge.

To alleviate these issues in LDM, we propose a novel Faithful Latent Diffusion
Model for VTON, termed FLDM-VTON. To achieve faithful try-on generation, our
FLDM-VTON improves the training of conventional latent diffusion process in
two major aspects: (i) supplying the model with _faithful clothes priors_ by
leveraging warped clothes as both the starting point and local condition to
mitigate the initial and in-process added stochasticity, respectively, and
(ii) providing _clothes-consistent faithful supervision_ through a novel
clothes flattening network to bring additional image-level constraints from
the original flat clothes. In addition to the training improvements, our FLDM-
VTON also improves the inference process by devising a clothes-posterior
sampling, further enhancing the model performance over conventional clothes-
agnostic Gaussian sampling.

Contributions. Our contributions are as follows. (i) We propose a novel
faithful latent diffusion model for VTON to address the unfaithful issue
caused by the diffusion stochastic nature and latent supervision. (ii) We
propose incorporating warped clothes as both the starting point and local
condition, supplying the model with faithful clothes priors. (iii) We
introduce a novel clothes flattening network to constrain the generated try-on
images, providing clothes-consistent faithful supervision. (iv) We devise a
clothes-posterior sampling for faithful inference, further enhancing the model
performance over conventional clothes-agnostic Gaussian sampling. (v)
Extensive experimental results on the VITON-HD and Dress Code datasets
demonstrate that our FLDM-VTON outperforms state-of-the-art baselines and is
able to generate photo-realistic try-on images with faithful clothing details.

Figure 2: Overview of the proposed FLDM-VTON. Our FLDM-VTON is trained with
main and prior denoising input, constrained by DINO-V2, and the proposed
clothes flattening network to generate the try-on images $\widehat{\bm{T}}$.

##  2 Related Work

#### Image-based virtual try-on.

Numerous VTON studies have investigated generating high-resolution photo-like
try-on images by transferring a in-shop flat clothes item onto one person’s
body, enhancing the online shopping experience. Currently, most VTON’s works
can be divided into two stages: pre-warping and refining. In the pre-warping
stage, thin plate spline (TPS)-based [?; ?; ?] or appearance flow-based [?; ?;
?] algorithms are used to warp the flat clothes to the corresponding positions
on the person body. In the refinement stage, convolutional neural network
(CNN) or GAN techniques are employed to further refine generated images.
Although there are some diffusion-based VTON methods [?; ?; ?], they often
generate unfaithful clothing details. In contrast, our FLDM-VTON prioritizes
generating realistic try-on images with faithful clothing details.

#### Diffusion models.

Inspired by non-equilibrium statistical physics, diffusion models have
recently presented strong capability in image synthesis. To generate high-
resolution images with limited computational resources, LDM, e.g. DALL-E and
Stable Diffusion (SD) series [?; ?; ?], conducts the diffusion process in the
latent space. Moreover, customized or subject-driven image generation has
recently become an important research topic, including text-driven [?; ?] and
image exemplar-based generation [?]. DreamBooth [?] embeds a given subject
instance in the output domain of a text-to-image diffusion model by binding
the subject to a unique identifier. Paint-by-Example [?] uses CLIP [?] to
convert the target image as an embedding for guidance. In contrast to their
emphasis on preserving general subject information, our FLDM-VTON focuses on
preserving more fine-grained clothing details.

##  3 Methodology

In this section, we present the try-on diffusion model with faithful clothes
priors in Sec. 3.1 and clothes-consistent faithful supervision in Sec. 3.2,
followed by an overview of the FLDM-VTON and a clothes-posterior sampling for
faithful inference in Sec. 3.3.

###  3.1 Try-on Diffusion with Clothes Priors

Given a person image $\bm{P}\in\mathbb{R}^{H\times W\times 3}$ and a mask
$\bm{m}\in\\{0,1\\}^{H\times W}$ indicating the try-on region, one can obtain
a clothes-agnostic person image $\bm{P}^{\text{a}}$ through element-wise
multiplication; $\bm{P}^{\text{a}}$ refers to the person image with the try-on
region being masked out. The goal of VTON is to transfer a piece of flat
clothes $\bm{C}\in\mathbb{R}^{H\times W\times 3}$ onto $\bm{P}^{\text{a}}$,
yielding a photo-realistic try-on image
$\widehat{\bm{T}}\in\mathbb{R}^{H\times W\times 3}$ with faithful clothing
details.

Current state-of-the-art (SOTA) LDM-based VTON methods [?; ?] first use TPS or
flow-based warping to generate the warped clothes $\bm{C}^{\text{w}}$ from the
flat clothes $\bm{C}$, and then employ an LDM for realistic refinement. With a
pretrained encoder $\mathcal{E}$ and decoder $\mathcal{D}$, LDM trains a
diffusion model in the latent space, involving forward and reverse processes.
In the forward process, Gaussian noise $\bm{\epsilon}\sim\mathcal{N}(0,1)$ is
added at arbitrary time-step $t$ to the resultant latent feature
$\bm{z}_{0}=\mathcal{E}(\bm{T})\in\mathbb{R}^{h\times w\times c}$, where
$\bm{T}$ is the ground-truth try-on image. In the reverse process, a diffusion
UNet is employed to estimate the added noise $\bm{\epsilon}$.

Although achieving realistic results, conventional LDM-based VTON methods lack
faithfulness to original clothing details. We identify the diffusion
stochastic nature as the key limiting factor, which can be reflected in two
primary aspects: (i) the initial Gaussian noise introduced at the sampling
process and (ii) the added Gaussian noise at each time-step. To alleviate
these, our idea is to supply the model with clothes priors, leveraging warped
clothes (i) as the starting point to address the initial stochasticity and
(ii) as the local condition to mitigate the in-process added stochasticity.

Figure 2 presents an overview of our FLDM-VTON. Unlike conventional forward
process that takes $\mathcal{E}(\bm{T})$ as the starting point, ours takes
either $\mathcal{E}(\bm{T})$ or warped clothes feature
$\mathcal{E}(\bm{C}^{\text{w}})$ as the starting point, in which warped
clothes feature provides clothes prior at the beginning. To differentiate
these two starting points, we refer to
$\bm{z}_{0}^{\text{m}}=\mathcal{E}(\bm{T})$ as _main_ starting point and
$\bm{z}_{0}^{\text{p}}=\mathcal{E}(\bm{C}^{\text{w}})$ as _prior_ starting
point. Moreover, we leverage the pre-warped try-on image feature
$\mathcal{E}(\widehat{\bm{T}}^{\text{w}})$ as prior local condition for all
time-steps, where
$\widehat{\bm{T}}^{\text{w}}=\bm{C}^{\text{w}}+\bm{P}^{\text{a}}$.

Next, we detail the concrete forward and reverse processes.

#### Forward process.

We gradually add Gaussian noise $\bm{\epsilon}\sim\mathcal{N}(0,1)$ on the
main and prior starting latent features, $\bm{z}_{0}^{\text{m}}$ and
$\bm{z}_{0}^{\text{p}}$, with an arbitrary time-step $t$, yielding $t$-th
corresponding latent features as follows:

$\displaystyle\bm{z}_{t}^{\text{m}}=\\!\sqrt{\alpha_{t}}\bm{z}_{0}^{\text{m}}+\sqrt{1\\!-\\!\alpha_{t}}\bm{\epsilon},\quad\bm{z}_{t}^{\text{p}}=\\!\sqrt{\alpha_{t}}\bm{z}_{0}^{\text{p}}+\sqrt{1\\!-\\!\alpha_{t}}\bm{\epsilon},$
(1)

where $\alpha_{t}:={\textstyle\prod_{s=1}^{t}}(1-\beta_{s})$, and $\beta_{s}$
is a pre-defined variance schedule [?].

#### Reverse process.

We have down-sized mask $\bm{m}^{\text{r}}\in\\{0,1\\}^{h\times w}$ as the
denoising condition and pre-warped try-on image feature
$\mathcal{E}(\widehat{\bm{T}}^{\text{w}})$ as the prior local condition. We
concatenate the $t$-th latent feature, prior local condition, and denoising
condition along the channel dimension, severing as the input to the diffusion
UNet:

$\displaystyle\bm{\psi}^{\text{m}}_{t}=[\bm{z}_{t}^{\text{m}};\mathcal{E}(\widehat{\bm{T}}^{\text{w}});\bm{m}_{\text{r}}],\quad\bm{\psi}^{\text{p}}_{t}=[\bm{z}_{t}^{\text{p}};\mathcal{E}(\widehat{\bm{T}}^{\text{w}});\bm{m}_{\text{r}}],$
(2)

where $[\cdot;\cdot]$ denotes concatenation operation.

Given one image pair, we obtain two distinct inputs: the main denoising input
$\bm{\psi}^{\text{m}}_{t}$ and the prior denoising input
$\bm{\psi}^{\text{p}}_{t}$. These inputs are individually processed through
the same try-on diffusion UNet $\mathcal{U}$ to predict the main starting
latent feature ${\bm{z}}^{\text{m}}_{0}$. In addition, we also encode the flat
clothes $\bm{C}$ through DINO-V2 [?], a currently powerful self-supervised
visual encoder $\mathcal{V}$, which serves as the global controller being
injected into each UNet layer via cross-attention. Therefore, the diffusion
training loss function over one single sample and one time-step $t$ is defined
as follows:

$\displaystyle\mathcal{L}_{\text{diff}}=\frac{1}{2}\big{(}$
$\displaystyle\|\mathcal{U}(\bm{\psi}^{\text{m}}_{t},\mathcal{V}(\bm{C}),t)-\bm{z}^{\text{m}}_{0}\|^{2}_{2}$
$\displaystyle+$
$\displaystyle\|\mathcal{U}(\bm{\psi}^{\text{p}}_{t},\mathcal{V}(\bm{C}),t)-\bm{z}^{\text{m}}_{0}\|^{2}_{2}\big{)},$
(3)

where $\bm{\psi}^{\text{m}}_{t}$ contributes to preserving the photo-realistic
quality as established by existing diffusion models and
$\bm{\psi}^{\text{p}}_{t}$ contributes to enhancing the faithfulness of
generated clothes.

###  3.2 Clothes-consistent Faithful Supervision

Although clothes priors can help enhance the faithfulness from the input, it
is still challenging to preserve the fine details such as pattern and text
since the training is only supervised by the ground-truth try-on latent
feature. To further improve the faithfulness to fine details, we introduce
clothes-consistent faithful supervision, drawing inspiration from the fact
that the clothes item you try on should be identical to the flat one once you
take it off and flatten it out. To this end, we introduce a clothes flattening
network $\mathcal{F}$ that can take off clothes from the generated try-on
image and flatten it out like the original flat one.

#### Clothes flattening network.

Our clothes flattening network is a two-step method: (i) take-off step and
(ii) flatten-out step. The take-off step can be easily done by masking out the
generated try-on image with the pre-parsed clothes mask $\bm{m}^{\text{C}}$.
The flatten-out step is an inverse warping process, which is done by training
a flattening module to predict flattening flows. More specifically, our
clothes flattening network is designed with a U-shape structure, which
utilizes a Feature Pyramid Network (FPN) [?] to encode the clothes-parsed
feature at multiple scales, and then employs cascaded flow estimation blocks
to predict the flattening flows with down-sized flat clothes-position masks.
Note that we use five different multi-scale features in our experiments; three
of these are illustrated in Figure 3 for simplicity.

Following the SOTA appearance flow training strategy [?], we use mixed loss
function to train our clothes flattening network, including
$\mathcal{L}_{\text{1}}$ loss and perceptual loss $\mathcal{L}_{\text{per}}$
[?] at the image level and the second-order smooth loss
$\mathcal{L}_{\text{sec}}$ and the total-variation loss
$\mathcal{L}_{\text{TV}}$ at the flow level, which is defined as:

$\displaystyle\mathcal{L}_{\text{flat}}=\mathcal{L}_{\text{1}}+\lambda_{\text{per}}\mathcal{L}_{\text{per}}+\lambda_{\text{sec}}\mathcal{L}_{\text{sec}}+\lambda_{\text{TV}}\mathcal{L}_{\text{TV}},$
(4)

where $\lambda_{\text{*}}$ are the hyperparameters to adjust the weights among
different loss components.

#### Clothes-consistent supervision.

Once the clothes flattening network is trained, we can use the frozen clothes
flattening network $\mathcal{F}$ to process the generated try-on images
$\widehat{\bm{T}}=\mathcal{D}(\hat{\bm{z}}_{0}^{\text{m}})$, yielding
estimated flat clothes $\widehat{\bm{C}}$. Measuring the difference between
estimated flat clothes $\widehat{\bm{C}}$ and original flat clothes $\bm{C}$
can further provide clothes consistent faithful supervision for try-on
diffusion training, which is defined as:

$\displaystyle\mathcal{L}_{\text{cons}}=\|\widehat{\bm{C}}-\bm{C}\|_{1}=\|\mathcal{F}({\widehat{\bm{T}}})-\bm{C}\|_{1}.$
(5)

Figure 3: Illustration of the proposed clothes flattening network.

###  3.3 Overview of Our FLDM-VTON

#### Overview.

The overview of our FLDM-VTON is given in Figure 2. During the training phase,
we handle each sample by deriving its main and prior denoising inputs, i.e.
$\bm{\psi}^{\text{m}}_{t}$ and $\bm{\psi}^{\text{p}}_{t}$, by concatenating
the $t$-th latent feature with prior local and denoising conditions. And then,
our try-on diffusion UNet $\mathcal{U}$ individually processes these two types
of inputs guided by the global controller DINO-V2 $\mathcal{V}$, estimating
the try-on latent feature with the diffusion loss $\mathcal{L}_{\text{diff}}$
in Eq. (3.1). Moreover, our FLDM-VTON incorporates a clothes flattening
network, providing clothes-consistent faithful supervision with the clothes-
consistent loss $\mathcal{L}_{\text{cons}}$ in Eq. (5). The overall training
loss for the try-on diffusion UNet is defined as follows:

$\displaystyle\mathcal{L}_{\text{Try-
on}}=\mathcal{L}_{\text{diff}}+\lambda_{\text{cons}}\mathcal{L}_{\text{cons}},$
(6)

where $\lambda_{\text{cons}}$ is a trade-off hyperparameter. Note that only
the try-on diffusion UNet and the linear layer followed by the DINO V2 are
trained.

#### Faithful inference.

During the inference phase, conventional diffusion models initiate inference
by a clothes-agnostic noise sampled from a standard Gaussian distribution.
However, this introduces significant initial stochasticity, adversely
affecting the faithfulness of generated clothing details. To address this
issue, we devise a clothes-posterior sampling to further enhance the model
performance.

With the introduced clothes prior for model training, our FLDM-VTON can
initiate inference from a posterior Gaussian noise that is specifically
conditioned by the warped clothes feature $\mathcal{E}(\bm{C}^{\text{w}})$.
Specifically, the clothes-posterior noise is the $T$-th prior latent feature:
$\bm{z}_{T}^{\text{p}}=\sqrt{\alpha_{T}}\mathcal{E}(\bm{C}^{\text{w}})+\sqrt{1-\alpha_{T}}\bm{\epsilon}$,
corresponding to the warped clothes feature $\mathcal{E}(\bm{C}^{\text{w}})$
after $T$ diffusion forward time-steps using Eq. (1). By doing this, the
initial stochasticity of the sampling process is significantly reduced,
thereby ensuring the faithfulness of the generated clothing details.

Figure 4: Qualitative results of different methods and ours on the VITON-HD
dataset. Best viewed when zoomed in.

##  4 Experiments

###  4.1 Experimental Setup

#### Datasets.

We conduct experiments on two popular high-resolution VTON benchmarks: the
VITON-HD dataset [?] and Dress Code dataset [?]. Both datasets contain high-
resolution paired images of size $512\times 384$: in-shop flat clothes and
their corresponding persons wearing the clothes. The VITON-HD dataset contains
$13,679$ image pairs for upper-body clothes. The Dress Code dataset includes
$15,366$ image pairs for upper-body clothes, $8,951$ image pairs for lower-
body clothes, and $29,478$ image pairs for dresses. We follow the official
guidelines to divide the data into training and testing sets [?; ?].

Table 1: Comparison results on the VITON-HD dataset. I, II, and III represent
CNN-based, GAN-based, and diffusion-based methods, respectively. The best and
second best results are highlighted in bold and underlined, respectively.

Methods |  |  | Paired |  | Unpaired
---|---|---|---|---|---
|  | LPIPS$\downarrow$ | SSIM$\uparrow$ |  | FID$\downarrow$ | KID$\downarrow$
CP-VTON | I |  | 0.160 | 0.831 |  | 31.34 | 2.37
CP-VTON+ |  | 0.131 | 0.847 |  | 22.79 | 1.55
VITON-HD | II |  | 0.116 | 0.862 |  | 12.12 | 0.32
HR-VITON |  | 0.104 | 0.878 |  | 11.27 | 0.27
GP-VTON |  | 0.081 | 0.884 |  |  9.19 | 0.09
Paint-by-example | III |  | 0.143 | 0.803 |  | 11.94 | 0.39
Ladi-VTON |  | 0.096 | 0.863 |  |  9.47 | 0.19
DCI-VTON |  | 0.081 | 0.880 |  |  8.76 | 0.11
FLDM-VTON (ours) |  | 0.080 | 0.886 |  |  8.81 | 0.13

#### Implementation details.

We use the SOTA appearance flow-based warping method [?; ?] to generate a
warped clothes image aligning the flat clothes with the person. We adopt the
Adam optimizer to optimize all networks with a mini-batch size of $8$ and a
learning rate of $1.0\times 10^{-5}$ on 4 NVIDIA V100 GPUs. In addition, we
employ the encoder and decoder of the SD KL-regularized auto-encoder, with a
down-sampling factor of $d=8$ and a latent channel number of $c=4$, as our
encoder $\mathcal{E}$ and decoder $\mathcal{D}$, respectively. We set
$T=1,000$ for latent diffusion training as suggested by SD [?], and use the
DPM solver [?] with $50$ sampling steps for inference. Besides, we use FreeU
[?] to reweight the contributions of the backbone and skip connection features
with different scaling factors for inference, enhancing the denoising
capabilities of the LDM and reducing low-frequency information; please refer
to Appendix I for more implementation details, including visualizations and
detailed pipelines of $\bm{P}^{\text{a}}$ and $\widehat{\bm{T}}^{\text{w}}$, a
detailed warping procedure, and hyperparameter setting.

#### Evaluation metrics.

We evaluate performance under paired and unpaired test settings, where ground-
truth try-on images are available for the paired setting while not for the
unpaired setting. We adopt Learned Perceptual Image Patch Similarity (LPIPS)
[?] and Structural Similarity (SSIM) [?] for paired setting while using
Fréchet Inception Distance (FID) [?] and Kernel Inception Distance (KID) [?]
for unpaired setting. Note that we present all qualitative results under the
unpaired setting.

Figure 5: Qualitative results of different methods and ours on the Dress Code
dataset. Best viewed when zoomed in. Table 2: Comparison results on the Dress
Code dataset. The best results are highlighted with bold.

Methods | Upper |  | Lower |  | Dresses
---|---|---|---|---|---
Paired |  | Unpaired |  | Paired |  | Unpaired |  | Paired |  | Unpaired
LPIPS$\downarrow$ | SSIM$\uparrow$ |  | FID$\downarrow$ | KID$\downarrow$ |  | LPIPS$\downarrow$ | SSIM$\uparrow$ |  | FID$\downarrow$ | KID$\downarrow$ |  | LPIPS$\downarrow$ | SSIM$\uparrow$ |  | FID$\downarrow$ | KID$\downarrow$
GP-VTON | 0.271 | 0.775 |  | 21.58 | 0.99 |  | 0.283 | 0.759 |  | 23.61 | 1.13 |  | 0.221 | 0.789 |  | 17.73 | 0.70
Paint-by-Example | 0.165 | 0.858 |  | 38.78 | 2.70 |  | 0.197 | 0.818 |  | 23.56 | 0.99 |  | 0.346 | 0.700 |  | 21.51 | 0.87
Ladi-VTON | 0.049 | 0.928 |  | 13.26 | 0.27 |  | 0.051 | 0.922 |  | 14.80 | 0.31 |  | 0.089 | 0.868 |  | 13.40 | 0.25
FLDM-VTON (ours) | 0.045 | 0.930 |  | 11.45 | 0.16 |  | 0.051 | 0.924 |  | 13.27 | 0.24 |  | 0.079 | 0.891 |  | 12.61 | 0.19

Figure 6: Qualitative results on real-world data.  Figure 7: Results of
different source and target clothing types.

###  4.2 Comparison with SOTA Methods

We comprehensively compare our FLDM-VTON with several SOTA methods, which can
be categorized into three groups: CNN-based, GAN-based, and diffusion-based.
Specifically, the CNN-based methods are CP-VTON [?] and CP-VTON+ [?]. For the
GAN-based methods, we include VITON-HD [?], HR-VITON [?], and GP-VTON [?].
Moreover, we compare our FLDM-VTON with recently published SOTA diffusion-
based methods: Paint-by-example [?], Ladi-VTON [?], and DCI-VTON [?]. In
addition, due to the extensive size of the Dress Code dataset, we only compare
our FLDM-VTON with several SOTA methods, including GP-VTON, Paint-by-example,
and Ladi-VTON.

#### Quantitative comparison.

Tables 1 and 2 present quantitative results on the VITON-HD and Dress Code
datasets, respectively, which show that our FLDM-VTON outperforms most
competitors across various performance metrics under paired and unpaired
settings. We observe that diffusion-based methods perform better in terms of
FID and KID, two realistic metrics. However, our FLDM-VTON not only maintains
comparable realistic performance but also excels in faithfulness under the
paired setting, i.e. LPIPS and SSIM.

#### Qualitative comparison.

Figures 1, 4, and 5 present qualitative results, which demonstrate the
superior performance of our FLDM-VTON in generating realistic try-on images
while preserving faithful clothing details to the original flat clothes.
Although CNN-based methods generate try-on images that more closely resemble
the original flat clothes, they lack realism and don’t achieve a photo-like
quality. With adversarial training to enhance the initially warped clothes,
GP-VTON stands out with superior performance. However, its qualitative results
suggest a relatively simple composition of warped clothes and persons due to
GAN’s intrinsic model collapse problem. Among diffusion-based methods, Paint-
by-example and Ladi-VTON generate more realistic images. However, the
stochastic nature of conventional diffusion models poses a challenge in
faithfully preserving crucial clothing details, often leading to substantial
distortions. DCI-VTON faces challenges in correctly adapting the clothes’
style to the person, though generating relatively faithful images. In
contrast, our FLDM-VTON is able to generate more realistic images while
effectively handling complex style, pattern, and text; please refer to
Appendix II for more qualitative results.

Figure 8: Ablation qualitative results for modernizing our FLDM-VTON on the
VITON-HD dataset. Best viewed when zoomed in.

#### Application to real-world data.

Figure 6 also presents qualitative results on real-world data from the Amazon
website, illustrating the robustness of our FLDM-VTON. Our method effectively
generates corresponding try-on images for different persons with diverse flat
clothes, ensuring photo-realistic quality with faithful clothing details.

#### More challenging try-on situations.

We further examine our FLDM-VTON on challenging try-on situations, such as
different types of source and target clothes, by simply _masking out the
region related to the source and target clothes_. The challenging try-on
results in Figure 7 suggest the strong effectiveness of our FLDM-VTON.

###  4.3 Ablation Study

Here, we conduct detailed ablation studies to show the effectiveness of
different conditions and proposed components for modernizing our FLDM-VTON.
Figure 8 and Table 3 present ablation qualitative and quantitative results,
respectively.

Table 3: Ablation quantitative results on the VITON-HD dataset. The best
results are highlighted in bold.

Methods | Paired |  | Unpaired
---|---|---|---
LPIPS$\downarrow$ | SSIM$\uparrow$ |  | FID$\downarrow$ | KID$\downarrow$
Conventional inpainting LDM | 0.158 | 0.788 |  | 34.58 | 3.41
\+ Prior local condition (Sec. 3.1) | 0.127 | 0.864 |  | 13.43 | 0.34
\+ CLIP global condition | 0.121 | 0.870 |  | 13.02 | 0.32
\+ DINO V2 global condition | 0.114 | 0.874 |  | 12.24 | 0.29
\+ Prior denoising input training (Sec. 3.1) | 0.094 | 0.876 |  | 10.60 | 0.27
\+ Clothes-consistent supervision (Sec. 3.2) | 0.082 | 0.883 |  |  8.90 | 0.15
\+ Faithful inference (Sec. 3.3) | 0.080 | 0.886 |  |  8.81 | 0.13

#### Ablation on different conditions.

We take the conventional unconditional inpainting LDM as our initial baseline.
First, we enhance it with the prior local condition $\widehat{\bm{T}}^{w}$. We
observe that the generated try-on image can capture most coarse-grained
clothing details due to the prior information provided by warped clothes.
However, the style of complex patterns is significantly distorted. Second, by
integrating the CLIP or DINO V2 global conditions, there is a notable
improvement in the style of these complex patterns. Notably, DINO V2
outperforms CLIP, which can be attributed to its more discriminative global
feature extraction.

#### Ablation on proposed components.

Here, we take the inpainting LDM with the prior local condition and DINO V2
global condition as the initial baseline. We first investigate the impact of
the additional training for the prior denoising input. By including this
training process, we observe that the style of complex patterns is largely
preserved. However, there still remains a challenge in preserving fine
clothing details.

Second, we show the effect of clothes-consistent supervision in Figure 9,
including the real clothes-parsed image $\bm{T}^{C}$, estimated flat clothes
image $\widehat{\bm{C}}$, and real flat clothes image $\bm{C}$. We find that
our clothes flattening network effectively transforms the clothes from the
try-on state to the flat state. By comparing the estimated and the original
flat clothes, we observe that the original flat clothes have more fine
clothing details, which further validates the importance of providing clothes-
consistent supervision to guide the try-on diffusion process towards higher
faithfulness to the original flat clothes. In summary, both the quantitative
and qualitative results demonstrate that introducing the clothes flattening
network into the training improves the capability of the try-on diffusion UNet
to preserve more faithful clothing details.

Figure 9: Clothes-consistent results. $\bm{T}^{C}$, $\widehat{\bm{C}}$, and
$\bm{C}$ denote the real clothes-parsed image, estimated flat clothes image,
and real flat clothes image, respectively. Best viewed when zoomed in.

In addition, we observe that while the faithful inference contributes not as
much as other components to the quantitative results in Table 3, it
significantly enhances the faithfulness of generated clothing details, as
shown in Figure 8, without introducing additional computational burdens.
Please refer to Appendix III for more ablation results about FreeU, and
Appendix IV for more discussions, including the acceleration advantage, the
limitation, and the social impact.

##  5 Conclusion

In this paper, we proposed a novel faithful latent diffusion model for virtual
try-on. With the introduced faithful clothes priors and clothes-consistent
faithful supervision, the proposed FLDM-VTON can significantly alleviate the
unfaithful generation issue caused by the diffusion stochastic nature and
latent supervision in LDM. In addition, the devised clothes-posterior sampling
for faithful inference can further improve the model performance. Extensive
experimental results on two popular VTON benchmarks validate the superior
performance of our FLDM-VTON—generating photo-realistic try-on images with
faithful clothing details.

## References

  * [Bai et al., 2022] Shuai Bai, Huiling Zhou, Zhikang Li, Chang Zhou, and Hongxia Yang.  Single stage virtual try-on via deformable attention flows.  In ECCV, pages 409–425, 2022.
  * [Bau et al., 2019] David Bau, Jun-Yan Zhu, Jonas Wulff, William Peebles, Hendrik Strobelt, Bolei Zhou, and Antonio Torralba.  Seeing what a GAN cannot generate.  In ICCV, pages 4502–4511, 2019.
  * [Bińkowski et al., 2018] Mikołaj Bińkowski, Danica J Sutherland, Michael Arbel, and Arthur Gretton.  Demystifying MMD GANs.  In ICLR, 2018.
  * [Chen et al., 2023] Tao Chen, Chenhui Wang, and Hongming Shan.  BerDiff: Conditional bernoulli diffusion model for medical image segmentation.  In MICCAI, 2023.
  * [Choi et al., 2021] Seunghwan Choi, Sunghyun Park, Minsoo Lee, and Jaegul Choo.  VITON-HD: High-resolution virtual try-on via misalignment-aware normalization.  In CVPR, pages 14131–14140, 2021.
  * [Fele et al., 2022] Benjamin Fele, Ajda Lampe, Peter Peer, and Vitomir Struc.  C-VTON: Context-driven image-based virtual try-on network.  In WACV, pages 3144–3153, 2022.
  * [Ge et al., 2021] Yuying Ge, Yibing Song, Ruimao Zhang, Chongjian Ge, Wei Liu, and Ping Luo.  Parser-free virtual try-on via distilling appearance flows.  In CVPR, pages 8485–8493, 2021.
  * [Goodfellow et al., 2014] Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio.  Generative adversarial nets.  In NIPS, 2014.
  * [Gou et al., 2023] Junhong Gou, Siyu Sun, Jianfu Zhang, Jianlou Si, Chen Qian, and Liqing Zhang.  Taming the power of diffusion models for high-quality virtual Try-On with appearance flow.  In ACM MM, pages 7599–7607, 2023.
  * [Han et al., 2019] Xintong Han, Xiaojun Hu, Weilin Huang, and Matthew R Scott.  ClothFlow: A flow-based model for clothed person generation.  In ICCV, pages 10471–10480, 2019.
  * [He et al., 2022] Sen He, Yi-Zhe Song, and Tao Xiang.  Style-based global appearance flow for virtual try-on.  In CVPR, pages 3470–3479, 2022.
  * [Heusel et al., 2017] Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter.  GANs trained by a two time-scale update rule converge to a local nash equilibrium.  NIPS, 30, 2017.
  * [Ho and Salimans, 2022] Jonathan Ho and Tim Salimans.  Classifier-free diffusion guidance.  arXiv:2207.12598, 2022.
  * [Ho et al., 2020] Jonathan Ho, Ajay Jain, and Pieter Abbeel.  Denoising diffusion probabilistic models.  In NIPS, pages 6840–6851, 2020.
  * [Huang et al., 2023] Zhizhong Huang, Siteng Ma, Junping Zhang, and Hongming Shan.  Adaptive nonlinear latent transformation for conditional face editing.  In ICCV, pages 21022–21031, 2023.
  * [Johnson et al., 2016] Justin Johnson, Alexandre Alahi, and Li Fei-Fei.  Perceptual losses for real-time style transfer and super-resolution.  In ECCV, pages 694–711, 2016.
  * [Lee et al., 2022] Sangyun Lee, Gyojung Gu, Sunghyun Park, Seunghwan Choi, and Jaegul Choo.  High-resolution virtual try-on with misalignment and occlusion-handled conditions.  In ECCV, pages 204–219. Springer, 2022.
  * [Li et al., 2021] Kedan Li, Min Jin Chong, Jeffrey Zhang, and Jingen Liu.  Toward accurate and realistic outfits visualization with attention to details.  In CVPR, pages 15546–15555, 2021.
  * [Lin et al., 2017] Tsung-Yi Lin, Piotr Dollár, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie.  Feature pyramid networks for object detection.  In CVPR, pages 2117–2125, 2017.
  * [Lu et al., 2022] Cheng Lu, Yuhao Zhou, Fan Bao, Jianfei Chen, Chongxuan Li, and Jun Zhu.  DPM-Solver: A fast ODE solver for diffusion probabilistic model sampling in around 10 steps.  In NIPS, volume 35, pages 5775–5787, 2022.
  * [Minar et al., 2020] Matiur Rahman Minar, Thai Thanh Tuan, Heejune Ahn, Paul Rosin, and Yu-Kun Lai.  CP-VTON+: Clothing shape and texture preserving image-based virtual try-on.  In CVPRW, volume 3, pages 10–14, 2020.
  * [Morelli et al., 2022] Davide Morelli, Matteo Fincato, Marcella Cornia, Federico Landi, Fabio Cesari, and Rita Cucchiara.  Dress code: High-resolution multi-category virtual try-on.  In CVPR, pages 2231–2235, 2022.
  * [Morelli et al., 2023] Davide Morelli, Alberto Baldrati, Giuseppe Cartella, Marcella Cornia, Marco Bertini, and Rita Cucchiara.  LaDI-VTON: Latent diffusion textual-inversion enhanced virtual try-on.  In ACM MM, 2023.
  * [Nichol and Dhariwal, 2021] Alexander Quinn Nichol and Prafulla Dhariwal.  Improved denoising diffusion probabilistic models.  In ICML, pages 8162–8171, 2021.
  * [Oquab et al., 2023] Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy Vo, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al.  DINOv2: Learning robust visual features without supervision.  arXiv:2304.07193, 2023.
  * [Podell et al., 2023] Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach.  SDXL: Improving latent diffusion models for high-resolution image synthesis.  arXiv:2307.01952, 2023.
  * [Radford et al., 2021] Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al.  Learning transferable visual models from natural language supervision.  In ICML, pages 8748–8763, 2021.
  * [Ramesh et al., 2021] Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, Scott Gray, Chelsea Voss, Alec Radford, Mark Chen, and Ilya Sutskever.  Zero-shot text-to-image generation.  In ICML, pages 8821–8831. PMLR, 2021.
  * [Rombach et al., 2022] Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer.  High-resolution image synthesis with latent diffusion models.  In CVPR, pages 10684–10695, 2022.
  * [Ruiz et al., 2023] Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Yael Pritch, Michael Rubinstein, and Kfir Aberman.  DreamBooth: Fine tuning text-to-image diffusion models for subject-driven generation.  In CVPR, pages 22500–22510, 2023.
  * [Si et al., 2023] Chenyang Si, Ziqi Huang, Yuming Jiang, and Ziwei Liu.  FreeU: Free lunch in diffusion U-Net.  arXiv:2309.11497, 2023.
  * [Song et al., 2020] Jiaming Song, Chenlin Meng, and Stefano Ermon.  Denoising diffusion implicit models.  In ICLR, 2020.
  * [Vaswani et al., 2017] Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin.  Attention is all you need.  In NIPS, 2017.
  * [Wang et al., 2004] Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli.  Image quality assessment: from error visibility to structural similarity.  IEEE TIP, 13(4):600–612, 2004.
  * [Wang et al., 2018] Bochao Wang, Huabin Zheng, Xiaodan Liang, Yimin Chen, Liang Lin, and Meng Yang.  Toward characteristic-preserving image-based virtual try-on network.  In ECCV, pages 589–604, 2018.
  * [Wang et al., 2024] Chenhui Wang, Sirong Piao, Zhizhong Huang, Qi Gao, Junping Zhang, Yuxin Li, Hongming Shan, et al.  Joint learning framework of cross-modal synthesis and diagnosis for alzheimer’s disease by mining underlying shared modality information.  Medical Image Analysis, 91:103032, 2024.
  * [Wei et al., 2024] Yujie Wei, Shiwei Zhang, Zhiwu Qing, Hangjie Yuan, Zhiheng Liu, Yu Liu, Yingya Zhang, Jingren Zhou, and Hongming Shan.  Dreamvideo: Composing your dream videos with customized subject and motion.  In CVPR, 2024.
  * [Xie et al., 2023] Zhenyu Xie, Zaiyu Huang, Xin Dong, Fuwei Zhao, Haoye Dong, Xijin Zhang, Feida Zhu, and Xiaodan Liang.  GP-VTON: Towards general purpose virtual try-on via collaborative local-flow global-parsing learning.  In CVPR, pages 23550–23559, 2023.
  * [Yang et al., 2023] Binxin Yang, Shuyang Gu, Bo Zhang, Ting Zhang, Xuejin Chen, Xiaoyan Sun, Dong Chen, and Fang Wen.  Paint by Example: Exemplar-based image editing with diffusion models.  In CVPR, pages 18381–18391, 2023.
  * [Zhang et al., 2018] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang.  The unreasonable effectiveness of deep features as a perceptual metric.  In ICCV, pages 586–595, 2018.
  * [Zhu et al., 2023] Luyang Zhu, Dawei Yang, Tyler Zhu, Fitsum Reda, William Chan, Chitwan Saharia, Mohammad Norouzi, and Ira Kemelmacher-Shlizerman.  TryOnDiffusion: A tale of two unets.  In CVPR, pages 4606–4615, 2023.

## Appendix

##  I More Implementation Details

This section presents visualizations and detailed pipelines of
$\bm{P}^{\text{a}}$ and $\widehat{\bm{T}}^{\text{w}}$, the clothes warping
procedure, and more training and sampling details.

#### Visualizations and detailed pipelines of $\bm{P}^{\text{a}}$ and
$\widehat{\bm{T}}^{\text{w}}$.

The clothes-agnostic person image $\bm{P}^{\text{a}}$ refers to the person
image with the try-on region being masked out. The pre-warped try-on image
$\widehat{\bm{T}}^{\text{w}}$ is simply obtained by adding the warped clothes
image $\bm{C}^{\text{w}}$ to $\bm{P}^{\text{a}}$, i.e.
$\widehat{\bm{T}}^{\text{w}}=\bm{C}^{\text{w}}+\bm{P}^{\text{a}}$. Figure A1
presents the visualizations and detailed processing pipelines of these two
definitions to facilitate the understanding. Of note, all preprocessing
pipelines of our FLDM-VTON follow previous VTON research [?]. We will
certainly make them clearer in the future version.

Figure A1: Visualizations and detailed pipelines of $\bm{P}^{\text{a}}$ and
$\widehat{\bm{T}}^{\text{w}}$.

#### Clothes warping procedure.

Figure A2 presents the detailed clothes warping procedure. To adapt the flat
clothes image $\bm{C}$ to fit a person $\bm{P}$, we exploit the appearance
flow-based network proposed in [?; ?] to estimate the warping flow
$\widehat{\bm{F}}$, which is then utilized to warp the original flat clothes
$\bm{C}$, generating the corresponding warped clothes image
$\bm{C}^{\text{w}}$.

Figure A2: Workflow of the clothes warping procedure.

Specifically, our appearance flow-based warping network processes both _human
perception data_ and _clothes-related data_ to estimate the warping flow
$\widehat{\bm{F}}$. The human perception data comprises the clothes-agnostic
segmentation mask $\bm{S}^{\text{a}}$ and dense pose image
$\bm{D}_{\text{p}}$. Meanwhile, the clothes-related data include the flat
clothes image $\bm{C}$ and flat clothes position mask $\bm{m}^{\text{cp}}$.
Then, the warping network takes them as input to estimate the final warping
flow $\widehat{\bm{F}}$, generating the corresponding warped clothes image
$\bm{C}^{\text{w}}$.

Following the training procedure of the proposed clothes flattening network
and other appearance flow-based methods [?], we optimize the warping network
with the following loss function, including both pixel- and flow-level
components:

$\displaystyle\mathcal{L}_{\text{Warp}}=\mathcal{L}_{\text{1}}^{\text{w}}+\lambda_{\text{per}}^{\text{w}}\mathcal{L}_{\text{per}}^{\text{w}}+\lambda_{\text{sec}}^{\text{w}}\mathcal{L}_{\text{sec}}^{\text{w}}+\lambda_{\text{TV}}^{\text{w}}\mathcal{L}_{\text{TV}}^{\text{w}},$
(A7)

where $\lambda_{\text{*}}^{\text{w}}$ are hyperparameters to adjust the
weights among different loss components. We set
$\lambda_{\text{per}}^{\text{w}}=0.2$, $\lambda_{\text{sec}}^{\text{w}}=0.01$,
and $\lambda_{\text{TV}}^{\text{w}}=6.0$, in line with the settings suggested
by [?; ?].

#### Training and sampling details.

In our experiments, we first train the clothes flattening network and then
freeze its weights to train our try-on diffusion UNet.

First, we train the clothes flattening network with a mini-batch size of 32
over 100 epochs. Specifically, we start with an initial learning rate of
$5.0\times{10}^{-5}$ for the first 30 epochs, which is then linearly decreased
for the remaining epochs. The hyperparameters $\lambda_{\text{per}}$,
$\lambda_{\text{sec}}$, and $\lambda_{\text{TV}}$ in Eq. (4) are set as 0.1,
10, and 0.01, respectively, ensuring consistency in the order of magnitude.
Note that both the warping and clothes flattening networks are trained with a
resolution of size $256\times 192$, aligning with existing appearance flow-
based methods [?; ?; ?]. When needed, we upsample the predicted warping or
flattening flow to the corresponding size.

Subsequently, we train the try-on diffusion UNet over $200,000$ iterations,
with a mini-batch size of $8$. The initial learning rate is
$1.0\times{10}^{-6}$, which is gradually increased to $2\times{10}^{-5}$ over
the first $3000$ iterations. The hyperparameter $\lambda_{\text{cons}}$ is
empirically set as $0.15$, also ensuring consistency in order of magnitude.
The try-on diffusion UNet is initialized with the official pre-trained weights
of Paint-by-example [?], offering a basic inpainting ability. In line with the
standard SD framework, classifier-free guidance [?] with an unconditional
probability of $0.2$ is used for the global condition.

During the sampling phase, we employ the DPM solver [?] method with 50 steps.
Following the official adjusted suggestion of FreeU [?], we empirically adjust
the weights of the backbone ($b$) and skip-connection ($s$) features at the
first two stages with the value of $b_{1}=1.1$, $b_{2}=1.2$, $s_{1}=0.9$,
$s_{2}=0.6$; please refer to Appendix III for more FreeU ablation results. In
addition, for the implementation of competing methods presented in this paper,
we use pre-trained weights if available; otherwise, we train the models
following their official codes.

##  II More Qualitative Results

To further demonstrate the effectiveness of our FLDM-VTON, this section
presents more qualitative results on the VITON-HD and Dress Code datasets.

#### Results on the VITON-HD dataset.

Figure A4 presents more qualitative results on the VITON-HD dataset.
Specifically, we comprehensively compare our FLDM-VTON with 8 SOTA competing
methods, including CP-VTON [?], CP-VTON+ [?], VITON-HD [?], HR-VITON [?], GP-
VTON [?], Paint-by-example [?], Ladi-VTON [?], and DCI-VTON [?].

#### Results on the Dress Code dataset.

Figure A5 presents more qualitative results of our FLDM-VTON and other
competing methods on the Dress Code dataset. Due to the extensive size of the
Dress Code dataset, we only compare our FLDM-VTON with several SOTA methods
that have disclosed pre-training weights, including GP-VTON, Paint-by-example,
and Ladi-VTON.

##  III More Ablation Study

Figure A3 and Table A1 present the qualitative and quantitative ablation
results on FreeU, respectively. We observe that FreeU does not contribute as
much as other proposed components. However, the experimental results show that
the use of FreeU is able to enhance the sample quality of diffusion models
without introducing any additional computational costs, as demonstrated in the
original paper [?].

Table A1: Quantitative ablation results of FreeU used in our FLDM-VTON. The
best results are highlighted in bold.

Methods | Paired |  | Unpaired
---|---|---|---
LPIPS$\downarrow$ | SSIM$\uparrow$ |  | FID$\downarrow$ | KID$\downarrow$
Our FLDM-VTON | 0.080 | 0.886 |  |  8.81 | 0.13
w/o FreeU | 0.081 | 0.884 |  |  8.83 | 0.13

Figure A3: Qualitative ablation results of FreeU used in our FLDM-VTON.

##  IV Discussions

#### Acceleration advantage.

We employ the same DDIM sampler [?] with various sampling steps for inference
in both DCI-VTON and our FLDM-VTON, as illustrated in Figure A6. As the number
of sampling steps increases, we observe that DCI-VTON exhibits an _unstable
and inconsistent_ sampling process. Notably, the open neckline of the
presented person intermittently appears and disappears. In contrast, our FLDM-
VTON can generate photo-realistic try-on images while preserving most clothing
details with fewer sampling steps. Moreover, our sampling process exhibits a
more stable and consistent refinement phenomenon.

#### Limitation.

While our FLDM-VTON exhibits promising performance, we acknowledge some
limitations in preserving _extremely small or complex_ logos and patterns.
Figure A7 presents some failure cases. Despite its ability to generate high-
quality try-on images and preserve most clothing details, handling intricate
scenarios remains a struggle. This limitation arises from the inherent loss of
vital clothing information during the latent diffusion process, which may be
alleviated by performing the diffusion process in pixel space with sufficient
computational resources or providing a more robust pretrained LDM.

#### Social impact.

The VTON technology has a significant social impact by revolutionizing the
retail experience. It offers consumers a personalized shopping journey,
reducing clothing returns and waste. In addition, it supports sustainable
practices in the fashion industry by minimizing the need for physical
fittings. Moreover, it fosters social engagement as users can share their
virtual try-on experiences on social platforms, creating a dynamic and
interactive online community.

Figure A4: More qualitative results of different methods and our FLDM-VTON on
the VITON-HD dataset. Figure A5: More qualitative results of different methods
and our FLDM-VTON on the Dress Code dataset. Figure A6: Comparison results of
DCI-VTON and our FLDM-VTON using the same DDIM sampler with various sampling
steps. Figure A7: Some failure cases by our FLDM-VTON.