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In this video I cover a new paper from OpenAI – “GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models” where they combine diffusion models with transformers to outperform their older DALL-E model.
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GLIDE paper: https://arxiv.org/abs/2112.10741
GLIDE code: https://github.com/openai/glide-text2im
Learning about diffusion models
Papers:
Seminal (2015): https://arxiv.org/pdf/1503.03585.pdf
DDPM (2020): https://arxiv.org/pdf/2006.11239.pdf
OpenAI (1): https://arxiv.org/pdf/2102.09672.pdf
OpenAI (2): https://arxiv.org/pdf/2105.05233.pdf
Blogs:
Score-based models: https://yang-song.github.io/blog/2021/score/
Diffusion models: https://lilianweng.github.io/lil-log/2021/07/11/diffusion-models.html
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Timetable:
00:00 Intro to GLIDE – results
04:00 Intro to diffusion models
07:10 Inpainting and other awesome results
11:05 Diffusion models in depth
20:45 VAE inspired loss
31:30 GLIDE pipeline (diffusion + transformers)
34:15 Guided diffusion
38:00 Classifier-free guidance
42:25 CLIP guidance
45:25 Comparison with other models
48:30 Safety considerations
49:25 Failure cases
51:40 Outro
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#glide #openai #diffusionmodels
