Awesome GitHub RepositoriesDiffusion Model Frameworks

Implementation of Denoising Diffusion Probabilistic Model in Pytorch

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

Instruct-pix2pix is an instruction-based image model and PyTorch library designed to modify visual content by following natural language directions. It functions as a diffusion model image editor that applies human-written instructions to existing pictures rather than using traditional text-to-image prompts. The project provides a fine-tunable diffusion framework for adapting pre-trained checkpoints to specific image editing datasets. It includes a synthetic dataset generator that creates paired images and text triplets to train models on various image editing tasks. The system covers a rang

Diffusion Policy is a robot learning framework that uses diffusion models to map visual observations to precise action trajectories. It functions as an imitation learning toolkit and visuomotor policy learner, providing a system to train neural networks that replicate human behavior by generating robotic movements based on image and sensor data. The framework employs a conditional denoising process to sample sequences of robotic movements, allowing it to handle multimodal action distributions where multiple valid trajectories may exist for a single state. It utilizes score-based action modeli

Acesta este un framework de deep learning PyTorch și un instrument pentru sinteza mișcării umane care generează animații de personaje 3D din prompt-uri text sau descrieri de acțiuni. Funcționează ca un generator text-to-motion care convertește limbajul natural și etichetele categorice în secvențe de mișcare scheletică 3D consistente temporal. Sistemul utilizează un model de difuzie bazat pe transformer pentru a denoise iterativ datele de mișcare. Include capabilități pentru generarea condiționată de acțiuni, lifting-ul mișcării de la monocular la 3D și editarea secvențelor de mișcare folosind constrângeri textuale. Framework-ul încorporează aplicarea constrângerilor geometrice de mișcare pentru a asigura plauzibilitatea fizică prin pierderi (losses) de poziție a articulațiilor și viteză. Acoperă, de asemenea, întregul pipeline de animație, inclusiv antrenarea modelului de mișcare, evaluarea performanței față de seturi de date de benchmark, randarea mesh-urilor 3D și controlul simulării bazate pe fizică pentru interacțiunea cu mediul.

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

Discoart is a diffusion model orchestration framework and distributed GPU generation engine designed to automate and scale image generation workflows across hardware clusters. It functions as a generative AI model API, providing HTTP and gRPC endpoints to trigger and retrieve images from diffusion models as a network service. The system distinguishes itself through a comprehensive task management layer that includes timeline-based prompt and parameter scheduling. It manages the generative art lifecycle by supporting state-based session serialization for recovery, YAML-based configuration mana

FastVideo is a comprehensive system for accelerated video generation, serving as a video generation inference engine, a video diffusion training framework, and a modular pipeline orchestrator. It provides a distributed transformer optimizer and a distillation toolkit designed to reduce denoising steps and model complexity to increase frame rates. The project distinguishes itself through specialized acceleration techniques, including joint distillation and sparse attention training. It implements low-step video generation and weight quantization to FP8 or FP4 precision to increase throughput a

Acest repository servește drept resursă educațională structurată pentru învățarea modului de construire, antrenare și deployment a rețelelor neuronale folosind framework-ul PyTorch. Oferă o colecție de exemple de cod practice și tutoriale concepute pentru a ghida practicienii prin implementarea modelelor de deep learning. Proiectul acoperă o gamă largă de domenii de machine learning, inclusiv computer vision, procesarea limbajului natural, modelarea generativă și reinforcement learning. Prin utilizarea componentelor modulare și a calculului automat al gradienților, materialele demonstrează cum să construiești arhitecturi complexe și să optimizezi procesele de antrenare prin algoritmi specializați și tehnici de augmentare a datelor. Conținutul este organizat într-o serie de exerciții practice care abordează întregul ciclu de viață al dezvoltării modelelor. Aceasta include configurarea buclelor de antrenare, gestionarea parametrilor modelului și integrarea modelelor antrenate în mediile de producție.

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