Projects sharing features with Deep Daze

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

GLIDE is a generative model designed for text-to-image synthesis, image editing, and the contextual filling of masked image regions. It uses a guided diffusion process to transform random noise into high-resolution imagery that aligns with descriptive text prompts. The system provides specialized capabilities for modifying existing visuals, including the ability to alter specific image elements and iteratively refine selected regions through text-driven guidance. It also functions as an inpainting tool, filling missing or masked sections of an image with new content that blends naturally with

Sana is a framework for high-resolution image and video synthesis based on a linear diffusion transformer. It provides a toolkit for the training, fine-tuning, and execution of text-to-image and text-to-video models, as well as a video generative world model capable of simulating physical environments with precise spatial control. The project is distinguished by its use of linear complexity layers to handle high resolutions and its support for long-form, minute-length video generation in real time. It implements a two-stage inference paradigm that separates structural generation from visual t

Stable Diffusion is a generative machine learning pipeline that synthesizes high-resolution visual content by performing iterative denoising within a compressed latent space. By mapping natural language embeddings into pixel outputs through conditioned probabilistic processes, the framework enables the generation of images from text prompts and the transformation of existing visual inputs based on semantic instructions. The architecture utilizes a modular execution environment that decouples model loading, scheduler logic, and inference components to support diverse hardware configurations. I

HunyuanDiT is a bilingual text-to-image generative model and diffusion transformer image generator. It uses a latent diffusion system to synthesize high-resolution images from text prompts, with a specific focus on understanding and generating content from both Chinese and English language descriptions. The project features a multi-resolution transformer architecture and a bilingual embedding space to map different scripts into a shared semantic area. It supports iterative multi-turn image refinement, which translates conversational dialogue into updated prompts to progressively modify visual

This project is a WhatsApp chatbot that integrates large language models and image generation into the WhatsApp messaging platform. It acts as a bridge connecting WhatsApp messages to OpenAI services to provide automated text and visual responses. The bot features the ability to convert spoken audio messages into written text using automated speech recognition, facilitating conversational interactions via voice. It also functions as a generative image bot, creating custom visual assets from text descriptions. The system is designed for containerized deployment, using Docker to package the ap

This project is a framework for training consistency models and performing diffusion model distillation. It functions as a few-step text-to-image generator and an image-to-image transformation tool designed to produce high-resolution visuals from text prompts or existing images. The system focuses on converting pre-trained diffusion models into consistency models to reduce the number of required inference steps. It enables the training of lightweight model adaptors to inject specific visual styles into large models without requiring full network fine-tuning. The project covers broad capabili

This project provides a cloud-based notebook configuration for deploying a Stable Diffusion web interface. It functions as a specialized environment for image generation, incorporating a model trainer for fine-tuning weights and creating training datasets. The system emphasizes infrastructure persistence by saving software installations and model files to cloud storage, avoiding repetitive setups between sessions. It uses a tunnel-based interface to expose the web dashboard to a public URL for remote interaction. The project covers end-to-end AI workflows, including dataset preparation and t

Janus is a multimodal large language model and unified framework that integrates visual understanding and image generation within a single neural network. It functions as both a visual understanding model for analyzing images and a text-to-image generator. The system uses a unified transformer backbone and a multimodal latent space to bridge the gap between text and visual data. This architecture employs decoupled visual encoding and cross-modal tokenization to separate the paths for discriminative understanding and generative tasks, representing images as grids of discrete codes. The projec

This project is a research framework and toolkit designed for training large-scale vision transformers and multimodal language models. It provides a comprehensive suite for vision-language pretraining, enabling the development of models that map images and text into shared latent spaces. The framework is distinguished by its capabilities in high-fidelity image generation and multimodal research, utilizing normalizing flows and variational autoencoders to produce images from text prompts or class labels. It supports the development of both generative and contrastive models, allowing for a wide

Latent Diffusion is a framework for high-resolution image synthesis that performs the denoising process within a compressed latent space. It uses variational autoencoders to encode images into a lower-dimensional representation, reducing the computational cost of noise prediction compared to operating on raw pixels. The project enables text-to-image generation by integrating natural language descriptions through cross-attention conditioning. It also supports image inpainting and restoration, filling masked or missing image areas with generated content, and example-based synthesis using retrie

StableCascade is a generative AI system and latent diffusion framework designed for text-to-image synthesis and image-to-image transformations. It utilizes a multi-stage cascade architecture that encodes and decodes images via a latent space to produce high-fidelity visual imagery. The system includes a cascade diffusion pipeline for controlling image structure through inpainting, outpainting, and super-resolution. It also provides a toolkit for image-to-image generation and the creation of image variations using embeddings. The framework supports model optimization through low-rank adaptati

Learn_Prompting is an educational project focused on prompt engineering, providing the principles and techniques required to craft effective inputs and improve the quality of generative AI outputs. The project covers advanced prompting strategies to enhance reasoning, reliability, and output quality. This includes techniques for task decomposition, chain-of-thought reasoning, and the use of few-shot and zero-shot guidance. It also addresses model security through the study of prompt hacking, vulnerability analysis, and privacy auditing to prevent sensitive data leaks. The scope extends to th

sd-scripts is a suite of utilities designed for fine-tuning generative models, preprocessing datasets, and converting model weights. It provides a collection of scripts for executing Stable Diffusion training through methods such as DreamBooth, textual inversion, and full fine-tuning, alongside a framework for creating and managing Low-Rank Adaptation weights. The project features specialized capabilities for model weight conversion between different architectures and precision formats. It includes tools for merging adaptation weights into base models, extracting weights from trained models,

Instruct-pix2pix is an instruction-based image model and PyTorch library designed to modify visual content by following natural language directions. It functions as a diffusion model image editor that applies human-written instructions to existing pictures rather than using traditional text-to-image prompts. The project provides a fine-tunable diffusion framework for adapting pre-trained checkpoints to specific image editing datasets. It includes a synthetic dataset generator that creates paired images and text triplets to train models on various image editing tasks. The system covers a rang

Vercel is a cloud platform for building, deploying, and scaling web applications. It provides a unified infrastructure that automates the build process by detecting project frameworks and distributing static and dynamic content through a global content delivery network. The platform executes application logic using serverless functions that scale automatically based on real-time traffic demand. The platform distinguishes itself through a centralized AI gateway that proxies requests to multiple model providers, enabling standardized authentication, observability, and cost tracking. It supports

This project is a Go library that provides a programmatic interface for interacting with generative AI services. It serves as a comprehensive software development kit for integrating large language models into applications, enabling developers to perform tasks such as text and chat completion, image generation, and audio transcription. The library distinguishes itself through a unified infrastructure designed for robust network communication and service management. It features structured request mapping and error normalization to ensure type-safe interactions and simplified debugging. Further

StyleCLIP is a framework for steering generative models using contrastive language-image pre-training to edit and synthesize imagery. It functions as a latent space image editor that manipulates high-dimensional vector representations to align visual features with natural language descriptions. The system modifies StyleGAN generated images by calculating the difference between neutral and target text embeddings. This process allows for the isolation of specific visual attributes, such as age, gender, or expression, by projecting semantic changes onto the latent manifold. The project covers c

This project is an educational resource providing practical code examples and implementations of machine learning algorithms using the Python language. It serves as a guide for constructing predictive pipelines, clustering models, and dimensionality reduction within the Scikit-Learn ecosystem. The repository includes comprehensive demonstrations for supervised and unsupervised learning, as well as detailed examples for implementing neural networks and deep architectures. It also provides practical guidance on exporting model parameters to JSON and wrapping trained models in web APIs for produ

Sglang is a high-performance inference engine and serving system designed for large language and multimodal models. It provides a programmable interface for orchestrating complex generation workflows, enabling developers to coordinate multi-turn dialogues, tool invocations, and reasoning chains through a domain-specific language. The platform is built to support production-scale deployments, offering an OpenAI-compatible API that allows for integration with existing application ecosystems. The system distinguishes itself through a disaggregated architecture that separates compute-intensive pr

This project is an educational codebase and reference library that translates theoretical deep learning concepts into executable PyTorch code. It serves as a practical implementation of a deep learning textbook, providing a course-like structure of guided exercises and architectural examples for learning purposes. The repository includes a library of standard neural network architectures, including linear, convolutional, recurrent, and transformer models. It specifically implements a variety of deep learning patterns such as multilayer perceptrons, VGG networks, gated recurrent units, and lon

This project is a reference implementation and application template for Retrieval-Augmented Generation (RAG). It integrates Azure OpenAI with Azure AI Search to enable conversational chat interfaces that provide grounded responses based on private enterprise data. The system is distinguished by its multimodal AI interface, allowing it to process and reason over combined text, image, and PDF content. It employs a hybrid search architecture that combines vector and keyword retrieval with semantic reranking to prioritize the most relevant documents for prompt augmentation. The project covers a

PraisonAI is an autonomous AI agent platform that coordinates multiple LLM-powered agents for research, planning, and execution of complex workflows. It functions as a multi-agent orchestration framework, a workflow builder, and a Model Context Protocol server, while also providing retrieval-augmented generation through vector knowledge bases. Agents can interact via CLI, web, or standardized protocols with sandboxed code execution. The platform distinguishes itself with a rich set of agent communication protocols, including A2A, REST, WebSocket, voice and telephony integration, and MCP, allo

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

This repository is a collection of node-based pipeline configurations, examples, and templates for generating AI media. It provides a workflow library and a curated gallery of blueprints designed for creating images, videos, and 3D assets using diffusion models. The project specifically offers a set of pre-configured node graphs for implementing advanced image generation and refinement techniques, with a focus on Stable Diffusion workflows. These examples demonstrate how to interconnect processing nodes to define complex generative logic without writing code. The available templates cover a

Stable Diffusion Web UI is a browser-based interface for generating, editing, and upscaling images and videos using latent diffusion models. It functions as a text-to-image generator, an AI image editor, and a tool for increasing image resolution and clarity. The system includes capabilities for custom model training, specifically allowing the creation of textual inversion embeddings to teach a model new concepts and visual styles from user photos. It also provides tools for AI video production, generating short clips from text prompts. The software covers image-to-image transformation, imag

Easy Diffusion is a desktop application that generates images from text descriptions using AI. It provides a straightforward interface for creating visuals by simply typing what you want to see, with the ability to preview images as they are being generated. The application supports loading custom AI models, allowing users to switch between different artistic capabilities and styles. It includes tools for editing existing images through text prompts or masks, applying predefined artistic styles like "Realistic" or "Pencil Sketch", and upscaling or correcting facial details after generation. F