Popular GitHub Repositories for Subject Mixing

[ICLR 2024] Official implementation of "TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting"

Phantom: Subject-Consistent Video Generation via Cross-Modal Alignment

[ECCV 2024 Oral?] Arc2Face: A Foundation Model for ID-Consistent Human Faces

Official implementation of "XVerse: Consistent Multi-Subject Control of Identity and Semantic Attributes via DiT Modulation".

Papers and resources on Controllable Generation using Diffusion Models, including ControlNet, DreamBooth, IP-Adapter.

An implementation of "MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing" (ICML 2019).

In-context subject-driven image generation while preserving foreground fidelity

Official implementation of the NeurIPS 2023 paper "Photoswap: Personalized Subject Swapping in Images"

Unofficial implementation of Google's FNet: Mixing Tokens with Fourier Transforms

Front-end speech processing aims at extracting proper features from short- term segments of a speech utterance, known as frames. It is a pre-requisite step toward any pattern recognition problem employing speech or audio (e.g., music). Here, we are interesting in voice disorder classification. That is, to develop two-class classifiers, which can discriminate between utterances of a subject suffering from say vocal fold paralysis and utterances of a healthy subject.The mathematical modeling of the speech production system in humans suggests that an all-pole system function is justified [1-3]. As a consequence, linear prediction coefficients (LPCs) constitute a first choice for modeling the magnitute of the short-term spectrum of speech. LPC-derived cepstral coefficients are guaranteed to discriminate between the system (e.g., vocal tract)

Annif is a multi-algorithm automated subject indexing tool for libraries, archives and museums.

Open-source test management and generation for conversational Gen AI applications. Build and run context-specific test sets. Collaborate with subject matter experts to ensure relevance and quality.

OpenS2V-Nexus: A Detailed Benchmark and Million-Scale Dataset for Subject-to-Video Generation

A generalized framework for subspace tuning methods in parameter efficient fine-tuning.

Implementation of paper: Flux Already Knows – Activating Subject-Driven Image Generation without Training

Video Content Customization Using First Frame

LearnGo, a ios versatile learning tool with RAG and Prompting under LLM like GPT-4o-mini, enabling subject Q&A, smart note generation from uploaded materials, offline photo(or AR) object recognition with translations, and multilingual voice input (Cantonese, English, Mandarin). It also features customizable themes for a personalized experience

Mixing Language Models with Self-Verification and Meta-Verification

Important papers and associated code on automatic mixing research

Official code base for paper EZIGen: Enhancing zero-shot personalized image generation with precise subject encoding and decoupled guidance