text-to-text-transfer-transformer
imagen-pytorch
text-to-text-transfer-transformer | imagen-pytorch | |
---|---|---|
29 | 47 | |
5,909 | 7,787 | |
1.1% | - | |
5.0 | 6.8 | |
3 months ago | 28 days ago | |
Python | Python | |
Apache License 2.0 | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
text-to-text-transfer-transformer
- T5: Text-to-Text-Transfer-Transformer
-
Gemma: New Open Models
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.
-
[P] Free and Fast LLM Finetuning
[2] - https://arxiv.org/abs/1910.10683
- Free and Fast LLM Finetuning
-
[Discussion] Is there a better way than positional encodings in self attention?
T5-style relative encodings https://arxiv.org/abs/1910.10683
-
What were the 40 research papers on the list Ilya Sutskever gave John Carmack?
11. T5: Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer" (2020) - https://arxiv.org/abs/1910.10683 (Google Research)
-
[P] T5 Implementation in PyTorch
You can find a link to the paper here: https://arxiv.org/abs/1910.10683
-
Text-to-Text Transformer (T5-Base Model) Testing For Summarization, Sentiment Classification, and Translation Using Pytorch and Torchtext
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
- AlphaCode by DeepMind
-
[R] LiBai: a large-scale open-source model training toolbox
Found relevant code at https://github.com/google-research/text-to-text-transfer-transformer + all code implementations here
imagen-pytorch
-
Google's StyleDrop can transfer style from a single image
If google doesnt, someone like lucidrains probably would implement it, just like he did for imagen and muse.
- Create a Stable diffusion neural network from scratch.
-
Google just announced an Even better diffusion process.
lucidrains/imagen-pytorch: Implementation of Imagen, Google's Text-to-Image Neural Network, in Pytorch (github.com)
- Karlo, the first large scale open source DALL-E 2 replication is here
-
training imagen
Hi Can someone guide me a little, as to how i can use LAION dataset to train my imagen model? like how i can download the data, and in which format it should be fed to https://github.com/lucidrains/imagen-pytorch code?
-
If everyone in this sub make a donation of $10 then we can train truly open stable diffusion.
If we were to put money into training something, I'd hope we use a better model, like Imagen.
- AI Content Generation, Part 1: Machine Learning Basics
-
DALL-E 2 is switching to a credits system (50 generations for free at first, 15 free per month)
I've been messing around with this open-source implementation. You can get a pretty good idea of the model size by just copying the parameters from the paper.
-
Protests erupt outside of DALL-E offices after pricing implementation, press photograph
I'm waiting on this implementation/training of imagen: https://github.com/lucidrains/imagen-pytorch
-
Show HN: Food Does Not Exist
I'm honestly surprised that they trained a StyleGAN. Recently, the Imagen architecture has been show to be both easier in structure, easier to train, and even faster to produce good results. Combined with the "Elucidating" paper by NVIDIA's Tero Karras you can train a 256px Imagen to tolerable quality within an hour on a RTX 3090.
Here's a PyTorch implementation by the LAION people:
https://github.com/lucidrains/imagen-pytorch
And here's 2 images I sampled after training it for some hours, like 2 hours base model + 4 hours upscaler:
https://imgur.com/a/46EZsJo
What are some alternatives?
fairseq - Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
dalle-mini - DALLĀ·E Mini - Generate images from a text prompt
tortoise-tts - A multi-voice TTS system trained with an emphasis on quality
DALLE2-pytorch - Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis neural network, in Pytorch
DeepCreamPy - Decensoring Hentai with Deep Neural Networks
DALLE-pytorch - Implementation / replication of DALL-E, OpenAI's Text to Image Transformer, in Pytorch
latent-diffusion - High-Resolution Image Synthesis with Latent Diffusion Models
DeepCreamPy - deeppomf's DeepCreamPy + some updates
majesty-diffusion - Majesty Diffusion by @Dango233(@Dango233max) and @apolinario (@multimodalart)
CogVideo - Text-to-video generation. The repo for ICLR2023 paper "CogVideo: Large-scale Pretraining for Text-to-Video Generation via Transformers"