Lab for MAchine Perception and LEarning (MAPLE)

Official inference implementation of the paper "DON'T SETTLE TOO EARLY: SELF-REFLECTIVE REMASKING FOR DIFFUSION LANGUAGE MODELS". [ICLR 2026]

Implementation of "Reinforcing the Diffusion Chain of Lateral Thought with Diffusion Language Models" [NeurIPS 2025]

Implementation of "Schedule On the Fly: Diffusion Time Prediction for Faster and Better Image Generation" [CVPR 2025]

Inference-only implementation of "One-Step Diffusion Distillation through Score Implicit Matching" [NIPS 2024]

Equilibrated Diffusion: Frequency-aware Textual Embedding for EquilibratedImage Customization. https://maple-aigc.github.io/EqDiff/

CaCo: Both Positive and Negative Samples are Directly Learnable via Cooperative-adversarial Contrastive Learning

EnAET: Self-Trained Ensemble AutoEncoding Transformations for Semi-Supervised Learning

code for "AdPE: Adversarial Positional Embeddings for Pretraining Vision Transformers via MAE+"