Popular GitHub Repositories for Conditions

[768 Resolution] [Any "SDXL" Model] [Various Conditions] [Arbitrary Views] Official impl. of "MV-Adapter: Multi-view Consistent Image Generation Made Easy"

[ICLR 2025] Phidias: A Generative Model for Creating 3D Content from Text, Image, and 3D Conditions with Reference-Augmented Diffusion

Chargym simulates the operation of an electric vehicle charging station (EVCS) considering random EV arrivals and departures within a day. This is a generalised environment for charging/discharging EVs under various disturbances (weather conditions, pricing models, stochastic arrival-departure EV times and stochastic Battery State of Charge (BOC) at arrival). This is an open source OpenAI Gym environment for the implementation of Reinforcement Learning (RL), Rule-based approaches (RB) and Intelligent Control (IC).

4D Radar Object Detection Dataset and Benchmark for Autonomous Driving in Various Weather Conditions

Fog Simulation using Generative Adversarial Networks (GAN). This code is the implementation of the master thesis Simulating Weather Conditions on Digital Images. It uses a modified CycleGAN model to synthesize fog on clear images.

Nvidia DLI workshop on AI-based predictive maintenance techniques to identify anomalies and failures in time-series data, estimate the remaining useful life of the corresponding parts, and map anomalies to failure conditions.

OmniVCus: Feedforward Subject-driven Video Customization with Multimodal Control Conditions

[IV2024] MultiCorrupt: A benchmark for robust multi-modal 3D object detection, evaluating LiDAR-Camera fusion models in autonomous driving. Includes diverse corruption types (e.g., misalignment, miscalibration, weather) and severity levels. Assess model performance under challenging conditions.

Analyzing the Drugs Descriptions, conditions, reviews and then recommending it using Deep Learning Models, for each Health Condition of a Patient.

Gated Differentiable Image Processing (GDIP) for Object Detection in Adverse Conditions | Accepted at ICRA 2023

This project implements Q-Learning to find the optimal policy for charging and discharging electric vehicles in a V2G scheme under conditions of uncertain commitment of EV owners. The problem is modelled as a multi-objective multi-agent cooperative game. Project is part of fulfillment criteria for ECE 730 course at the University of Alberta.

To address some of the failure modes in training of physics informed neural networks, a Lagrangian architecture is designed to conform to the direction of travel of information in convection-diffusion equations, i.e., method of characteristic; The repository includes a pytorch implementation of PINN and proposed LPINN with periodic boundary conditions

[ICPR Best Paper]:"AllWeatherNet:Unified Image enhancement for autonomous driving under adverse weather and lowlight-conditions"

? IMAGGarment-1: Fine-Grained Garment Generation with Controllable Structure, Color, and Logo. It supports precise and customizable garment synthesis guided by multi-conditions (e.g., sketch, color, logo), achieving high realism and controllability for digital fashion design.

Collection of leaked ChatGPT system prompts, documenting base tools, conditional features, and specialized assistants. Provides insights into the internal structure and behavior of different ChatGPT components and their activation conditions.

[CVPR 2020] Re-hosting of the LUVLi Face Alignment codebase. Please download the codebase from the original MERL website by agreeing to all terms and conditions. By using this code, you agree to MERL's research-only licensing terms.

Source code for "Online Unsupervised Domain Adaptation for Semantic Segmentation in Ever-Changing Conditions", ECCV 2022. This is the code has been implemented to perform training and evaluation of UDA approaches in continuous scenarios. The library has been implemented in PyTorch 1.7.1. Some newer versions should work as well.

Optimising electricity expenditure in an HVAC system under dynamic electricity pricing scheme and weather conditions using a DDPG model.

In this work we propose two postprocessing approaches applying convolutional neural networks (CNNs) either in the time domain or the cepstral domain to enhance the coded speech without any modification of the codecs. The time domain approach follows an end-to-end fashion, while the cepstral domain approach uses analysis-synthesis with cepstral domain features. The proposed postprocessors in both domains are evaluated for various narrowband and wideband speech codecs in a wide range of conditions. The proposed postprocessor improves speech quality (PESQ) by up to 0.25 MOS-LQO points for G.711, 0.30 points for G.726, 0.82 points for G.722, and 0.26 points for adaptive multirate wideband codec (AMR-WB). In a subjective CCR listening test, the proposed postprocessor on G.711-coded speech exceeds the speech quality of an ITU-T-standardized pos

A sophisticated Formula 1 race simulation tool that models and predicts F1 race outcomes with realistic parameters based on driver skills, team performance, track characteristics, and dynamic weather conditions.