tanelp/tiny-diffusion
A minimal PyTorch implementation of denoising diffusion probabilistic models trained on 2D point datasets.
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This repository provides a concise implementation of DDPM-style diffusion models for 2D datasets, designed as a learning resource rather than a production tool. It includes both the forward diffusion process (gradually adding noise) and the reverse denoising process (learning to recover data distributions). The README documents ablation studies on hyperparameters including learning rate, model size, number of timesteps, and variance schedules, with visualizations showing how each affects the quality of generated 2D point distributions.
Frequently asked
- What is tanelp/tiny-diffusion?
- A minimal PyTorch implementation of denoising diffusion probabilistic models trained on 2D point datasets.
- Is tiny-diffusion open source?
- Yes — tanelp/tiny-diffusion is an open-source project tracked on heatdrop.
- What language is tiny-diffusion written in?
- tanelp/tiny-diffusion is primarily written in Jupyter Notebook.
- How popular is tiny-diffusion?
- tanelp/tiny-diffusion has 1k stars on GitHub.
- Where can I find tiny-diffusion?
- tanelp/tiny-diffusion is on GitHub at https://github.com/tanelp/tiny-diffusion.