Compositional-Visual-Generation-with-Composable-Diffusion-Models-PyTorch
diffusion_models
| Compositional-Visual-Generation-with-Composable-Diffusion-Models-PyTorch | diffusion_models | |
|---|---|---|
| 2 | 4 | |
| 431 | 152 | |
| 1.2% | 0.0% | |
| 3.8 | 0.0 | |
| 11 months ago | almost 2 years ago | |
| Jupyter Notebook | Jupyter Notebook | |
| GNU General Public License v3.0 or later | MIT License |
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Compositional-Visual-Generation-with-Composable-Diffusion-Models-PyTorch
- Help with Composable-Diffusion
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Compositional Diffusion
Composable Diffusion
diffusion_models
- [Variational and Diffusion Methods] Minimal standalone example of diffusion model
- A minimal standalone example of diffusion model
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(unguided) Sampling from a diffusion model
I'm trying to figure out a way to reproduce the sampling method used in the DDPM model arxiv link. The codebase link is roughly here for the original model and for improved DDPMs here. There is also implementations recently posted to r/MachineLearning such as this one (check the reverse process section) and finally this last one.
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[P] Hands on diffusion models
A minimal example of the forward and reverse flow of diffusion models with equations from the paper and visualizations alongside the code: https://github.com/InFoCusp/diffusion_models I coded it up since I wanted to familiarize myself with rhe end to end flow. It uses a simple 2d dataset that can train within minutes. Hope others on this subreddit find it useful.
What are some alternatives?
gansformer - Generative Adversarial Transformers
nlpaug - Data augmentation for NLP
stable-diffusion-compositional
score_sde_pytorch - PyTorch implementation for Score-Based Generative Modeling through Stochastic Differential Equations (ICLR 2021, Oral)
Catlab.jl - A framework for applied category theory in the Julia language
DiffusionFastForward - DiffusionFastForward: a free course and experimental framework for diffusion-based generative models
Awesome-Diffusion-Models - A collection of resources and papers on Diffusion Models
machine-learning-for-trading - Code for Machine Learning for Algorithmic Trading, 2nd edition.
score_sde - Official code for Score-Based Generative Modeling through Stochastic Differential Equations (ICLR 2021, Oral)
Reinforcement-Learning - Learn Deep Reinforcement Learning in 60 days! Lectures & Code in Python. Reinforcement Learning + Deep Learning
computervision-recipes - Best Practices, code samples, and documentation for Computer Vision.