Projects sharing features with AnimateAnyone

HunyuanVideo-1.5 is a video generation foundation model and text-to-video diffusion framework. It utilizes a latent video diffusion model and a spatio-temporal transformer architecture to generate high-definition video sequences from text descriptions and images. The project enables cinematic camera control for directing pans and tilts and provides image-to-video animation capabilities. It supports visual style adaptation through low-rank adaptation tuning and uses a language model for prompt refinement to improve visual alignment. The model covers high-resolution video upscaling via a super

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.

CogVideo is a generative video framework that uses diffusion models and transformer-based architectures to synthesize high-resolution video clips. It functions as both a text-to-video and image-to-video generator, converting textual descriptions or static images into temporal visual sequences. The system integrates large language model capabilities to expand short user prompts into detailed descriptions for better visual alignment. It supports the animation of static images through latent seeding and provides the ability to extend the length of existing video sequences. The project includes

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

EchoMimic is a multimodal human animation framework and diffusion-based video generator. It produces lifelike facial and semi-body animations of a reference image by synthesizing motion and appearance from various source data. The system enables portrait animation driven by audio, pose sequences, or driver videos. It features a landmark conditioning tool that allows for the precise control of facial movements by modifying specific landmark points. The framework covers multi-modal motion synthesis and the synchronization of reference images to match the physical movements of a target driver.

AnimatedDrawings is a system for transforming static 2D drawings of humanoid figures into animated characters. It provides a workflow for character rigging, motion mapping, and scene assembly to turn hand-drawn sketches into moving visual sequences. The project utilizes a motion retargeting framework to map movement data from motion capture files onto custom character skeletons regardless of their physical proportions. It employs a rigging tool that uses pose estimation to automatically predict joint locations and create digital skeletons, which can be manually refined to improve animation ac

TurboDiffusion is a video diffusion inference engine and generator designed to create high-resolution videos from text prompts and images. It provides a runtime environment for executing optimized diffusion model checkpoints with a focus on reducing latency and GPU memory usage. The project features a specialized training framework for aligning sparse-linear attention models with pretrained full-attention models. This system includes capabilities for sparse attention parameter merging and sparse-linear model alignment to reduce computational costs during inference while maintaining output qua

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

AniPortrait is an AI video synthesis pipeline designed to generate photorealistic speaking portraits and facial animations. It functions as a talking head generator and audio-driven animator that synchronizes lip movements, expressions, and head poses to speech or reference video sources. The system includes a facial expression transfer tool for reenacting movements from a source video onto a static reference image. It utilizes a latent diffusion model with reference-based image conditioning to maintain visual identity and consistency across generated frames. The pipeline covers audio-to-exp

This project is a neural network extension for Stable Diffusion that provides spatial control and geometric consistency for text-to-image generation. It functions as an image structure controller and conditioning tool, enabling the use of external inputs to guide the layout and geometry of generated imagery. The framework is distinguished by its ability to transform input images into structural guides through various preprocessors. These include the extraction of depth maps, normal maps, and human pose landmarks, as well as the detection of Canny edges, anime lineart, and straight architectur

CogVideo is a video generation framework and large language model architecture designed for synthesizing high-resolution video clips from natural language descriptions and images. It functions as a text-to-video and image-to-video generator, while also providing a model for video captioning to analyze visual content into descriptive text summaries. The system supports animating static images into motion sequences and transforming series of images into video based on prompts. It includes capabilities for extending the length of generated video clips to create longer sequences of motion. The f

EchoMimic is an audio-driven portrait animation framework and latent diffusion video generator. It transforms static reference images into dynamic talking head videos by synchronizing facial movements with audio tracks and motion drivers. The system functions as a hybrid motion synthesis engine that combines audio inputs and pose data. It utilizes a facial landmark motion controller to edit positioning markers, enabling precise synchronization and video-to-video pose transfer. The pipeline covers image-to-video animation through latent diffusion and facial landmark conditioning. This allows

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

Magic Animate is a diffusion model video generator designed for human image animation. It transforms a static human photo into a temporally consistent video by mapping movements from a reference motion clip, acting as a tool to create realistic animations from a single image. The system ensures visual stability and minimizes flicker through temporal attention injection and motion-controlled noise scheduling. To accelerate the generation of high-resolution video, it includes a distributed GPU inference engine that splits model workloads across multiple graphics cards. The project covers a com

Stable-Video-Infinity is a video synthesis tool based on Stable Video Diffusion designed for creating long-form animations and consistent visual content. It serves as an AI video extension framework and a conditioned animation synthesizer capable of producing video sequences of arbitrary length. The project enables infinite video extension by bypassing standard model duration constraints through an error-recycling loop. It supports conditioned animation synthesis using external inputs such as image streams, audio files, or skeletal motion data to guide the generation process. The framework i

PaddleGAN is a generative AI framework and deep learning computer vision library built on the PaddlePaddle framework. It serves as a toolkit for image and video synthesis, providing a collection of generative adversarial network implementations for creating synthetic visual content. The library focuses on advanced synthesis capabilities, including the generation of talking heads through lip motion synchronization and the creation of synthetic videos via motion transfer from driving sequences. It provides tools for domain-to-domain translation, allowing for image style transfer and the transfo

LivePortrait is a deep learning framework for portrait animation that transfers facial expressions from a driving video to a static image. It functions as an AI motion retargeting tool, mapping movements between different identities while preserving the unique features of the source portrait. The system includes specialized capabilities for cross-species portrait animation, adapting human-centric models to non-human subjects and animals. It also features a motion template generator that converts driving videos into portable files to accelerate inference and protect the identity of the origina

MAGI-1 is an autoregressive video generation model designed to synthesize high-resolution video sequences from text prompts and image references. It functions as a generative system for text-to-video, image-to-video, and video-to-video transformations. The model utilizes an autoregressive architecture that treats spatio-temporal patches as a sequence of discrete tokens to maintain temporal motion. It employs a variational autoencoder to compress the spatial and temporal dimensions of video data and uses distillation-based step scaling to allow for inference budget control. The system integra

I've made everythign less reliant on torch.compile for VRAM efficiency, so things should work better even without it. Also figured workaround for some issues when using compile that made first run use drastically more VRAM, issue I battled with myself a lot.

Open-Sora-Plan is a text-to-video framework and distributed video training system. It utilizes a diffusion transformer architecture and large language model components to transform written descriptions or image prompts into high-quality video sequences. The system features a distributed infrastructure designed for large-scale video training and inference. It employs sequence parallelism to split high-resolution or long-duration video samples across multiple GPUs and uses a sparse attention mechanism to increase processing speed. The project includes capabilities for both text-to-video and im

This project is a research-oriented PyTorch framework designed for the implementation and training of generative video diffusion models. It provides a modular toolkit that extends standard image-based diffusion techniques into three dimensions, enabling the synthesis of coherent video sequences through iterative denoising processes. The framework distinguishes itself by utilizing factored space-time attention, which decomposes high-dimensional video data into separate spatial and temporal layers to maintain motion consistency while managing computational complexity. It supports multi-modal tr

FastVideo is a comprehensive system for accelerated video generation, serving as a video generation inference engine, a video diffusion training framework, and a modular pipeline orchestrator. It provides a distributed transformer optimizer and a distillation toolkit designed to reduce denoising steps and model complexity to increase frame rates. The project distinguishes itself through specialized acceleration techniques, including joint distillation and sparse attention training. It implements low-step video generation and weight quantization to FP8 or FP4 precision to increase throughput a

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

SkyReels-V2 is a video generation system that creates, extends, and refines video clips from text descriptions, images, or both. It operates as a diffusion-based video generation model that can produce videos of any duration by denoising frames sequentially, with each new frame conditioned on the ones that came before it. The system supports generating videos from scratch using text prompts, starting from a single image and producing subsequent frames, or constraining both the first and last frames to match user-provided images. What distinguishes SkyReels-V2 is its combination of infinite-le

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

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

Facechain is a generative AI toolchain and portrait generator designed to create personalized synthetic identities and consistent digital portraits. It provides a pipeline for training and refining diffusion models to produce subject-driven image synthesis from reference photos. The project focuses on digital twin generation, enabling the creation of a personalized model from a single image to maintain identity consistency across various poses and artistic styles. It utilizes identity fusion and similarity sorting to balance facial accuracy with stylized visual effects. The toolkit covers a