Search Results for "method"
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Showing 12 open source projects for "method"
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ChatGPT.Net
Unofficial .Net Client for ChatGPT
...With this API, developers can send queries to ChatGPT and receive responses in real-time, making it easy to integrate ChatGPT into their own applications. The new method operates without a browser by utilizing a server that has implemented bypass methods to function as a proxy. The library sends requests to the server, which then redirects the request to ChatGPT while bypassing Cloudflare and other bot detection measures. The server then returns the ChatGPT response, ensuring that the method remains effective even if ChatGPT implements changes to prevent bot usage. ... -
2
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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TextGen
textgen, Text Generation models
...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. This project implements the training and prediction of Seq2Seq, ConvSeq2Seq, and BART models based on PyTorch, which can be used for text generation tasks such as text translation. -
4
Petals
Run 100B+ language models at home, BitTorrent-style
Run 100B+ language models at home, BitTorrent‑style. Run large language models like BLOOM-176B collaboratively — you load a small part of the model, then team up with people serving the other parts to run inference or fine-tuning. Single-batch inference runs at ≈ 1 sec per step (token) — up to 10x faster than offloading, enough for chatbots and other interactive apps. Parallel inference reaches hundreds of tokens/sec. Beyond classic language model APIs — you can employ any fine-tuning and... -
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5
revChatGPT
This app allows you to chat with ChatGPT using reverse-engineered API
...You can choose from three authentication methods: Email/Password, Session token, or Access token. Once you have your credentials, you can select your authentication method in the sidebar and provide the required information. If you choose Email/Password, you'll need to provide your email and password. If you choose Session token, you'll need to provide your session token. If you choose Access token, you'll need to provide your access token. revChatGPT is a reverse-engineered ChatGPT API that is not affiliated with OpenAI. ... -
6
audio-diffusion-pytorch
Audio generation using diffusion models, in PyTorch
...Includes models for unconditional audio generation, text-conditional audio generation, diffusion autoencoding, upsampling, and vocoding. The provided models are waveform-based, however, the U-Net (built using a-unet), DiffusionModel, diffusion method, and diffusion samplers are both generic to any dimension and highly customizable to work on other formats. Note: no pre-trained models are provided here, this library is meant for research purposes. -
7
NÜWA - Pytorch
Implementation of NÜWA, attention network for text to video synthesis
Implementation of NÜWA, state of the art attention network for text-to-video synthesis, in Pytorch. It also contains an extension into video and audio generation, using a dual decoder approach. It seems as though a diffusion-based method has taken the new throne for SOTA. However, I will continue on with NUWA, extending it to use multi-headed codes + hierarchical causal transformer. I think that direction is untapped for improving on this line of work. In the paper, they also present a way to condition the video generation based on segmentation mask(s). You can easily do this as well, given you train a VQGanVAE on the sketches beforehand. ... -
8
ruDALL-E
Generate images from texts. In Russian
...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 query in Russian and other languages. You can even combine different languages within a single query. This neural network has been developed and trained by Sber AI researchers in close collaboration with scientists from Artificial Intelligence Research Institute using joined datasets by Sber AI and SberDevices. ... -
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Deep Feature Rotation Multimodal Image
Implementation of Deep Feature Rotation for Multimodal Image
...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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10
CLIP Guided Diffusion
A CLI tool/python module for generating images from text
...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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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) -
12
Text Gen
Almost state of art text generation library
...Something sweet built with Tensorflow and Pytorch(coming soon). Load your data, your data must be in a text format. Download the example data from the example folder. Tune your model to know the best optimizer, activation method to use.
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