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Back to aigc-apps/sd-webui-easyphoto

Open-source alternatives to Sd Webui EasyPhoto

30 open-source projects similar to aigc-apps/sd-webui-easyphoto, ranked by how many features they have in common. Compare stars, activity and what each one does to find the best Sd Webui EasyPhoto alternative.

  • badtobest/echomimicBadToBest 的头像

    BadToBest/EchoMimic

    4,258在 GitHub 上查看↗

    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

    Python
    在 GitHub 上查看↗4,258
  • humanaigc/outfitanyoneHumanAIGC 的头像

    HumanAIGC/OutfitAnyone

    5,979在 GitHub 上查看↗

    OutfitAnyone is a diffusion-based virtual try-on system and AI person-garment integration tool. It functions as an image-to-image clothing transfer model designed to visualize how specific clothing items look on any person regardless of their pose. The system adapts garment textures and shapes to a person's body and pose to produce photorealistic results. It specifically focuses on adjusting clothing deformation based on body shape to maintain high fidelity and detail consistency during the fitting process. The project covers AI fashion visualization and virtual garment fitting, providing ca

    在 GitHub 上查看↗5,979
  • aliaksandrsiarohin/first-order-modelAliaksandrSiarohin 的头像

    AliaksandrSiarohin/first-order-model

    15,003在 GitHub 上查看↗

    This project is a generative adversarial network designed for image animation and motion transfer. It functions as a computer vision framework that synthesizes video sequences by applying motion patterns extracted from a driving video onto a static source image. The model distinguishes itself by using a keypoint-based representation to decouple object appearance from temporal movement. By tracking structural deformations through learned latent coordinates, it performs motion retargeting and synthetic media production without requiring manual annotations or object-specific training data. The

    Jupyter Notebookdeep-learninggenerative-modelimage-animation
    在 GitHub 上查看↗15,003

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  • antgroup/echomimicantgroup 的头像

    antgroup/echomimic

    4,255在 GitHub 上查看↗

    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.

    Pythonaaai2025audio-driven-portrait-animationsaudio-driven-talking-face
    在 GitHub 上查看↗4,255
  • astriaai/headshots-starterastriaai 的头像

    astriaai/headshots-starter

    4,461在 GitHub 上查看↗

    This project is an AI headshot generator and SaaS boilerplate designed to train custom models on uploaded photos to produce professional, high-resolution portraits. It functions as an image generation pipeline and model training orchestrator that manages the end-to-end workflow of processing user images to create stylized avatars. The system includes a credit-based monetization framework that handles payments via automated webhooks. It provides a complete infrastructure for AI-driven services, incorporating user account management and automated email notifications to alert users when their ge

    TypeScript
    在 GitHub 上查看↗4,461
  • iperov/deepfacelabiperov 的头像

    iperov/DeepFaceLab

    19,256在 GitHub 上查看↗

    DeepFaceLab is a deep learning software suite designed for face swapping and the creation of deepfake videos. It functions as a neural network image compositor that replaces human faces or entire heads in video files to produce synthetic media. The tool provides capabilities for digital facial manipulation, including the ability to modify the perceived age of people in video sequences. It uses automated pattern recognition to blend source faces onto target frames to create seamless visual composites. The system covers a broad technical surface including landmark-based face alignment, autoenc

    Python
    在 GitHub 上查看↗19,256
  • yisol/idm-vtonyisol 的头像

    yisol/IDM-VTON

    4,881在 GitHub 上查看↗

    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

    Python
    在 GitHub 上查看↗4,881
  • fudan-generative-vision/champfudan-generative-vision 的头像

    fudan-generative-vision/champ

    4,253在 GitHub 上查看↗

    Champ is a generative vision system and controllable image-to-video generator designed for human image animation. It uses a diffusion-based video synthesizer and 3D parametric guidance to transform a single reference image into a consistent sequence of motion based on external driving data. The framework distinguishes itself through a human pose transfer system that employs 3D body parametric extraction and coordinate-space alignment. This allows the model to map motion from a driving video to a reference person by adjusting for body scales and camera perspectives using depth and semantic con

    Pythonhuman-animationimage-animatiolnvideo-generation
    在 GitHub 上查看↗4,253
  • kwaivgi/liveportraitKwaiVGI 的头像

    KwaiVGI/LivePortrait

    18,632在 GitHub 上查看↗

    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

    Python
    在 GitHub 上查看↗18,632
  • magic-research/magic-animatemagic-research 的头像

    magic-research/magic-animate

    10,908在 GitHub 上查看↗

    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

    Python
    在 GitHub 上查看↗10,908
  • zejun-yang/aniportraitZejun-Yang 的头像

    Zejun-Yang/AniPortrait

    5,020在 GitHub 上查看↗

    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

    Python
    在 GitHub 上查看↗5,020
  • antgroup/echomimic_v2antgroup 的头像

    antgroup/echomimic_v2

    4,597在 GitHub 上查看↗

    EchoMimic V2 is an AI video generation pipeline and computer vision animation model designed to produce synthetic human animations. It functions as a generative framework that creates semi-body videos by aligning a static reference image with pose movements extracted from a driving video. The system utilizes a diffusion-based generation process combined with latent space compression and a temporal attention mechanism to ensure smooth transitions between frames. It maintains consistent person identity through reference-based encoding and guides spatial placement via pose-driven motion conditio

    Pythonaudio-driven-body-animationaudio-driven-portrait-animationsaudio-driven-talking-face
    在 GitHub 上查看↗4,597
  • fudan-generative-vision/hallo2fudan-generative-vision 的头像

    fudan-generative-vision/hallo2

    3,713在 GitHub 上查看↗

    Hallo2 is an AI video generation tool and audio-driven portrait animation framework designed to transform static images into speaking videos. It functions as a portrait image animator that synchronizes a single photo with an audio track to produce high-resolution talking head videos. The system includes a distributed animation trainer for fine-tuning deep learning models using custom datasets and distributed computing resources. It employs hierarchical video generation and temporal consistency modeling to produce long-form character animations that remain stable over extended durations. The

    Python
    在 GitHub 上查看↗3,713
  • nateraw/stable-diffusion-videosnateraw 的头像

    nateraw/stable-diffusion-videos

    4,695在 GitHub 上查看↗

    This project is a Stable Diffusion video generator that creates moving imagery by interpolating between text prompts within a generative model's latent space. It functions as a tool for AI video generation and latent space interpolation, transforming descriptive text into visual sequences. The system specifically enables audio-reactive visuals by synchronizing the rate of image interpolation to the beat and rhythm of an audio file. It produces these sequences through morphing video generation, which transitions smoothly between different text prompts. The project includes a graphical user in

    Python
    在 GitHub 上查看↗4,695
  • modelscope/facechainmodelscope 的头像

    modelscope/facechain

    9,496在 GitHub 上查看↗

    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

    Jupyter Notebook
    在 GitHub 上查看↗9,496
  • humanaigc/emoHumanAIGC 的头像

    HumanAIGC/EMO

    7,616在 GitHub 上查看↗

    EMO is an AI portrait animator and audio-to-video diffusion model designed to generate expressive talking head videos. It transforms a single static portrait image and an audio track into a synchronized video of a person speaking. The system focuses on digital human synthesis, producing high-fidelity facial movements and emotional cues. It synchronizes lip movements and facial gestures to match spoken voice recordings to create realistic portrait animations. The framework utilizes a diffusion process and a cross-modal alignment mechanism to ensure timing between audio signals and visual land

    在 GitHub 上查看↗7,616
  • meituan-longcat/longcat-videomeituan-longcat 的头像

    meituan-longcat/LongCat-Video

    4,460在 GitHub 上查看↗

    LongCat-Video is a collection of specialized models for video synthesis, featuring a large language model based architecture for creating high-resolution videos from text, images, or existing sequences. It includes dedicated systems for text-to-video generation, image-to-video animation, and the creation of talking avatars. The project provides specific capabilities for extending the length of existing clips through a video continuation model that predicts subsequent frames. It also enables the synchronization of character lip movements with audio and text prompts to produce speaking videos.

    Python
    在 GitHub 上查看↗4,460
  • fudan-generative-vision/hallofudan-generative-vision 的头像

    fudan-generative-vision/hallo

    8,644在 GitHub 上查看↗

    Hallo is an audio-driven talking head generator and portrait animation framework. It synchronizes a static portrait image with an audio file to produce realistic talking head videos by mapping audio spectral features to facial expressions and lip movements. The system utilizes a diffusion video synthesis model that employs iterative denoising and latent representations to generate temporally consistent video frames. It incorporates identity-preserving feature extraction and latent space motion modeling to maintain visual consistency and control facial poses. The toolkit provides capabilities

    Pythonface-animationimage-animationvideo-animation
    在 GitHub 上查看↗8,644
  • lkwq007/stablediffusion-infinitylkwq007 的头像

    lkwq007/stablediffusion-infinity

    3,878在 GitHub 上查看↗

    stablediffusion-infinity is a browser-based generative image workspace and infinite canvas editor. It provides a non-destructive environment for expanding image boundaries and synthesizing content using latent diffusion models. The project enables generative image outpainting and inpainting, allowing users to extend image boundaries or fill masked regions. It utilizes an infinite coordinate system to manage large-scale compositions and maintain spatial relationships between original and generated image patches. The workspace employs patch-based inference and contextual blending to ensure vis

    Python
    在 GitHub 上查看↗3,878
  • comfyanonymous/comfyuicomfyanonymous 的头像

    comfyanonymous/ComfyUI

    117,322在 GitHub 上查看↗

    ComfyUI is a modular generative AI workflow orchestrator and node-based GUI for designing and executing complex diffusion model pipelines. It functions as both a visual interface for building generative logic graphs and a programmable backend API that exposes diffusion model operations for external integration. The system distinguishes itself through a graph-based execution model that supports differential workflow execution, re-running only modified nodes to reduce computation. It features dynamic model offloading to manage memory between system RAM and GPU VRAM and utilizes metadata-embedde

    Python
    在 GitHub 上查看↗117,322
  • hillobar/ropeHillobar 的头像

    Hillobar/Rope

    5,334在 GitHub 上查看↗

    Rope is a graphical user interface for swapping faces in images and videos. It functions as a deepfake video editor and image face swapper that utilizes pre-trained deep learning models to replace identities in visual media. The tool includes specialized capabilities for AI video post-production, such as occlusion-aware blending to handle foreground objects and mouth-parsing refinement to align facial expressions. It also serves as an AI face restoration tool, using saliency-based restoration to recover clarity and sharpness in swapped facial regions. The software provides a pipeline for vis

    Python
    在 GitHub 上查看↗5,334
  • kohya-ss/sd-scriptskohya-ss 的头像

    kohya-ss/sd-scripts

    7,133在 GitHub 上查看↗

    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,

    Python
    在 GitHub 上查看↗7,133
  • sensity-ai/dotsensity-ai 的头像

    sensity-ai/dot

    4,529在 GitHub 上查看↗

    Dot is a deep learning face swap tool used to replace faces in live video streams, recorded media, and static images. It functions as a deepfake media processor and real-time video manipulator that applies facial transformations through neural network mapping. The system includes a virtual camera video injector that routes processed output into a system-level virtual device to simulate a physical hardware webcam. This allows generated video to be used within third-party video conferencing software. The tool supports real-time source switching via keyboard inputs to toggle between different s

    Python
    在 GitHub 上查看↗4,529
  • cubiq/comfyui_ipadapter_pluscubiq 的头像

    cubiq/ComfyUI_IPAdapter_plus

    6,031在 GitHub 上查看↗

    ComfyUIIPAdapterplus is a node-based extension for ComfyUI that implements IPAdapter models to guide image generation using reference images. It functions as an image prompting tool and a Stable Diffusion image adapter, allowing reference files to serve as visual prompts for controlling style, composition, and subject identity. The project provides specialized capabilities for maintaining facial identity and high-fidelity features across generated portraits. It enables the transfer of visual characteristics and artistic styles from reference images, as well as the extraction of spatial layo

    Python
    在 GitHub 上查看↗6,031
  • levihsu/ootdiffusionlevihsu 的头像

    levihsu/OOTDiffusion

    6,556在 GitHub 上查看↗

    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

    Python
    在 GitHub 上查看↗6,556
  • instantx-research/instantidinstantX-research 的头像

    instantX-research/InstantID

    11,955在 GitHub 上查看↗

    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

    Python
    在 GitHub 上查看↗11,955
  • premieroctet/photoshotpremieroctet 的头像

    premieroctet/photoshot

    3,874在 GitHub 上查看↗

    Photoshot is a commercial SaaS image platform and web application used for creating personalized AI avatars and portraits. It functions as an AI avatar creator that trains custom machine learning models on user-uploaded photos to produce consistent digital personas. The platform includes an LLM prompt generator that uses large language models to craft detailed text descriptions for image generation engines. It integrates a secure third-party payment gateway to manage user access to these creative tools and services. The system architecture handles asynchronous task queueing for machine learn

    TypeScriptaidreamboothnextjs
    在 GitHub 上查看↗3,874
  • tencentarc/photomakerTencentARC 的头像

    TencentARC/PhotoMaker

    10,122在 GitHub 上查看↗

    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

    Jupyter Notebook
    在 GitHub 上查看↗10,122
  • bing-su/adetailerBing-su 的头像

    Bing-su/adetailer

    4,763在 GitHub 上查看↗

    Adetailer is a Stable Diffusion inpainting extension and automated detail enhancer that identifies specific image regions to improve quality through targeted inpainting. It functions as an AI image masking tool that uses detection models to create precise masks for automated image editing. The system distinguishes itself by integrating structural guides, such as depth and pose, to constrain the inpainting process and maintain anatomical consistency. It also supports object-specific prompt assignment, allowing unique text instructions to be mapped to multiple detected objects within a single i

    Pythonsd-webuistable-diffusion-webuistable-diffusion-webui-plugin
    在 GitHub 上查看↗4,763
  • xavierxiao/dreambooth-stable-diffusionXavierXiao 的头像

    XavierXiao/Dreambooth-Stable-Diffusion

    7,738在 GitHub 上查看↗

    This project is a Dreambooth implementation designed to personalize Stable Diffusion models. It serves as an AI image personalization tool and model tuner that enables the creation of unique subject identifiers to generate consistent, personalized images. The system focuses on subject-driven image synthesis by fine-tuning pre-trained diffusion models on small, custom datasets. This allows the model to recognize specific people, objects, or artistic styles and place those learned subjects into diverse contexts via text-to-image conditioning. The implementation includes a diffusion model optim

    Jupyter Notebookpytorchpytorch-lightningstable-diffusion
    在 GitHub 上查看↗7,738