#glide #openai #diffusion

Diffusion models learn to iteratively reverse a noising process that is applied repeatedly during training. The result can be used for conditional generation as well as various other tasks such as inpainting. OpenAI's GLIDE builds on recent advances in diffusion models and combines textconditional diffusion with classifierfree guidance and upsampling to achieve unprecedented quality in texttoimage samples.

Try it yourself: https://huggingface.co/spaces/valhall...

OUTLINE:
0:00 Intro & Overview
6:10 What is a Diffusion Model?
18:20 Conditional Generation and Guided Diffusion
31:30 Architecture Recap
34:05 Training & Result metrics
36:55 Failure cases & my own results
39:45 Safety considerations

Paper: https://arxiv.org/abs/2112.10741
Code & Model: https://github.com/openai/glidetext2im

More diffusion papers:
https://arxiv.org/pdf/2006.11239.pdf
https://arxiv.org/pdf/2102.09672.pdf

Abstract:
Diffusion models have recently been shown to generate highquality synthetic images, especially when paired with a guidance technique to trade off diversity for fidelity. We explore diffusion models for the problem of textconditional image synthesis and compare two different guidance strategies: CLIP guidance and classifierfree guidance. We find that the latter is preferred by human evaluators for both photorealism and caption similarity, and often produces photorealistic samples. Samples from a 3.5 billion parameter textconditional diffusion model using classifierfree guidance are favored by human evaluators to those from DALLE, even when the latter uses expensive CLIP reranking. Additionally, we find that our models can be finetuned to perform image inpainting, enabling powerful textdriven image editing. We train a smaller model on a filtered dataset and release the code and weights at this https URL.

Authors: Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, Mark Chen

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