Diffusion Models in AI

Diffusion Models

Introduction

Diffusion models are generative models that create new data such as images, audio, or video. They learn how to remove noise step by step. This process produces clean samples that match the training distribution. This guide explains the workflow, key ideas, and simple examples.

Diffusion models هما generative models لي كيبداو بالنويز و كيحيدوه شوية بشوية حتى كيخرجو image ولا audio واضح.

Core Concepts Explained

The model learns how data turns into noise and then learns how to reverse the process. During generation, it starts from random noise and denoises it across steps. This multi step pipeline builds high quality outputs.

الموديل كيتعلم كيفاش data كتولي noise و من بعد كيفاش يرجع noise ل output واضح.

How Diffusion Models Work

1. Forward Process

The model adds noise to training samples for many steps. After enough steps, samples become pure noise. This forward diffusion stays fixed.

2. Reverse Process

The model learns how to remove noise at each step. It predicts cleaner versions until reaching a full sample.

3. Sampling

The generation starts from noise. The model applies reverse steps until it forms the final output.

Why Diffusion Models Work

Stable training
Fine grain detail learning
Outputs aligned with the training distribution

Popular Diffusion Architectures

1. DDPM

Denoising Diffusion Probabilistic Model. The base architecture for many diffusion systems.

2. DDIM

Uses fewer steps for faster sampling while keeping strong output quality.

3. Latent Diffusion Models

Run diffusion inside a compressed latent space. This speeds up training and sampling.

Applications

Image generation
Image editing
Super resolution
Audio and speech generation
Video synthesis

Strengths of Diffusion Models

Stable learning
High quality samples
Flexible architectures

Limitations

Sampling slow
Heavy compute
Many steps needed

Improving Diffusion Models

Use latent space compression
Use fast samplers
Use optimized noise schedules

Syntax or Model Structure Example

This example shows a small PyTorch structure used inside diffusion models.

import torch
import torch.nn as nn

class SimpleDenoiser(nn.Module):
    def __init__(self):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(256, 512),
            nn.ReLU(),
            nn.Linear(512, 256)
        )

    def forward(self, x):
        return self.net(x)

model = SimpleDenoiser()
x = torch.randn(1, 256)
y = model(x)
print(y.shape)

هادا مثال بسيط كيورّي structure ديال denoiser ف diffusion models.