pytorch-seq2seq
ru-dalle
| pytorch-seq2seq | ru-dalle | |
|---|---|---|
| 3 | 50 | |
| 5,169 | 1,639 | |
| - | -0.3% | |
| 5.4 | 0.0 | |
| 3 months ago | over 1 year ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | Apache License 2.0 |
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.
pytorch-seq2seq
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A Good Github Repo to Look at (CS388 Natural Language Processing)
I don't know how many people are taking CS388 NLP in Fall 2022, but the assignment is really putting lots of stress on me. I was searching some good materials to prepare for NLP class, and a really good resource to look at is this github repo: https://github.com/bentrevett/pytorch-seq2seq.
- [D] How to truly understand attention mechanism in transformers?
- [D] Resources for Understanding The Original Transformer Paper
ru-dalle
- I trained a custom AI model for fakemon outputs. Feel free to use them for inspiration! No credit needed.
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I trained an AI model to help me design fakebadge concepts. Full album in comments. Please feel free to take these for your own inspiration, too!
It’s a custom trained model, built in rudalle https://github.com/ai-forever/ru-dalle
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Using AI to draft new ideas for legendaries.
It's a custom model, built from rudalle
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SD photorealism to the extreme, is MJ really that better?
ru-dalle has had that feature for quite a while, as it was their first inpainting example notebook:
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2 Google Colab notebooks are available for the large ruDALL-E Kandinsky model (12 billion parameters). The smaller ruDALL-E model has 1.3 billion parameters.
GitHub repo.
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Colab notebook "pharmapsychotic modified rudalle" lets the user choose which of 4 ruDALL-E models to use
Colab notebook. There are actually 5 models, but I doubt the 12B parameter Kandinsky model is actually available per looking at this code.
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Tree in a field.
This was made with a mini version of DALL-E: ruDALL-E
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I trained an AI model to generate images of ancient Roman imperial denarii
Specifically, I fine-tuned ru-DALLE using a dataset consisting of ~1000 images of imperial denarii (ranging from Augustus through Maximinus Thrax) coupled with descriptions of each coin grabbed from OCRE. For example, the obverse description of this coin would be "Head of Augustus, bare, right" and the reverse description would be "Round shield, spear-head, and curved sword".
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New ruDALL-E 1.3 billion parameter model version 3 has been released with ruDALL-E v1.0.0
One way to use the version 3 model is to use this official Colab notebook linked to in the ruDALL-E GitHub repo. I recommend making the changes mentioned in this post. If you want to use the older version 2 model with this Colab notebook, change 'Malevich' to 'Malevich_v2' in line "dalle = get_rudalle_model('Malevich', pretrained=True, fp16=True, device=device)" (relevant source code).
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Preview of ruDALL-E v0.5.0 from the developer
# !pip install rudalle==0.0.1rc8 > /dev/null !pip3 install git+https://github.com/sberbank-ai/ru-dalle.git@feature/new_malevich
What are some alternatives?
Time-Series-Forecasting-Using-LSTM - Time-Series Forecasting on Stock Prices using LSTM
NeuralTextToImage - Colabs for text prompt steered image generators
tensor2tensor - Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.
fastai - The fastai deep learning library
poolformer - PoolFormer: MetaFormer Is Actually What You Need for Vision (CVPR 2022 Oral)
naver-webtoon-faces - Generative models on NAVER Webtoon faces
Behavior-Sequence-Transformer-Pytorch - This is a pytorch implementation for the BST model from Alibaba https://arxiv.org/pdf/1905.06874.pdf
ganspace - Discovering Interpretable GAN Controls [NeurIPS 2020]
sequitur - Library of autoencoders for sequential data
FinRL-Meta - FinRL-Meta: Dynamic datasets and market environments for FinRL.
awesome-speech-recognition-speech-synthesis-papers - Automatic Speech Recognition (ASR), Speaker Verification, Speech Synthesis, Text-to-Speech (TTS), Language Modelling, Singing Voice Synthesis (SVS), Voice Conversion (VC)
gpt-3-simple-tutorial - Generate SQL from Natural Language Sentences using OpenAI's GPT-3 Model