Open-source alternatives to PatchDiffusion Pytorch

"Applying Regularized Schrödinger-Bridge-Based Stochastic Process in Generative Modeling"

AAAI23 Oral Official implementations of Video Implicit Diffusion Models

CVPR 2024: Residual Denoising Diffusion Models

GENIE: Higher-Order Denoising Diffusion Solvers

Official code for "Conditional Generation from Unconditional Diffusion Models using Denoiser Representations" (BMVC 2023)

Code for Fast Training of Diffusion Models with Masked Transformers

Official implementation of Cold-Diffusion for different transformations in pytorch.

Speed up Stable Diffusion with this one simple trick!

Official implementation for Estimating the Optimal Covariance with Imperfect Mean in Diffusion Probabilistic Models (ICML 2022), and a reimplementation of Analytic-DPM: an Analytic Estimate of the Optimal Reverse Variance in Diffusion Probabilistic Models (ICLR 2022)

Official implementation of "DreamPose: Fashion Image-to-Video Synthesis via Stable Diffusion"

Elucidating The Design Space of Classifier-Guided Diffusion Generation

Implementation of MagViT2 Tokenizer in Pytorch

DiT is a latent diffusion model and transformer-based generative AI framework implemented in PyTorch. It functions as a class-conditional image generator that replaces traditional convolutional backbones with a transformer architecture to synthesize high-fidelity images. The project utilizes patch-based latent processing and latent space compression to operate on low-dimensional image representations. It incorporates class-conditional guidance and adjustable guidance scales to control the visual content of generated images during the sampling process. The framework covers distributed model t

ICML 2023 official implementation for "Input Perturbation Reduces Exposure Bias in Diffusion Models"

PyTorch implementation for "Parallel Sampling of Diffusion Models", NeurIPS 2023 Spotlight

Implementation of Particle Guidance: non-I.I.D. Diverse Sampling with Diffusion Models

ICLR2024 The official implementation of paper "VDT: General-purpose Video Diffusion Transformers via Mask Modeling", by Haoyu Lu, Guoxing Yang, Nanyi Fei, Yuqi Huo, Zhiwu Lu, Ping Luo, Mingyu Ding.