Search Results for "random map generation"
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Showing 28 open source projects for "random map generation"
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Hunyuan3D 2.0
High-Resolution 3D Assets Generation with Large Scale Diffusion Models
The Hunyuan3D-2 model, developed by Tencent, is designed for generating high-resolution 3D assets using large-scale diffusion models. This model offers advanced capabilities for creating detailed 3D models, including texture enhancements, multi-view shape generation, and rapid inference for real-time applications. It is particularly useful for industries requiring high-quality 3D content, such as gaming, film, and virtual reality. Hunyuan3D-2 supports various enhancements and is available... -
2
Anything to NotebookLM
Multi-source content processor for NotebookLM
...It is built for users who want to convert articles, web pages, videos, PDFs, office files, podcasts, images, and search results into more usable study or presentation formats. The project uses natural-language commands, so the user can ask for a podcast, slide deck, mind map, report, quiz, flashcards, or infographic without manually building the workflow. It supports multilingual material, with especially strong use cases for Chinese and English content. The tool can process files locally, extract or transcribe content when needed, and hand the cleaned material to NotebookLM for generation. It is best suited for researchers, students, content curators, and knowledge workers who regularly turn scattered information into organized learning assets. -
3
Agent Sprite Forge
Agent Skill for generating 2D sprite sheets and map, transparent PNG
Agent Sprite Forge is an AI-powered asset generation toolkit designed to create 2D game sprites, transparent PNG frames, animated GIFs, and sprite sheets directly from text prompts. The project functions as an “agent skill” that can integrate with coding assistants and AI workflows to automate parts of the game asset creation pipeline. It focuses on generating production-friendly pixel art and animation assets that can be used in indie games, prototypes, and rapid iteration workflows. The... -
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pix2pixHD
Synthesizing and manipulating 2048x1024 images with conditional GANs
pix2pixHD is a PyTorch-based implementation of a conditional generative adversarial network designed for high-resolution image-to-image translation, capable of producing photorealistic outputs at resolutions up to 2048×1024. It is widely used to convert structured inputs such as semantic label maps into realistic images, making it particularly valuable in applications like autonomous driving simulation, face synthesis, and scene generation. The model improves upon earlier GAN approaches by... -
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comfyui-mixlab-nodes
Workflow and speech recognition app
...The project also brings Real-time Design features like screen capture and floating video nodes, enabling creative pipelines that mix live screen content, generative models, and visual effects. For audio and speech, it provides nodes for SpeechRecognition and SpeechSynthesis, plus workflows that combine voice generation with real-time face swapping and other audio-visual effects. On the AI side, it integrates multiple LLM providers (cloud and local), supports OpenAI-compatible endpoints, Siliconflow models, and includes prompt-focused utilities for random prompt generation, Chinese prompts, clip interrogation. -
6
No Cost AI
80+ free AI services for chat, image, video, voice & APIs
...The repository also warns that free AI services can change, disappear, rate-limit users, or become unreliable over time. Its main value is discovery, not guaranteed infrastructure, so it works best as a resource map rather than a production dependency. -
7
gplearn
Genetic Programming in Python, with a scikit-learn inspired API
...Symbolic regression is a machine learning technique that aims to identify an underlying mathematical expression that best describes a relationship. It begins by building a population of naive random formulas to represent a relationship between known independent variables and their dependent variable targets in order to predict new data. Each successive generation of programs is then evolved from the one that came before it by selecting the fittest individuals from the population to undergo genetic operations. -
8
Regex
Generate matching and non matching strings based on regex patterns
...It is highly doubtful anyone would require a string of infinite length, thus I've artificially limited repetitions in such patterns to 100 symbols when generating random values. Use a{n,m} if you require some specific number of repetitions. It is suggested to avoid using such infinite patterns to generate data based on regex. -
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Audiomentations
A Python library for audio data augmentation
A Python library for audio data augmentation. Inspired by albumentations. Useful for deep learning. Runs on CPU. Supports mono audio and multichannel audio. Can be integrated in training pipelines in e.g. Tensorflow/Keras or Pytorch. Has helped people get world-class results in Kaggle competitions. Is used by companies making next-generation audio products. Mix in another sound, e.g. a background noise. Useful if your original sound is clean and you want to simulate an environment where... -
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RAG-Retrieval
Unify Efficient Fine-tuning of RAG Retrieval, including Embedding
RAG-Retrieval is an open-source framework for building and training retrieval systems used in retrieval-augmented generation pipelines. Retrieval-augmented generation combines large language models with external knowledge retrieval to improve factual accuracy and domain-specific reasoning. This repository provides end-to-end infrastructure for training retrieval models, performing inference, and distilling embedding models for improved performance. It includes implementations of modern embedding architectures designed to map documents and queries into vector spaces for efficient similarity search. ... -
11
TextGen
textgen, Text Generation models
Implementation of Text Generation models. textgen implements a variety of text generation models, including UDA, GPT2, Seq2Seq, BART, T5, SongNet and other models, out of the box. UDA, non-core word replacement. EDA, simple data augmentation technique: similar words, synonym replacement, random word insertion, deletion, replacement. This project refers to Google's UDA (non-core word replacement) algorithm and EDA algorithm, based on TF-IDF to replace some unimportant words in sentences with synonyms, random word insertion, deletion, replacement, etc. method, generating new text and implementing text augmentation This project realizes the back translation function based on Baidu translation API, first translate Chinese sentences into English, and then translate English into new Chinese. ... -
12
Diffusers-Interpret
Model explainability for Diffusers
...Model explainability for Diffusers. Get explanations for your generated images. Install directly from PyPI. It is possible to visualize pixel attributions of the input image as a saliency map. diffusers-interpret also computes these token/pixel attributions for generating a particular part of the image. To analyze how a token in the input prompt influenced the generation, you can study the token attribution scores. You can also check all the images that the diffusion process generated at the end of each step. Gradient checkpointing also reduces GPU usage, but makes computations a bit slower. -
13
RQ-Transformer
Implementation of RQ Transformer, autoregressive image generation
Implementation of RQ Transformer, which proposes a more efficient way of training multi-dimensional sequences autoregressively. This repository will only contain the transformer for now. You can use this vector quantization library for the residual VQ. This type of axial autoregressive transformer should be compatible with memcodes, proposed in NWT. It would likely also work well with multi-headed VQ. I also think there is something deeper going on, and have generalized this to any number of... -
14
text_renderer
Generate text images for training deep learning ocr model
...Optional CUDA-enabled OpenCV processing can accelerate generation, although the repository points users to a newer successor project. -
15
SVL Simulator
A ROS/ROS2 Multi-robot Simulator for Autonomous Vehicles
LG Electronics America R&D Lab has developed an HDRP Unity-based multi-robot simulator for autonomous vehicle developers. We provide an out-of-the-box solution which can meet the needs of developers wishing to focus on testing their autonomous vehicle algorithms. It currently has integration with The Autoware Foundation's Autoware.auto and Baidu's Apollo platforms, can generate HD maps, and can be immediately used for testing and validation of a whole system with little need for custom... -
16
CAM
Class Activation Mapping
This repository implements Class Activation Mapping (CAM), a technique to expose the implicit attention of convolutional neural networks by generating heatmaps that highlight the most discriminative image regions influencing a network’s class prediction. The method involves modifying a CNN model slightly (e.g., using global average pooling before the final layer) to produce a weighted combination of feature maps as the class activation map. Integration with existing CNNs (with light... -
17
BNFGen
Generates random text based on context-free grammars defined in BNF
BNFGen generates random text based on context-free grammar. You give it a file with your grammar, defined using BNF-like syntax, it gives you a string that follows that grammar. BNFGen is a CLI tool, an OCaml library. There are also official JS bindings available via NPM. Project goals are to make it easy to write and share grammar and give the user total control of and insight into the generation process. -
18
ALAE
Adversarial Latent Autoencoders
ALAE (Adversarial Latent Autoencoders) is a deep learning research implementation that combines autoencoders with generative adversarial networks to produce high-quality image synthesis models. The project implements the architecture introduced in the CVPR research paper on Adversarial Latent Autoencoders, which focuses on improving generative modeling by learning latent representations aligned with adversarial training objectives. Unlike traditional GANs that directly generate images from... -
19
VideoPose3D
Efficient 3D human pose estimation in video using 2D keypoint
...The framework includes pretrained models, data preprocessing utilities, visualization tools, and evaluation scripts for standard benchmarks like Human3.6M. VideoPose3D has been used widely in computer vision research for human motion understanding, activity recognition, and animation generation. -
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chatbot chung is a keywords based probabilities algorythm simple entertainment chatbot with 3D talking openGL avatars written in freebasic. Can import aiml simple question/answer or question/random/answers or single star/ multi srai data saved from "AIML_chung" open source application . Online html5 javascript version with 44 languages multilingual auto detection available on the website (source included in the zip file). SORT gentext text generation algorythm option added (desktop version) .
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imgaug
Image augmentation for machine learning experiments
...Rotate image and segmentation map on it by the same value sampled. Convert keypoints to distance maps, extract pixels within bounding boxes from images, clip polygon to the image plane, etc. Scale segmentation maps, average/max pool of images/maps, pad images to aspect ratios (e.g. to square them). Draw heatmaps, segmentation maps, keypoints, bounding boxes, etc. -
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Accord.NET Framework
Scientific computing, machine learning and computer vision for .NET
The Accord.NET Framework provides machine learning, mathematics, statistics, computer vision, computer audition, and several scientific computing related methods and techniques to .NET. The project is compatible with the .NET Framework. NET Standard, .NET Core, and Mono. -
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Genetic Algorithms Engine - Blackjack
A genetic algortihm engine that evolves blackjack basic strategy.
...The genetic algorithm engine supports various mutation rates, ranked parental selection, stochastic sampling parental selection, cyclic crossover, crossover at each gene, cloning the best individual each generation, and creating random individuals each generation. To use the genetic algorithm engine to search for a different problem's solution, one needs to program a fitness function, the project settings, and a few virtual functions. -
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AI Path
AI Pathfinding Presentation
Presentation of pathfinding algorithms Howto: 1. Create Map (manual or use Map -> Rapid Deployment) 2. Select start point 3. Select end point 4. Choose alg 5. Click Path -> Find -
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WAP RSSI 2 ARFF
This program scans WAP RSSI readings and turns then into an ARFF file.
This program runs on a laptop with a wireless card. It scan for wireless access points (WAPs) and notes their Base Station ID or BSSID and their Received Signal Strength Indicator or RSSI. This data, along with an identifier for the physical location or 'ROOM', and the name of the computer it is being run on are stored as an ARFF file, which is compatible with programs such as WEKA and RAPIDMINER.