Search Results for "generate"
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Showing 31 open source projects for "generate"
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YData Synthetic
Synthetic data generators for tabular and time-series data
...Several example Jupyter Notebooks and Python scripts are included, to show how to use the different architectures. YData synthetic has now a UI interface to guide you through the steps and inputs to generate structure tabular data. The streamlit app is available form v1.0.0 onwards. -
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InvokeAI
InvokeAI is a leading creative engine for Stable Diffusion models
...It runs on Windows, Mac and Linux machines, and runs on GPU cards with as little as 4 GB or RAM. InvokeAI is a leading creative engine built to empower professionals and enthusiasts alike. Generate and create stunning visual media using the latest AI-driven technologies. InvokeAI offers an industry leading Web Interface, interactive Command Line Interface, and also serves as the foundation for multiple commercial products. This fork is supported across Linux, Windows and Macintosh. Linux users can use either an Nvidia-based card (with CUDA support) or an AMD card (using the ROCm driver). ... -
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CodiumAI PR-Agent
AI-Powered tool for automated pull request analysis
...You can try GPT-4 powered PR-Agent, on your public GitHub repository, instantly. Just mention @CodiumAI-Agent and add the desired command in any PR comment. The agent will generate a response based on your command. -
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CTGAN
Conditional GAN for generating synthetic tabular data
CTGAN is a collection of Deep Learning based synthetic data generators for single table data, which are able to learn from real data and generate synthetic data with high fidelity. If you're just getting started with synthetic data, we recommend installing the SDV library which provides user-friendly APIs for accessing CTGAN. The SDV library provides wrappers for preprocessing your data as well as additional usability features like constraints. When using the CTGAN library directly, you may need to manually preprocess your data into the correct format, for example, continuous data must be represented as floats. ... -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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Diffusers
State-of-the-art diffusion models for image and audio generation
Diffusers is the go-to library for state-of-the-art pretrained diffusion models for generating images, audio, and even 3D structures of molecules. Whether you're looking for a simple inference solution or training your own diffusion models, Diffusers is a modular toolbox that supports both. Our library is designed with a focus on usability over performance, simple over easy, and customizability over abstractions. State-of-the-art diffusion pipelines that can be run in inference with just a... -
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Synthetic Data Vault (SDV)
Synthetic Data Generation for tabular, relational and time series data
The Synthetic Data Vault (SDV) is a Synthetic Data Generation ecosystem of libraries that allows users to easily learn single-table, multi-table and timeseries datasets to later on generate new Synthetic Data that has the same format and statistical properties as the original dataset. Synthetic data can then be used to supplement, augment and in some cases replace real data when training Machine Learning models. Additionally, it enables the testing of Machine Learning or other data dependent software systems without the risk of exposure that comes with data disclosure. ... -
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BERTopic
Leveraging BERT and c-TF-IDF to create easily interpretable topics
BERTopic is a topic modeling technique that leverages transformers and c-TF-IDF to create dense clusters allowing for easily interpretable topics whilst keeping important words in the topic descriptions. BERTopic supports guided, supervised, semi-supervised, manual, long-document, hierarchical, class-based, dynamic, and online topic modeling. It even supports visualizations similar to LDAvis! Corresponding medium posts can be found here, here and here. For a more detailed overview, you can... -
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Simple StyleGan2 for Pytorch
Simplest working implementation of Stylegan2
...You can increase the network capacity (which defaults to 16) to improve generation results, at the cost of more memory. By default, if the training gets cut off, it will automatically resume from the last checkpointed file. Once you have finished training, you can generate images from your latest checkpoint. If a previous checkpoint contained a better generator, (which often happens as generators start degrading towards the end of training), you can load from a previous checkpoint with another flag. A technique used in both StyleGAN and BigGAN is truncating the latent values so that their values fall close to the mean. ... -
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Kaleidoscope-SDK
User toolkit for analyzing and interfacing with Large Language Models
...It provides a simple interface to launch LLMs on an HPC cluster, asking them to perform basic features like text generation, but also retrieve intermediate information from inside the model, such as log probabilities and activations. Users must authenticate using their Vector Institute cluster credentials. This can be done interactively instantiating a client object. This will generate an authentication token that will be used for all subsequent requests. The token will expire after 30 days, at which point the user will be prompted to re-authenticate. -
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Shap-E
Generate 3D objects conditioned on text or images
...The model is built with a two-stage architecture: first an encoder that maps existing 3D assets into parameterizations of implicit functions, and then a conditional diffusion model trained on those parameterizations to generate new assets. Because it works at the level of implicit functions, Shap-E can render output both as textured meshes and NeRF-style volumetric renderings. The repository contains sample notebooks (e.g. sample_text_to_3d.ipynb, sample_image_to_3d.ipynb) so users can try out text → 3D or image → 3D generation. The code is distributed under the MIT license, and includes a “model card” that documents limitations, recommended use, and ethical considerations. -
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DALL-E 2 - Pytorch
Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis
...To train CLIP, you can either use x-clip package, or join the LAION discord, where a lot of replication efforts are already underway. Then, you will need to train the decoder, which learns to generate images based on the image embedding coming from the trained CLIP. -
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ChatFred
Alfred workflow using ChatGPT, DALL·E 2 and other models for chatting
...Verify answers if they are important. Talk with ChatGPT via the cf keyword. Answers will show as Large Type. Alternatively, use the Universal Action, Fallback Search, or Hotkey. To generate text with InstructGPT models and see results in-line, use the cft keyword. ⤓ Install on the Alfred Gallery or download it over GitHub and add your OpenAI API key. If you have used ChatGPT or DALL·E 2, you already have an OpenAI account. Otherwise, you can sign up here - You will receive $5 in free credit, no payment data is required. ... -
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GPT-Code UI
An open source implementation of OpenAI's ChatGPT Code interpreter
An open source implementation of OpenAI's ChatGPT Code interpreter. Simply ask the OpenAI model to do something and it will generate & execute the code for you. You can put a .env in the working directory to load the OPENAI_API_KEY environment variable. For Azure OpenAI Services, there are also other configurable variables like deployment name. See .env.azure-example for more information. Note that model selection on the UI is currently not supported for Azure OpenAI Services. -
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texturize
Generate photo-realistic textures based on source images
Generate photo-realistic textures based on source images. Remix, remake, mashup! Useful if you want to create variations on a theme or elaborate on an existing texture. A command-line tool and Python library to automatically generate new textures similar to a source image or photograph. It's useful in the context of computer graphics if you want to make variations on a theme or expand the size of an existing texture. -
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Quote2Image
A Python library for turning text quotes into graphical images
A Python library for turning text quotes into graphical images. Generate an image using RGB background and foreground. The package comes with a built-in GenerateColors function that generates a fg and bg color with the correct amount of luminosity and returns them in tuples. Generate an image using a custom background image. The package comes with a builtin GenerateColors function that generates a fg and bg color with the correct amount of luminosity and returns them in tuples. ... -
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DeepMozart
Audio generation using diffusion models
Audio generation using diffusion models in PyTorch. The code is based on the audio-diffusion-pytorch repository. -
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Point-E
Point cloud diffusion for 3D model synthesis
point-e is the official repository for Point-E, a generative model developed by OpenAI that produces 3D point clouds from textual (or image) prompts. Its principal advantage is speed: it can generate 3D assets in just 1–2 minutes on a single GPU, which is significantly faster than many competing text-to-3D models. The model works via a two-stage diffusion approach: first, it uses a text → image diffusion network to produce a synthetic 2D view consistent with the prompt; then a second diffusion model converts that image into a 3D point cloud. ... -
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Minimal text diffusion
A minimal implementation of diffusion models for text generation
A minimal implementation of diffusion models of text: learns a diffusion model of a given text corpus, allowing to generate text samples from the learned model. The main idea was to retain just enough code to allow training a simple diffusion model and generating samples, remove image-related terms, and make it easier to use. To train a model, run scripts/train.sh. By default, this will train a model on the simple corpus. However, you can change this to any text file using the --train_data argument. ... -
19
BCI
BCI: Breast Cancer Immunohistochemical Image Generation
...We have released the trained model on BCI and LLVIP datasets. We host a competition for breast cancer immunohistochemistry image generation on Grand Challenge. Project pix2pix provides a python script to generate pix2pix training data in the form of pairs of images {A,B}, where A and B are two different depictions of the same underlying scene, these can be pairs {HE, IHC}. Then we can learn to translate A(HE images) to B(IHC images). The evaluation of human epidermal growth factor receptor 2 (HER2) expression is essential to formulate a precise treatment for breast cancer. ... -
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AI Chatbots based on GPT Architecture
Training & Implementation of chatbots leveraging GPT-like architecture
...This repo is a simple attempt to help solve that problem. ai-msgbot covers the practical use case of building a chatbot that sounds like you (or some dataset/persona you choose) by training a text-generation model to generate conversation in a consistent structure. This structure is then leveraged to deploy a chatbot that is a "free-form" model that consistently replies like a human. Some of the trained models can be interacted with through the HuggingFace spaces and model inference APIs on the ETHZ Analytics Organization page on huggingface.co. -
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min(DALL·E)
min(DALL·E) is a fast, minimal port of DALL·E Mini to PyTorch
This is a fast, minimal port of Boris Dayma's DALL·E Mini (with mega weights). It has been stripped down for inference and converted to PyTorch. The only third-party dependencies are numpy, requests, pillow and torch. The required models will be downloaded to models_root if they are not already there. Set the dtype to torch.float16 to save GPU memory. If you have an Ampere architecture GPU you can use torch.bfloat16. Set the device to either cuda or "cpu". Once everything has finished... -
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ruDALL-E
Generate images from texts. In Russian
We present a family of generative models from SberDevices and Sber AI! Models allow you to create images that did not exist before. All you need is a text description in Russian or another language. Try to create unique images together with generative artists using your own formulations. Ask generative artists to depict something special for you as well. The Kandinsky 2.0 model uses the reverse diffusion method and creates colorful images on various topics in a matter of seconds by text... -
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RQ-Transformer
Implementation of RQ Transformer, autoregressive image generation
...A short sequence length is important for an AR model to reduce its computational costs to consider long-range interactions of codes. However, we postulate that previous VQ cannot shorten the code sequence and generate high-fidelity images together in terms of the rate-distortion trade-off. -
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VQGAN-CLIP web app
Local image generation using VQGAN-CLIP or CLIP guided diffusion
...Thus, this simple Streamlit app for generating VQGAN-CLIP images on a local environment. Be advised that you need a beefy GPU with lots of VRAM to generate images large enough to be interesting. (Hello Quadro owners!). -
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CLIP Guided Diffusion
A CLI tool/python module for generating images from text
A CLI tool/python module for generating images from text using guided diffusion and CLIP from OpenAI. Text to image generation (multiple prompts with weights). Non-square Generations (experimental) Generate portrait or landscape images by specifying a number to offset the width and/or height. Uses fewer timesteps over the same diffusion schedule. Sacrifices accuracy/alignment for quicker runtime. options: - 25, 50, 150, 250, 500, 1000, ddim25,ddim50,ddim150, ddim250,ddim500,ddim1000 (default: 1000) Prepending a number with ddim will use the ddim scheduler. e.g. ddim25 will use the 25 timstep ddim scheduler. ...