OpenAI DALL·E: Creating Images from Text (Blog Post Explained)
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#openai #science #gpt3 OpenAI's newest model, DALL·E, shows absolutely amazing abilities in generating high-quality images from arbitrary text descriptions. Like GPT-3, the range of applications and the diversity of outputs is astonishing, given that this is a single model, trained on a purely autoregressive task. This model is a significant step towards the combination of text and images in future AI applications. OUTLINE: 0:00 - Introduction 2:45 - Overview 4:20 - Dataset 5:35 - Comparison to GPT-3 7:00 - Model Architecture 13:20 - VQ-VAE 21:00 - Combining VQ-VAE with GPT-3 27:30 - Pre-Training with Relaxation 32:15 - Experimental Results 33:00 - My Hypothesis about DALL·E's inner workings 36:15 - Sparse Attention Patterns 38:00 - DALL·E can't count 39:35 - DALL·E can't global order 40:10 - DALL·E renders different views 41:10 - DALL·E is very good at texture 41:40 - DALL·E can complete a bust 43:30 - DALL·E can do some reflections, but not others 44:15 - DALL·E can do cross-sections of some objects 45:50 - DALL·E is amazing at style 46:30 - DALL·E can generate logos 47:40 - DALL·E can generate bedrooms 48:35 - DALL·E can combine unusual concepts 49:25 - DALL·E can generate illustrations 50:15 - DALL·E sometimes understands complicated prompts 50:55 - DALL·E can pass part of an IQ test 51:40 - DALL·E probably does not have geographical / temporal knowledge 53:10 - Reranking dramatically improves quality 53:50 - Conclusions & Comments Blog: https://openai.com/blog/dall-e/ Links: TabNine Code Completion (Referral): http://bit.ly/tabnine-yannick YouTube: https://www.youtube.com/c/yannickilcher Twitter: https://twitter.com/ykilcher Discord: https://discord.gg/4H8xxDF BitChute: https://www.bitchute.com/channel/yannic-kilcher Minds: https://www.minds.com/ykilcher Parler: https://parler.com/profile/YannicKilcher LinkedIn: https://www.linkedin.com/in/yannic-kilcher-488534136/ If you want to support me, the best thing to do is to share out the content :) If you want to support me financially (completely optional and voluntary, but a lot of people have asked for this): SubscribeStar: https://www.subscribestar.com/yannickilcher Patreon: https://www.patreon.com/yannickilcher Bitcoin (BTC): bc1q49lsw3q325tr58ygf8sudx2dqfguclvngvy2cq Ethereum (ETH): 0x7ad3513E3B8f66799f507Aa7874b1B0eBC7F85e2 Litecoin (LTC): LQW2TRyKYetVC8WjFkhpPhtpbDM4Vw7r9m Monero (XMR): 4ACL8AGrEo5hAir8A9CeVrW8pEauWvnp1WnSDZxW7tziCDLhZAGsgzhRQABDnFy8yuM9fWJDviJPHKRjV4FWt19CJZN9D4n

0:00 - Introduction 2:45 - Overview 4:20 - Dataset 5:35 - Comparison to GPT-3 7:00 - Model Architecture 13:20 - VQ-VAE 21:00 - Combining VQ-VAE with GPT-3 27:30 - Pre-Training with Relaxation 32:15 - Experimental Results 33:00 - My Hypothesis about DALL·E's inner workings 36:15 - Sparse Attention Patterns 38:00 - DALL·E can't count 39:35 - DALL·E can't global order 40:10 - DALL·E renders different views 41:10 - DALL·E is very good at texture 41:40 - DALL·E can complete a bust 43:30 - DALL·E can do some reflections, but not others 44:15 - DALL·E can do cross-sections of some objects 45:50 - DALL·E is amazing at style 46:30 - DALL·E can generate logos 47:40 - DALL·E can generate bedrooms 48:35 - DALL·E can combine unusual concepts 49:25 - DALL·E can generate illustrations 50:15 - DALL·E sometimes understands complicated prompts 50:55 - DALL·E can pass part of an IQ test 51:40 - DALL·E probably does not have geographical / temporal knowledge 53:10 - Reranking dramatically improves quality 53:50 - Conclusions & Comments
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