Diffusion models have emerged as a powerful class of generative
models that revolutionise image synthesis. By iteratively adding and
subsequently removing noise from data, these models learn to generate
high-quality images with remarkable detail and realism. Models like Stable
Diffusion, leveraging U-Net architectures, demonstrate the efficacy of this
approach, producing impressive results in image generation tasks. While
potentially computationally more intensive than some other generative models,
diffusion models exhibit notable advantages, including enhanced stability and a
reduced propensity for mode collapse. The incorporation of positional encoding
further enhances their ability to generate high-quality images by enabling the
model to effectively process images at varying noise levels. Powerful,
open-source diffusion model like Stable diffusion runs efficiently on
consumer-grade hardware. It can generate photorealistic images from text
descriptions and offers additional capabilities like image-to-image style
transfer and upscaling. Stable Diffusion excels at transforming text prompts
into visually stunning images. The latest version, Stable Diffusion 3, further
enhances the model's ability to handle complex prompts and generate
high-quality images. Additionally, Stable Diffusion's outpainting feature
allows users to extend images beyond their original boundaries. This paper
explores the basic diffusion concepts in generative AI, the mathematical
formulation of forward and reverse diffusion, the structure of Denoising
Diffusion Probabilistic Models (DDPMs), stable diffusion frameworks, comparative
advantages of stable diffusion and other popular diffusion models (Eshratifar
et al. A. E. 2024, C., Wu, H. et al. 2024, Pan, X. et al. 2022, Yang, J. et al.
2024).
Author(s)
Details
Babychen
Kunnel Mathew
Shah and Anchor Kutchhi Engineering College, Mumbai-88, India.
Please see the book here:- https://doi.org/10.9734/bpi/erpra/v9/5911
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