Blogs / Diffusion Models in AI: Revolution in Image and Video Generation

Diffusion Models in AI: Revolution in Image and Video Generation

مدل‌های انتشار در هوش مصنوعی: انقلاب در تولید تصویر و ویدیو

Introduction

Imagine typing a simple sentence and having it generate an incredibly realistic or artistically creative image. This is no longer science fiction; Diffusion Models have made this miracle possible. These models, among the most advanced deep learning technologies, have sparked a revolution in generative AI.
From popular tools like Midjourney and Stable Diffusion to advanced video generation systems like Sora, all leverage the power of diffusion models. But how exactly do these models work? Why have they become so successful? And what makes them different from Generative Adversarial Networks (GANs)?
In this comprehensive article, we'll explore diffusion models in depth, examining everything from mathematical foundations and operational mechanisms to practical applications and the future of this technology.

What Are Diffusion Models?

Diffusion Models are a type of generative machine learning model inspired by thermodynamics physics and the diffusion process that generate new data. These models operate in a two-stage process that closely resembles the physical process of particle diffusion in an environment.
In the first stage, called the Forward Process, Gaussian noise is gradually added to the original image until it eventually becomes pure noise. This process involves thousands of small steps, with a specific amount of noise added at each stage. Imagine you have a clear image of a red rose, and you slowly spray random colored dots on it until you can no longer recognize the rose and only colored noise remains.
The second stage, called the Reverse Process, is the magic that enables image generation. The model learns to reverse this process, starting from pure noise and gradually removing noise until it reaches a clear and meaningful image. It's like the model learning how to reconstruct the original image from a completely corrupted one.

History and Evolution of Diffusion Models

Diffusion models have roots in research from 2015, but their real growth has occurred in recent years. In 2015, researchers introduced the concept of Diffusion Probabilistic Models based on statistical physics and Markov processes. This early research laid the theoretical foundations for these models, but they didn't yet have widespread practical applications.
The real turning point came in 2020 with the publication of DDPM (Denoising Diffusion Probabilistic Models). This paper demonstrated that diffusion models could achieve quality similar to or even better than GANs, while enjoying greater training stability. This discovery opened a new window in the world of generative AI.
From 2021 to 2022, we witnessed an explosion of practical applications for this technology. OpenAI's introduction of DALL-E 2 showed that diffusion models could create amazing images from complex textual descriptions. Then Stable Diffusion was released as open-source, providing public access to this technology. Simultaneously, tools like Midjourney emerged, demonstrating that this technology could produce professional results even in the hands of ordinary users.
From 2023 onwards, this technology moved beyond image generation and expanded into other areas such as video, audio, and 3D model generation. Tools like Sora, Kling AI, and Google Veo3 demonstrated that the same principles could be used to generate realistic videos. Additionally, integration with large language models made control and guidance of these models more precise and user-friendly.

Technical Architecture of Diffusion Models

To understand more deeply how these models work, we need to examine their architectural details. The heart of diffusion models is typically a U-Net network specifically designed for image denoising. This architecture gets its name from the U-shape of its graphical representation and consists of three main parts.
The first part, called the Encoder Path, is responsible for gradually reducing image dimensions and extracting its features. Along this path, the image gradually becomes smaller but the number of feature channels increases. This allows the model to move from surface details toward more abstract concepts.
At the center of this architecture is the Bottleneck, which is the deepest layer of the network and stores the most compressed representation of image information. This part is key to the model's conceptual understanding of the image.
The third part, the Decoder Path, has the task of reconstructing the image at its original dimensions from this compressed representation. But the interesting point is that this reconstruction isn't simply reversing the encoder process. Using Skip Connections, information from encoder layers is directly transferred to corresponding decoder layers. These shortcut connections help the model preserve fine image details.
One key innovation in diffusion models is adding Time Embedding. The model needs to know which stage of the denoising process it's at, because noise levels differ across stages. In early stages with high noise, the model should make coarser changes, but in final stages when the image is nearly clear, it should focus on fine details.
Using Attention Mechanisms is another important feature, inspired by Transformer models and added to diffusion models. Attention mechanisms allow the model to focus on important parts of the image and generate better details. This mechanism works similarly to how humans pay attention to images, naturally focusing on the most important parts.
For text-to-image generation, Conditioning techniques are used. In this method, textual features extracted from language models are combined with image features. The Cross-Attention technique allows the model to establish connections between text words and different parts of the image. Also, the Classifier-Free Guidance method is used, helping the model generate images that better align with the input text.

Types of Diffusion Models

Diffusion models have evolved over time, and various types have been developed. DDPM or Denoising Diffusion Probabilistic Models is the base and original model using Markov chains. This model is very accurate but its main weakness is the slowness of the generation process, as it requires performing thousands of denoising steps.
To solve the speed problem, DDIM or Denoising Diffusion Implicit Models was developed. This faster version can generate quality images with far fewer steps, for example 50 steps instead of 1000. This reduction in steps multiplies generation speed several times without significant quality loss.
Latent Diffusion Models were one of the most important advances. Instead of working directly on image pixels, these models operate in a more compressed space called Latent Space. Stable Diffusion, one of the most popular models, uses this approach, which is why it's much more efficient and can run even on consumer graphics cards.
Another type called Cascaded Diffusion Models uses multiple models in a chain. First, one model generates a low-resolution image, then subsequent models gradually add details and improve quality. OpenAI's DALL-E 2 uses this method and produces extraordinary results.

Comparing Diffusion Models with GANs

One important question is why diffusion models have replaced Generative Adversarial Networks (GANs). In terms of quality, diffusion models have been able to produce very high-quality and stable results, while GANs, although capable of creating excellent images, sometimes suffer from instability.
Diversity is one of the great advantages of Diffusion Models. GANs sometimes suffer from a problem called Mode Collapse, where the model only generates limited types of images, but diffusion models can have very high diversity in their outputs.
Training GANs is a major challenge because they require precise balance between the Generator and Discriminator. If one becomes stronger, the entire training process collapses. But diffusion models have a more stable and simpler training process and don't need this delicate balance.
In terms of generation speed, GANs have an advantage and can create images in a fraction of a second, while diffusion models typically take several seconds. However, this speed difference is decreasing, and new methods for accelerating diffusion models have been developed.
Controllability is one of the main reasons for the popularity of diffusion models. Through prompt engineering and Conditioning techniques, very precise control over generated images is possible, while in GANs this control is more limited.

Feature Diffusion Models GANs
Quality Very high and stable Excellent but unstable
Diversity Very high diversity May have Mode Collapse
Training Stable and easier Difficult, needs Generator-Discriminator balance
Generation Speed Slower (seconds) Faster (fraction of second)
Controllability Highly controllable More limited
Computational Resources High Medium to high

Practical Applications of Diffusion Models

The most popular application of diffusion models is Text-to-Image Generation. This capability has been implemented in tools like Stable Diffusion, DALL-E 3, Midjourney, and Flux AI. Users can create their desired images by writing detailed descriptions. This capability is very useful for designers, artists, marketers, and even ordinary people who want to visualize their ideas.
Image Editing is another powerful application. The Inpainting technique allows you to delete parts of an image and the model intelligently fills that section with appropriate content. Outpainting, conversely, extends the image beyond its original boundaries and creates its logical continuation. The Image-to-Image capability also allows you to convert one image to another style, for example turning an ordinary photo into an oil painting. Tools like Nano Banana from Google provide these capabilities.
Video Generation is one of the most exciting recent developments. Diffusion models can now generate realistic, high-quality videos from textual descriptions. Sora from OpenAI, Kling AI, and Google Veo3 are examples of these tools that can create videos ranging from several seconds to several minutes long. This technology has the potential to massively transform the cinema, advertising, and content production industries.
In the field of 3D Content Generation, diffusion models are used to create 3D models, textures, and even virtual reality environments. This application is very valuable for the game development, architecture, and industrial design industries.
In Medicine and Life Sciences, this technology has amazing applications. Synthetic medical images can be generated for training students and doctors without violating patient privacy. It's also used in predicting protein structures and simulating biological tissues. The connection of this technology with AI in diagnosis and treatment is expanding.
For Design and Architecture, diffusion models have provided a powerful tool for generating concept designs, creating architectural renders, and product design. Architects can receive various designs just by describing their idea and then select the best one.
The impact of this technology on Art and Creativity has been profound. As examined in the article on the impact of AI on art and creativity, these tools allow artists to visualize their ideas faster and experiment with different styles.

Challenges and Limitations of Diffusion Models

Despite their amazing capabilities, diffusion models face several challenges. High Computational Cost is one of the most important limitations. Training these models requires powerful graphics cards like professional GPUs or TPUs, very high RAM memory, and long training time. Even running these models for image generation requires relatively powerful hardware, although recent advances have reduced this requirement.
Precise Control is still one of the remaining challenges. Despite significant advances in prompt engineering, precise control of specific details like exact finger count, specific facial expressions, or precise object placement in scenes remains challenging. Sometimes the model presents its own interpretation of the input text, which may differ from the user's intent.
Ethical Issues are a serious concern. This technology can be used to generate fake content (Deepfakes) that poses security and social risks. Also, the issue of violating artists' copyright is raised, as these models have been trained on millions of existing images and may imitate artists' styles without permission. The risk of misuse for malicious purposes like generating inappropriate or misleading content also exists. These issues fall within the broader framework of ethics in artificial intelligence and require regulations and technical solutions for control.
Bias and Uniformity is another problem models may face. If training data has cultural or social biases, the model learns these biases and reproduces them in its outputs. Also, uniformity is sometimes seen in generating people from different races and cultures.
Hallucination, similar to hallucination in language models, occurs when the model generates unreasonable or unrealistic details, such as extra fingers, incorrect physics, or illogical combinations of objects. This problem is gradually improving but hasn't been completely solved yet.

Optimization and Acceleration Techniques

To overcome the problems of low speed and high computational cost, various techniques have been developed. Latent Space Diffusion is one of the most effective methods. Instead of working directly on high-resolution images, we first convert the image to a more compressed space, perform denoising operations in that space, and then convert the result back to a high-resolution image. This multiplies speed several times without significant quality loss.
Progressive Distillation is another approach where smaller models are trained to mimic the behavior of large models but work much faster. It's like a talented student learning from a skilled teacher and being able to do the same work faster.
Consistency Models are one of the recent advances with a different approach. Instead of performing thousands of denoising steps, these models can generate quality images with one or a few steps. This creates a major transformation in speed.
Using Quantization and Pruning techniques is also very effective for reducing model size. Using methods like LoRA, models can be made smaller without significant performance loss. This allows these models to run on more ordinary hardware.
Parallel Sampling is another technique where multiple denoising process steps are executed simultaneously. Using the parallel computational power of GPUs, overall generation time can be reduced.

Training Diffusion Models

For those who want to train these models themselves or work with them, specific technical knowledge is required. Familiarity with the Python programming language is essential, as most tools and libraries work with Python. You should also be familiar with deep learning frameworks like TensorFlow or PyTorch.
Knowledge of scientific libraries like NumPy for working with arrays and numerical computations is also important. Understanding basic concepts of neural networks and deep learning is also a prerequisite.
The training process starts with data collection and processing. You need a large dataset of quality images. The more diverse and larger this dataset, the more powerful your model will be. Then you must define the appropriate U-Net architecture, including the number of layers, filter sizes, and other parameters.
Setting the Noise Schedule is one of the important steps. You must determine how much noise to add or remove at each stage. This setting has a major impact on final quality. Then the training process is performed on GPUs or TPUs, which can take days or even weeks. You can use Google Colab or Cloud services.
Finally, the Fine-tuning stage is performed, where you optimize the model on specific data or for a specific application. This stage is usually faster than initial training and improves results.
For practical work with these models, various tools and frameworks are available. Hugging Face Diffusers is a comprehensive and user-friendly library that supports most types of diffusion models and makes using them very simple. Stable Diffusion WebUI is a popular graphical interface that allows non-technical users to easily work with these models.
ComfyUI is an advanced node-based interface that gives very precise control over the generation process and is suitable for professional users. AUTOMATIC1111 is also a powerful WebUI with many extensions that has an active developer community and constantly adds new features.

Future of Diffusion Models

Diffusion models are rapidly evolving, and multiple research directions are being pursued. One main goal is increasing speed. Researchers are working on methods to reduce generation time to milliseconds so these models can be used in real-time applications.
More Precise Control is also one of the research priorities. In the future, we expect to be able to control every detail of the image precisely, from exact facial expressions to the spatial position of every object in the scene.
Multimodal Models, meaning models that can work seamlessly with text, image, audio, and video, are the future of this technology. Models like Gemini and GPT-4 have shown how powerful this integration is. Complete combination of these capabilities with diffusion models could create entirely new experiences.
Better Efficiency, meaning developing smaller models with better performance, is another goal. Inspired by small language models (SLM), researchers are working on smaller diffusion models that can run on mobile devices and limited hardware.
Real-time Generation is a long-term goal where images can be generated immediately without delay. This capability could have amazing applications in games, interactive applications, and virtual reality.
In terms of emerging applications, Personalized Content Generation for marketing and advertising is growing. Companies can generate unique visual content for each customer that aligns with their interests and tastes. This topic is linked with using AI tools in financial analysis and digital marketing.
Training and Simulation is another expanding application. Realistic virtual training environments can be created where students can practice without the dangers of the real world. This topic has also been discussed in the impact of AI on the education industry.
In Game Development, this technology can enable automatic asset and environment generation. Imagine a game where environments are generated dynamically based on player needs. This topic is related to creating video games with AI.
Fashion Design is another industry that can benefit from this technology. Designers can generate hundreds of different clothing and accessory designs and select the best ones or even create personalized designs for each customer.
In Architecture and Urban Planning, modeling and designing urban spaces can be done much faster and more accurately with these tools. Architects can immediately visualize their conception of a building or public space and see people's reactions.
Of course, there are challenges ahead. Regulating laws and regulations for using this technology is necessary to prevent misuse while not suppressing innovation. Protecting intellectual property rights of artists and content creators must be considered. Solutions like Watermarking and tracking image sources can help.
Preventing misuse requires developing fake content detection tools and content authentication systems. Also, ensuring AI trustworthiness in the digital age is critically important.

Connection with Emerging Technologies

Diffusion models are being combined with other advanced technologies. One of the most exciting areas is combination with Quantum Computing. Quantum computing can dramatically increase the speed of training and running these models, and quantum artificial intelligence has a bright future.
Combination with Blockchain and Cryptocurrencies is also being explored. AI in blockchain can help preserve digital rights and prove ownership of generated works.
Integration with Internet of Things (IoT) also has high potential. AI and IoT integration can enable smart devices to generate personalized visual content.
Edge AI is also an important area. Local processing with Edge AI means running diffusion models on local devices without needing to send data to servers, which has privacy and speed advantages.
Using RAG (Retrieval-Augmented Generation) can increase the accuracy and controllability of these models. The complete RAG guide shows how specific information can be incorporated into the generation process.
The connection with Metaverse is also significant. AI transformation in virtual worlds can create entirely new experiences where virtual environments are generated dynamically.

Conclusion

Diffusion Models are undoubtedly one of the most important innovations of the last decade in artificial intelligence. This technology has not only brought visual content generation quality to an unprecedented level but has also opened doors to countless applications across various industries.
From generating stunning artwork to medical and scientific applications, from architectural design to creating cinematic videos, this technology is fundamentally changing how we interact with digital content. The ability to convert a simple idea into a realistic or artistic visual work is a power that seemed imaginative just a few years ago.
With continuous advances in speed, quality, and controllability, we can expect diffusion models to play an even more central role in the future of AI and the future of work. This technology is not just a tool for creativity but can help solve complex problems in science, medicine, engineering, and many other fields.
For those who want to work in this field, now is the best time to learn and experiment. With open-source tools like Stable Diffusion and abundant educational resources available, access to this advanced technology has never been easier. Whether you want to be a digital artist, a scientific researcher, or a software developer, this technology provides powerful tools at your disposal.
Of course, we shouldn't ignore the ethical and social challenges of this technology. The responsibility for proper and ethical use of these tools rests with all of us. We must ensure this technology is used to improve human lives, not to deceive or harm them.
The future of diffusion models is bright and exciting. With continued research and development, we can expect to see more advances that we perhaps can't even imagine today. This technology is becoming one of the main pillars of generative AI and will play a key role in shaping our digital future.