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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
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 is a PyTorch implementation of a text-to-image model designed for synthesizing high-fidelity images from natural language descriptions. It utilizes a diffusion image generator to transform latent embeddings into visual data through an iterative denoising process. The system employs a two-stage latent mapping process, using a CLIP-based latent prior to map text embeddings to image embeddings before decoding them into pixels. It features a cascading diffusion decoder that produces high-resolution imagery by passing low-resolution outputs through a sequence of models at increasing scales.
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
Implementation of Denoising Diffusion Probabilistic Model in Pytorch
The main features of lucidrains/denoising-diffusion-pytorch are: Diffusion Models, Image Diffusion Models, Diffusion Sampling Methods, Diffusion Model Training, Automated Folder-Based Training, Noise-to-Image Generation, U-Net Architectures, Sinusoidal Timestep Embeddings.
Open-source alternatives to lucidrains/denoising-diffusion-pytorch include: openai/improved-diffusion — This project is a diffusion model framework for training and sampling from denoising probabilistic models to generate… lucidrains/imagen-pytorch — This is a PyTorch-based implementation of diffusion models for synthesizing photorealistic images and video. It… lucidrains/dalle2-pytorch — This is a PyTorch implementation of a text-to-image model designed for synthesizing high-fidelity images from natural… hao-ai-lab/fastvideo — FastVideo is a comprehensive system for accelerated video generation, serving as a video generation inference engine,… datawhalechina/tiny-universe — Tiny Universe is an educational monorepo that delivers multiple independent implementations of core AI subsystems as… hojonathanho/diffusion — This project is a diffusion model training framework and image synthesis pipeline. It provides the tools necessary to…