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A tool to automatically optimize stable diffusion prompts
AI Image Prompts — 10,000+ curated prompts for any model. Works with Nano Banana Pro, Nano Banana 2, Seedream 5.0, GPT Image 1.5, Midjourney, DALL-E, Flux, Stable Diffusion, and more.
📚 AIGC 求职面经、必备基础知识、提示词工程、ChatGPT、Stable Diffusion、Prompt、Embedding、Fintune 等 AIGC 求职你所需要知道的一切~
Using a Model to generate prompts for Model applications. / 使用模型来生成作图咒语的偷懒工具，支持 MidJourney、Stable Diffusion 等。
Stable Diffusion: macOS install help with homebrew, python, anaconda, dream, etc.
永久免费开源的 AIGC 课程, 目前已支持Prompt Engineering, ChatGPT, Midjourney, Runway, Stable Diffusion, AI数字人，AI声音&音乐，开源大模型
A simple standalone viewer for reading prompts from Stable Diffusion generated image outside the webui.
[ICLR'24 Spotlight] The official codes of our work on AIGC detection: "Multiscale Positive-Unlabeled Detection of AI-Generated Texts"
本爬虫用于爬取知乎网站问题、回答的相关字段信息，问题的标题、内容、发布时间、话题、回答数量、评论数、点击数、关注数等字段，及对该问题回答的内容，作者、点赞数、评论数、回答时间等等字段信息。可用于对社会话题、热点进行数据分析。
小红书笔记 | 评论爬虫、抖音视频 | 评论爬虫、快手视频 | 评论爬虫、B 站视频 ｜ 评论爬虫、微博帖子 ｜ 评论爬虫、百度贴吧帖子 ｜ 百度贴吧评论回复爬虫 | 知乎问答文章｜评论爬虫
A Benchmark for Anytime Person Re-Identification (AT-ReID), which aims to retrieve a person at any time, including both daytime and nighttime, ranging from short-term to long-term.
Recommend new arxiv papers of your interest daily according to your Zotero libarary.
一套为研究生和学术研究者设计的完整AI Prompt库 📖 包含内容： ✨ 40+ 精心设计的AI Prompt ✨ 论文选题系统方法（生成、评估、论证） ✨ 论文查找快速方案（8个不同方案） ✨ 文献综述框架和工具 ✨ Excel自动评估表格 ✨ 3个完整的论证模板 🚀 核心优势： ⚡ 节省时间 50-70%（选题3-5天而不是2-3周） 🎯 科学方法（基于系统的5维度评估体系） 💡 即插…
记录了武汉大学国家网络安全学院密码学实验课所有加密算法的复现代码
A next.js web application that integrates AI capabilities with draw.io diagrams. This app allows you to create, modify, and enhance diagrams through natural language commands and AI-assisted visual…
Elevate your AI research writing, no more tedious polishing ✨
Code and hyperparameters for the paper "Generative Adversarial Networks"
Awesome paper list with code about generative adversarial nets
Keras implementations of Generative Adversarial Networks.
此项目是机器学习(Machine Learning)、深度学习(Deep Learning)、NLP面试中常考到的知识点和代码实现，也是作为一个算法工程师必会的理论基础知识。
Implementation of "Frustratingly Easy Edit-based Linguistic Steganography with a Masked Language Model"