Projects sharing features with AudioLDM

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

Text2Video-Zero is a text-to-video diffusion model and framework designed to synthesize temporally consistent video sequences from textual prompts. It functions as a zero-shot video generator, repurposing pre-trained image diffusion models to create video content without requiring additional training on video datasets. The system includes a conditional video synthesizer that allows for guided generation using depth, edge, or pose maps to control structural layout and movement. It also provides text-based video editing capabilities to modify the style or content of existing video clips through

Open-Sora is a video generation framework designed to produce cinematic sequences from text prompts and images. It functions as a generative system that transforms written descriptions or reference images into video content featuring realistic textures and lighting. The project includes a dedicated prompt engineering tool that uses large language models to expand simple user inputs into detailed descriptions. It also features a motion controller for adjusting movement intensity in generated sequences and evaluating motion levels in existing video files. The framework incorporates text-to-vid

This is a PyTorch implementation of a text-to-image model designed for synthesizing high-fidelity images from natural language descriptions. It utilizes a diffusion image generator to transform latent embeddings into visual data through an iterative denoising process. The system employs a two-stage latent mapping process, using a CLIP-based latent prior to map text embeddings to image embeddings before decoding them into pixels. It features a cascading diffusion decoder that produces high-resolution imagery by passing low-resolution outputs through a sequence of models at increasing scales.

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

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

Heartlib is an audio processing library for large language models that provides tools for audio tokenization, compression, and cross-modal alignment. It implements core models for audio-text embedding, automatic speech recognition, neural codecs, and text-driven audio synthesis. The project features a text-to-audio synthesis engine capable of generating high-fidelity music and speech from text descriptions or reference files. It also includes a neural audio codec designed for low-bitrate compression that preserves acoustic structure and sound quality. Additional capabilities cover audio-text

Wan2.2 is a generative video artificial intelligence system designed to synthesize visual media by interpreting natural language instructions. It functions as a text-to-video diffusion model that transforms written concepts into coherent motion sequences through deep learning and latent space manipulation. The system utilizes a transformer-based architecture to process video data as a series of tokens, allowing it to capture complex spatial and temporal relationships. By employing a temporal attention mechanism, the model maintains visual consistency across frames, while its latent space appr

IC-Light is a diffusion-based image editor and generative tool designed for controlling the illumination of foreground subjects. It functions as an image relighting system that uses latent diffusion models to modify lighting effects on isolated subjects. The project provides two primary methods for lighting control: text-based relighting, which uses descriptive prompts and lighting directions, and background-based relighting, which conditions the foreground lighting to match the visual properties of a provided background image. Beyond illumination, the system includes a surface normal estima

This project is an end-to-end text-to-speech engine and deep learning voice synthesizer. It functions as a neural speech synthesis framework that converts written text directly into audio waveforms using a single neural network. The system implements an adversarial framework and a conditional variational autoencoder to generate high-fidelity artificial speech. It utilizes a generative adversarial network to ensure synthesized audio is indistinguishable from real human speech. The toolkit provides capabilities for neural speech synthesis, text-to-audio generation, and the training of custom v

This repository provides a collection of reference implementations and code examples for training and deploying machine learning models using the MLX framework. It serves as a practical guide for executing distributed training, fine-tuning large language models, converting model weights, and implementing multimodal generative workflows. The project distinguishes itself through specialized examples for local hardware execution, featuring weight quantization to reduce memory usage and low-rank adaptation for parameter-efficient fine-tuning. It also includes scripts for transforming external mod

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

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

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

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,

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

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

This project is a TensorFlow implementation of a neural network for raw audio waveform generation. It functions as a conditioned speech synthesis model that produces synthetic audio samples using a dilated convolutional neural network architecture. The system supports custom voice modeling by incorporating global conditioning and categorical identifiers during training and generation. This allows the model to mimic specific speakers or distinct audio characteristics for neural text-to-speech applications. The framework covers deep learning audio synthesis, including audio dataset processing,

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

imaginAIry is a system for generating and refining images and videos using diffusion models. It operates as a web-based server that triggers generation requests through standard API calls, allowing for the creation of visuals and video sequences from text prompts or existing files. The project provides a suite for AI image editing and upscaling, enabling the modification of visuals through natural language instructions and super-resolution tools to increase detail and image size. The system includes capabilities for structural image control using depth maps, edge maps, and body poses to main

This project provides a clean implementation of the latent diffusion model architecture using the PyTorch framework. It functions as a generative machine learning pipeline designed to synthesize images from text prompts by loading pre-trained model weights into a modular neural network structure. The implementation focuses on the mechanics of image generation, utilizing a tensor-based computational graph to execute the complex linear algebra required for inference. It incorporates transformer-based text encoding to map natural language into vector embeddings, which are then integrated into th

This project provides a TensorFlow implementation of the Stable Diffusion model, serving as a generative engine for creating and modifying visual content. It functions as a machine learning architecture that translates natural language descriptions into high-quality images by iteratively refining noise within a compressed latent space. The system enables a variety of generative tasks, including text-to-image synthesis, image inpainting to fill missing or masked regions, and image editing to transform existing visuals based on text prompts. Beyond static imagery, the framework supports the gen

AnimateDiff is a latent diffusion video generator and text-to-video diffusion framework. It converts existing text-to-image diffusion models into animation generators by applying specialized motion modules, allowing for the creation of video sequences without modifying the original base model. The project provides an image-to-video animation framework that uses sparse RGB images, sketches, or structural keyframe constraints to guide generation. It further distinguishes itself with a motion adapter system that injects cinematic camera movements, such as zooming, panning, and tilting, into anim

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.

Tortoise-tts is a neural text-to-speech engine and voice cloning toolkit designed for high-quality audio generation. It functions as a zero-shot synthesis system, meaning it can generate speech for unseen speakers without requiring additional training or fine-tuning for each new voice. The system specializes in replicating human vocal characteristics using small sets of reference audio clips. It allows for the extraction of voice latents to mimic specific speakers, the generation of random synthetic identities, and the blending of multiple voice profiles to create hybrid vocal identities. Th

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

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