ACL'2023: DiffusionBERT: Improving Generative Masked Language Models with Diffusion Models

Update-to-data resources for conditional content generation, including human motion generation, image or video generation and editing.

This is the official implementation of our neural-network-based fast diffuse room impulse response generator (FAST-RIR) for generating room impulse responses (RIRs) for a given acoustic environment.

Few-Shot Diffusion Models

Code for "Optimal Transport-Guided Conditional Score-Based Diffusion Model (NeurIPS, 8,7,7,6)"

Repository for the paper: 'Diffusion-based Conditional ECG Generation with Structured State Space Models'

Official Implementation of KnobGen: Controlling the Sophistication of Artwork in Sketch-Based Diffusion Models

[NeurIPS 2023] VPP: Efficient Conditional 3D Generation via Voxel-Point Progressive Representation

The code for the NeurIPS 2021 paper "A Unified View of cGANs with and without Classifiers".

Controllable Face Generation via pretrained Conditional Adversarial Latent Autoencoder (ALAE)

Official PyTorch implementation of "Stochastic Conditional Diffusion Models for Robust Semantic Image Synthesis" (ICML 2024).

TRGAN: A Time-Dependent Generative Adversarial Network for Synthetic Transactional Data Generation

Forward-backward conditional sampling

[ICLR 2022] Denoising Likelihood Score Matching for Conditional Score-based Data Generation

Code for the paper "FAME: Fragment-based Conditional Molecular Generation for Phenotypic Drug Discovery", published on SDM 2022.

A PyTorch implementation of various deep generative models, including Diffusion (DDPM), GAN, cGAN, and VAE.

[ICLR 2022] Toy Experiments for Denoising Likelihood Score Matching for Conditional Score-based Data Generation

TRGAN: A Time-Dependent Generative Adversarial Network for Synthetic Transactional Data Generation

A partial pytorch implementation of "Latent Constraints: Learning to Generate Conditionally from Unconditional Generative Models" for practice

Chinese couplet generation with transformer and simple transformer-based variants.