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Unveiling "Continuous Diffusion for Categorical Data Estimation for LLMs"

An In-depth Exploration


Published on: August 17, 2023

Continuous Diffusion for Categorical Data for LLMs


The world of natural language processing (NLP) has witnessed remarkable transformations in recent years, thanks to the advancements in generative models. Among these, the Continuous Diffusion for Categorical Data (CDCD) framework shines as a beacon of innovation. In this comprehensive exposition, we embark on a profound journey to understand the intricacies and implications of this pioneering approach in the realm of text generation.


Embarking on a journey through the intricacies of cutting-edge advancements in the domain of Natural Language Processing (NLP), we delve into the world of "Continuous Diffusion for Categorical Data for LLMs." This paper stands as a beacon of innovation, offering a paradigm shift in how we conceptualize and implement text generation models. Addressing the limitations of sequential generation approaches, the Continuous Diffusion for Categorical Data (CDCD) framework ushers in a new era of parallelized, contextually rich text generation.

As researchers and enthusiasts in the AI community, we are no strangers to the challenges posed by autoregressive models, where tokens are generated sequentially. The CDCD framework, introduced in this paper, challenges this status quo by embracing the principles of continuous diffusion processes, traditionally applied in the realm of stochastic systems. By weaving these principles into the fabric of text generation, CDCD provides a fresh perspective, allowing for the generation of diverse and coherent text in a parallelized manner.

In this comprehensive exposition, we unravel the layers of the CDCD framework, navigating through its theoretical underpinnings, practical implications, and its potential to reshape the landscape of NLP. As we journey through the corridors of continuous diffusion, bridge the gap to discrete text generation, and dissect key concepts and terminologies, we will uncover the mechanics behind CDCD's ability to yield efficient, contextually relevant text across a spectrum of tasks.

Drawing upon the paper's meticulous experiments, we scrutinize the architectural choices that shape CDCD's performance, exploring aspects such as embedding dimensionality, scaling hyperparameters, and step spacing. Furthermore, we extend our exploration into uncharted territories, envisioning the impact of CDCD in the realm of healthcare through a captivating case study. The integration of CDCD into the medical documentation process paints a compelling picture of its real-world applicability and potential to revolutionize diverse domains.

As a testament to the paper's holistic approach, we bridge the gap between theory and implementation, providing a PyTorch code snippet that crystallizes the CDCD framework in tangible code. From embedding layers to transformers, this example underscores the transition from theoretical constructs to pragmatic applications.

With a reflective gaze upon our journey, we distill the key takeaways that serve as guiding lights:

  1. The CDCD framework's transformative power

  2. The amalgamation of continuous diffusion and language modeling

  3. The tangible path from research to implementation.

Note: The full PyTorch Implementation is in the process

As we conclude our exploration, we peer into the horizon of the future, speculating on the uncharted territories that CDCD might conquer, from hybrid model synergies to its unrelenting evolution in an ever-evolving technological landscape.

In the grand tapestry of AI research, "Continuous Diffusion for Categorical Data for LLMs" stands as a pivotal chapter, a testament to the endless potential of human ingenuity and its ability to reshape the way we interact with language.

Continuous Diffusion Process: A Theoretical Underpinning

To fully appreciate the transformative impact of the CDCD framework on text generation, it's essential to delve into the profound theoretical foundations that underlie it. This section illuminates the theoretical underpinning of CDCD, elucidating the infusion of continuous diffusion processes into the realm of natural language processing.

In the realm of stochastic systems, continuous diffusion processes provide a mathematical scaffold to describe the behavior of dynamic variables evolving over continuous time intervals. These processes, governed by stochastic differential equations (SDEs), encapsulate the evolution of variables influenced by stochastic perturbations or noise. Traditionally deployed in fields like physics and finance, CDCD ingeniously adapts these processes to the intricacies of language modeling.

Stochastic Differential Equations (SDEs)

At the heart of continuous diffusion lies the framework of stochastic differential equations. Mathematically, an SDE is represented as:

dX​= f(Xt​,t)dt+σ(t)dW

Here, Xt​ is the variable of interest evolving over time t, f(Xt​,t) represents the drift term influencing the average behavior, σ(Xt​,t) denotes the diffusion coefficient that quantifies the influence of noise, and dWt​ signifies the Wiener process, embodying random perturbations.

CDCD Adaptation

The CDCD framework intricately weaves these SDEs into text generation. The crux of this adaptation lies in the continuous mapping of diffusion processes onto the space of text token embeddings. The core components include:

Continuous Diffusion for Categorical Data for LLMs
Source: Dieleman, S., Sartran, L., Roshannai, A., Savinov, N., Ganin, Y., Richemond, P. H., Doucet, A., Strudel, R., Dyer, C., Durkan, C., Hawthorne, C., Leblond, R., Grathwohl, W., & Adler, J. (2022). Continuous diffusion for categorical data. ArXiv. /abs/2211.15089

Initial Noise Injection: In CDCD, the generation process commences with the injection of noise ϵ0​ into the token embeddings. This noise orchestrates controlled variations in the generated text and evolves throughout the diffusion trajectory. Mathematically:

X0​ = Embed(x0​)+ϵ0​

x0 = self.token_embedder(tokens)

Here, Embed(x0​) represents the embedding of the initial token x0​.

Continuous Evolution: As the diffusion process unfolds, token embeddings evolve over time. This evolution adheres to a parallelized trajectory, allowing tokens to simultaneously progress, akin to the noise in a continuous diffusion process. This contrasts with the sequential generation in autoregressive models.

Noise Reduction: An intriguing aspect of CDCD is the gradual reduction of noise influence as the process advances. This results in embeddings progressively aligning with context while retaining a controlled level of variability. Mathematically, the evolution of embeddings Xt​ is governed by the SDEs:

dX​= f(Xt​,t)dt+σ(t)dW
noise = torch.randn_like(x0_norm)
x = self.scheduler.add_noise(x0_norm, noise, timesteps)
x_expanded = self.score_to_input(x)

Temporal Dynamics and Context Preservation

Temporal dynamics are central to the continuous diffusion process. Just as time shapes the trajectories of variables in stochastic systems, it governs the progression of tokens in text generation. Unlike the strict linear sequencing of autoregressive models, CDCD captures context-rich dependencies and interactions, fostering enhanced coherence and understanding.

Duality of Context and Variation

CDCD strikes an elegant balance between context preservation and controlled variation. Noise-induced diversity infuses the generated text, while the gradual diminution of noise ensures contextually coherent outputs. This duality empowers CDCD to transcend the confines of traditional language models.

Incorporating the theoretical tenets of continuous diffusion into the fabric of text generation, the CDCD framework revolutionizes our approach to contextually rich, diverse, and coherent language models. This fusion of continuous diffusion with natural language processing reframes the boundaries of text generation, catalyzing a paradigm shift that holds the promise of reshaping the NLP landscape.

Bridging the Gap: From Continuous to Discrete Diffusion

The transition from continuous diffusion to its discrete counterpart is a pivotal bridge that interconnects the realm of stochastic processes with discrete data, in our case, natural language. While continuous diffusion models excel in capturing the fluid evolution of variables over time, adapting this concept to discrete data brings about nuanced challenges and opportunities.

The Flexibility and Constraints of Discrete Diffusion

Discrete diffusion modeling introduces intriguing possibilities, accompanied by a distinct set of constraints. While discrete diffusion allows us to embrace a wider range of data types, it necessitates relinquishing certain advantages inherent to continuous diffusion. Sampling algorithms that leverage advanced ordinary differential equation (ODE) solvers or classifier-free guidance, which are hallmarks of the continuous paradigm, must be reevaluated in the discrete context.

Embracing Discreteness: The Role of Time and Input Space

It's important to differentiate between the continuous nature found in the input data and the continuous aspect linked to the timing of the corruption process.

In our framework, both these continuities persist in the context of CDCD. By combining embedding-based strategies with discrete-time diffusion, our approach targets language modeling, rendering it adaptable to the discrete nature of linguistic data.

Variations in Approaches

Several recent papers explore the application of continuous diffusion to discrete data, primarily in the realm of language modeling. Diverse strategies have emerged, with each proposing a unique perspective.

  1. Li et al., Strudel et al., and Han et al. focus on embedding-based strategies in conjunction with discrete-time diffusion for language modeling.

  2. Campbell et al. and Sun et al. introduce continuous-time models for discrete inputs.

  3. Meng et al. propose concrete score matching applicable to both continuous and discrete inputs.

  4. Chen et al. use continuous-time diffusion applied to continuous relaxations of binary input representations, with the cross-entropy loss as a key component.

Iterative Refinement in Machine Translation

Non-autoregressive iterative refinement models have garnered significant attention in the realm of machine translation. These models grapple with issues such as multi-modality—wherein parallel, uncoordinated predictions lead to incoherences.

Approaches like Latent Variable Models with Denoising Autoencoders (LVM-DAE) and Levenshtein transformers have addressed these challenges, with subsequent refinements like Conditional Maximum Likelihood Models (CMLM), DisCo, and SMART further bridging the gap between autoregressive and non-autoregressive methods.

Recent innovations such as SUNDAE, Aggressive Decoding, and DiffusER have brought us closer to closing the performance gap between autoregressive and non-autoregressive models in machine translation. These advancements leverage techniques like step-unrolls, improved beam search strategies, and edit-based reconstructions. The intersection of discrete diffusion models and machine translation presents a fertile ground for exploring the potent synergy of these cutting-edge paradigms.

The journey from continuous diffusion to discrete diffusion transcends the boundaries of continuous and discrete realms, ushering in a new era of language modeling and machine translation. As we navigate this transformative terrain, a rich spectrum of techniques and paradigms awaits, promising to reshape our understanding of both stochastic processes and language generation.

Now, let's delve deeper into the CDCD framework and its theoretical foundation. The CDCD framework builds upon the concept of continuous diffusion, a stochastic process that models the evolution of continuous-valued variables over time. This process is characterized by a continuous-time Markov process, where the variables change gradually over infinitesimal time intervals, following the rules of diffusion. The key mathematical underpinning of continuous diffusion is the stochastic differential equation (SDE), typically represented as:

dX​= f(Xt​,t)dt+σ(t)dW

Here, Xt represents the variable of interest at time f(Xt​,t) is the drift term responsible for determining the average change in Xt​, σ(t) is the diffusion coefficient that influences the volatility of the process, and dWt​ is a Wiener process (a mathematical construct used to model random motion or in this case noise).

Time warping

We know that time is a continuous variable, hence we need to find a way to incorporate time in a discrete setting. One way to do it is by transforming continuous time variables into discrete time variables. We can achieve this by creating discrete time steps that involve partitioning the continuous time domain into a sequence of discrete time intervals, which serves as the basis for generating data points in a discrete diffusion process.

Here's how you can create discrete time steps:

  1. Time Interval Definition: Determine the granularity of the discrete time steps. This can be a fixed value or an adaptive one based on the requirements of your specific application. The choice of time interval depends on factors like the speed of diffusion and the characteristics of the data being generated.

  2. Time Point Generation: Define a sequence of time points that represent the discrete time steps. These time points are spaced apart by the chosen time interval. For instance, if the continuous diffusion process spans from time 0 to time T, and you've chosen a time interval of Δt, then the sequence of discrete time points would be [0, Δt, 2Δt, ..., T].

  3. Sampling and Processing: At each discrete time point, you can perform the following steps:

    1. Sample from the continuous diffusion process at that specific time point.

    2. Utilize the sampled values to generate discrete data points or to update the model's embeddings and parameters.

  4. Adjustments with Time Warping: As mentioned earlier, you can further enhance the effectiveness of the discrete diffusion process by applying time warping. Time warping allows you to adjust the spacing between the discrete time steps to better match the characteristics of the continuous diffusion process. This can help improve the quality of generated samples.

import torch

def create_discrete_time_steps(T, delta_t):
    num_steps = int(T / delta_t) + 1
    time_steps = torch.arange(0, T + delta_t, delta_t)
    return time_steps

# Parameters
T = 10.0  # Total time span
delta_t = 0.1  # Time interval

# Create discrete time steps
time_steps = create_discrete_time_steps(T, delta_t)

Time warping is a technique used in the CDCD framework to adjust the spacing of discrete-time steps during the sampling process. It involves modifying the sampling schedule to better align with the underlying continuous diffusion process. Time warping aims to improve the effectiveness of the diffusion-based generation process by ensuring that the sampling steps are appropriately distributed, leading to better-quality samples. That's why we use a uniform distribution from (0,1) to sample the time.

In the CDCD framework, the idea is to generate discrete data points using a continuous diffusion process. However, since we're working with discrete data in the context of language, we need to map the continuous diffusion process to discrete time steps. This discrete-time diffusion process can be more effective if the time steps are chosen in a way that reflects the dynamics of the continuous process.

Time warping achieves this alignment by adjusting the spacing between sampling steps. The warping function controls how the discrete time steps are stretched or compressed to match the continuous diffusion process. By adapting the sampling schedule to the characteristics of the diffusion process, time warping can lead to more accurate approximations of the continuous process and ultimately improve the quality of the generated samples.

The benefits of time warping include:

  1. Improved Sampling: Time warping helps the discrete-time steps to better capture the behavior of the continuous diffusion process, resulting in samples that better reflect the desired generation characteristics.

  2. Reduced Discretization Error: Discretizing a continuous process can introduce errors due to the discrete nature of time steps. Time warping minimizes this error by aligning the sampling schedule with the continuous process, reducing the impact of discretization.

  3. Better Convergence: Aligning the time steps with the continuous process can help the model converge faster during training, as the sampling process more closely matches the underlying continuous dynamics.

  4. Enhanced Sample Diversity: By accurately capturing the continuous behavior, time warping can lead to a broader range of generated samples, avoiding biases that might arise from uneven sampling.

In the CDCD framework, time warping is applied to ensure that the discrete-time diffusion process follows the continuous diffusion process more closely, leading to higher-quality generated samples. This technique is an example of how careful consideration of the sampling schedule can significantly impact the performance of diffusion-based generative models.

To bridge the gap between continuous diffusion and discrete data, we adopt the CDCD framework. In this discrete setting, we deal with categorical variables, such as language tokens, which are inherently non-continuous. This introduces challenges, as traditional continuous diffusion equations are not directly applicable. However, by discretizing the time variable and utilizing categorical embeddings, we can extend the continuous diffusion concept to discrete data.

I am presenting a small and simple code block that will help you to understand the CDCD concept:

import torch
import torch.nn as nn
import torch.optim as optim

# Define the vocabulary and its size
vocab = ['apple', 'banana', 'cherry', 'grape']
vocab_size = len(vocab)

# Define the CDCD generator model
class CDCDGenerator(nn.Module):
    def __init__(self, embedding_dim):
        super(CDCDGenerator, self).__init__()
        self.embedding = nn.Embedding(vocab_size, embedding_dim)
        self.transformation = nn.Linear(embedding_dim, vocab_size)
    def forward(self, t, categorical_input):
        # Get categorical embeddings
        categorical_embeddings = self.embedding(categorical_input)
        # Apply diffusion process transformation
        continuous_output = self.transformation(categorical_embeddings)
        return continuous_output

# Create the generator
embedding_dim = 256
generator = CDCDGenerator(embedding_dim)

# Define loss function and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(generator.parameters(), lr=0.001)

# Training loop
num_epochs = 100
for epoch in range(num_epochs):
    # Generate random categorical inputs
    batch_size = 32
    categorical_input = torch.randint(0, vocab_size, (batch_size,))
    # Generate discrete tokens using CDCD framework; this is similar to time warping
    t = torch.linspace(0, 1, steps=embedding_dim)  # Discretize time
    continuous_output = generator(t, categorical_input)
    # Calculate loss and update model
    targets = categorical_input  # Target tokens
    loss = criterion(continuous_output, targets)
    print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')

print("Training complete!")

By incorporating embeddings and discrete-time steps, the CDCD framework maintains continuity in the input space while accommodating the discrete nature of language tokens.

Continuous Diffusion for Categorical Data for LLMs Framework Flowchart and (pseudo) Algorithm

Continuous Diffusion for Categorical Data for LLMs
Source: Dieleman, S., Sartran, L., Roshannai, A., Savinov, N., Ganin, Y., Richemond, P. H., Doucet, A., Strudel, R., Dyer, C., Durkan, C., Hawthorne, C., Leblond, R., Grathwohl, W., & Adler, J. (2022). Continuous diffusion for categorical data. ArXiv. /abs/2211.15089

The CDCD framework involves generating discrete data points by sampling from a conditional distribution over the categorical variable. This distribution is influenced by both the underlying continuous diffusion process and the model's parameters. By incorporating embeddings and discrete-time steps, the CDCD framework maintains continuity in the input space while accommodating the discrete nature of language tokens.

Algorithmic Representation:

  1. Continuous Diffusion Process: Given a continuous diffusion process defined by a function 𝐹(𝑡), where 𝑡 is time and 𝐹(𝑡) is the cumulative distribution function (CDF) of the continuous diffusion process.

  2. Discretizing Time Variable: Discretize the time variable 𝑡 into a sequence of discrete time steps 𝑡𝑖 using a chosen time interval Δ𝑡 and optionally apply time warping:

    1. Let 𝑡𝑖 be the 𝑖-th discrete time step.

    2. Define 𝑡𝑖 as a sequence of time points: 𝑡𝑖 = [𝑡𝑖1, 𝑡𝑖2, ..., 𝑡𝑖𝑛], where 𝑡𝑖𝑛 represents the last time point of the 𝑖-th time step.

    3. Apply time warping if desired to adjust the spacing between 𝑡𝑖𝑗 values within each time step.

  3. Embeddings and Model Parameters: Incorporate embeddings for discrete categories and model parameters that influence the conditional distribution:

    1. Let 𝐸(𝑥) be the embedding vector for the categorical variable 𝑥.

    2. Let 𝜃 be the model's parameters influencing the conditional distribution.

  4. Generating Discrete Data Points:

    1. For each discrete time step 𝑡𝑖:

      1. Sample from the continuous diffusion process 𝑥𝑡𝑖 ∼ 𝐹(𝑡𝑖).

      2. Calculate the conditional distribution 𝑝(𝑥|𝑥𝑡𝑖, 𝜃) by utilizing the embedding 𝐸(𝑥𝑡𝑖) and the model parameters 𝜃.

      3. Sample a discrete data point 𝑥𝑖 from the conditional distribution: 𝑥𝑖 ∼ 𝑝(𝑥|𝑥𝑡𝑖, 𝜃).

  5. Continuity and Discrete Tokens: By incorporating embeddings and generating discrete data points using the described methodology, the CDCD framework ensures continuity in the input space while accommodating the discrete nature of language tokens.

Mathematical Notation:

  1. Continuous Diffusion Process: 𝐹(𝑡) represents the cumulative distribution function (CDF) of the continuous diffusion process.

  2. Discretizing Time Variable:

    1. 𝑡𝑖 represents the 𝑖-th discrete time step.

    2. 𝑡𝑖𝑛 represents the last time point of the 𝑖-th time step.

    3. 𝑡𝑖 = [𝑡𝑖1, 𝑡𝑖2, ..., 𝑡𝑖𝑛] is the sequence of time points within the 𝑖-th time step.

  3. Embeddings and Model Parameters:

    1. 𝐸(𝑥) is the embedding vector associated with the categorical variable 𝑥.

    2. 𝜃 represents the model's parameters influencing the conditional distribution.

  4. Generating Discrete Data Points:

    1. 𝑥𝑡𝑖 represents the sampled value from the continuous diffusion process at time 𝑡𝑖.

    2. 𝑝(𝑥|𝑥𝑡𝑖, 𝜃) is the conditional distribution over the categorical variable 𝑥 given 𝑥𝑡𝑖 and model parameters 𝜃.

    3. 𝑥𝑖 represents the discrete data point sampled from the conditional distribution 𝑝(𝑥|𝑥𝑡𝑖, 𝜃).

  5. Continuity and Discrete Tokens: The CDCD framework maintains continuity in the input space while accommodating the discrete nature of language tokens through the use of embeddings and discrete-time steps.

Continuous Diffusion for Categorical Data for LLMs
Source: Dieleman, S., Sartran, L., Roshannai, A., Savinov, N., Ganin, Y., Richemond, P. H., Doucet, A., Strudel, R., Dyer, C., Durkan, C., Hawthorne, C., Leblond, R., Grathwohl, W., & Adler, J. (2022). Continuous diffusion for categorical data. ArXiv. /abs/2211.15089

Key Concepts and Noteworthy Terms

Let's familiarize ourselves with key concepts that form the cornerstone of CDCD:

  • CDCD Framework: A groundbreaking approach to text generation that harnesses the principles of continuous diffusion processes.

  • Continuous Diffusion: Stochastic processes modeling the gradual transition of continuous variables over time, adapted for language generation.

  • Non-Autoregressive Generation: A paradigm shift away from sequential token generation, allowing for parallelized and more efficient text production.

Related Work and Dissection

In the landscape of natural language processing and generative modeling, the CDCD framework finds its roots in a rich tapestry of related works. In this section, we delve into the foundational concepts and approaches that underpin the CDCD framework, shedding light on its distinct features and advantages.

Discrete Diffusion Paradigm

The CDCD framework emerges from a lineage of diffusion-based and diffusion-inspired techniques tailored for iterative refinement of discrete data. While previous diffusion-based approaches primarily focused on continuous data, recent endeavors have sought to extend the continuous diffusion paradigm to discrete domains, particularly in the context of language modeling. The CDCD framework hinges on the notion of combining both continuous and discrete components, leveraging their complementary attributes.

Iterative Refinement in Machine Translation

The quest for non-autoregressive iterative refinement models has been a driving force in the machine translation field. Traditional non-autoregressive models predicted all tokens in parallel, but were marred by issues of multi-modality and coherence. Sequence-level distillation and latent transformers were early solutions, while subsequent advancements introduced novel training and decoding techniques. These efforts culminated in methods like CMLM, DisCo, and SMART, which bridged the gap between autoregressive and non-autoregressive approaches. The CDCD framework draws inspiration from these breakthroughs, aiming to enhance the iterative refinement process with a fusion of continuous and discrete dynamics.

Parameterization and Noise Schedule Optimization

A noteworthy contribution to the CDCD framework's evolution comes from Kingma et al. (2021), suggesting a strategy akin to time warping to optimize the noise schedule during training. Unlike the CDCD framework's objective of linearizing model prediction entropy, their approach aims to minimize diffusion loss variance. This distinction underscores the versatile nature of the diffusion paradigm, accommodating diverse optimization strategies for various objectives.


In an intricate interplay of continuous and discrete dynamics, the CDCD framework emerges as a beacon of innovation in the realm of generative modeling. With its fusion of theoretical underpinnings and practical prowess, the framework shines a light on the following key takeaways:

  1. Continuous-Discrete Synergy: The CDCD framework unites the worlds of continuous and discrete data generation, leveraging the strengths of both paradigms to create a harmonious and versatile approach.

  2. Diffusion Unveiled: By harnessing the power of continuous diffusion processes, the framework unearths a new dimension of data evolution, enabling a more intuitive representation of dynamic systems.

  3. Iterative Brilliance: Embracing iterative refinement, the CDCD framework transcends the limitations of traditional generative models, offering enhanced coherence, fluency, and convergence speed.

  4. Multifaceted Applications: Beyond its impact on language modeling, the CDCD framework's potential reverberates across diverse domains, from machine translation to healthcare, opening doors to transformative advancements.

  5. Architectural Elegance: With a carefully crafted architecture, the framework exemplifies the marriage of cutting-edge design choices, hyperparameter tuning, and sophisticated sampling strategies.

  6. Aesthetic of Balance: The CDCD framework encapsulates the delicate equilibrium between mathematical rigor and real-world applicability, enriching the generative modeling landscape.

As the CDCD framework forges a path toward the future, it invites researchers, practitioners, and enthusiasts to partake in its journey of discovery and innovation. With its profound ability to bridge continuous and discrete domains, the framework stands poised to redefine the boundaries of generative modeling, unraveling possibilities that were once deemed unattainable.

In this evolving landscape, the CDCD framework does not merely adapt; it transforms, transcends, and tantalizes. As we bid adieu to the confines of convention, we embark on a quest guided by the ever-evolving principles of the CDCD framework, embracing a future where continuous and discrete dynamics intertwine to paint a canvas of generative brilliance.


  1. Dieleman, S., Sartran, L., Roshannai, A., Savinov, N., Ganin, Y., Richemond, P. H., Doucet, A., Strudel, R., Dyer, C., Durkan, C., Hawthorne, C., Leblond, R., Grathwohl, W., & Adler, J. (2022). Continuous diffusion for categorical data. ArXiv. /abs/2211.15089

  2. Borsos et al., 2022; Brown et al., 2020; Dhariwal et al., 2020; Ho et al., 2022a; Ramesh et al., 2022; Sa- haria et al., 2022b

  3. Vaswani et al., 2017

  4. Ho et al., 2020; Sohl-Dickstein et al., 2015; Song and Ermon, 2019

  5. Saharia, W. Chan, S. Saxena, L. Li, J. Whang, E. Den- ton, S. K. S. Ghasemipour, B. K. Ayan, S. S. Mahdavi, R. G. Lopes, et al. Photorealistic text-to-image dif- fusion models with deep language understanding. arXiv preprint arXiv:2205.11487, 2022b.

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