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Tsinghua U & BAAI’s CogView2 Achieves SOTA Competitive Text-to-Image Generation With 10x Speedups
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In the new paper CogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformers, Tsinghua University and the Beijing Academy of Artificial Intelligence researchers pretrain a Cross-Modal general Language Model (CogLM) for text and image token prediction and finetune it for fast super-resolution. The resulting CogView2 hierarchical text-to-image system achieves significant speedups while generating images with better quality at comparable resolutions.
The post Tsinghua U & BAAI’s CogView2 Achieves SOTA Competitive Text-to-Image Generation With 10x Speedups first appeared on Synced.
ai artificial intelligence baai deep-neural-networks generation image image generation machine learning machine learning & data science ml research sota technology text text-to-image transformers