# Latent Diffusion Model

DiffRhythm is an innovative music generation model that utilizes latent diffusion technology to achieve fast and high-quality full-song generation. This technology breaks through the limitations of traditional music generation methods, eliminating the need for complex multi-stage architectures and cumbersome data preparation. Only lyrics and style prompts are needed to generate a complete song up to 4 minutes and 45 seconds in a short time. Its autoregressive structure ensures fast inference speed, greatly improving the efficiency and scalability of music creation. The model was jointly developed by the Audio, Speech, and Language Processing group (ASLP@NPU) at Northwestern Polytechnical University and the Big Data Institute of the Chinese University of Hong Kong (Shenzhen), aiming to provide a simple, efficient, and creative solution for music creation.

StructLDM is a structured latent diffusion model designed to learn 3D human generation from 2D images. It can generate diverse, viewpoint-consistent human figures and supports various levels of controllable generation and editing, such as combined generation and local clothing editing. The model enables garment-independent generation and editing without requiring clothing types or mask conditions. This project was proposed by Tao Hu, Fangzhou Hong, and Ziwei Liu from the S-Lab of Nanyang Technological University, with related research published at ECCV 2024.

SHMT is a self-supervised hierarchical makeup transfer technology achieved through latent diffusion models. This technology allows for the natural transfer of one facial makeup to another without the need for explicit labeling. Its main advantages include the ability to handle complex facial features and expression changes, providing high-quality transfer results. This technology has been accepted at NeurIPS 2024, showcasing its innovation and practicality in the field of image processing.

AnyDressing is an innovative virtual fitting technology that implements personalized customization through latent diffusion models. This technology can generate realistic virtual fitting images based on user-provided clothing combinations and personalized text prompts. Its key advantages include high-precision handling of clothing texture details, compatibility with various plugins, and strong adaptability to different scenarios. Background information indicates that it was co-developed by the ByteDance and Tsinghua University research teams to advance the development of virtual fitting technology. The product is currently in the research phase and is not yet priced, primarily targeting academic research and effect demonstration.

Stable Video Diffusion (SVD) 1.1 Image-to-Video is a diffusion model that generates videos corresponding to static images as conditioning frames. This latent diffusion model is trained to generate short video clips from images. At a resolution of 1024x576, the model is trained to generate 25-frame videos using the same-sized context frames and is fine-tuned from SVD Image-to-Video [25 frames]. During fine-tuning, conditions like 6FPS and Motion Bucket Id 127 are fixed to improve output consistency without adjusting hyperparameters.

Stable Signature is a method for embedding watermarks in images. It uses latent diffusion models (LDMs) to extract and embed watermarks. This method has high stability and robustness, and can maintain watermark readability under various attacks. Stable Signature provides pre-trained models and code implementations, which users can use to embed and extract watermarks.