Better Aligning Text-to-Image Models with Human Preference. ICCV 2023

Stable Diffusion web UI

The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

An open-source project dedicated to tracking and segmenting any objects in videos, either automatically or interactively. The primary algorithms utilized include the Segment Anything Model (SAM) fo…

[NeurIPS 2023] ImageReward: Learning and Evaluating Human Preferences for Text-to-image Generation

Official Pytorch Implementation for "TokenFlow: Consistent Diffusion Features for Consistent Video Editing" presenting "TokenFlow"

[ICCV 2023] StableVideo: Text-driven Consistency-aware Diffusion Video Editing

Official PyTorch implementation of CoDeF: Content Deformation Fields for Temporally Consistent Video Processing

[ICCV 2023 Oral] "FateZero: Fusing Attentions for Zero-shot Text-based Video Editing"

[Arxiv 2023] Official pytorch implementation of "ControlVideo: Training-free Controllable Text-to-Video Generation"

A latent text-to-image diffusion model

Official implementation for "ControlVideo: Adding Conditional Control for One Shot Text-to-Video Editing"

ECCV2022 - Real-Time Intermediate Flow Estimation for Video Frame Interpolation

Text-to-Image Diffusion Models are Zero-Shot Video Generators

Let us control diffusion models!

WebUI extension for ControlNet

Elucidating the Design Space of Diffusion-Based Generative Models (EDM)

Official code for "DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps" (Neurips 2022 Oral)

Implementation of various DDPM papers to understand how they work

Denoising Diffusion Implicit Models

PyTorch implementation for Score-Based Generative Modeling through Stochastic Differential Equations (ICLR 2021, Oral)

Karras et al. (2022) diffusion models for PyTorch

Release for Improved Denoising Diffusion Probabilistic Models

Google Research

This may be the simplest implement of DDPM. You can directly run Main.py to train the UNet on CIFAR-10 dataset and see the amazing process of denoising.