#stable diffusion

Riffusion enables real-time music synthesis through stable diffusion. Built with Next.js and React, it provides a platform for dynamic music creation. Despite being inactive, it offers compatibility with inference servers for efficient model output generation. The app exemplifies the innovative use of modern technology in creative fields, complete with clear guidance for setup and usage.

Riffusion offers a library leveraging stable diffusion for real-time music and audio generation, despite no longer being actively maintained. It serves as a valuable asset for developers working with spectrogram-image-to-audio conversions. The project encompasses a command-line interface, a Streamlit interactive app, and a Flask server for API-based model inference. Compatible with Python 3.9 or 3.10, it supports CUDA for enhanced performance, providing a robust tool for audio processing and third-party application integration.

Explore a new method for generating pose-controllable videos using easily accessible datasets and pre-trained models. This approach employs a two-stage training process, combining image pose pairs and videos to create text-editable digital animations with pose control. Utilizing a convolutional encoder and temporal self-attention, this project maintains the editing capabilities of pre-trained models for dynamic video generation. Access our code and models to discover advancements in digital human creation.