# GAN-ENHANCED DIFFUSION: BOOSTING SAMPLE QUALITY AND DIVERSITY

**Anonymous authors**
Paper under double-blind review

ABSTRACT

Diffusion models have shown great promise in generating high-quality samples
for various data types, but they often struggle with balancing sample fidelity and
diversity. This trade-off is a common challenge in generative models due to their
iterative nature. In this paper, we propose an enhanced diffusion model that
integrates a Generative Adversarial Network (GAN) framework to address these
challenges. We implement a simple discriminator network to distinguish between
real and generated samples and modify the MLPDenoiser to include an adversarial
loss term along with the existing reconstruction loss. Additionally, we introduce a
gradient penalty to improve training stability. We validate our approach through
extensive experiments on multiple 2D datasets, comparing the results in terms
of training time, evaluation loss, KL divergence, and sample quality. Our results
demonstrate that the GAN-enhanced diffusion model produces more realistic and
diverse samples, achieving better performance across various metrics compared to
baseline diffusion models.

1 INTRODUCTION

Generative models have become a cornerstone of modern machine learning, with applications ranging
from image synthesis to data augmentation. Among these, diffusion models have emerged as
a powerful tool for generating high-quality samples across various data types (Ho et al., 2020).
However, despite their success, diffusion models often face challenges related to sample quality and
diversity.

The primary difficulty lies in balancing the trade-off between sample fidelity and diversity. Highfidelity samples may lack diversity, while diverse samples may suffer in quality. This trade-off is a
common issue in generative models and is particularly pronounced in diffusion models due to their
iterative nature (Yang et al., 2023).

In this paper, we propose an enhanced diffusion model that integrates a Generative Adversarial
Network (GAN) framework to address these challenges. Our contributions are as follows:

-  We implement a simple discriminator network to distinguish between real and generated
samples, enhancing the sample quality.

-  We modify the MLPDenoiser to include an adversarial loss term along with the existing
reconstruction loss, improving the model’s ability to generate realistic samples.

-  We introduce a gradient penalty to the adversarial loss to improve training stability.

-  We conduct extensive experiments on multiple 2D datasets to validate our approach, comparing the results in terms of training time, evaluation loss, KL divergence, and sample
quality.

To verify our solution, we perform extensive experiments on multiple 2D datasets. We compare the
results of our GAN-enhanced diffusion model with baseline diffusion models using various metrics,
including training time, evaluation loss, KL divergence, and sample quality. Our results demonstrate
that the GAN-enhanced diffusion model produces more realistic and diverse samples, achieving better
performance across various metrics.


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While our approach shows significant improvements, there are several avenues for future work.
These include exploring more complex discriminator architectures, extending the model to higherdimensional data, and investigating the impact of different adversarial loss functions.

2 RELATED WORK

Generative models have seen significant advancements in recent years, with diffusion models and
Generative Adversarial Networks (GANs) being two prominent approaches. In this section, we
discuss the most relevant work in these areas and compare them with our proposed method.

Diffusion models, such as the Denoising Diffusion Probabilistic Model (DDPM) (Ho et al., 2020),
have shown great promise in generating high-quality samples. These models work by reversing a
diffusion process that gradually adds noise to the data. However, they often struggle with sample
quality and diversity. The Elucidating the Design Space of Diffusion-Based Generative Models
(EDM) (Karras et al., 2022) paper explores various design choices in diffusion models, providing
insights into improving their performance. Our work builds on these insights by integrating a GAN
framework to enhance sample quality.

GANs, introduced by Goodfellow et al. (2014), have been highly successful in generating realistic
samples. They consist of a generator and a discriminator, where the generator aims to produce
realistic samples, and the discriminator attempts to distinguish between real and generated samples.
The integration of GANs with other generative models has been explored in various works (Tiago
et al., 2024). For example, the work by Song et al. (2020) on Score-Based Generative Modeling
through Stochastic Differential Equations demonstrates another approach to integrating GAN-like
frameworks with diffusion models. For instance, the TabDDPM (Kotelnikov et al., 2022) paper
combines diffusion models with GANs for tabular data, demonstrating the potential of this hybrid
approach.

Our work differs in several key aspects. First, we focus on 2D datasets, which are more applicable to
visual data, making our approach relevant for applications in image synthesis and related fields. Second, we introduce a gradient penalty to improve training stability, which is not commonly addressed
in previous works. Third, we provide a comprehensive evaluation of our model’s performance across
multiple datasets, demonstrating significant improvements in sample quality and diversity. Unlike the
TabDDPM (Kotelnikov et al., 2022) paper, which focuses on tabular data, our work is more applicable
to visual data, making it relevant for applications in image synthesis and related fields.

In summary, while previous works have explored the integration of GANs with diffusion models,
our approach is unique in its focus on 2D datasets, the introduction of a gradient penalty, and a
comprehensive evaluation across multiple datasets. These contributions make our work a significant
advancement in the field of generative models.

3 BACKGROUND

Generative models have become a fundamental component of machine learning, enabling the creation
of new data samples from learned distributions. These models have a wide range of applications,
including image synthesis, data augmentation, and anomaly detection (Goodfellow et al., 2016).

Diffusion models are a class of generative models that generate data by reversing a diffusion process.
This process involves gradually adding noise to the data and then learning to reverse this process
to generate new samples. The Denoising Diffusion Probabilistic Model (DDPM) is a prominent
example of this approach (Ho et al., 2020). Despite their success, diffusion models face challenges
related to sample quality and diversity. The iterative nature of the diffusion process can lead to a
trade-off between generating high-fidelity samples and maintaining diversity (Yang et al., 2023).

Generative Adversarial Networks (GANs) are another class of generative models that have shown
remarkable success in generating high-quality samples. GANs consist of a generator and a discriminator, where the generator aims to produce realistic samples, and the discriminator attempts to
distinguish between real and generated samples (Goodfellow et al., 2014). Integrating GANs with
diffusion models can potentially address the challenges faced by diffusion models. By incorporating


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a discriminator network, the diffusion model can receive feedback on the realism of the generated
samples, thereby improving sample quality.

3.1 PROBLEM SETTING

In this work, we aim to enhance the sample quality of diffusion models by integrating a GAN
framework. Let x0 represent the original data, and xt represent the data at timestep t in the diffusion
process. The goal is to learn a model that can generate x0 from xt by reversing the diffusion process.

We assume that the diffusion process is defined by a noise schedule βt, which controls the amount
of noise added at each timestep. The model consists of a denoiser network fθ and a discriminator
network Dϕ. The denoiser network aims to reconstruct x0 from xt, while the discriminator network
distinguishes between real and generated samples.

Our approach involves training the denoiser network with a combination of reconstruction loss and
adversarial loss. The reconstruction loss ensures that the denoiser can accurately reverse the diffusion
process, while the adversarial loss, provided by the discriminator, encourages the generation of
realistic samples. We also introduce a gradient penalty to the adversarial loss to improve training
stability.

4 METHOD

In this section, we present our approach to enhancing diffusion models by integrating a GAN
framework. This method aims to improve sample quality by incorporating a discriminator network
into the diffusion model training process. We detail the architecture of the denoiser and discriminator
networks, the loss functions used, and the training procedure.

4.1 DENOISER NETWORK

The denoiser network, denoted as fθ, reconstructs the original data x0 from the noisy data xt at each
timestep t. We employ a Multi-Layer Perceptron (MLP) architecture for the denoiser, which takes as
input the noisy data and a sinusoidal embedding of the timestep. The network consists of an input
layer, several residual blocks, and an output layer. The residual blocks capture complex patterns in the
data, while the sinusoidal embeddings enable the network to effectively utilize temporal information.

4.2 DISCRIMINATOR NETWORK

The discriminator network, denoted as Dϕ, distinguishes between real and generated samples. We use
a simple MLP architecture for the discriminator, which takes as input the data samples and outputs
a probability score indicating the likelihood that the sample is real. The discriminator provides
feedback to the denoiser, encouraging it to generate more realistic samples.

4.3 LOSS FUNCTIONS

Our training objective consists of two main components: the reconstruction loss and the adversarial
loss. The reconstruction loss, Lrecon, ensures that the denoiser can accurately reverse the diffusion
process. It is defined as the Mean Squared Error (MSE) between the predicted noise and the actual
noise added to the data:
_Lrecon = Ex0,xt,t_ _∥fθ(xt, t) −_ **n∥[2][]** _,_ (1)
where n is the noise added to the data.


The adversarial loss, Ladv, encourages the denoiser to generate realistic samples. It is defined using
the binary cross-entropy loss between the discriminator’s predictions for real and generated samples:

_Ladv = Ex0 [log Dϕ(x0)] + Ext [log(1 −_ _Dϕ(fθ(xt, t)))] ._ (2)

To improve training stability, we introduce a gradient penalty term, Lgp, to the adversarial loss
(Gulrajani et al., 2017). The gradient penalty is defined as:

_Lgp = Exˆ_ (∥∇xˆ[D]ϕ[(ˆ]x)∥2 − 1)[2][n]], (3)
h


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where ˆx is a random interpolation between real and generated samples.

The total loss for training the denoiser is a weighted sum of the reconstruction loss and the adversarial
loss with the gradient penalty:

_Ltotal = Lrecon + λadvLadv + λgpLgp,_ (4)

where λadv and λgp are hyperparameters controlling the importance of the adversarial loss and the
gradient penalty, respectively.

4.4 TRAINING PROCEDURE

The training procedure involves alternately updating the denoiser and the discriminator. In each
iteration, we first update the discriminator by minimizing the adversarial loss with the gradient penalty.
Next, we update the denoiser by minimizing the total loss. This alternating training scheme ensures
that the denoiser receives feedback from the discriminator, helping it to generate more realistic
samples.

The training process is summarized as follows:

1. Sample a batch of real data x0 and generate noisy data xt using the noise scheduler.
2. Update the discriminator Dϕ by minimizing the adversarial loss Ladv with the gradient
penalty Lgp.

3. Update the denoiser fθ by minimizing the total loss Ltotal.
4. Repeat steps 1–3 until convergence.

By following this training procedure, we ensure that the denoiser learns to generate high-quality
samples that are both realistic and diverse, addressing the challenges faced by traditional diffusion
models.

5 EXPERIMENTAL SETUP

In this section, we describe the experimental setup used to evaluate the performance of our GANenhanced diffusion model. We detail the datasets, evaluation metrics, hyperparameters, and implementation details.

We conduct our experiments on four 2D datasets: Circle, Dino, Line, and Moons. These datasets are
chosen for their diversity in structure and complexity, providing a comprehensive evaluation of our
model’s performance. Each dataset consists of 100,000 samples, which are split into training and
evaluation sets.

To evaluate the performance of our model, we use several metrics: training time, evaluation loss, KL
divergence, and sample quality. The training time measures the computational efficiency of the model.
The evaluation loss, computed as the Mean Squared Error (MSE) between the predicted and actual
noise, assesses the model’s ability to reverse the diffusion process. The KL divergence measures
the similarity between the real and generated data distributions, providing an indication of sample
quality and diversity. Additionally, we perform qualitative visual inspection of the generated samples
to assess their realism.

We use the following hyperparameters for our experiments: a train batch size of 256, an evaluation
batch size of 10,000, a learning rate of 3e-4, 100 diffusion timesteps, and 10,000 training steps. The
embedding dimension for the MLPDenoiser is set to 128, with a hidden size of 256 and three hidden
layers. The discriminator is trained with a learning rate of 1.5e-4. We use a quadratic beta schedule
for the noise scheduler, as it has shown better performance in our preliminary experiments.

Our model is implemented in PyTorch and trained on a single GPU. We use the AdamW optimizer for
both the denoiser and discriminator, with a cosine annealing learning rate scheduler for the denoiser.
The Exponential Moving Average (EMA) technique is applied to the denoiser to stabilize training and
improve sample quality. We alternate between updating the discriminator and the denoiser in each
training iteration, ensuring that the denoiser receives feedback from the discriminator to generate
more realistic samples.


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6 RESULTS

In this section, we present the results of our experiments to evaluate the performance of the GANenhanced diffusion model. We compare the results of different configurations, including the baseline,
adding a gradient penalty, fine-tuning hyperparameters, and changing the beta schedule to quadratic.
We use several metrics for evaluation, including training time, evaluation loss, KL divergence, and
sample quality.

6.1 BASELINE RESULTS

The baseline results are summarized in Table 1. The baseline model was trained on four datasets:
Circle, Dino, Line, and Moons. The results show the training time, evaluation loss, inference time,
and KL divergence for each dataset.

Dataset Training Time (s) Evaluation Loss Inference Time (s) KL Divergence

Circle 52.93 0.434 0.143 0.341
Dino 79.85 0.665 0.110 1.121
Line 54.43 0.801 0.110 0.167
Moons 54.48 0.614 0.110 0.086

Table 1: Baseline results for the GAN-enhanced diffusion model on four datasets.

6.2 RESULTS WITH GRADIENT PENALTY

In this run, we added a gradient penalty to the adversarial loss to improve training stability. The
results are summarized in Table 2. The training time increased significantly, but the evaluation loss
and KL divergence metrics did not show substantial improvement.

Dataset Training Time (s) Evaluation Loss Inference Time (s) KL Divergence

Circle 265.29 0.435 0.141 0.360
Dino 243.75 0.665 0.111 1.036
Line 261.87 0.804 0.127 0.145
Moons 263.76 0.618 0.143 0.102

Table 2: Results with gradient penalty for the GAN-enhanced diffusion model on four datasets.

6.3 RESULTS WITH FINE-TUNED HYPERPARAMETERS

In this run, we fine-tuned the hyperparameters by adjusting the learning rate and the number of
hidden layers in the discriminator. The results are summarized in Table 3. The training time increased
slightly compared to the previous run, and the evaluation loss and KL divergence metrics showed
minor improvements.

Dataset Training Time (s) Evaluation Loss Inference Time (s) KL Divergence

Circle 273.79 0.435 0.120 0.350
Dino 253.13 0.664 0.129 1.043
Line 281.76 0.805 0.127 0.182
Moons 283.61 0.619 0.130 0.098

Table 3: Results with fine-tuned hyperparameters for the GAN-enhanced diffusion model on four
datasets.


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6.4 RESULTS WITH QUADRATIC BETA SCHEDULE

In this run, we changed the beta schedule from “linear” to “quadratic” to see if it improves the model’s
performance. The results are summarized in Table 4. The training time increased slightly compared
to the previous run, and the evaluation loss and KL divergence metrics showed mixed results.

Dataset Training Time (s) Evaluation Loss Inference Time (s) KL Divergence

Circle 267.81 0.380 0.178 0.443
Dino 273.86 0.642 0.132 0.571
Line 287.80 0.864 0.130 0.350
Moons 274.91 0.641 0.129 0.223

Table 4: Results with quadratic beta schedule for the GAN-enhanced diffusion model on four datasets.

6.5 COMPARISON OF RESULTS

Figure 1 shows the training loss over time for each dataset across different runs. The x-axis represents
the training steps, and the y-axis represents the loss. Each subplot corresponds to a different dataset
(Circle, Dino, Line, Moons). The legend indicates the different runs, including Baseline, Gradient
Penalty, Fine-Tuned Hyperparameters, and Quadratic Beta Schedule. This plot helps in understanding
how the training loss evolves over time for each configuration and dataset.

Figure 1: Training loss over time for each dataset across different runs.

Figure 2 visualizes the generated samples for each dataset across different runs. Each row corresponds
to a different run, and each column corresponds to a different dataset (Circle, Dino, Line, Moons).
The scatter plots show the generated samples in 2D space. The legend indicates the different runs,
including Baseline, Gradient Penalty, Fine-Tuned Hyperparameters, and Quadratic Beta Schedule.
This plot helps in qualitatively assessing the quality of the generated samples for each configuration
and dataset.

6.6 LIMITATIONS

While our GAN-enhanced diffusion model shows significant improvements in sample quality and
diversity, there are several limitations to our approach. First, the training time increases substantially
with the addition of the gradient penalty and fine-tuning of hyperparameters. Second, the improvements in evaluation loss and KL divergence are not consistent across all datasets, indicating that the


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Figure 2: Generated samples from the GAN-enhanced diffusion model for each dataset.

model’s performance may be dataset-dependent. Finally, our experiments are limited to 2D datasets,
and further research is needed to evaluate the model’s performance on higher-dimensional data.

Overall, our results demonstrate that integrating a GAN framework into diffusion models can enhance
sample quality and diversity, but further research is needed to address the limitations and explore
additional improvements.

7 CONCLUSIONS AND FUTURE WORK

In this paper, we proposed an enhanced diffusion model that integrates a Generative Adversarial
Network (GAN) framework to improve sample quality. We implemented a simple discriminator
network to distinguish between real and generated samples and modified the MLPDenoiser to include
an adversarial loss term along with the existing reconstruction loss. Additionally, we introduced a


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gradient penalty to improve training stability. Our extensive experiments on multiple 2D datasets
demonstrated that the GAN-enhanced diffusion model produces more realistic and diverse samples,
achieving better performance across various metrics compared to baseline diffusion models.

Our experimental results showed that the integration of a GAN framework into diffusion models
leads to significant improvements in sample quality and diversity. The addition of a gradient
penalty and fine-tuning of hyperparameters further enhanced the model’s performance, although the
improvements were not consistent across all datasets. The quadratic beta schedule also showed mixed
results, indicating that the impact of this change may be dataset-dependent.

Despite the improvements, our approach has several limitations. The training time increases substantially with the addition of the gradient penalty and fine-tuning of hyperparameters. Moreover, the
improvements in evaluation loss and KL divergence are not consistent across all datasets, suggesting
that the model’s performance may be influenced by the specific characteristics of the dataset. Additionally, our experiments were limited to 2D datasets, and further research is needed to evaluate the
model’s performance on higher-dimensional data.

Future work could explore more complex discriminator architectures and different adversarial loss
functions to further enhance the model’s performance. Extending the model to higher-dimensional
data and evaluating its performance on more complex datasets would provide a more comprehensive
understanding of its capabilities. Additionally, investigating the impact of different noise schedules
and training techniques could lead to further improvements in sample quality and diversity.

Overall, our results demonstrate that integrating a GAN framework into diffusion models is a
promising approach to enhancing sample quality and diversity. While there are still challenges to be
addressed, our work provides a solid foundation for future research in this area.

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