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DeepBeepMeep – AI solutions specifically optimized for low spec GPUs
https://huggingface.co/DeepBeepMeep
https://github.com/deepbeepmeep
Wan2GP – A fast AI Video Generator for the GPU Poor. Supports Wan 2.1/2.2, Qwen Image, Hunyuan Video, LTX Video and Flux.
mmgp – Memory Management for the GPU Poor, run the latest open source frontier models on consumer Nvidia GPUs.
YuEGP – Open full-song generation foundation that transforms lyrics into complete songs.
HunyuanVideoGP – Large video generation model optimized for low-VRAM GPUs.
FluxFillGP – Flux-based inpainting and outpainting tool for low-VRAM GPUs.
Cosmos1GP – Text-to-world and image/video-to-world generator for the GPU Poor.
Hunyuan3D-2GP – GPU-friendly version of Hunyuan3D-2 for 3D content generation.
OminiControlGP – Lightweight version of OminiControl enabling 3D, pose, and control tasks with FLUX.
SageAttention – Quantized attention achieving 2.1–3.1× and 2.7–5.1× speedups over FlashAttention2 and xformers without losing end-to-end accuracy.
insightface – State-of-the-art 2D and 3D face analysis project for recognition, detection, and alignment.
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Skill Foundry – ARTIFICIAL INTELLIGENCE WITH PYTHON
INTRODUCTION 3
Setting Up AI Development Environment with Python 7
Understanding Machine Learning — The Heart of AI 11
Supervised Learning Deep Dive — Regression and Classification Models 16
Unsupervised Learning Deep Dive — Discovering Hidden Patterns 21
Neural Networks Fundamentals — Building Brains for AI 26
Project — Build a Neural Network to Classify Handwritten Digits 30
Deep Learning for Image Classification — CNNs Explained 33
Advanced Image Classification — Transfer Learning 37
Natural Language Processing (NLP) Basics with Python 41
Spam Detection Using Machine Learning 45
Deep Learning for Text Classification (with NLP) 48
Computer Vision Basics and Image Classification 51
AI for Automation: Files, Web, and Emails 56
AI Chatbots and Virtual Assistants 61 -
Eyeline Labs VChain – Chain-of-Visual-Thought for Reasoning in Video Generation for better AI physics
https://eyeline-labs.github.io/VChain/
https://github.com/Eyeline-Labs/VChain
Recent video generation models can produce smooth and visually appealing clips, but they often struggle to synthesize complex dynamics with a coherent chain of consequences. Accurately modeling visual outcomes and state transitions over time remains a core challenge. In contrast, large language and multimodal models (e.g., GPT-4o) exhibit strong visual state reasoning and future prediction capabilities. To bridge these strengths, we introduce VChain, a novel inference-time chain-of-visual-thought framework that injects visual reasoning signals from multimodal models into video generation. Specifically, VChain contains a dedicated pipeline that leverages large multimodal models to generate a sparse set of critical keyframes as snapshots, which are then used to guide the sparse inference-time tuning of a pre-trained video generator only at these key moments. Our approach is tuning-efficient, introduces minimal overhead and avoids dense supervision. Extensive experiments on complex, multi-step scenarios show that VChain significantly enhances the quality of generated videos.
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