b) Diffusion Models

Diffusion Models

Diffusion models in the context of deep learning are a class of 'Generative Models' that generate new data samples by simulating a 'Diffusion process'.

Diffusion models are a class of generative models that have gained popularity for their ability to generate high-quality samples. They work by modeling the data generation process as a diffusion process, starting from a simple noise distribution and gradually refining it until it matches the data distribution.

Here are a few popular diffusion models:

i). Denoising Diffusion Probabilistic Models (DDPMs): Introduced by Sohl-Dickstein et al., DDPMs are a type of diffusion model where the noise is gradually added to the data to convert it into a simple prior distribution (like Gaussian noise), and the reverse process is learned as a denoising function.

ii). Score-Based Generative Models: These models, introduced by Song and Ermon, learn the score function of the data distribution, which can then be used with a Langevin dynamics sampler to generate new samples.

iii). WaveGrad: This is a diffusion model specifically designed for high-quality speech synthesis. It's based on DDPMs but with some modifications for better efficiency.

iv). Improved Denoising Diffusion Probabilistic Models (iDDPMs): This is an improvement on the original DDPM model which includes techniques for better sample quality and sampling efficiency.

Diffusion models are still an active area of research, with new techniques and improvements being proposed regularly.

In the context of image generation, the diffusion model starts with a random noise image and progressively refines it through a series of steps until it converges to the desired output image.

This process is guided by a learned denoising function that captures the structure and patterns of the target distribution (i.e., the type of images to generate). Examples of diffusion models include denoising score matching and denoising diffusion probabilistic models.

This process involves starting with a random noise sample and progressively refining it into a meaningful and realistic output, such as an image or text. The diffusion process is guided by a series of learned "noise schedules" that determine the level of noise added or removed at each step.

A recent example of a diffusion model is the "Denoising Diffusion Probabilistic Model" (DDPM), which has been used for tasks like image synthesis and text generation.

DDPMs learn to denoise data samples by training a neural network to predict the original data from progressively noisier versions of the data.

Qs? Is it correct to say that 'Diffusion Model' is not a network but a method/process of image processing?

Yes, it is correct. A "Diffusion Model," particularly in the context of the 'Denoising Diffusion Probabilistic Model' or DDPM we discussed earlier, is not a specific network architecture but rather a method or process for generative modeling, which can be applied to tasks like image synthesis, text generation, and more.

The diffusion model describes a process where data samples are progressively transformed from a random noise distribution to a data distribution through a series of learned noise schedules. The neural network, which can be any suitable architecture (such as a CNN for image synthesis), is trained to predict the original data from progressively noisier versions of the data.

In summary, the "Diffusion Model" is a method or process for generative modeling that can be applied to different tasks and used in conjunction with various neural network architectures, rather than being a specific network architecture itself.

What is the Diffusion process?

The name “diffusion” comes from the fact that the model starts with a high-entropy image (i.e., a random image with no structure) and then gradually diffuses the entropy away, making the image more structured and realistic.

Training Process:

Diffusion models are trained by starting with a random image and then gradually adding noise to it. The model is then asked to predict the original image from the noisy image. This process is repeated many times, with the noise being added in smaller and smaller increments each time. Eventually, the model learns to predict the original image with high accuracy. Basically It works by starting with a random image and then gradually adding detail to it until it matches the text prompt to generate images.

Generating Process:

Once the model is trained, it can be used to generate images by starting with a random image and then gradually adding detail to it. The model is able to do this by predicting the next step in the diffusion process. This process is repeated until the image matches the text prompt.

Diffusion models have been shown to be effective at generating realistic images. However, they can sometimes produce images that are blurry or have artifacts. Additionally, they can be slow to generate images, especially for complex prompts.

CLIP (Contrastive Language-Image Pre-training), a neural network model developed by OpenAI can be used with Diffusion models to improve the quality of the generated images. CLIP can be used to guide the diffusion model, ensuring that the generated image is both realistic and relevant to the text prompt. This can help to produce images that are sharper, more detailed, and more creative.

Overall, Diffusion models are a promising approach to text-to-image generation. They are relatively easy to train and can generate realistic images. However, they can sometimes produce blurry or low-quality images.