Diffusion models explained. How does OpenAI's GLIDE work?

Diffusion models beat GANs in image synthesis, GLIDE generates images from text descriptions, surpassing even DALL-E in terms of photorealism! Check out this video to learn how diffusion models work. Enjoy the visuals!
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Recommended videos:
📺 DALL-E video:
📺 GAN explained video:
📺 CLIP video:

📜 GLIDE paper: Nichol, Alex, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, and Mark Chen. “Glide: Towards photorealistic image generation and editing with text-guided diffusion models.” arXiv preprint arXiv:2112.10741 (2021).
🔗 GLIDE mini, demo:
📜 Diffusion models for image generation: Dhariwal, Prafulla, and Alexander Nichol. “Diffusion models beat GANs on image synthesis.” Advances in Neural Information Processing Systems 34 (2021).
📜 Original diffusion models paper: Sohl-Dickstein, Jascha, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. “Deep unsupervised learning using nonequilibrium thermodynamics.” In International Conference on Machine Learning, pp. 2256-2265. PMLR, 2015.
🔗 Check out this awesome blogpost by Lilian Weng:
🔗 Flow-based models:
🔗 DALL-E blog post:

00:00 Diffusion models are cool
00:33 Weights & Biases (Sponsor)
01:51 4 types of generative models (in 2022)
05:13 Diffusion models explained
08:27 Why are diffusion models good at photorealism? – Diffusion models beat GANs
10:36 GLIDE explained
12:16 Classifier-guided diffusion, CLIP-guided diffusion
13:56 Classifier-free guidance

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Video contains the rock emoji designed by OpenMoji – the open-source emoji and icon project. License: CC BY-SA 4.0

Music 🎵 : Tell Me That I Can’t (Instrumental) by NEFFEX