score_sde_pytorch
diffusion_models
score_sde_pytorch | diffusion_models | |
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4 | 4 | |
1,401 | 152 | |
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0.0 | 0.0 | |
over 1 year ago | almost 2 years ago | |
Jupyter Notebook | Jupyter Notebook | |
Apache License 2.0 | MIT License |
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score_sde_pytorch
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[D] score based vs. Diffusion models
there's an implementation of score-based models from the paper that showed how score based models and diffusion models are the same here: https://github.com/yang-song/score_sde_pytorch
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Machine learning and black box numerical solver[D]
Someone has already mentioned Neural Ordinary Differential Equations, which is also the first thing that came to mind. There are also extensions to it, where one can use PDEs(Neural Hamiltonian Flows) or even stochastic DEs(Score-Based Generative Models) in the model. All of them covering different but overlapping use cases.
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[Discussion] Could someone explain the math behind the number of distinct images that can be generated with a latent diffusion model?
I was considering an unconditional latent diffusion model, but for conditional models, the computation becomes much more complex (we might have to use bayes here). If we use Score-Based Generative Modeling (https://arxiv.org/abs/2011.13456), we could try to find and count all the unique local minima and saddle points, but it is not clear how we can do this...
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[D] Machine Learning - WAYR (What Are You Reading) - Week 138
You can find an implementation here: https://github.com/yang-song/score_sde_pytorch/blob/main/models/ddpm.py
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?
dpm-solver - Official code for "DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps" (Neurips 2022 Oral)
nlpaug - Data augmentation for NLP
Compositional-Visual-Generation-with-Composable-Diffusion-Models-PyTorch - [ECCV 2022] Compositional Generation using Diffusion Models
score_sde - Official code for Score-Based Generative Modeling through Stochastic Differential Equations (ICLR 2021, Oral)
DiffusionFastForward - DiffusionFastForward: a free course and experimental framework for diffusion-based generative models
Magic123 - [ICLR24] Official PyTorch Implementation of Magic123: One Image to High-Quality 3D Object Generation Using Both 2D and 3D Diffusion Priors
machine-learning-for-trading - Code for Machine Learning for Algorithmic Trading, 2nd edition.
Reinforcement-Learning - Learn Deep Reinforcement Learning in 60 days! Lectures & Code in Python. Reinforcement Learning + Deep Learning
Self-Attention-Guidance - Official implementation of the paper "Improving Sample Quality of Diffusion Models Using Self-Attention Guidance" (ICCV 2023)
computervision-recipes - Best Practices, code samples, and documentation for Computer Vision.