hent-AI VS text-to-text-transfer-transformer

This existed years ago; https://github.com/natethegreate/hent-AI

1. DeepCreamPy draws over hentai sensor bars if you direct it where the bar is: https://github.com/gguilt/DeepCreamPy

2. hentAI automates the process: https://github.com/natethegreate/hent-AI

3. [NSFW] Should look at this person on Twitter: https://twitter.com/nate_of_hent_ai

4. [NSFW] PornHub released vintage porn videos upscaled to 4k with AI a while back. The called it the "Remastured Project": https://www.pornhub.com/art/remastured

5. [NSFW] This project shows the limit of AI-wthout-big-tech-or-corporate-support projects. This project creates female genitalia that don't exist in the real world. Project is "This Vagina Does Not Exist": https://thisvaginadoesnotexist.com/about.html

Allo w me to introduce: hent.ai

Yes, if you're talking about anime pictures. There is DeepCreamPy. The original repo has been deleted from Github, but there's information on how to find it in this thread. There's also hent-AI which includes a more experimental method.

Nah, that award goes to this project (Hent-AI)- https://github.com/natethegreate/hent-AI

If I recall correctly, hentAI is the spiritual successor to DeepCreamPy

use https://github.com/natethegreate/hent-AI to automatically detect censored parts

Google released the T5 paper about 5 years ago:

https://arxiv.org/abs/1910.10683

This included full model weights along with a detailed description of the dataset, training process, and ablations that led them to that architecture. T5 was state-of-the-art on many benchmarks when it was released, but it was of course quickly eclipsed by GPT-3.

Following GPT-3, it became much more common for labs to not release full details or model weights. Prior to that, it was common practice from Google (BERT, T5), Meta (BART), OpenAI (GPT1, GPT2) and others to release full training details and model weights.

[2] - https://arxiv.org/abs/1910.10683

T5-style relative encodings https://arxiv.org/abs/1910.10683

11. T5: Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer" (2020) - https://arxiv.org/abs/1910.10683 (Google Research)

You can find a link to the paper here: https://arxiv.org/abs/1910.10683

The Text-to-Text Transformer is a type of neural network architecture that is particularly well-suited for natural language processing tasks involving the generation of text. It was introduced in the paper "Attention is All You Need" by Vaswani et al. and has since become a popular choice for many NLP tasks, including language translation, summarization, and text generation

Found relevant code at https://github.com/google-research/text-to-text-transfer-transformer + all code implementations here