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

这是一个 PyTorch 深度学习框架和人体运动合成工具，可从文本提示或动作描述生成 3D 角色动画。它作为一个文本到运动生成器，将自然语言和分类标签转换为时间上一致的 3D 骨骼运动序列。 该系统利用基于 Transformer 的扩散模型来迭代去噪运动数据。它包括动作条件生成、单目到 3D 运动提升以及使用文本约束进行运动序列编辑的功能。 该框架结合了几何运动约束强制执行，以通过关节位置和速度损失确保物理合理性。它进一步涵盖了完整的动画流水线，包括运动模型训练、针对基准数据集的性能评估、3D 网格渲染以及用于环境交互的基于物理的模拟控制。

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

该仓库作为结构化的教育资源，用于学习使用 PyTorch 框架构建、训练和部署神经网络。它提供了一系列实用的代码示例和教程，旨在引导从业者完成深度学习模型的实现。 该项目涵盖了广泛的机器学习领域，包括计算机视觉、自然语言处理、生成式建模和强化学习。通过利用模块化组件和自动梯度计算，这些材料展示了如何构建复杂的架构，并通过专门的算法和数据增强技术优化训练过程。 内容被组织成一系列动手练习，涵盖了模型开发的完整生命周期。这包括训练循环的配置、模型参数的管理以及将训练好的模型集成到生产环境中。

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