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Introduction to Diffusion Model in AI

Introduction to Diffusion model in AI




Diffusion models lie in the domain of Computer Vision. Diffusion is the model of deep learning that deals with latent or hidden variables in an image by adding or removing noise. The diffusion model operates by iteratively adding noise to an image and then attempting to reverse this process to reconstruct the original image. Diffusion models have been used for tasks such as image generation, denoising, and inpainting, and they have shown promising results in producing high-quality and diverse samples.

• Noise

Unwanted information that disrupts the accuracy of the picture.This noise disrupts the clarity and fidelity of the image, making it more challenging for the Artificial Intelligence model to accurately process or reconstruct the original image. 

  •       Stable Diffusion
Stable diffusion is a type of Diffusion model used to generate text to image. This approach involves using diffusion techniques to generate images, particularly in the context of text-to-image generation tasks. Stable diffusion methods aim to create high-quality and coherent images by gradually introducing and refining details in the image generation process. 

Latent Diffusion Model (LDM)

In the beginning, Stable diffusion used but, it is very expensive and more computational. Therefore, Latent Diffusion Models are made. LDM converts the picture into latent(hidden)  representation (vector representation) due to which its computations decrease, and then apply diffusion model concept on it. LDM is 48 times faster than Diffusion.This approach enables faster and more scalable training and inference compared to traditional diffusion models, which operate directly on pixel-level data.

Two types of Diffusion process 

1. Forward Diffusion Process 

In this process, noise is added into the image continuously.

2. Reverse/ Backward Diffusion Process

In this process, noise is removed from the image until it gets the original image.  

Two ways of adding noise into the picture. 

1. By unit time

In this method, noise is incrementally introduced into the image over time. This gradual addition of noise allows for finer control over the level of distortion applied to the image.

2. By CoSin

This method involves adding noise to the picture using a specific mathematical function, typically involving cosine transformations. By applying this function, noise can be introduced in a controlled manner to achieve desired effects or levels of distortion in the image.

Some popular AI image generation tools 

• DALL·E 2

Created by OpenAI, DALL·E 2 is an impressive image generator that can indulge your wildest imagination. It produces great results and allows you to expand on AI-generated images with additional prompts. You can even add AI elements to your real-life photos using DALL·E 2.

• Midjourney

Midjourney offers excellent AI image results. It’s a great platform for creative exploration and collaboration with other users. 

• DreamStudio (Stable Diffusion)

 DreamStudio provides customization and control over your AI images. It allows you to fine-tune your generated images according to your preferences. 

  • Here are some generic prompt with generated AI images examples. Use these prompts and change only highlighted words or phrases to your desired output from AI tools. 
“ Prompt:Create an image of natural view with hand drawing with waterfall  lightly colorful for kids
Negative Prompt:Blurriness“



“ A future car with features like transparent outer body, open doors and lavish seats. An advanced engine on inner body which can be seen from outside of the car. The car is standing for the presentation “





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