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Official PyTorch Implementation of "Scalable Diffusion Models with Transformers"
Learning in infinite dimension with neural operators.
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.
Elucidating the Design Space of Diffusion-Based Generative Models (EDM)
TorchCFM: a Conditional Flow Matching library
ResShift: Efficient Diffusion Model for Image Super-resolution by Residual Shifting (NeurIPS@2023 Spotlight, TPAMI@2024)
[AAAI 2023] Exploring CLIP for Assessing the Look and Feel of Images
PyTorch library for solving imaging inverse problems using deep learning
[CVPRW 2022] MANIQA: Multi-dimension Attention Network for No-Reference Image Quality Assessment
FMBoost: Boosting Latent Diffusion with Flow Matching (ECCV 2024 Oral)
Official code repository for ICLR 2024 paper "DiffusionSat: A Generative Foundation Model for Satellite Imagery"
[CVPR 2023] Guided Depth Super-Resolution by Deep Anisotropic Diffusion
Crack Segmentation for Low-Resolution Images using Joint Learning with Super-Resolution (CSSR) was accepted to international conference on MVA2021 (oral), and selected for the Best Practical Paper …
Super-resolution of 10 Sentinel-2 bands to 5-meter resolution, starting from L1C or L2A (Theia format) products.
The official repository of BFSR: "Boosting Flow-based Generative Super-Resolution Models via Learned Prior" [CVPR 2024]
Semantic segmentation from multi-source optical data (baseline for the FLAIR#2 challenge)
Implementation of 'DynamicEarthNet: Daily Multi-Spectral Satellite Dataset for Semantic Change Segmentation' [CVPR 2022]
PyTorch implementation of NeurIPS 2021 paper "Spatial-Temporal Super-Resolution of Satellite Imagery via Conditional Pixel Synthesis"
A comprehensive benchmark for real-world Sentinel-2 imagery super-resolution