[MICCAI 2024] Easy diffusion models (optionally with segmentation guidance) for medical images and beyond.
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Updated
Jun 18, 2025 - Python
[MICCAI 2024] Easy diffusion models (optionally with segmentation guidance) for medical images and beyond.
A simple PyTorch implementation of conditional denoising diffusion probabilistic models (DDPM) on MNIST, Fashion-MNIST, and Sprite datasets
[ECCV 2024] Diffusion Models for Monocular Depth Estimation: Overcoming Challenging Conditions
ICML 2024, Official Implementation of "Cross-view Masked Diffusion Transformers for Person Image Synthesis."
[ICML'24] Adsorbate Placement via Conditional Denoising Diffusion
A PyTorch implementation of various deep generative models, including Diffusion (DDPM), GAN, cGAN, and VAE.
A project focused on generating synthetic fingerprint images using deep learning and image processing techniques. Aims to create realistic fingerprints for use in biometric research, security testing, and dataset augmentation.
[ICLR 2026] One step further with Monte-Carlo sampler to guide diffusion better
Official Implementation of WaveStitch: Flexible and Fast Conditional Time Series Generation with Diffusion Models, (ACM SIGMOD 2026)
📸 Computer vision project for soccer player detection, multi-object tracking and behavior analysis from broadcast videos. Evaluated using HOTA0.5 and normalized MAE, with final PTBS score. Developed for an Artificial Vision academic challenge.
A light conditional diffusion model trained for generating images of the MNIST digits given the input number
Generative models nano version for fun. No STOA here, nano first.
A diffusion model trained on FairFace with discrete conditioning on age, gender, and race. Generates high-quality face images using a custom UNet and DDPM scheduler.
Diffusion Models crash course with Pytorch from DeepLearningAI
PyTorch implementation for Conditional Image Generation via Score-Based Diffusion Generative Models
Class-conditional diffusion model for CIFAR-10 with Streamlit + Google Gemini interface.
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