Understanding Diffusion Models via Code Execution
This provides researchers with a clearer, implementation-first understanding of diffusion models, addressing a gap in existing tutorials, but it is incremental as it focuses on educational clarification rather than advancing the models themselves.
The authors tackled the difficulty in bridging the theoretical foundations of diffusion models with practical implementations by providing a concise code example of about 300 lines that explains diffusion models from a code-execution perspective, including essential components like forward diffusion and training loops.
Diffusion models have achieved remarkable performance in generative modeling, yet their theoretical foundations are often intricate, and the gap between mathematical formulations in papers and practical open-source implementations can be difficult to bridge. Existing tutorials primarily focus on deriving equations, offering limited guidance on how diffusion models actually operate in code. To address this, we present a concise implementation of approximately 300 lines that explains diffusion models from a code-execution perspective. Our minimal example preserves the essential components -- including forward diffusion, reverse sampling, the noise-prediction network, and the training loop -- while removing unnecessary engineering details. This technical report aims to provide researchers with a clear, implementation-first understanding of how diffusion models work in practice and how code and theory correspond. Our code and pre-trained models are available at: https://github.com/disanda/GM/tree/main/DDPM-DDIM-ClassifierFree.