What are Diffusion Models? Introduction and Applications

Diffusion models have revolutionized artificial intelligence by excelling at modeling complex data distributions and generating new samples for task-desired properties. With proven successful applications in reinforcement learning, computer vision, audio, and computational biology, diffusion models improve image data generation quality.

The article will discuss emerging applications and techniques behind the ground-breaking performance of the latent diffusion models. Additionally, our guide will expose the challenges of narrowing the gap between theories and practices in implementing this generative model.

What Are Diffusion Models?

Diffusion models, or score-based models/diffusion probabilistic models, are generative models used to recover data from the original dataset. The model constructs desired data samples through the learned denoising process based on a fixed procedure and high-dimensional latent variable. Basically, the denoising steps include a forward process and reverse process to transform the data point with random noise into an accurate and detailed output.

Besides computer vision tasks such as inpainting, super-resolution, or image denoising, diffusion models prove high-performing results in natural language processing, like text summarization and generation. Popular image-generating tools such as Stable Diffusion and DALL-E have combined diffusion models with other models to generate images from text-based prompts.     

How Do Diffusion Models Work?

Diffusion models work through a step-by-step process, including forward and reverse diffusion, data preprocessing, and model training. The following part will explain the dual-phase mechanism of diffusion models in detail throughout each step.

Step 1: Data Preprocessing

The implementation process initiates with formatting data used for model training to ensure proper scaling and centering. Specifically, the processing stages encompass cleaning data for outliers removal, organizing data entries (data normalization) for consistent feature scale, and generating new data for dataset diversity.

Data standardization also converts noisy image data into a distribution with a mean of zero and a variance of one. A meticulous data preprocessing practice ensures high-quality samples based on meaningful patterns generated from trusted training data.

Step 2: Forward Diffusion Process

The forward diffusion uses a sequence of reversible and incremental transformations to diffuse the initial sample from a normal distribution, typically a Gaussian distribution. Each step of the transformation process layers the sample on complexity by gradually adding Gaussian noise through a Markov chain.

A large size of step causes the data sample to lose its distinguishable features and is controlled by a variance schedule. The forward process enables the transformation of minimal information into diverse and realistic outputs matching the desired complex data distribution.

Step 3: Reverse Diffusion Process

The reverse process applies a sequence of inverse transformations to generate a simple distribution resembling the original data. Specifically, this process recognizes specific noise patterns added via each step of the forward process and denoise them accordingly by training the neural network. The reverse formula applied for all steps allows the model to predict specific characteristics, such as the data spread at each point in time or the average value.

What Are Diffusion Models’s Techniques?

Several critical mechanisms contribute to the performance of the latent diffusion model to generate complex data, including the stochastic differential equations, score-based generative models, and denoising diffusion probabilistic models:

• Denoising Diffusion Probabilistic Models (DDPMs): Denoising diffusion probabilistic models (DDPMs) refer to the data-denoising approach based on probabilities. This technique performs educated guesses by leveraging probabilities to infer the data’s original state before adding noise. Consequently, the output will be reconstructed accurately without noise and closely resemble the original data distribution.
• Score-Based Models (SGMs): Score-based generative modeling uses the score function to estimate the data’s probability density at any given point. It delivers crucial insights into local structural data to enable effective modeling of complex and high-dimensional data distributions.
• Stochastic Differential Equations (Score SDEs): The mathematical tools describe the system changes under deterministic and random forces over time. The detailed blueprint provided by this technique enables flexible work with different data types and applications to tailor for multiple generative tasks.

Benefits of Diffusion Models

Many industries take advantage of diffusion models thanks to the unique data processing approaches. Practically, this model aids gaming businesses in creating interacting elements, characters, and detailed environments. In the Architecture, Engineering, and Construction (AEC) industry, diffusion models can create high-quality visualizations, analyze the impact of natural light on building designs, and speed up the design process to help decision-makers visualize the proposed projects.

Regarding the marketing and e-commerce industries, diffusion models generate images with high-quality graphics and enhance visualization to personalize customer needs. Moreover, sophisticated medical images generated by diffusion models can help healthcare providers improve patient’s treatment and diagnosis. Meanwhile, businesses leverage this generative model in the fashion industry to experience unique and aesthetical pattern designs.

Generally, diffusion models prove the following excellent benefits:

• Image quality and consistency: Surpass traditional models like VAEs and GANs in generating high-resolution images. Indeed, high-quality images generated from this model possess a coherent structure with fine details and realistic textures.
• Stable training process: Ensures stability while mitigating mode collapse based on likelihood-based training.
• Effective handling of missing data: Effectively proceed with incomplete input data to generate coherent samples.
• Interpretable latent space: Integrate latent variables into reverse diffusion to enable fine-grained control over the image generation process with more insights into the data.
• Resilience to overfitting: The ability to create consistent and varied samples enables reverse diffusion to handle overfitting.
• Scalability in high-dimensional data: The step-by-step diffusion process effectively scales high-dimensional data with diverse and intricate datasets.

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4 Main Applications of Diffusion Models

Many artists leverage diffusion models to generate images from textual descriptions or abstract concepts. By integrating technologies in creating artwork, creators can increase the possibility of new styles and ideas that were difficult to achieve.

Graphic Design

Diffusion models support graphic designers in generating visual content from rough ideas. Indeed, the models can convert input images and layouts into high-quality generated images with an extensive range of possibilities. Additionally, diffusion models accelerate the design process by automating visualization ideas, including materials or object positioning, to create innovative and optimized solutions.   

Music and Sound Design

Generative diffusion models excel at creating unique auditory experiences by allowing artists to expand their creativity with music and sound design ideas. A paper by Mark Levy et al., accepted at the Diffusion Models workshop at NeurIPS 2023, mentioned that diffusion models can handle various music-producing tasks in 44.1kHz stereo audio through sampling-time guidance. The research experienced different scenarios to ensure the audio matched the surrounding context, encompassing smooth transitions between two tracks and inpainting, continuation, and regeneration of musical audio.    

Film and Animation

Diffusion models facilitate film and animation production by accelerating the process of creating characters, backgrounds, and other dynamic elements. While surpassing traditional production methods in the time and effort required, the diffusion process allows unlimited creativity in visual storytelling in diverse animation styles. A real-life example of applying this model in movie production is the first AI animation film, Seasons, which combines the effort of artists, AI, and several software tools.    

Media and Gaming Industry

Diffusion models have revolutionized users’ experience when interacting with digital products with various features. Indeed, the model converts digital assets like characters, objects, and surrounding environments into realistic and interactive elements that promote users’ imagination and engagement. As a result of granting players and viewers more freedom to interact with digital content, businesses grab more chances to advertise their brands and generate more conversions when using diffusion models.

Explore more applications of Generative AI for individuals and businesses in this blog: Top Applications of Generative AI for Individuals and Businesses  

Popular Diffusion Model Tools

Implementing diffusion models is accessible for beginners thanks to various user-friendly applications. Dall-E 3, MidJourney, or DreamStudio offer simple features for inpainting, outpainting, and conditional image generation with limited initial credits for free usage.

Here is the list of popular diffusion models to create sophisticated and realistic images:

• Dall-E by OpenAI: OpenAI has developed Dall-E to welcome all users to the innovative technology of diffusion models. Its intuitive user interface and powerful features in image generation contribute to its advantages in bringing diverse ideas to life. Artists and researchers can leverage this diffusion model’s versatile toolkit to seek inspiration or explore novel applications.
• Stable Diffusion: This standard diffusion model offers a two-stage training process to ensure remarkable success in multi-subject prompts, unconditional image generation, text-to-image synthesis, and super-resolution tasks. By overcoming the computational challenges regarding pixel space operation, Stable Diffusion can adapt to users’ scalability and quality requirements that align with their creativity.
• DreamStudio: Developed by Stability AI, DreamStudio mitigates the burden of infrastructure details as a simpler version of Stable Diffusion. This tool welcomes new users with 200 free credits and easy-to-use tools for high-quality image generation, inpainting, and outpainting.
• Sora: Sora, a user-friendly diffusion model, makes exploring cutting-edge modeling techniques accessible. It helps newcomers generate images and videos by handling high-dimensional datasets with ease.
• Midjourney: The exceptional capabilities in handling large and high-dimensional datasets highlight Midjourney as a game-changer diffusion model. Indeed, its reversible transformations are facilitated with optimized training and sampling algorithms to ensure accurate modeling. Moreover, this text-to-image generation tool satisfies users’ experience with an intuitive interface and extensive documentation to leverage its power for research and real-life applications.    
• Imagen: This diffusion model allows users to create high-performing images based on powerful transformer-based language models. Imagen performs excellently in understanding textual descriptions to encode them for image synthesis. Additionally, it enhances photorealism and image-text alignment using sizeable classifier-free guidance weights for guidance and control over image generation.

Challenges of Deploying Diffusion Models

Regardless of impressive generative capabilities, businesses encounter certain diffusion model limitations to scale effectively, including:

• Computational efficiency: The guide diffusion models can perform slower than GANs for sample generation due to their iterative nature.
• Complexity: Implementing diffusion models requires deep knowledge of foundational theories for customization and optimization.
• Facial distortion: The input prompt, which includes more than 3 subjects, can cause the generated image to be substantially distorted.
• Text generation issues: Diffusion models meet limitations in generating texts within images. Specifically, the model is not optimized to create exact-match texts from prompts and place them in the proper position, which is expected to improve in future versions.

Implementing Diffusion Models Effectively with Neurond AI Today

Neurond AI supports businesses in scaling effectively with optimized generative AI consulting services. Besides clarifying the meaning of diffusion models, our Generative AI consulting experts help strategize a roadmap to implement this model, combined with other generative models, to adapt to unique business needs. Contact us for detailed consultancies to understand your business use cases and an overview of the deployment process.