DALL·E | 台中監視器地圖
![DALL·E](https://i.imgur.com/9rbDwju.jpg)
2021年1月5日—AI-generatedimages.Editpromptorviewmoreimages.Text&imageprompt.theexactsamecatonthetopasasketchonthebottom.
![DALL·E](https://i.imgur.com/9rbDwju.jpg)
DALL·E is a simple decoder-only transformer that receives both the text and the image as a single stream of 1280 tokens—256 for the text and 1024 for the image—and models all of them autoregressively. The attention mask at each of its 64 self-attention layers allows each image token to attend to all text tokens. DALL·E uses the standard causal mask for the text tokens, and sparse attention for the image tokens with either a row, column, or convolutional attention pattern, depending on the layer. We provide more details about the architecture and training procedure in our paper[1].
Text-to-image synthesis has been an active area of research since the pioneering work of Reed et. al,[reference-1] whose approach uses a GAN conditioned on text embeddings. The embeddings are produced by an encoder pretrained using a contrastive loss, not unlike CLIP. StackGAN[refer...
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