Finn's Projects
Config files for my GitHub profile.
Shape Completion with Meso-Skeleton Learning
Official implementations for paper: Anydoor: zero-shot object-level image customization
[CVPR 2023] Are We Ready for Vision-Centric Driving Streaming Perception? The ASAP Benchmark
Implicit neural reconstruction, manipulation and scene composition for 3D object
The official repository for BEVerse
[ECCV 2022] This is the official implementation of BEVFormer, a camera-only framework for autonomous driving perception, e.g., 3D object detection and semantic map segmentation.
[ICRA'23] BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation
A devkit for the Canadian Adverse Driving Conditions (CADC) dataset.
Python calls ChatGPT API, multi-turn dialogue support
The most powerful and modular stable diffusion GUI, api and backend with a graph/nodes interface.
Let us control diffusion models!
Nightly release of ControlNet 1.1
Officail Implementation for "Cross-Image Attention for Zero-Shot Appearance Transfer"
Official pytorch implementation of the paper: "An Edit Friendly DDPM Noise Space: Inversion and Manipulations". CVPR 2024.
End-to-End Learned Multi-View Stereo Reconstruction
We'll cover some of the most common Deep Learning Interview Questions and answers and provide detailed answers to help you
🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch and FLAX.
PyTorch code and models for the DINOv2 self-supervised learning method.
FB-BEV and FB-OCC are vision-centric autonomous driving perception algorithm based on forward-backward view transformation strtegies.
FreeU: Free Lunch in Diffusion U-Net (CVPR2024 Oral)
"FreeU: Free Lunch in Diffusion U-Net" for Huggingface Diffusers
One-step image-to-image with Stable Diffusion turbo: sketch2image, day2night, and more
Official implementation of the paper “Inversion-Based Style Transfer with Diffusion Models” (CVPR 2023)
Neural Surface reconstruction based on Instant-NGP. Efficient and customizable boilerplate for your research projects. Train NeuS in 10min!