Implementation of Denoising Diffusion Probabilistic Model in Pytorch
-
Updated
Oct 9, 2024 - Python
Implementation of Denoising Diffusion Probabilistic Model in Pytorch
Code for the paper "Jukebox: A Generative Model for Music"
Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.
This repository is a curated collection of links to various courses and resources about Artificial Intelligence (AI)
Collection of generative models in Tensorflow
Simplest working implementation of Stylegan2, state of the art generative adversarial network, in Pytorch. Enabling everyone to experience disentanglement
[CVPR2020] Adversarial Latent Autoencoders
Synthetic data generation for tabular data
[ICLR 2024] Official implementation of DreamCraft3D: Hierarchical 3D Generation with Bootstrapped Diffusion Prior
[ICLR 2025] From anything to mesh like human artists. Official impl. of "MeshAnything: Artist-Created Mesh Generation with Autoregressive Transformers"
Implementation of papers in 100 lines of code.
Official implementation of the RAVE model: a Realtime Audio Variational autoEncoder
[NeurIPS 2023] ImageReward: Learning and Evaluating Human Preferences for Text-to-image Generation
Research Framework for easy and efficient training of GANs based on Pytorch
[CVPR 2025 Oral]Infinity ∞ : Scaling Bitwise AutoRegressive Modeling for High-Resolution Image Synthesis
This repository contains the code for the CVPR 2021 paper "GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fields"
High-fidelity performance metrics for generative models in PyTorch
KakaoBrain KoGPT (Korean Generative Pre-trained Transformer)
Official JAX implementation of MAGVIT: Masked Generative Video Transformer
[ICLR 2024 Spotlight] SyncDreamer: Generating Multiview-consistent Images from a Single-view Image
Add a description, image, and links to the generative-model topic page so that developers can more easily learn about it.
To associate your repository with the generative-model topic, visit your repo's landing page and select "manage topics."