Awesome GitHub RepositoriesNoise-to-Image Generation

Implementation of Denoising Diffusion Probabilistic Model in Pytorch

VAR is a visual autoregressive model and image generation framework that applies large language model scaling laws to visual data. It functions as an image generator that uses a coarse-to-fine next-scale prediction approach rather than traditional raster-scan tokenization. The system utilizes scale-based tokenization to represent images as a hierarchy of discrete tokens. It generates high-resolution content by iteratively predicting the next resolution level, refining coarse predictions into fine-grained details. The project covers a broad range of capabilities including autoregressive image

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

mmagic is a multimodal training pipeline and framework for generative AI, focusing on visual synthesis and restoration. It provides the infrastructure to build and train models for tasks such as text-to-image and text-to-video generation, 3D-aware content synthesis, and high-fidelity image translation using diffusion models and generative adversarial networks. The project distinguishes itself through specialized capabilities for generative model personalization, including techniques for fine-tuning subjects and styles. It also supports advanced visual manipulations such as latent space interp

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 TensorFlow implementation of the Deep Convolutional Generative Adversarial Network (DCGAN) architecture, providing a framework for training generative models that produce synthetic images from random noise vectors. The project implements the core DCGAN design, using transposed convolutions for upsampling, batch normalization for training stability, and leaky ReLU activations in the discriminator, all executed as static TensorFlow computation graphs. The implementation supports training on custom image datasets by accepting user-supplied image folders without requiring a predefined f

This repository is a collection of practical deep learning implementations and examples built using the TensorFlow framework. It provides a variety of neural network architectures focusing on natural language processing, recommendation systems, reinforcement learning, and time series prediction. The project features a range of specialized models, including sequence-to-sequence and transformer architectures for text processing, and factorization machines for personalized ranking and retrieval. It also includes implementations of reinforcement learning agents using actor-critic and policy gradi

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

StableCascade is a generative AI system and latent diffusion framework designed for text-to-image synthesis and image-to-image transformations. It utilizes a multi-stage cascade architecture that encodes and decodes images via a latent space to produce high-fidelity visual imagery. The system includes a cascade diffusion pipeline for controlling image structure through inpainting, outpainting, and super-resolution. It also provides a toolkit for image-to-image generation and the creation of image variations using embeddings. The framework supports model optimization through low-rank adaptati

This project is a framework for training and sampling generative models designed to produce high-quality images in few steps. It provides implementations for image generation models that transform random noise into structured visual data through an optimized sampling process. The system specializes in accelerating image generation through consistency distillation and consistency training. It includes tools to transform pre-trained diffusion models into faster versions by distilling knowledge from a teacher model into a student model, as well as methods to train consistency models from scratch

StoryDiffusion is a generative AI system designed for consistent character image and video generation. It utilizes a pluggable cross-attention module to inject shared character representations into pretrained diffusion models, allowing for visual identity stability across multiple images and scenes without retraining the base model. The project features a video generation pipeline that produces temporally coherent sequences from text prompts or condition images. It employs a latent space motion interpolator to predict intermediate frames and semantic motion, enabling long-range video generati

该项目是一系列用于股票价格预测的预测模型和定量工具。它实现了多种机器学习架构，包括生成对抗网络（GAN）、长短期记忆网络（LSTM）以及用于金融分析的语言模型。 该系统的独特之处在于将时间序列预测与自然语言处理相结合，将金融新闻转换为数值情感分数。它还结合了合成市场数据生成，以及使用贝叶斯和强化学习方法进行自动超参数优化，以减少预测误差。 该代码库涵盖了广泛的定量金融功能，包括通过傅里叶变换进行的信号分解和通过自回归模型进行的数据去噪。它还提供了用于技术指标计算、特征重要性排序以及集成外部经济指数的工具。

AnimeGANv2 是一个生成对抗网络训练框架和图像风格化工具，旨在将真实世界的照片和视频转换为动漫风格的图像。它作为动漫风格生成器，通过监督式风格迁移将真实场景转换为动画。 该项目提供了一个用于训练风格模型并从深度学习检查点提取特定生成器权重参数的系统，从而创建用于推理的轻量级模型。它专注于风景图像风格化以及从提供的训练集中模仿特定艺术风格的能力。 该框架支持照片转动漫和视频转动漫，通过在单个视频帧之间应用一致的美学效果。其功能涵盖通用 AI 艺术生成以及训练生成器以复制目标风格。

StarGAN 是一个 PyTorch 图像到图像翻译框架，旨在跨多个域合成视觉风格和属性。它实现了一个生成对抗网络（GAN），作为深度学习图像翻译器，用于修改图像数据集中的特定视觉特征。 该框架使用单个统一模型来处理多个图像域之间的翻译，而无需成对的模型。这是一个研究性实现，无需成对的训练数据即可学习不同图像属性之间的映射。 该项目涵盖了生成模型的训练和优化，包括应用预训练模型检查点进行推理。它还提供了基于标签和拆分进行图像数据集准备和组织的实用程序。

DragGAN 是一个交互式生成式图像编辑器和操纵器，允许用户通过移动生成网络流形上的控制点来重塑视觉输出。它作为一个基于点的图像编辑工具，将用户定义的坐标偏移映射到生成模型的潜在空间，从而对图像进行变形。 该系统包含一个生成式图像反转工具，可将真实照片转换为潜在表示。此过程通过将真实世界图像转换为与生成对抗网络兼容的格式，实现了对非生成内容的交互式操纵。 该项目涵盖了广泛的功能，包括图像到潜在空间反转、可微点跟踪和迭代梯度下降优化。这些组件协同工作，将图形控制点的移动转换为精确的视觉修改，同时将输出限制在逼真图像的分布范围内。

AnimeGAN 是一个使用 TensorFlow 开发的生成对抗网络（GAN）和图像转换器。它专为照片转动漫风格迁移而设计，利用深度学习系统将现实世界的照片和视频帧转换为动漫风格的图像。 该系统包括一个视频转动漫转换器，可在连续帧之间应用一致的视觉转换。它既支持在艺术数据集上训练生成网络以复制特定风格，也支持从检查点（Checkpoints）提取生成器权重以进行高效推理。 该项目提供图像优化工具，包括边缘平滑和模糊处理，以增强视觉过渡。它还通过连续帧流水线管理视频文件的处理。

这是一个基于 PyTorch 的生成模型框架，旨在通过学习向量场和概率路径将噪声转换为复杂的数据分布。它作为一个多模态生成工具包，通过学习到的概率流来生成合成文本和图像。 该库的独特之处在于支持连续、离散和黎曼流形（Riemannian manifold）集成。这使得该框架能够处理多种数据类型，包括通过离散状态流匹配处理分类数据，以及通过黎曼流形集成处理非欧几里得空间。 该工具包涵盖了完整的生成流水线，包括概率路径定义、向量场回归以及用于数据采样的微分方程求解器。这些功能使得训练和推理能够跨多种模态生成合成内容的生成模型成为可能。

本项目是 AnimeGANv2 的 PyTorch 实现，这是一个生成对抗网络和图像到图像翻译模型，旨在将现实世界的照片转换为风格化的动漫图像。 该仓库包含一个模型权重转换器，能够实现跨不同运行时环境的检查点翻译。该工具执行权重键重映射和张量维度置换，以确保框架实现之间的兼容性。 该系统通过预训练权重加载支持 AI 照片风格化，并提供可配置的上采样对齐，以在图像分辨率增加期间保持视觉连贯性。

RestorePhotos 是一款 AI 面部修复工具和深度学习图像超分辨率工具，旨在消除模糊并重建退化面部照片中丢失的细节。它作为面部照片增强器和生成对抗网络（GAN）图像处理器，将低质量像素转换为高分辨率的面部特征。 该系统利用 GPU 加速的推理引擎来运行机器学习模型，进行实时图像修复。这种硬件加速支持锐化面部图像和提高视觉保真度所需的繁重矩阵乘法和基于张量的运算。 该项目涵盖了 AI 照片修复和数字档案修复，特别专注于面部图像增强。它采用顺序处理管道来执行深度学习图像超分辨率，通过重建缺失的细节来提高图像的分辨率和清晰度。

本项目是一个专注于生成式扩散模型的教育课程和培训材料集合。它提供了一个课程大纲和实践指南，用于训练、微调和部署能够合成图像、音频和视频的模型。 该材料涵盖了特定的实现策略，包括基于噪声的合成、迭代细化和潜在空间压缩。它提供了关于通过条件合成和提示词遵循优化来引导生成式输出的指导，以及图像修复和基于文本的编辑技术。 该项目包括关于模型优化和开发的内容，涵盖概念微调和推理步骤的减少。它还提供了用于生成合成媒体的工作流，例如生成视频序列和将视觉频谱图转换为音频。 实践实现通过 PyTorch 代码示例和将模型权重及元数据发布到 Hugging Face Hub 的教程提供。