In this blog post, I will explain the difference between two types of generative models: LLM (Large language model) and diffusion model. Generative models are AI models that can create new data samples, such as images, text, audio, or video, based on some input or prior distribution. These models are useful for many applications, such as web3, NFTs, games, art, and more.
What is an LLM (Large language model)?
An LLM (Large language model) is a type of deep learning model that can understand and generate natural language in a human-like fashion. An LLM is trained in a large corpus of text data, such as books, articles, websites, social media posts, etc., and learns to predict the next word or token given some context. For example, given the sentence “The sky is”, an LLM might predict “blue” or “cloudy” as the next word.
An LLM can also be given a text prompt and produce a coherent and relevant text output. Given the prompt “Write a summary of this blog post,” an LLM could come up with something like: “This blog post explains the difference between LLM (Large language model) and diffusion model, two types of generative models that can generate new data samples based on some input or prior distribution.” The author illustrates how LLMs can interpret and generate natural language from enormous volumes of text data, and likewise how diffusion models can generate realistic and multifaceted images by reversing a diffusion process. The author evaluates the benefits and limitations of both approaches and provides instances of text-to-image generation using these techniques.”
LLMs have the advantage of being able to do a wide range of natural language tasks without the need for fine-tuning or task-specific training. An LLM, for example, can use different prompts to answer questions, write summaries, construct poems, create conversations, translate languages, and so on. As a result, LLMs are extremely versatile and powerful tools for natural language processing.
What is a diffusion model?
A diffusion model is a type of generative model that works by reversing a diffusion process. A diffusion process is a random process that transforms data into noise gradually by adding small perturbations at each step. For example, imagine starting with an image of a cat and applying a diffusion process to it. At each step, you add some random noise to the image, making it more blurry and less recognizable. After many steps, you end up with pure noise.
A diffusion model learns to do the opposite: it starts with noise and gradually removes it to generate a realistic data sample. It does this by learning a series of transition functions that map noise to data at each step. For example, imagine starting with pure noise and applying a diffusion model to it. At each step, you remove some noise from the image, making it more clear and more recognizable. After many steps, you end up with an image of a cat.
A diffusion model can be trained by using a large dataset of real data samples as targets and applying a diffusion process to them to create noisy inputs. Then, the model learns to reconstruct the targets from the inputs by minimizing a reconstruction loss.
One of the advantages of diffusion models is that they can generate high-quality and diverse samples with fewer parameters than other generative models, such as GANs or VAEs. They are also more stable and easier to train than GANs.
How do they differ?
The main difference between LLMs and diffusion models is that LLMs work on natural language data, while diffusion models work on image data. LLMs use large corpora of text data to learn the structure and semantics of natural language, while diffusion models use large datasets of image data to learn the appearance and variation of visual objects.
This difference has several implications for their performance and applications:
- LLMs can perform various natural language tasks without requiring any fine-tuning or task-specific training, while diffusion models can only perform image generation tasks.
- LLMs can generate coherent and relevant text outputs given any text prompt, while diffusion models can only generate realistic and diverse image outputs given any noise input.
- LLMs can handle complex linguistic phenomena such as syntax, semantics, pragmatics, etc., while diffusion models can handle complex visual phenomena such as texture, shape, color, etc.
- LLMs are more suitable for tasks that require natural language understanding and generation, such as question answering, summarization, translation, etc., while diffusion models are more suitable for tasks that require image synthesis and manipulation, such as style transfer, super-resolution, inpainting, etc.
How does GAN work?
GAN (Generative Adversarial Network) is another type of generative model that can create new data samples based on some input or prior distribution. GAN consists of two components: a generator and a discriminator. The generator tries to generate realistic data samples that can fool the discriminator, while the discriminator tries to distinguish between real and fake data samples. The generator and the discriminator are trained in an adversarial manner, where they compete against each other to improve their performance.
For example, imagine a GAN that can generate images of faces. The generator takes a random vector as input and outputs an image of a face. The discriminator takes an image of a face as input and outputs a probability of whether the image is real or fake. The generator and the discriminator are trained by using a large dataset of real images of faces as targets. The generator tries to generate images that look like real faces and can deceive the discriminator, while the discriminator tries to correctly classify the images as real or fake.
One of the advantages of GANs is that they can generate realistic and diverse samples with high resolution and quality. They can also learn the latent features and attributes of the data, such as style, pose, expression, etc., and manipulate them to generate novel and creative samples.
Examples
To illustrate the difference between LLMs, diffusion models, and GANs, let’s look at some examples of text-to-image generation using these models.
Text-to-image generation is a task where the model takes a text prompt as input and generates an image that matches the description. For example, given the prompt “a blue car on a green field”, the model should generate an image of a blue car on a green field.
Here are some examples of text-to-image generation using diffusion models, taken from this paper:
As you can see, Diffusion models can generate realistic and diverse images that capture the appearance and variation of the objects well, but they may struggle with some prompts that require spatial or common-sense reasoning. GANs can generate realistic and diverse images with high resolution and quality, but they may produce some artifacts or inconsistencies.
Conclusion
In this blog post, I explained the difference between LLM (Large language model), diffusion model, and GAN (Generative Adversarial Network), three types of generative models that can create new data samples based on some input or prior distribution. I also showed some examples of text-to-image generation using these models.
I hope you learned something new and enjoyed reading this blog post. If you have any questions or feedback, please leave a comment below. Thank you for reading!
