Diffusion_Model

A diffusion model is a type of generative model that gradually denoises data starting from pure noise to generate realistic samples, such as images. It has gained popularity due to its high-quality output in image synthesis tasks. 

Libraries and Setup

python

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!pip install -q -U einops datasets matplotlib tqdm

import torch

import torch.nn as nn

import matplotlib.pyplot as plt

from einops import rearrange

from tqdm.auto import tqdm

These libraries are essential for:

• Building the neural network (torch)
  
  

• Visualizing results (matplotlib)
  
  

• Structuring tensors (einops)
  
  

• Progress display (tqdm)

2. Helper Functions
Functions like exists(), default() are utilities for model flexibility and clean code.

3. Neural Network Components

• Residual: A block that adds the input back after applying a function (skip connection).

• Upsample and Downsample: Used to change the resolution of feature maps during generation.

4. Diffusion Model Structure
The model follows a UNet-like architecture and includes:

• Time embedding

• Noise schedule

• Training loop to minimize denoising error

5. Sampling and Visualization
Once trained, the model can:

• Start from random noise

• Iteratively denoise to generate an image

• Show intermediate steps using matplotlib
  
  

Input image :

Downsampled Input

The image is resized to a lower resolution (128×128) for training and input.

Noise Input

This is the starting point — random noise used by the diffusion model in the reverse denoising process.