A latent text-to-image diffusion model

18 Lessons to Get Started Building AI Agents

The repository provides code for running inference with the SegmentAnything Model (SAM), links for downloading the trained model checkpoints, and example notebooks that show how to use the model.

CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image

✅（已完结）超级全面的 深度学习 笔记【土堆 Pytorch】【李沐 动手学深度学习】【吴恩达 深度学习】【大飞 大模型Agent】

Instruct-tune LLaMA on consumer hardware

StableLM: Stability AI Language Models

High-Resolution Image Synthesis with Latent Diffusion Models

LangGPT: Empowering everyone to become a prompt expert! 🚀 📌 结构化提示词（Structured Prompt）提出者 📌 元提示词（Meta-Prompt）发起者 📌 最流行的提示词落地范式 | Language of GPT The pioneering framework for structured & meta-prompt…

Build your neural network easy and fast, 莫烦Python中文教学

Implementation of Dreambooth (https://arxiv.org/abs/2208.12242) with Stable Diffusion

Using Low-rank adaptation to quickly fine-tune diffusion models.

A unified framework for 3D content generation.

The image prompt adapter is designed to enable a pretrained text-to-image diffusion model to generate images with image prompt.

Chinese version of CLIP which achieves Chinese cross-modal retrieval and representation generation.

【🔞🔞🔞 内含不适合未成年人阅读的图片】基于我擅长的编程、绘画、写作展开的 AI 探索和总结：StableDiffusion 是一种强大的图像生成模型，能够通过对一张图片进行演化来生成新的图片。ChatGPT 是一个基于 Transformer 的语言生成模型，它能够自动为输入的主题生成合适的文章。而 Github Copilot 是一个智能编程助手，能够加速日常编程活动。

Implementation of Dreambooth (https://arxiv.org/abs/2208.12242) by way of Textual Inversion (https://arxiv.org/abs/2208.01618) for Stable Diffusion (https://arxiv.org/abs/2112.10752). Tweaks focuse…

Jupyter Notebooks to help you get hands-on with Pinecone vector databases

Sharing both practical insights and theoretical knowledge about LLM evaluation that we gathered while managing the Open LLM Leaderboard and designing lighteval!

deeplearning.ai , By Andrew Ng, All slide and notebook + data + solutions and video link

Translations of TensorFlow documentation

骆驼:A Chinese finetuned instruction LLaMA. Developed by 陈启源 @ 华中师范大学 & 李鲁鲁 @ 商汤科技 & 冷子昂 @ 商汤科技

Human Preference Score v2: A Solid Benchmark for Evaluating Human Preferences of Text-to-Image Synthesis

what I learned about fine-tuning stable diffusion