hieuristic/Tutorial_4
A Tutorial for Diffusion Models
This tutorial helps machine learning practitioners understand and implement diffusion models for generating data, specifically images. It guides users through the theoretical underpinnings of stochastic differential equations (SDEs) and their application to diffusion models, starting with simple synthetic datasets like the Swiss Roll and progressing to more complex image generation tasks. The input is theoretical knowledge and starter code, and the output is a working understanding and implementation of diffusion model components. This is ideal for researchers, students, or practitioners in AI and machine learning.
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Use this if you want a hands-on guide to building and understanding diffusion models, from foundational SDEs to practical image generation techniques like DDIM and inpainting.
Not ideal if you're looking for a ready-to-use diffusion model application or an academic paper on advanced diffusion model research.
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Jul 17, 2023
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