Open-source alternatives to Shap E

This project is a diffusion-based 3D generator and image-to-3D reconstruction system. It translates natural language descriptions or two-dimensional images into three-dimensional assets using neural radiance fields and diffusion models. The system utilizes score-distillation sampling and diffusion-based guidance to refine 3D shapes without requiring 3D training data. It includes specialized tools for transforming neural representations into exportable meshes with texture and material data, as well as a pipeline for iterative optimization of geometry and textures. The project covers a broad r

TRELLIS is a 3D generative AI model and latent diffusion framework designed to transform natural language descriptions or reference images into textured 3D assets. It operates as a text-to-3D asset generator that utilizes structured latent representations to produce high-quality 3D meshes, Gaussians, and Radiance Fields. The system functions as a multi-format 3D decoder, converting internal representations into standard exchange formats such as GLB and PLY. It also serves as a 3D asset editing tool, enabling the modification of specific regions of generated objects through targeted text or im

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

LatentSync is an audio-driven video generator and latent diffusion lip sync model designed to synchronize a speaker's lip movements in a video to a target audio track. It provides a lip synchronization training framework for developing synchronization networks on custom video and audio datasets. The system utilizes a video preprocessing pipeline to clean, segment, and align face data. It includes a visual sync evaluation tool that calculates confidence scores to measure the accuracy of audio and visual alignment in generated videos. The project covers capabilities for custom synchronization

Kolors is a generative model implementation for synthesizing photorealistic images from natural language descriptions and visual references. It utilizes a latent diffusion model framework to produce high-fidelity imagery, operating within a compressed latent space to improve generation efficiency and quality. The system functions as a multilingual image generator, interpreting text prompts in multiple languages to produce semantically accurate visual outputs. It includes a custom model training pipeline that uses low-rank adaptation to teach the model specific subjects or artistic styles from

IDM-VTON is an AI virtual try-on framework and fashion synthesis tool designed to generate realistic images of people wearing specific garments. It operates as a diffusion-based image generator that blends garment textures with human poses to create synthetic fashion imagery. The system implements virtual fitting room capabilities through a generative model that combines person and clothing inputs. It includes a web-based interface to run interactive visual demonstrations and synthesize try-on images in real-time. The framework covers the broader domain of AI fashion visualization, enabling

DreamGaussian is a generative system and converter designed to create textured three-dimensional models from text or images using Gaussian Splatting. It functions as a pipeline for transforming two-dimensional inputs into high-fidelity 3D assets. The project provides specific workflows for converting 3D Gaussian point clouds into standard textured mesh formats compatible with external 3D software. It supports the generation of textured meshes from single images via volumetric refinement and UV texture optimization, as well as the creation of 3D models from text prompts through intermediate im

Hunyuan3D-2.1 is a generative 3D framework and image-to-3D pipeline that transforms single 2D images into textured 3D geometries. It functions as an asset generator that produces high-quality 3D meshes and textures using a flow-matching system. The project includes a specialized synthesizer for creating photorealistic textures with physically based rendering properties. These tools allow for the simulation of metallic reflections and light interactions on generated models. The system covers 3D asset pipeline automation through a sequence of shape generation and mesh refinement. It also provi

TRELLIS.2 is a generative image-to-3D system that creates high-resolution 3D assets with physically based rendering materials from 2D images. It utilizes a sparse voxel representation to handle complex topologies and internal structures without relying on iso-surface fields. The project features a structured latent space representation that maps geometry and texture attributes to maintain visual fidelity. It employs an optimization-free geometry reconstruction process to decode latent representations directly into voxel grids and includes a PBR texture generator for synthesizing base color, r

ComfyUI-LTXVideo is a generative framework and ComfyUI custom node extension for synthesizing high-fidelity video. It utilizes a latent diffusion and transformer-based system to create cinematic clips from text, image, and audio inputs, providing a modular interface for precise control over subject behavior and temporal consistency. The tool distinguishes itself with production-grade capabilities, including the generation of High Dynamic Range video in linear formats such as ARRI LogC3. It supports multimodal synchronization for audio-driven animation and lip-syncing, and allows for the creat

Modly is a local AI 3D model generator that converts two-dimensional images into three-dimensional meshes. It is a privacy-focused tool that processes data directly on the host graphics card using GPU-accelerated inference. The system serves as an extensible AI model framework, allowing the integration of external model extensions and runtime files from remote repositories. It utilizes a manifest-driven plugin architecture to add new generation methods by loading metadata and files from external version control systems. The toolset includes a command-line interface for triggering generation

OOTDiffusion is an AI virtual try-on system designed for controllable image synthesis. It generates images of people wearing specific clothing items by superimposing garments onto human figures for both half-body and full-body compositions. The project facilitates digital fashion prototyping and virtual clothing fitting by creating garment-to-person overlays. It aims to maintain the original identity of the wearer and the specific details of the clothing during the synthesis process. The system utilizes a latent diffusion model and conditioning-based image generation to control the output. I

Sygil-webui is a web interface for Stable Diffusion latent diffusion models, providing a creative suite for text-to-image and text-to-video synthesis. It functions as an image generation tool and a latent diffusion image editor, allowing users to create visuals and video sequences from textual descriptions. The project includes a dedicated model training interface for creating custom textual inversion embeddings, which introduces specific new concepts or styles into the diffusion models. It also features specialized tools for generative image editing, including mask-based inpainting, image-to

AudioLDM is a latent diffusion framework for generating high-fidelity audio, music, and sound effects. It functions as a text-to-audio generator that converts natural language descriptions into synthetic audio signals with control over pitch and environment. The system provides specialized tools for audio-to-audio synthesis and generative repair. This includes the ability to perform audio style transfer and replicate specific acoustic events based on existing files. The project covers a broad range of audio transformation tasks, including audio super-resolution for increasing signal fidelity

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

VACE is a set of software tools and frameworks for reference-guided video generation, diffusion-based editing, and video-to-video translation. It provides utilities to produce new video content and modify existing sequences by using reference materials to guide visual style, subject matter, and composition. The framework enables video-to-video translation and synthesis, allowing for the update of visual styles and depth. It also functions as a video editor for modifying properties and content through reference-guided transformations. The system covers localized video editing and inpainting,

Stable-audio-tools is a toolkit for training and deploying latent diffusion models for high-fidelity audio synthesis. It provides a framework for generating audio by iteratively refining noise within a compressed latent space, using specialized encoders to preserve temporal and spectral features of the audio signal. The project features a system for adapting pre-trained audio checkpoints to new datasets through modular initialization and configuration files. It includes utilities for weight extraction and inference model export, which remove training metadata and optimizer states to create li

This is a framework for training and sampling diffusion models to generate high-fidelity images, video, and 4D assets. It provides a modular environment for managing generative AI training pipelines, including the handling of datasets, noise sampling, and loss weighting to stabilize the creation of synthetic content. The project features a modular model configuration system that uses YAML-based assembly to define network submodules and conditioners. It also includes a dedicated toolset for AI image watermarking, allowing for the embedding and detection of invisible markers to verify the origi

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

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

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

ToonCrafter is a model that combines latent diffusion, reference-based colorization, and sketch-guided control for cartoon animation and interpolation. It functions as a cartoon video interpolation model, a reference-based colorization model, and a sketch-guided animation tool, all built on a latent diffusion animation framework. The project distinguishes itself by integrating three core capabilities into a single pipeline: generating smooth intermediate frames between two cartoon images using diffusion-based priors, transferring color and style from a reference image onto black-and-white ske

Taming Transformers is a generative system for high-resolution image synthesis that combines a vector-quantized GAN image encoder with an autoregressive transformer. It utilizes a discrete latent space to represent images as codebook tokens, enabling the production of high-fidelity visuals through a hybrid architecture. The project provides specialized capabilities for layout-based scene synthesis, allowing for the creation of complex images by placing objects according to defined bounding box coordinates. It also includes tools for image inpainting to fill missing sections of an image by ana

stable-diffusion.cpp is a high-performance C++ inference engine designed for generating images and video from text prompts using Stable Diffusion models. It functions as a latent diffusion model runtime and a lightweight machine learning framework that enables local diffusion model execution on consumer hardware. The project distinguishes itself as a CPU-based image generator capable of running without a dedicated GPU. It employs a specialized C++ tensor backend and cross-backend hardware abstraction to dispatch compute tasks across different processor instruction sets and graphics APIs. The

Diffusers is a PyTorch-based library and generative AI framework used to build, train, and deploy diffusion pipelines for producing multi-modal media. It provides a suite of tools for generating images, video, and audio from natural language descriptions, as well as specialized systems for text-to-image generation. The project differentiates itself through a modular architecture that separates noise schedulers, pretrained model blocks, and pipeline compositions. This structure allows for the construction of custom generation workflows and the ability to swap individual components of the diffu

InstantID is a diffusion-based identity preservation framework designed for zero-shot image generation. It allows for the synthesis of images featuring a specific person's facial identity using a single reference photo without requiring additional model training or fine-tuning. The project distinguishes itself through the use of consistency model distillation to accelerate inference, reducing the number of steps needed to produce high-quality results. It combines identity-preserving feature extraction with multi-modal prompt integration to merge visual embeddings from a reference image with t

Videocrafter is a latent diffusion model designed for AI video synthesis. It functions as both a text-to-video and image-to-video generation system, synthesizing high-quality video sequences from descriptive text prompts or static image inputs. The model utilizes a diffusion-based neural network to transform inputs into animated content, ensuring visual consistency and temporal coherence throughout the generated sequences. This allows for the creation of custom video clips and the animation of static images into fluid motion.