Lists (1)
Stars
An LLM-powered knowledge curation system that researches a topic and generates a full-length report with citations.
Support for miscellaneous image models. Currently supports: DiT, PixArt, HunYuanDiT, MiaoBi, and a few VAEs.
GGUF Quantization support for native ComfyUI models
The ultimate training toolkit for finetuning diffusion models
JohannesBuchner / imagehash
Forked from bunchesofdonald/photohashA Python Perceptual Image Hashing Module
A python module for scientific analysis of 3D data based on VTK and Numpy
GPT4V-level open-source multi-modal model based on Llama3-8B
Official release of InternLM series (InternLM, InternLM2, InternLM2.5, InternLM3).
A small neural network to provide interoperability between the latents generated by the different Stable Diffusion models.
Ever wanted to yoinkysploinky SDXL's clip and use them in SD3? Well, now you can!
A knowledge graph RAG app using LangChain and Neo4j.
Empower Large Language Models (LLM) using Knowledge Graph based Retrieval-Augmented Generation (KG-RAG) for knowledge intensive tasks
RAG with knowledge graphs implemented from scratch
Retrieval Augmented Generation (RAG) chatbot powered by Weaviate
Weaviate is an open-source vector database that stores both objects and vectors, allowing for the combination of vector search with structured filtering with the fault tolerance and scalability of …
All-in-one infrastructure for search, recommendations, RAG, and analytics offered via API
A free, open-source SaaS app starter for React & Node.js with superpowers. Full-featured. Community-driven.
RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding.
Langflow is a powerful tool for building and deploying AI-powered agents and workflows.
OneDiff: An out-of-the-box acceleration library for diffusion models.
Implementation of Post-training Quantization on Diffusion Models (CVPR 2023)
The official implementation of PTQD: Accurate Post-Training Quantization for Diffusion Models
[ECCV2024] IDM-VTON : Improving Diffusion Models for Authentic Virtual Try-on in the Wild