Open Source Generative AI - Page 4
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BCI
BCI: Breast Cancer Immunohistochemical Image Generation
Breast Cancer Immunohistochemical Image Generation through Pyramid Pix2pix. 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. The routine evaluation of HER2 is conducted with immunohistochemical techniques (IHC), which is very expensive. Therefore, for the first time, we propose a breast cancer immunohistochemical (BCI) benchmark attempting to synthesize IHC data directly with the paired hematoxylin and eosin (HE) stained images. -
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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 read the paper or see a brief overview. After having trained our BERTopic model, we can iteratively go through hundreds of topics to get a good understanding of the topics that were extracted. However, that takes quite some time and lacks a global representation. Instead, we can visualize the topics that were generated in a way very similar to LDAvis. By default, the main steps for topic modeling with BERTopic are sentence-transformers, UMAP, HDBSCAN, and c-TF-IDF run in sequence. -
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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. BNFGen provides a "DSL" for grammar definitions. It's a familiar BNF-like syntax with a few additions. One problem with using straight BNF for driving language generators is that you have no control over the process. BNFGen adds two features to fix that. The canonical way to express repetition in BNF is to use a self-referential recursive rule. In classic BNF, that can easily lead to the process terminating to early, since there's a 50% chance that it will take the non-recursive alternative. -
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BertViz
BertViz: Visualize Attention in NLP Models (BERT, GPT2, BART, etc.)
BertViz is an interactive tool for visualizing attention in Transformer language models such as BERT, GPT2, or T5. It can be run inside a Jupyter or Colab notebook through a simple Python API that supports most Huggingface models. BertViz extends the Tensor2Tensor visualization tool by Llion Jones, providing multiple views that each offer a unique lens into the attention mechanism. The head view visualizes attention for one or more attention heads in the same layer. It is based on the excellent Tensor2Tensor visualization tool. The model view shows a bird's-eye view of attention across all layers and heads. The neuron view visualizes individual neurons in the query and key vectors and shows how they are used to compute attention. -
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Big Sleep
A simple command line tool for text to image generation
A simple command line tool for text to image generation, using OpenAI's CLIP and a BigGAN. Ryan Murdock has done it again, combining OpenAI's CLIP and the generator from a BigGAN! This repository wraps up his work so it is easily accessible to anyone who owns a GPU. You will be able to have the GAN dream-up images using natural language with a one-line command in the terminal. User-made notebook with bug fixes and added features, like google drive integration. Images will be saved to wherever the command is invoked. If you have enough memory, you can also try using a bigger vision model released by OpenAI for improved generations. You can set the number of classes that you wish to restrict Big Sleep to use for the Big GAN with the --max-classes flag as follows (ex. 15 classes). This may lead to extra stability during training, at the cost of lost expressivity. -
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Bottery
A conversational agent prototyping platform
Bottery is a prototyping environment and mini-language for designing conversational agents as explicit state machines rather than opaque bundles of code. It encourages authors to model intents, dialog turns, and transitions in a compact, declarative form that’s easy to visualize and simulate. A built-in simulator lets you “play” the conversation, test edge cases, and tweak flows without wiring up back-end services first. Because behavior is defined in a DSL, teams can version the conversation design alongside code and keep product, research, and engineering aligned. The repo includes examples of slots, branching, and side effects, showing how to express common patterns like confirmations, fallbacks, and small talk. Bottery’s value is in reducing the gap between whiteboard diagrams and a running prototype you can test with real users. -
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CIPS-3D
3D-aware GANs based on NeRF (arXiv)
3D-aware GANs based on NeRF (arXiv). This repository contains the code of the paper, CIPS-3D: A 3D-Aware Generator of GANs Based on Conditionally-Independent Pixel Synthesis. The problem of mirror symmetry refers to the sudden change of the direction of the bangs near the yaw angle of pi/2. We propose to use an auxiliary discriminator to solve this problem. Note that in the initial stage of training, the auxiliary discriminator must dominate the generator more than the main discriminator does. Otherwise, if the main discriminator dominates the generator, the mirror symmetry problem will still occur. In practice, progressive training is able to guarantee this. We have trained many times from scratch. Adding an auxiliary discriminator stably solves the mirror symmetry problem. -
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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. This method may be better at shorter timestep_respacing values. Multiple prompts can be specified with the | character. You may optionally specify a weight for each prompt. -
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CRSLab
CRSLab is an open-source toolkit
CRSLab is an open-source toolkit for building Conversational Recommender System (CRS). It is developed based on Python and PyTorch. CRSLab has the following highlights. Comprehensive benchmark models and datasets: We have integrated commonly-used 6 datasets and 18 models, including graph neural network and pre-training models such as R-GCN, BERT and GPT-2. We have preprocessed these datasets to support these models, and release for downloading. Extensive and standard evaluation protocols: We support a series of widely-adopted evaluation protocols for testing and comparing different CRS. General and extensible structure: We design a general and extensible structure to unify various conversational recommendation datasets and models, in which we integrate various built-in interfaces and functions for quickly development. Easy to get started: We provide simple yet flexible configuration for new researchers to quickly start in our library. Human-machine interaction interfaces. -
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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. Discrete data must be represented as ints or strings. The data should not contain any missing values. -
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ChatFred
Alfred workflow using ChatGPT, DALL·E 2 and other models for chatting
Alfred workflow using ChatGPT, DALL·E 2 and other models for chatting, image generation and more. Access ChatGPT, DALL·E 2, and other OpenAI models. Language models often give wrong information. 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. Afterward you can create your API key. To start a conversation with ChatGPT either use the keyword cf, setup the workflow as a fallback search in Alfred or create your custom hotkey to directly send the clipboard content to ChatGPT. -
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ChatGPT Console Client in Golang
ChatGPT Console client in Golang
chatgpt: Chat GPT console client in Golang. A Golang console client for ChatGPT using GPT. Request your OpenAPI key. -
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ChatGPT-Reviewer
Automated pull requests reviewing and issues triaging with ChatGPT
Automated pull requests reviewing and issues triaging with ChatGPT. Create an OpenAI API key here, and then set the key as an action secret in your repository named OPENAI_API_KEY. The ChatGPT reviewer PRs are also getting reviewed by ChatGPT, refer the pull requests for the sample review comments. In order to protect public repositories for malicious users, Github runs all pull request workflows raised from repository forks with a read-only token and no access to secrets. -
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Coframe
Coframe brings your UX to life with AI-powered optimization
Bring your UX to life with AI-powered optimization and personalization. Coframe brings the content of your app or website to life through AI-powered optimization, personalization, and overall self-improvement. It takes minutes to integrate, and the ROI is clear to measure. Your website or app gains self-enhancing abilities with Coframe, learning from real-world performance. It's A/B testing, but with a serious upgrade. Coframe uses the latest in AI to generate copy that is tailored to your users. Resulting performance data is fed back in to continuously improve your platform's content. With Coframe, your website or app works for you 24/7, not the other way around. All it takes to get up and running is a few lines of code. Coframe gives you full control and visibility. Our mission is to give every digital interface its own sense of intelligence. -
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Critterding2
Evolving Artificial Life
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DALL-E 2 - Pytorch
Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis
Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis neural network, in Pytorch. The main novelty seems to be an extra layer of indirection with the prior network (whether it is an autoregressive transformer or a diffusion network), which predicts an image embedding based on the text embedding from CLIP. Specifically, this repository will only build out the diffusion prior network, as it is the best performing variant (but which incidentally involves a causal transformer as the denoising network) To train DALLE-2 is a 3 step process, with the training of CLIP being the most important. 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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DALL-E in Pytorch
Implementation / replication of DALL-E, OpenAI's Text to Image
Implementation / replication of DALL-E (paper), OpenAI's Text to Image Transformer, in Pytorch. It will also contain CLIP for ranking the generations. Kobiso, a research engineer from Naver, has trained on the CUB200 dataset here, using full and deepspeed sparse attention. You can also skip the training of the VAE altogether, using the pretrained model released by OpenAI! The wrapper class should take care of downloading and caching the model for you auto-magically. You can also use the pretrained VAE offered by the authors of Taming Transformers! Currently only the VAE with a codebook size of 1024 is offered, with the hope that it may train a little faster than OpenAI's, which has a size of 8192. In contrast to OpenAI's VAE, it also has an extra layer of downsampling, so the image sequence length is 256 instead of 1024 (this will lead to a 16 reduction in training costs, when you do the math). -
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DCGAN in TensorLayerX
The Simplest DCGAN Implementation
This is an implementation of Deep Convolutional Generative Adversarial Networks. First, download the aligned face images from google or baidu to a data folder. Please place dataset 'img_align_celeba.zip' under 'data/celebA/' by default. -
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DCVGAN
DCVGAN: Depth Conditional Video Generation, ICIP 2019.
This paper proposes a new GAN architecture for video generation with depth videos and color videos. The proposed model explicitly uses the information of depth in a video sequence as additional information for a GAN-based video generation scheme to make the model understands scene dynamics more accurately. The model uses pairs of color video and depth video for training and generates a video using the two steps. Generate the depth video to model the scene dynamics based on the geometrical information. To add appropriate color to the geometrical information of the scene, the domain translation from depth to color is performed for each image. This model has three networks in the generator. In addition, the model has two discriminators. -
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Dalai
The simplest way to run LLaMA on your local machine
Run LLaMA and Alpaca on your computer. Dalai runs on all of the following operating systems, Linux, Mac, and Windows. Runs on most modern computers. Unless your computer is very very old, it should work. -
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Data augmentation
List of useful data augmentation resources
List of useful data augmentation resources. You will find here some links to more or less popular github repos, libraries, papers, and other information. Data augmentation can be simply described as any method that makes our dataset larger. To create more images for example, we could zoom in and save a result, we could change the brightness of the image or rotate it. To get a bigger sound dataset we could try to raise or lower the pitch of the audio sample or slow down/speed up. Keypoints/landmarks Augmentation, usually done with image augmentation (rotation, reflection) or graph augmentation methods (node/edge dropping) Spectrograms/Melspectrograms, usually done with time series data augmentation (jittering, perturbing, warping) or image augmentation (random erasing) -
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Deep Daze
Simple command line tool for text to image generation
Simple command-line tool for text to image generation using OpenAI's CLIP and Siren (Implicit neural representation network). In true deep learning fashion, more layers will yield better results. Default is at 16, but can be increased to 32 depending on your resources. Technique first devised and shared by Mario Klingemann, it allows you to prime the generator network with a starting image, before being steered towards the text. Simply specify the path to the image you wish to use, and optionally the number of initial training steps. We can also feed in an image as an optimization goal, instead of only priming the generator network. Deepdaze will then render its own interpretation of that image. The regular mode for texts only allows 77 tokens. If you want to visualize a full story/paragraph/song/poem, set create_story to True. -
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Deep Feature Rotation Multimodal Image
Implementation of Deep Feature Rotation for Multimodal Image
Official implementation of paper Deep Feature Rotation for Multimodal Image Style Transfer [NICS'21] We propose a simple method for representing style features in many ways called Deep Feature Rotation (DFR), while still achieving effective stylization compared to more complex methods in style transfer. Our approach is a representative of the many ways of augmentation for intermediate feature embedding without consuming too much computational expense. Prepare your content image and style image. I provide some in the data/content and data/style and you can try to use them easily. We provide a visual comparison between other rotation angles that do not appear in the paper. The rotation angles will produce a very diverse number of outputs. This has proven the effectiveness of our method with other methods. -
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Dickinson
Text generation language
Dickinson is a text-generation language. You can try out the language on the web without installing anything. Binaries for some platforms are available on the releases page. There is an install script that will try to download the right release for your computer. -
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Diffusers-Interpret
Model explainability for Diffusers
diffusers-interpret is a model explainability tool built on top of 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.
