Denoising Diffusion

Denoising diffusion has recently emerged as the foremost method in the field of generative modeling. It stands as a formidable contender against other generative models, including Generative Adversarial Networks (GANs). Furthermore, these models underpin remarkable AI products such as DALL-E 2 and Imagen. While diffusion models enjoy robust theoretical underpinnings, their theoretical foundation offers only limited practical insights. In this tutorial, we will delve into the practical elements that drive these models and establish a common framework to determine the optimal practices for various choices.

At its core, the concept of diffusion models revolves around initiating with random noise and passing it through a denoiser, typically implemented as a Convolutional Neural Network (CNN). Through a series of iterative steps, ranging from 50 to 2000 iterations, this process gradually eliminates noise, ultimately revealing a random image hidden beneath the initial noise

Before delving into practical considerations, it is essential to explore the foundational principles behind the formulation of diffusion models, which are typically structured using ordinary or stochastic differential equations. A significant portion of this section draws upon the insights from the paper titled Score-Based Generative Modeling through Stochastic Differential Equations.

To illustrate the underlying concepts, let’s consider a simplified 1D toy example that mirrors the essential processes involved when dealing with real images. Real image data resides in a high-dimensional space of pixel values. For instance, specifying a megapixel image requires a million pixel values. In this case, we are visualizing it in 1D, even though our dataset is one-dimensional, the analogy remains valid.

On the vertical axis, we represent pixel values, with different images corresponding to distinct points in this vertical dimension. The horizontal axis denotes time, and we incrementally increase the noise level over time. The core idea here is that we have a dataset comprising samples, and our objective is to train or formulate a model capable of generating additional examples that resemble the dataset. The dataset is illustrated by the bimodal density in red within this toy example.

Consider drawing a sample from this dataset, hypothetically representing an image from it. If we imagine this dataset as a collection of pictures of cats and dogs, we can use this scenario as a stepping stone toward understanding the denoising diffusion approach. Specifically, we explore what transpires when we gradually introduce noise to this image.

In this example, the process corresponds to taking a random walk within the pixel value space. The image gradually deteriorates until it transforms into pure white noise. When multiple samples are involved, they collectively converge toward this indistinguishable white noise distribution.

Analyzing these trajectories reveals the emergence of a density in the XT plane. If we were to visualize this density, it would appear as follows:

On the far-left end of the spectrum, we have the original probability density of the data. As time progresses, this density becomes gradually diffused and blurred, ultimately leading to a state where, after a sufficient duration, all data converges to a normally distributed, effectively indistinguishable white noise.

The fundamental concept behind denoising diffusion methods is that the distribution at this final stage is easy to sample from. These methods enable the reverse journey in time, retracing the path back to the original distribution from a random sample. This process effectively implements the gradual denoising of the image.

This yields a random sample from the data distribution. Similarly, when dealing with multiple samples, they collectively approach the data distribution at time zero.

We can understand this formulation in terms of Stochastic Differential Equations (SDE). The simplest SDE that describes the forward approach, where noise is added, states that over an infinitesimal time interval, the change in the image is simply a random white noise:

()\[d\mathbf{x} = d\omega\]

When we take small steps, a small amount of white noise is added at each step, causing the image to evolve randomly over time. Although there are more general versions of this equation, we will keep it simple for now. Importantly, there exists a reverse version of the same SDE, crucial to the entire diffusion framework:

()\[d\mathbf{x} = - \nabla_{\mathbf{x}} \log p_t(\mathbf{x})dt + d\omega_{\mathbf{x}}\]

This SDE introduces a stochastic component, but additionally, it includes a deterministic drift component related to the density of the data at any given time and its gradient. In essence, this term attracts the point toward the data’s location at each time step. This well-known function is referred to as the score function and possesses the valuable property that it can be evaluated through denoising:

()\[\left( D(\mathbf{x}; \sigma) - \mathbf{x} \right) / \sigma^2\]

If we have an optimal \(L_2\) denoiser, denoted as \(D\), then this equation allows us to evaluate the term. Notably, this eliminates the need to have access to the generally intractable density function \(p\); instead, having some form of denoiser suffices. In practice, this is often approximated using a Convolutional Neural Network (CNN). In essence, this is where the CNN is integrated into these models.

Song et al. 2021 introduced an alternative approach to formulate diffusion models as deterministic Ordinary Differential Equations (ODEs):

()\[d\mathbf{x} = - \frac{1}{2} \nabla_{\mathbf{x}} \log p_t(\mathbf{x})dt\]

This formulation solely consists of a score-based term and does not involve a random walk component.

Qualitatively, the evolution of the image follows a distinct pattern. Rather than randomly perturbing the image, it exhibits a gradual transition from noise to the clean image. Armed with this insight, we can overlay these ideal trajectories on top of the density. These trajectories are known as flow curves, and they represent the solutions to the ODE. In practice, the ODE is solved through discretization in time. Given an initial sample, we take finite-length time steps that aim to follow these trajectories.

Zooming in, the process involves determining how much the image should change over a time interval \(dx\) for a given change in time \(dt\). These steps are repeated until time zero is reached, resulting in the generated sample.

In SDEs, noise is also injected at each of these steps, but we’ll explore that aspect later. For now, let’s focus on analyzing the ODE formulation as it provides valuable insights into the dynamics of this stepping procedure.

This concludes the background on previous works related to ODEs/SDEs for diffusion in a nutshell. While the theory is illuminating, it also imposes certain constraints on how the ODE or SDE must operate to recover the correct distribution. However, many questions and design choices remain, such as those related to sampling, stochasticity, and network training. These questions include considerations like the choice of ODE/PDE solver, step length, the need for stochasticity, the amount of noise to inject, scaling signals, predicting signals or noise, and determining loss weighting.

In the remainder of this tutorial, we will address these questions based on insights from the EDM Paper: Elucidating the Design Space of Diffusion-Based Generative Models. The approach taken in this paper involves dissecting key previous works to understand their components and design choices, particularly focus on Score-Based Generative Modeling through Stochastic Differential Equations, Denoising Diffusion Probabilistic Models, and Improved Denoising Diffusion Probabilistic Models.

The former work presents the Variance Preserving (VP) and Variance Exploding (VE) methods, which are fundamentally different in terms of architectures, stepping schedules, training methods, and more. The latter two are the DDPM and iDDPM methods. By analyzing these methods and their specific design choices related to sampling, preconditioning, training, and more, we aim to construct a comprehensive table of best practices for each of these design choices.

The ultimate goal is to develop a table where each entry represents an optimized design choice, drawing from the theory presented in the paper. It’s important to note that all these design choices are independent and can be studied in isolation.

To assess the generation quality, the Fréchet Inception Distance (FID) is a widely used metric in generative modeling. The remainder of this tutorial will be divided into two parts: Part 1 covering deterministic sampling and Part 2 exploring preconditioning and training. The detailed examination of neural network architectures for diffusion models falls outside the scope of this tutorial. Let’s begin with deterministic sampling!

In this section, we aim to understand the best approach for formulating and solving the ODE when we have a pre-trained neural network denoiser. The central challenge here is the presence of discretization error. The discrete trajectory obtained through discrete sampling deviates from the ideal trajectory.

As illustrated, the discrete samples do not precisely align with the ideal trajectory, leading to generated samples that are incorrectly distributed in practice. This can result in visually poor samples or incorrect variations. The most straightforward solution to address this discretization error is to use shorter steps.

However, it is desirable to minimize the number of steps since each step involves network evaluations, which constitute the primary computational cost in this sampling process. Examining previous works, we observe that the step sizes used are not of the same length. Generally, longer steps are employed at high noise levels, while relatively shorter steps are used at low noise levels.

This concept can be generalized into a family of polynomial step length discretizations where the step lengths follow a polynomial curve. This encapsulates the notion of squeezing and stretching towards time zero. It allows for empirical studies by conducting a grid search over the exponents to determine what provides the best results. With this approach, we can fill in the first row of our table. While the specific formulas may appear complex, in the EDM formula, a polynomial progression with the optimal exponent set to seven is found to be a broad optimum. The precise value doesn’t significantly impact the results, as anything in the ballpark of five to ten works well.

The next step, which is fairly intuitive to those familiar with ODEs, is to employ higher-order solvers. The basic Euler scheme mentioned earlier is somewhat simplistic, as it follows the trajectory locally. However, when there’s significant curvature in the trajectory, the steps tend to deviate. This issue can be mitigated by using higher-order methods that incorporate substepping strategies. Extra steps are taken to correct the naive steps, but this increases computational expenses. EDM’s evaluation has led to the conclusion that the second-order Heun method strikes an excellent balance between computational cost and accuracy. The Heun step is formulated as follows:

After performing the Euler step, an additional step is taken at the point where it landed. However, this additional step is then moved back to the original position, and the actual step is calculated as the average of the original Euler step and this correction step. This approach closely follows the underlying trajectory and adds another entry to our table:

Now, let’s examine another design choice, which is the noise schedule. There are various noise schedules available in the literature. For instance, this corresponds to the straightforward variance-exploding scheme that adds noise at a constant rate:

()\[\sigma(t) = \sqrt{(t)}\]

As we add noise at a constant rate, the variance grows linearly over time, which implies that the standard deviation increases as the square root of time. We are particularly interested in the standard deviation as a function of time.

This schedule also corresponds to DDIM:

()\[\sigma(t) = t\]

Here, the standard deviation itself increases at a constant rate, resulting in different trajectories with varying noise levels.

Another approach is to scale the distributions, such as the variance-preserving schedules that introduce a scaling term that changes over time. The goal is to constrain the noisy data density to remain within a horizontal tunnel of unit standard deviation as time progresses:

There are numerous options for scaling and noise schedules, but fundamentally, they all represent warps of the XT plane. These schedules do not alter the underlying densities but rather distort the trajectories in different ways:

Given these observations, EDM has formulated a generalized ODE, expressed as follows:

()\[d\mathbf{x} = \left[ \dot{s}(t) / s(t) - {s(t)}^2 \dot{\sigma}(t) \sigma(t) \nabla_{\mathbf{x}} \log p(\mathbf{x}/s(t);\sigma(t)) \right] dt\]

The key point is that EDM parameterizes this ODE in terms of the sigma and scale functions, which define the desired noise levels at different time instances and the appropriate scaling. This approach provides a clear representation of the trajectory shapes. However, it raises questions about whether all these choices are equally effective.

The question then becomes, are some schedules better than others, and if so, what makes them better? To explore this, let’s consider higher noise levels, where we introduce a substantial amount of noise:

In this scenario, we interpolate between different schedules and examine the relationship between the ideal continuous trajectory and the tangent approximations. We observe that for certain schedules, the approximation is particularly accurate, while for others, it is less precise. Notably, the linear schedule, where the standard deviation grows linearly with time and no scaling function is used, stands out as an excellent choice. This schedule shares similarities with DDIM and offers several advantages.

In the linear schedule, ideal trajectories become linear straight lines pointing toward the data as the noise level increases. Another interpretation is that if we shoot a tangent all the way toward time zero from our sample, it will land at the point corresponding to the denoiser’s output, which is essentially a blurred version of the output image. It represents the average of all possible images compatible with the current noisy image, and we can expect this to change slowly over time:

As we step toward time zero, only minor corrections to the tangent’s direction are needed because it remains almost the same image throughout. As a result, fewer steps are required in practice because the tangent remains nearly correct over time. This is the rationale behind EDM’s recommendation that the standard deviation should grow linearly with time. These insights contribute to the completion of the last two rows in the sampling portion of the EDM table:

In the next part, we will delve into the preconditioning and training of these models, specifically focusing on addressing questions related to handling vastly different scales of noisy and noise-free data. For instance, when adding noise to clean data, the noisy data may have a standard deviation much larger than the original data, and both types of data need to be fed into the same network. This requires careful consideration of scaling.

Another critical question is what the network should be trained to predict: the signal or the noise. Additionally, there are questions about how training efforts should be allocated, including whether to train for many epochs at low noise levels and how to weight the losses. Concerns about the network potentially memorizing the dataset also need to be addressed through augmentation.

To recap and elaborate on denoising score matching, the ODE presented in the previous section essentially indicates that at each step, the image is modified by moving it toward higher or lower data density at the current noise level. A scale factor determines the rate of this movement:

()\[d\mathbf{x} = - \dot{\sigma}(t) \sigma(t) \nabla_{\mathbf{x}} \log p_t(\mathbf{x}; \sigma(t))dt\]

The score function, which provides the direction toward the data, can be evaluated without direct access to the density function \(p\) by using the denoiser. The denoiser is expected to be \(L_2\) optimal and is typically approximated using a CNN. The training of the denoiser involves taking a random training sample and introducing noise of a random strength. The noisy image is then passed through the denoiser, which typically contains CNN layers. The output is compared to the clean image using a mean squared loss.

However, there is significant flexibility in how the internals of the denoiser are implemented. To handle vastly different signal scales, input and output weighting is applied to normalize the signal magnitudes. Specifically, for the inputs, the magnitude of the noisy input image is scaled to a standardized size, often chosen to be a unit standard deviation, with knowledge of the noise level. Neglecting this scaling can lead to unreasonable results in many cases. Similarly, at the output, scaling is applied to ensure that the CNN layers output values with unit standard deviation. The idea is not to convert the output to a unit standard deviation but to set the task of the CNN in a way that it always deals with standardized inputs and outputs.

There is also the aspect of loss weighting. Each time the loss is evaluated, the penalty of the loss can be scaled to modulate the magnitude of the gradient feedback reaching the network layers during training. This can be set to the reciprocal of the output weight to normalize the backpropagation gradients at initialization. We will examine this in more detail later.

The network can explicitly learn to predict the noise-free image or the noise itself. In the latter case, the predicted noise can be subtracted from the input to obtain the denoised image. This can be achieved through a skip connection, where the CNN’s task implicitly becomes predicting the differential required for noise subtraction. This effectively means that the CNN is tasked with predicting the noise. The weighting of the skip connection can also be adjusted. If the weight is set to zero, the skip connection is disabled, and only the noise is predicted. This weighting can be further generalized to represent intermediate mixtures of the image and the noise, for example.

Now let’s address the open question regarding the skip weight. This weight is closely related to the output scaling because both need to be adjusted in a way that is compatible with the skip weight and the constraint that the network must predict a unit standard deviation output.

This forms a system of two unknowns: the skip weight and output weight, with the constraint that the output must have a unit standard deviation. Ideally, we would like another constraint to narrow down the choices for these two weights. To do that, we can focus on the output of the network, which contributes to the denoising result. Any errors it makes will be amplified by the output scaling and affect the sampling trajectory directly.

The second constraint, therefore, should be related to the skip weight. Ideally, the skip weight should be set in a way that minimizes the output weight. This ensures that any errors made by the CNN are downweighted, rather than upweighted. Let’s gain some practical intuition about what this means.

At very low noise levels, the inputs to the denoiser consist mostly of the signal, and the noise is a very tiny portion. In this scenario, it makes sense to let the clean image pass through the skip connection rather than having the CNN predict it. Instead, the focus should be on the challenging task of correcting the small amount of noise. If errors are made here, they will be downweighted since the noise is small.

Conversely, at high noise levels, the input to the denoiser is mostly uninformative about the clean image. In this case, we should not rely on the skip connection at all and should override it with the CNN’s prediction, which means setting the skip to zero to predict the signal.

With this understanding, we can smoothly transition between noise-level-dependent curves that go from one (predicting noise) to zero (predicting the signal). This transition can be represented as an explicit formula, and we can now fill in the rows for skip scaling, output scaling, and input scaling in the design table.

Now, let’s address the question of at which noise levels the network should be trained, which is also related to loss weighting. There are many different noise levels in the samples, and backpropagating the loss from each of these noise levels can lead to situations where the gradient feedback is imbalanced. To equalize these gradient magnitudes, loss weighting is used.

The role of loss weighting is to push the solution with a roughly constant magnitude of loss, independent of the noise level. The sampling frequency, then, specifies at which noise levels the network should be trained more. This is an empirical choice. The EDM paper provides a helpful figure with the noise level on the horizontal axis and the loss value on the vertical axis.

The loss value is initially set to one at initialization using loss weighting. Over time, the loss at different noise levels converges towards a curve that expresses where training progress can be made. We cannot make much progress at the very low noise levels or at very high noise levels, but there’s a regime in the middle where progress can be achieved. Training progress should be made proportional to the shape of this distribution of training samples.

This concludes the last part of the tutorial. In summary, we have presented a tutorial on EDM’s modular design of diffusion models. Training, sampling, and network architectures are not tightly coupled, but rather consist of a selection of different individual design choices that can and should be evaluated in isolation. By carefully considering this design, we can significantly improve results, even transforming existing methods with average performance into highly effective ones.

  • Mohammad Amin Nabian, NVIDIA

  • Miika Aittala, NVIDIA

A special expression of gratitude goes out to the authors of EDM for generously contributing the content used in this tutorial:

  • Tero Karras, NVIDIA

  • Miika Aittala, NVIDIA

  • Timo Aila, NVIDIA

  • Samuli Laine, NVIDIA

© Copyright 2023, NVIDIA Modulus Team. Last updated on Jan 25, 2024.