aigc-apps/sd-webui-EasyPhoto
Sd Webui EasyPhoto
Acest proiect este o extensie Stable Diffusion WebUI care oferă o interfață grafică pentru generarea de portrete personalizate și editarea foto cu AI. Permite utilizatorilor să antreneze modele de identitate personalizate dintr-un set mic de imagini încărcate pentru a crea versiuni digitale consistente ale unor persoane specifice.
Extensia include un sistem de probă virtuală (virtual try-on) care înlocuiește îmbrăcămintea din imagini prin alinierea articolelor de referință cu corpurile din template-uri. De asemenea, dispune de instrumente pentru schimbarea feței (face swapping) atât în imagini statice, cât și în videoclipuri, precum și un animator de portrete care transformă imaginile statice în videoclipuri dinamice folosind mișcare ghidată de referință și descrieri text.
Capabilitățile suplimentare acoperă manipularea atributelor faciale pentru ajustarea vârstei și a expresiei, sinteza de imagini cu mai multe persoane și generarea de tranziții line între imagini prin interpolare în spațiul latent.
Features
- Portrait Generation - Enables training custom identity models to generate consistent, high-quality digital portraits of specific people.
- Face Swapping Tools - Replaces the face of a subject in existing videos with the likeness of a specifically trained identity model.
- Personalized Identity Training - Provides capabilities to train custom AI models from a small set of images to generate digital versions of specific people.
- Personalized Image Synthesis - Provides capabilities for generating images of specific, identifiable real-world persons through custom model training.
- LoRA-Weighted Image Generations - Integrates trained LoRA models within a text-to-image workflow to produce personalized portraits via trigger words.
- Likeness Training - Trains Low-Rank Adaptation (LoRA) weights to inject specific human likenesses into a frozen base model.
- Stable Diffusion Ecosystem - Ships as an extension for the Stable Diffusion WebUI to simplify personalized photo editing.
- Virtual Try-On Systems - Implements a system for mapping reference clothing items onto human images using deep learning.
- Diffusion-Based Try-Ons - Executes virtual try-ons by using diffusion models to transfer garments from reference photos to target images.
- Facial Feature Refinement - Allows modification of expressions, facial structure, and age using sliders while maintaining the subject's identity.
- Portrait Animation Tools - Transforms static portraits into dynamic videos by mapping motion from reference sources.
- Video Diffusion Models - Produces motion sequences by applying stable video diffusion models to a starting frame and textual description.
- Multi-Identity Synthesis - Generates a single image featuring multiple distinct trained individuals simultaneously using a shared template.
- Template-Driven Identity Synthesis - Combines pre-defined visual layouts with identity models to generate consistent person-specific portraits.
- Identity-Preserving - Creates videos featuring a specific person's likeness by combining a trained identity model with text descriptions.
- Facial Manipulation - Refines a person's expression, age, and facial structure using sliders while maintaining their core identity.
- Multi-Person Identity Portraits - Produces images featuring multiple trained individuals simultaneously through user ID selection and multi-person templates.
- Image-to-Video Animators - Transforms static portraits into dynamic videos using reference images and text prompts to guide motion.
- Mask-Based Area Replacement - Uses binary masks to restrict image generation to specific pixel areas for clothing or feature replacement.
- Portrait Animation Engines - Transforms static portraits into dynamic videos by transferring motion and expressions from driving sources.
- Latent Space Interpolations - Generates smooth transitions between images by interpolating between latent vector representations.
