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README.md
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RuDOLPH: One Hyper-Modal Transformer can be creative as DALL-E and smart as CLIP
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<img src="https://raw.githubusercontent.com/
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Model was trained by [Sber AI](https://github.com/sberbank-ai) and [SberDevices](https://sberdevices.ru/) teams.
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The primary proposed method is to modify the sparse transformer's attention mask to better control multi-modalities and up to the next level with "hyper-modality". It allows us to calculate the transitions of modalities in both directions, unlike another similar work DALL-E Transformer, which used only one direction, "text to image". The proposed "image to right text" direction is achieved by extension sparse attention mask to the right for auto-repressively text generation with image condition without attention to left text.
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<img src="https://raw.githubusercontent.com/
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# Authors
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RuDOLPH: One Hyper-Modal Transformer can be creative as DALL-E and smart as CLIP
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<img src="https://raw.githubusercontent.com/sberbank-ai/ru-dolph/master/pics/rudolph-generated.png" height="60" border="2"/>
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Model was trained by [Sber AI](https://github.com/sberbank-ai) and [SberDevices](https://sberdevices.ru/) teams.
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The primary proposed method is to modify the sparse transformer's attention mask to better control multi-modalities and up to the next level with "hyper-modality". It allows us to calculate the transitions of modalities in both directions, unlike another similar work DALL-E Transformer, which used only one direction, "text to image". The proposed "image to right text" direction is achieved by extension sparse attention mask to the right for auto-repressively text generation with image condition without attention to left text.
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<img src="https://raw.githubusercontent.com/sberbank-ai/ru-dolph/master/pics/attention_masks.png" height="40" border="2"/>
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# Authors
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