MODELS

Diffusion Models: Denoising Diffusion Probabilistic Models (DDPM) and Image Generation

Diffusion models generate images by learning to reverse noise. Add noise to images, learn to denoise, then sample by denoising random noise. Enables stable, high-quality image generation.

Soham Sharma

AI Engineer, Botmartz · July 17, 2026 · 1 min read

◈ Open in Google Colab ⌥ View Code on GitHub

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1 min

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Diffusion Models: Denoising Diffusion Probabilistic Models (DDPM) and Image Generation

Forward Process Reverse Process (Denoising)Sampling Conclusion

Diffusion models generate images through an iterative denoising process. Add Gaussian noise to images gradually (forward process), then train a model to reverse this process (reverse process). Sampling starts with pure noise and denoises iteratively to generate new images.

Forward Process

Code Snippet

x_0 (real image)
  ↓ (add noise)
x_1 (slightly noisy)
  ↓ (add more noise)
x_2 (more noisy)
  ↓ ...
x_T (pure noise)

Reverse Process (Denoising)

Train a neural network to predict and remove noise:

Code Snippet

import torch
import torch.nn as nn

class DiffusionModel(nn.Module):
    def __init__(self):
        super().__init__()
        self.encoder = nn.Sequential(...)  # Image encoder
        self.decoder = nn.Sequential(...)  # Noise predictor
    
    def forward(self, x_t, t):
        # x_t: noisy image at step t
        # Predict and subtract noise
        noise_pred = self.decoder(self.encoder(x_t))
        x_{t-1} = (x_t - noise_pred) / sqrt(alpha_t)
        return x_{t-1}

Sampling

Code Snippet

Start with x_T ~ N(0, I) (pure noise)
For t = T, T-1, ..., 1:
    x_{t-1} = denoise_model(x_t, t)
Return x_0 (generated image)

Conclusion

Diffusion models are the foundation of modern image generation (DALL-E, Stable Diffusion). Understanding the forward/reverse processes explains how denoising generates realistic images. Next: video generation models that extend diffusion to temporal sequences.