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CRAIYON-Teddy_bears_working_on_new_AI_research_underwater_with_1990s_technology.jpg(256 × 256 na pixel, pakadakula: 16 KB, tipo nin MIME: image/jpeg)

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Sumaryo

Pagladawan Output of Craiyon (formerly "DALL-E Mini"). See the model card for information on the model used. Specifically (from the title card): "Images are encoded through a VQGAN encoder, which turns images into a sequence of tokens. Descriptions are encoded through a BART encoder. The output of the BART encoder and encoded images are fed through the BART decoder, which is an auto-regressive model whose goal is to predict the next token. Loss is the softmax cross-entropy between the model prediction logits and the actual image encodings from the VQGAN." Upscaled using Real-ESRGAN. Compare to DALL-E 2 equivalent.
Pinagkunan craiyon.com
Kagsurat Craiyon
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Paglisensya

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presente15:53, 27 Hulyo 2022Thumbnail para sa bersyon kaitong 15:53, 27 Hulyo 2022256 × 256 (16 KB)JPxG== {{int:filedesc}} == {{Information |Description=Output of Craiyon (formerly "DALL-E Mini"). See [https://huggingface.co/dalle-mini/dalle-mini the model card] for information on the model used. Specifically (from the title card): "Images are encoded through a VQGAN encoder, which turns images into a sequence of tokens. Descriptions are encoded through a BART encoder. The output of the BART encoder and encoded images are fed through the BART decoder, which is an auto-regressive model whose go...

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