Awesome GitHub RepositoriesComposition-Controlled Generators

Flux is a diffusion model inference engine designed for text-to-image generation and image-to-image manipulation. It provides a system for executing open-weight models to transform natural language descriptions into visual imagery or to modify existing images. The project distinguishes itself through a flow-matching framework for image generation and a structural image controller. This controller allows for guided synthesis by using depth maps and Canny edge detection to constrain the geometry and composition of the output. The toolkit covers a broad range of image editing capabilities, incl

This project is a framework for running Stable Diffusion image generation models on Apple Silicon using Core ML hardware acceleration. It provides a local generative AI pipeline for producing images from text prompts using Swift and Python without relying on external cloud APIs. The system includes a model converter to transform deep learning checkpoints into Core ML formats and a model optimizer to quantize weights and activations. It features a ControlNet integration layer to guide image generation using external signals such as edge and depth maps. Capabilities cover text-to-image generat

This project is a static educational website and comprehensive curriculum focused on computer vision and deep learning. It serves as a public repository of instructional materials, lecture notes, and technical guides specifically detailing convolutional neural networks and visual recognition. The site is developed using static-site generation to host course documentation and student project directories. It provides structured academic resources that guide learners through image classification, generative modeling, and the implementation of various neural network architectures. The curriculum

PhotoMaker is a diffusion-based identity generator designed for person-specific image synthesis. It creates high-fidelity photos and avatars of specific individuals using stacked embeddings, which allows for the generation of consistent human identities without the need for custom model training or fine-tuning. The system utilizes zero-shot identity synthesis and identity adapters to maintain recognizable facial features across various visual contexts. It supports artistic style transfer by combining identity information with specialized model weights and integrates external control framework

Qwen-Image is a text-to-image model and large language model image generation framework. It functions as an AI image editing suite and a personalized image trainer, capable of producing high-fidelity visuals and accurate typography from natural language descriptions. The system is distinguished by its precision text rendering engine, which integrates multi-script calligraphy and layout-coherent alphabetic text into images. It provides specialized capabilities for subject identity preservation and consistent subject generation across different poses and viewpoints, alongside a training pipelin

Ce projet est un outil d'upscaling d'images par IA et de génération haute résolution. Il utilise la diffusion par tuiles (tiled diffusion) pour créer des images ultra-larges en les traitant par régions plus petites et superposées afin d'éviter les plantages mémoire sur du matériel limité. Le système gère la composition spatiale via le prompting régional, qui route des prompts textuels spécifiques vers des zones désignées d'une image. Il maintient la stabilité visuelle et la cohérence globale pendant le processus d'upscaling en utilisant l'inversion de bruit et le guidage structurel. Des capacités supplémentaires incluent l'upscaling de détails par tuiles et l'optimisation mémoire pour les étapes d'encodage et de décodage de l'auto-encodeur variationnel. Ces fonctionnalités réduisent la mémoire de pointe requise pour la reconstruction d'images haute résolution.

ComfyUI-nunchaku is a 4-bit diffusion inference engine and a set of nodes for running low-precision quantized diffusion models within ComfyUI visual workflows. It provides a backend that reduces memory overhead and increases generation speed for transformer models. The project includes specialized tools for identity-preserving generation and an image-to-image guidance toolkit that uses depth maps and reference images. It also features a multimodal visual question answering implementation and a utility for merging multiple quantized model files into single unified files. The engine covers a b