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[–]Wiskkey[S] 6 points7 points  (26 children)

Technical report (Russian).

Technical report (translated to English by Google Translate).

English language article that is similar to the technical report.

English language demo for ruDALL-E Malevich (XL).

English language ruDALL-E home page.

GitHub repo for ruDALL-E Malevich (XL).

Google Colab notebook ruDALLE-example-generation.

Google Colab notebook ruDALLE-example-generation-A100.

Google Colab notebook ruDALLE-image-prompts-A100.

Notebook at Kaggle.

From the 2nd link:

We trained two versions of the model of different sizes and gave them the names of the great Russian abstract artists - Wassily Kandinsky and Kazimir Malevich:

[1]. ruDALL-E Kandinsky (XXL) with 12 billion parameters;

[2]. ruDALL-E Malevich (XL) containing 1.3 billion parameters.

The base output appears to be at 256x256, but this version of Real-ESRGAN is apparently used to upscale the images in the demo.

Input for the demo apparently needs to be in Russian, and is not auto-translated. Here is a language translator.

[–]Wiskkey[S] 1 point2 points  (0 children)

New Colab notebook mentioned in the GitHub repo: Malevich-3.5GB-vRAM-usage.

[–]Wiskkey[S] 0 points1 point  (0 children)

The demo site now has an English user interface here.

[–]Wiskkey[S] 0 points1 point  (0 children)

Colab notebook Text2Image_v4.

[–]Wiskkey[S] 0 points1 point  (0 children)

Emojich: finetuned ruDALL-E on emojis.

[–]theRIAA 2 points3 points  (0 children)

First two prompts I tried:
a sturdy red chair
an armchair in the shape of an avacado. an armchair imitating an avacado.

Pretty groundbreaking. topk=512 > 4.6 min each on P100.

This seems now good enough to be used as product design inspiration. It might prefer different prompting style than the original Dall-E.

edit: here is a better translator that also allows ru_to_en: 

!pip install -U deep_translator
import time
from deep_translator import GoogleTranslator, MyMemoryTranslator
# langs_dict = GoogleTranslator.get_supported_languages(as_dict=True)
# print(langs_dict)

text = 'text to translate'

tService = GoogleTranslator #GoogleTranslator, MyMemoryTranslator
translated = tService(source='en', target='ru').translate(text)
time.sleep(1)
rev_translated = tService(source='ru', target='en').translate(translated)
print(f'original: {text}\ntranslted: {translated}\nrev-tran: {rev_translated}')

text = translated

Reverse translation is very useful to confirm the intention of your prompt. I used this a lot for CogView.

удобное кресло в форме авокадо. rev-tran: comfortable armchair in the shape of an avocado. (512, 0.97, 3)