mgpt
ru-dalle
| mgpt | ru-dalle | |
|---|---|---|
| 1 | 50 | |
| 195 | 1,639 | |
| 0.5% | -0.3% | |
| 3.1 | 0.0 | |
| 7 months ago | over 1 year ago | |
| Jupyter Notebook | Jupyter Notebook | |
| GNU General Public License v3.0 or later | Apache License 2.0 |
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mgpt
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[P] mGPT model released: a multilingual gpt-3-like model for 61 language
The example usage is at the Github repo https://github.com/ai-forever/mgpt
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?
gpt-3-simple-tutorial - Generate SQL from Natural Language Sentences using OpenAI's GPT-3 Model
NeuralTextToImage - Colabs for text prompt steered image generators
SpeechSuper-API-Samples - Deep learning based speech and pronunciation assessment API for 8 languages.
pytorch-seq2seq - Tutorials on implementing a few sequence-to-sequence (seq2seq) models with PyTorch and TorchText.
link-grammar - The CMU Link Grammar natural language parser
fastai - The fastai deep learning library
practical-pytorch - Go to https://github.com/pytorch/tutorials - this repo is deprecated and no longer maintained
naver-webtoon-faces - Generative models on NAVER Webtoon faces
language-planner - Official Code for "Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents"
ganspace - Discovering Interpretable GAN Controls [NeurIPS 2020]
question_generation - Neural question generation using transformers
FinRL-Meta - FinRL-Meta: Dynamic datasets and market environments for FinRL.