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MM-EUREKA: Exploring Visual Aha Moment with Rule-based Large-scale Reinforcement Learning
Explore the Multimodal “Aha Moment” on 2B Model
Qwen2.5-VL is the multimodal large language model series developed by Qwen team, Alibaba Cloud.
Unified Efficient Fine-Tuning of 100+ LLMs & VLMs (ACL 2024)
EasyR1: An Efficient, Scalable, Multi-Modality RL Training Framework based on veRL
Official Implementation for "MyVLM: Personalizing VLMs for User-Specific Queries" (ECCV 2024)
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
PyTorch implementation of FractalGen https://arxiv.org/abs/2502.17437
Official PyTorch Implementation of "History-Guided Video Diffusion"
code for "Diffusion Forcing: Next-token Prediction Meets Full-Sequence Diffusion"
EQ-VAE: Equivariance Regularized Latent Space for Improved Generative Image Modeling.
[CVPR 2025] Magma: A Foundation Model for Multimodal AI Agents
MLGym A New Framework and Benchmark for Advancing AI Research Agents
Solve Visual Understanding with Reinforced VLMs
Official code of "MakeAnything: Harnessing Diffusion Transformers for Multi-Domain Procedural Sequence Generation"
Code for the paper "Adapt - $\infty$: Scalable Lifelong Multimodal Instruction Tuning"
Janus-Series: Unified Multimodal Understanding and Generation Models
Arbitrary-steps Image Super-resolution via Diffusion Inversion (CVPR 2025)
Implementation of the paper "MaskBit: Embedding-free Image Generation from Bit Tokens"
Efficient vision foundation models for high-resolution generation and perception.
Unofficial Implementation of E-LatentLPIPS(Ensembled-LatentLPIPS) of Diffusion2GAN
A PyTorch library for implementing flow matching algorithms, featuring continuous and discrete flow matching implementations. It includes practical examples for both text and image modalities.
Official PyTorch implementation of "Paralinguistics-Aware Speech-Empowered LLMs for Natural Conversation" (NeurIPS 2024)
Official inference repo for FLUX.1 models
Official PyTorch Implementation of "SiT: Exploring Flow and Diffusion-based Generative Models with Scalable Interpolant Transformers"