Semantics Lead the Way: Harmonizing Semantic and Texture Modeling with Asynchronous Latent Diffusion

FlowFeat: Pixel-Dense Embedding of Motion Profiles (NeurIPS 2025 Spotlight)

A practical guide to diffusion models, implemented from scratch.

Official implementation of Geometry Forcing: Marrying Video Diffusion and 3D Representation for Consistent World Modeling

All-in-one training for vision models (YOLO, ViTs, RT-DETR, DINOv3): pretraining, fine-tuning, distillation.

Official implementation of "Continuous Autoregressive Language Models"

[BabyLM@EMNLP 2025 - Challenge Award] Official Implementation: Masked Diffusion Language Models with Frequency-Informed Training

[NeurIPS 2025] VideoREPA: Learning Physics for Video Generation through Relational Alignment with Foundation Models

Official PyTorch Implementation of "Diffusion Transformers with Representation Autoencoders"

Large Concept Models: Language modeling in a sentence representation space

Train Your VAE: A VAE Training and Finetuning Script for SD/FLUX

[ICCV 2025] VideoVAE+: Large Motion Video Autoencoding with Cross-modal Video VAE

DC-VideoGen: Efficient Video Generation with Deep Compression Video Autoencoder

Code for "Reasoning to Learn from Latent Thoughts"

UniDisc: A discrete diffusion model for joint multimodal generation, enabling controllable and efficient text-image synthesis, editing, and inpainting.

PyTorch implementation of various distillation approaches for continual learning of Diffusion Models.

The official GitHub repo for the survey paper "A Survey on Diffusion Language Models".

Reference PyTorch implementation and models for DINOv3

PixArt-Σ: Weak-to-Strong Training of Diffusion Transformer for 4K Text-to-Image Generation

PixArt-α: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis

[ICML 2025] Implementation of Spatial Reasoning with Denoising Models

Official code of Franca: Nested Matryoshka Clustering for Scalable Visual Representation Learning

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Code for "A Principled Framework for Multi-View Contrastive Learning"