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Definition of Diffusion models
What are diffusion models?
Diffusion models are a type of generative AI that creates new data (such as images, audio, or text) by gradually reversing a noising process. During training, the model learns to take clean data and slowly add noise until it turns into random static.
To make this process more efficient, researchers developed latent diffusion models. Instead of applying diffusion directly to raw data like pixels, they first compress the data into a smaller "latent space." Diffusion happens there, and the result is then decoded back into the full image. This makes training and generation much faster while still keeping high fidelity
How do diffusion models work?
Diffusion models generate new data by learning to reverse a gradual noising process. During training, they take real data (like an image) and repeatedly add random noise until it becomes pure static. The model then learns how to undo this process step by step, reconstructing the original data distribution.
Once trained, the model can start with nothing but random noise and denoise it incrementally until a coherent, high-quality image appears. Instead of trying to create an image in one leap, the model makes small, manageable adjustments, each removing a little noise. This step-by-step denoising is what makes diffusion models powerful and stable.
The process has three main stages:
- Forward diffusion — A clean image is gradually turned into pure noise.
- Reverse diffusion — The model learns to reverse each step of this process.
- Generation — Starting from random noise, the model applies the learned reverse steps to produce a realistic output.
What is the difference between diffusion and generative models?
Generative models are a broad family of machine learning methods designed to create new data that resembles the training data. They include many approaches, such as Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), autoregressive models (like GPT for text), and diffusion models. In short, "generative model" is the category.
Diffusion models are one specific type of generative model. They work by gradually adding noise to data during training, then learning to reverse that process step by step. At generation time, they start with random noise and denoise it until a realistic sample emerges (image, audio, or video).
In summary, all diffusion models are generative models, but not all generative models are diffusion models. Generative models describe what the goal is, while diffusion models describe how one approach achieves it.
Are diffusion models better than transformers?
Neither diffusion models nor transformers are better. They are different types of generative models with strengths in different areas.
Diffusion models are great at creating high-quality, detailed images. They've become the go-to approach for AI art because their step-by-step denoising process produces realistic textures and fine details. The tradeoff is that they can be slow and require a lot of computation.
Transformers, on the other hand, are best at working with sequences such as text, code, and audio. They power models like GPT for language and MusicLM for music, and they generate results quickly and efficiently once trained.
What are the key advantages and disadvantages of diffusion models?
Diffusion models are among the most powerful generative AI methods today, but they come with both strengths and limitations.
✅ Advantages
- High-quality, detailed outputs – Diffusion models generate some of the most realistic and fine-grained results among generative AI methods. They can capture textures, shadows, and details that often surpass GANs or VAEs in visual fidelity.
- Stable training process – Unlike GANs, which can suffer from instability and mode collapse (when the model produces only a narrow set of outputs), diffusion models rely on a gradual denoising process that is easier to optimize and consistently converges to reliable results.
- Ability to capture diversity – Because they learn by reconstructing data from noise, diffusion models are better at reflecting the full variety within training data. This makes them strong in creative fields, where producing many different styles and patterns is essential.
- Strong scalability – The more data and computational power available, the better diffusion models perform. With larger datasets and models, they continue to improve in output quality.
❌ Disadvantages
- Slow generation speed – Producing results requires many sequential denoising steps, which makes inference slower.
- High computational cost – Training and running diffusion models demand significant GPU resources, memory, and energy. This limits accessibility and makes them more practical for organizations with large-scale infrastructure.
- Dependence on large datasets – To achieve high-quality, generalizable results, diffusion models require vast amounts of diverse training data. With smaller or biased datasets, outputs tend to lose quality, diversity, and fairness.
- Potential artifacts and biases – Despite their strengths, diffusion models are not flawless. They can still generate visual artifacts, inconsistencies, or unrealistic details, and like all AI models, they may reproduce biases present in the training data.
Key Takeaways
- Diffusion models are a type of generative AI that create new data by learning to reverse a noising process.
- Instead of producing an image or sound in one step, they gradually denoise random static until a realistic output emerges, which makes them powerful and stable. A more efficient variant, latent diffusion models, moves this process into a compressed latent space, speeding up training and generation while preserving quality.
- As part of the broader family of generative models, diffusion models focus on how to generate data, while generative models as a whole define the goal of producing new samples.
- Compared with transformers, diffusion models excel at creating high-quality, detailed images, while transformers dominate in handling sequences like text, code, and audio.
- The main advantage of diffusion models is their ability to generate highly detailed, realistic, and diverse outputs with stable training, making them the foundation of modern image generation.
- Their key drawbacks are that they can be slow, computationally demanding, and dependent on large datasets, sometimes still producing artifacts or biased results.
