Awesome GitHub RepositoriesGaussian Noise Diffusion

Diffusers is a PyTorch-based library and generative AI framework used to build, train, and deploy diffusion pipelines for producing multi-modal media. It provides a suite of tools for generating images, video, and audio from natural language descriptions, as well as specialized systems for text-to-image generation. The project differentiates itself through a modular architecture that separates noise schedulers, pretrained model blocks, and pipeline compositions. This structure allows for the construction of custom generation workflows and the ability to swap individual components of the diffu

This project is a machine learning research automation system designed to manage the full research lifecycle, from idea discovery to final paper submission. It utilizes markdown-based skill templates to execute autonomous research tasks and manage iterative loops of deep review and experimentation. The system distinguishes itself through integrated capabilities for academic communication and integrity auditing. It can automate the generation of LaTeX papers, conference slide decks, and evidence-grounded peer review rebuttals. To ensure rigor, it employs cross-model review routing and adversar

Magic Animate is a diffusion model video generator designed for human image animation. It transforms a static human photo into a temporally consistent video by mapping movements from a reference motion clip, acting as a tool to create realistic animations from a single image. The system ensures visual stability and minimizes flicker through temporal attention injection and motion-controlled noise scheduling. To accelerate the generation of high-resolution video, it includes a distributed GPU inference engine that splits model workloads across multiple graphics cards. The project covers a com

Implementation of Denoising Diffusion Probabilistic Model in Pytorch

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

This project is a comprehensive machine learning interview guide and technical study resource designed for individuals preparing for machine learning and AI engineering roles. It provides a collection of materials and practice problems covering core algorithms, theoretical fundamentals, and the implementation of neural network architectures. The resource serves as a technical reference for generative AI development, focusing on the design and optimization of large language models and diffusion systems. It includes frameworks for system design, covering the architecture of production machine l

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 project is a diffusion model training framework and image synthesis pipeline. It provides the tools necessary to train generative models to learn image data distributions through an iterative denoising process. The framework includes a generative model evaluation tool consisting of automated scripts used to measure the quality and accuracy of produced samples. The system covers model training pipelines and performance evaluation for generative diffusion models.

Acest proiect este o resursă educațională cuprinzătoare și un curs pentru construirea de rețele neuronale folosind PyTorch. Acoperă elementele fundamentale ale deep learning-ului, inclusiv manipularea tensorilor, diferențierea automată și construcția componentelor modulare de rețele neuronale. Repository-ul servește drept ghid tehnic pentru mai multe domenii specializate. Oferă detalii de implementare pentru sarcini de computer vision, cum ar fi clasificarea imaginilor, detecția obiectelor și segmentarea semantică, precum și fluxuri de lucru de procesare a limbajului natural (NLP) care implică transformatoare, rețele recurente și modele generative. În plus, include o referință pentru AI generativ, concentrându-se în mod specific pe sinteza de imagini prin modele de difuzie și rețele adversariale. Materialul se extinde către optimizarea modelelor și pipeline-uri de deployment. Acoperă tehnici pentru reducerea dimensiunii modelelor și creșterea vitezei de inferență prin cuantizare și exportul modelelor în formate precum ONNX și TensorRT. Alte domenii de capabilitate includ ingineria datelor pentru încărcarea paralelă, evaluarea modelelor folosind metrici personalizate și deployment-ul modelelor de limbaj mari (LLM) open-source. Proiectul este livrat în principal sub formă de serie de Jupyter Notebooks.

ACE-Step is a high-fidelity audio synthesis system and diffusion model designed to generate music and vocals from text descriptions. It functions as a music generator and vocal synthesizer, using a diffusion transformer decoder to produce audio across various languages and genres. The project provides tools for text-guided audio editing, including the ability to extend the duration of tracks, regenerate specific song segments, and perform latent-space audio inpainting to modify lyrics or styles. It also includes a framework for audio style fine-tuning using low-rank adaptation to adapt vocal

Multimodal is a machine learning library built on PyTorch for training large-scale models that combine text, image, audio, and video data streams. It functions as a deep learning framework dedicated to generative diffusion models, multi-task training, and vision-language tasks. The library supplies modular building blocks, discrete latent codebook quantization, shared-space embeddings, and stackable adapter layers to handle diverse conditional inputs during training and inference. The framework supports specific architectures for diffusion models, text-to-video generation, image-text retrieva

Acest proiect este un framework PyTorch orientat spre cercetare, conceput pentru implementarea și antrenarea modelelor generative de difuzie video. Acesta oferă un toolkit modular care extinde tehnicile standard de difuzie bazate pe imagini în trei dimensiuni, permițând sinteza secvențelor video coerente prin procese iterative de denoising. Framework-ul se distinge prin utilizarea atenției spațio-temporale factorizate, care descompune datele video de înaltă dimensiune în straturi spațiale și temporale separate pentru a menține consistența mișcării în timp ce gestionează complexitatea computațională. Suportă antrenarea multi-modală prin procesarea simultană a imaginilor statice și a secvențelor video și încorporează ghidarea latentă condiționată de text pentru a direcționa procesul de generare conform unor descrieri specifice în limbaj natural. Biblioteca include pipeline-uri cuprinzătoare pentru gestionarea antrenării end-to-end a acestor modele, acoperind ingestia seturilor de date, optimizarea parametrilor și exportul periodic al mostrelor. Este structurată pentru a susține cercetarea în deep learning privind modelarea temporală și alinierea output-urilor generative cu prompt-urile semantice.

Diffusion-LM

We present a demo of SSD-LM (Semi-autoregressive Simplex-based Diffusion Language Model) in Google Colab here!