Projects sharing features with Diffusion

This is a classifier-guided diffusion framework for high-fidelity image generation. It implements a cascaded diffusion pipeline that chains a base diffusion model with a dedicated upsampler to progressively increase image resolution in stages, and uses classifier-guided diffusion sampling to steer the reverse diffusion process toward higher-quality outputs. The framework provides tools for training diffusion models from scratch using distributed processes with gradient accumulation, as well as training classifier models that provide gradient-based guidance during sampling. It supports both un

This is a PyTorch deep learning framework and tool for human motion synthesis that generates 3D character animations from text prompts or action descriptions. It functions as a text-to-motion generator that converts natural language and categorical labels into temporally consistent 3D skeletal movement sequences. The system utilizes a transformer-based diffusion model to iteratively denoise motion data. It includes capabilities for action-conditioned generation, monocular-to-3D motion lifting, and motion sequence editing using text constraints. The framework incorporates geometric motion con

This project is a generative adversarial network implementation and research framework. It provides the tools and hyperparameters necessary to train and evaluate generative models across various datasets, specifically designed to reproduce results from academic research. The framework includes a Parzen density likelihood estimator to calculate model log likelihood. This allows for the quantitative evaluation of generative distributions and the measurement of overall model performance. The codebase covers machine learning research capabilities, focusing on the training of adversarial networks

Implementation of Denoising Diffusion Probabilistic Model in Pytorch

Tiny Universe is an educational monorepo that delivers multiple independent implementations of core AI subsystems as self-contained Jupyter notebooks. It provides from-scratch constructions of foundational architectures including a complete Transformer model built from the original paper specification, a denoising diffusion probabilistic model for image generation, and a ReAct-style autonomous agent framework that equips an LLM with tools for planning and multi-step task execution. The project distinguishes itself by covering the full lifecycle of modern AI systems through hands-on implementa

This is a PyTorch-based implementation of diffusion models for synthesizing photorealistic images and video. It provides a framework for text-to-image and text-to-video generation, as well as unconditional image synthesis. The system utilizes a cascading diffusion pipeline to produce high-resolution imagery by passing low-resolution outputs through a sequence of super-resolution models. It also includes capabilities for image inpainting, allowing the reconstruction of masked or missing regions of visual media guided by surrounding context and text prompts. The project includes tools for diff

Kolors is a generative model implementation for synthesizing photorealistic images from natural language descriptions and visual references. It utilizes a latent diffusion model framework to produce high-fidelity imagery, operating within a compressed latent space to improve generation efficiency and quality. The system functions as a multilingual image generator, interpreting text prompts in multiple languages to produce semantically accurate visual outputs. It includes a custom model training pipeline that uses low-rank adaptation to teach the model specific subjects or artistic styles from

This project is a comprehensive collection of educational examples and reference implementations for building vision and language models using PyTorch. It serves as a deep learning tutorial covering the end-to-end process of developing neural networks, from initial architecture definition to final production deployment. The repository provides detailed guides on implementing a wide range of domain-specific models, including convolutional neural networks for object detection and segmentation, as well as transformer and recurrent architectures for natural language processing. It emphasizes gene

This is a structured deep learning curriculum for programmers, delivered as a collection of Jupyter notebooks. It teaches the fundamentals of training neural networks for computer vision, natural language processing, tabular data analysis, and collaborative filtering using PyTorch and the fastai library. The course is designed to be hands-on, guiding learners from building a training loop from scratch to fine-tuning pretrained models for a variety of practical tasks. The curriculum distinguishes itself by covering the full lifecycle of a deep learning project, from data preparation and augmen

TRELLIS.2 is a generative image-to-3D system that creates high-resolution 3D assets with physically based rendering materials from 2D images. It utilizes a sparse voxel representation to handle complex topologies and internal structures without relying on iso-surface fields. The project features a structured latent space representation that maps geometry and texture attributes to maintain visual fidelity. It employs an optimization-free geometry reconstruction process to decode latent representations directly into voxel grids and includes a PBR texture generator for synthesizing base color, r

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

This project is a diffusion model framework for training and sampling from denoising probabilistic models to generate images from noise. It functions as a generative image model that creates visual content by iteratively refining random noise into coherent images. The system includes a distributed GPU trainer designed to scale complex neural network architectures across multiple graphics processing units. It also provides an image dataset preprocessor to prepare, scale, and standardize raw image collections for training. The framework covers model training and image generation, utilizing noi

Spleeter is an AI audio source separation library and deep learning toolkit designed to split mixed music files into individual audio stems, such as vocals and drums. It provides a suite of pretrained models for isolating different instruments and voices from a recording. The toolkit includes capabilities for training and evaluating custom audio separation models using labeled datasets and configuration files. It also features utilities for measuring model performance by comparing separation outputs against reference datasets. The system manages audio processing through spectral representati

IDM-VTON is an AI virtual try-on framework and fashion synthesis tool designed to generate realistic images of people wearing specific garments. It operates as a diffusion-based image generator that blends garment textures with human poses to create synthetic fashion imagery. The system implements virtual fitting room capabilities through a generative model that combines person and clothing inputs. It includes a web-based interface to run interactive visual demonstrations and synthesize try-on images in real-time. The framework covers the broader domain of AI fashion visualization, enabling

This is a generative AI model library containing a collection of PyTorch and TensorFlow implementations for creating synthetic data and modeling complex probability distributions. It serves as a multi-framework repository of deep learning models designed for learning and replicating data patterns. The project provides specialized implementation suites for several generative architectures. This includes Generative Adversarial Networks using competing generator and discriminator models, Variational Autoencoder frameworks that map data to a latent space, and Restricted Boltzmann Machine and Deep

Lit is a machine learning interpretability framework and model debugging tool designed to analyze model behavior and performance. It serves as an interpretability dashboard for large language models and a general performance analyzer for text, image, and tabular datasets. The project distinguishes itself through a comprehensive suite of interpretability tools, including salience map generation for feature attribution, the creation of synthetic and counterfactual examples to test robustness, and the projection of high-dimensional embeddings into visual spaces via UMAP or PCA. It further enable

Magenta is an AI creative suite and TensorFlow generative art framework used to train and deploy models for the production of artistic media. It functions as a generative music library and a deep learning art generator, providing tools to automate the creation of original musical compositions and visual artwork. The project covers AI music composition and generative visual art through neural art generation and machine learning creativity. It enables the training of generative models to produce original songs, images, and drawings based on learned patterns.

CycleGAN is a generative adversarial network framework designed for unpaired image-to-image translation. It enables the conversion of images between two distinct visual domains using datasets that do not require direct one-to-one matching examples. The project implements a deep learning style transfer tool capable of artistic style transfer, object transfiguration, and domain-to-domain conversion. It uses a dual-generator architecture and cycle-consistency loss to ensure that images translated to a target domain and back recover their original state. The framework covers core machine learnin

ParlAI is a conversational AI research framework designed for training, evaluating, and sharing dialogue models using a unified interface for datasets and agents. It functions as a PyTorch-based training platform and a dialogue data collection system, providing a centralized model zoo for the distribution of versioned pretrained agents. The project distinguishes itself through a knowledge-grounded retrieval system that combines dense and sparse indexing to ground responses in external information. It also provides a comprehensive infrastructure for gathering human-AI interaction data via inte

pix2pix is a framework for image-to-image translation using conditional generative adversarial networks. It functions as a supervised trainer and visual domain mapper designed to learn a mapping between input and output images for style and domain transfer. The system utilizes a U-Net encoder-decoder architecture combined with a PatchGAN local discriminator to enforce high-frequency local consistency. It employs L1 loss regularization to ensure generated outputs remain structurally close to the ground truth. The project covers a broad range of computer vision capabilities, including semantic

StyleGAN is a TensorFlow-based generative adversarial network framework designed for the synthesis of high-resolution synthetic imagery. It utilizes a style-based generator architecture to create realistic visual assets from latent vectors, focusing on the production of high-fidelity images. The system incorporates style mixing and stochastic noise injection to control visual attributes and fine-grained details. It uses adaptive instance normalization and progressive resolution upsampling to manage image quality and variety across different resolutions. The framework covers the full lifecycl

This is a framework for training and sampling diffusion models to generate high-fidelity images, video, and 4D assets. It provides a modular environment for managing generative AI training pipelines, including the handling of datasets, noise sampling, and loss weighting to stabilize the creation of synthetic content. The project features a modular model configuration system that uses YAML-based assembly to define network submodules and conditioners. It also includes a dedicated toolset for AI image watermarking, allowing for the embedding and detection of invisible markers to verify the origi

PyTorch-GAN is a research-oriented framework providing a collection of modular implementations for generative adversarial network architectures. It serves as a toolkit for training and evaluating models that utilize adversarial minimax optimization to produce synthetic data, offering a structured environment for exploring complex generative tasks within the PyTorch ecosystem. The library distinguishes itself through a comprehensive suite of image synthesis and manipulation capabilities, including super-resolution, inpainting, and cross-domain style translation. It supports advanced training m

This project is a deep learning framework for AI image super-resolution and facial synthesis. It provides a diffusion model image upscaler and a generative facial image synthesizer capable of transforming low-resolution images into high-resolution outputs using pretrained model weights. The system utilizes iterative diffusion refinement and low-resolution guided sampling to restore fine details and sharpness. It supports both unconditional image generation, where images are created from scratch, and guided resolution enhancement for high-fidelity facial reconstruction. The repository include

River is a Python framework for online machine learning, designed to train and evaluate models on streaming data. It enables incremental learning by updating model parameters one observation at a time, eliminating the need to store full training datasets in memory. The library distinguishes itself through a dedicated concept drift detection system that monitors changes in data distributions to trigger model adaptation. It also provides a progressive validation framework that simulates real-time deployment by testing models on samples before using them for training. The system covers a broad

StarGAN is a PyTorch image-to-image translation framework designed to synthesize visual styles and attributes across multiple domains. It implements a generative adversarial network that serves as a deep learning image translator for modifying specific visual characteristics within an image dataset. The framework uses a single unified model to handle translations between multiple image domains rather than requiring separate pairs of models. It is a research implementation that learns mappings between different image attributes without the need for paired training data. The project covers the

Pytorch implementation