#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 text-conditional diffusion with classifier-free guidance and upsampling to achieve unprecedented quality in text-to-image 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/glide-text2im

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 high-quality synthetic images, especially when paired with a guidance technique to trade off diversity for fidelity. We explore diffusion models for the problem of text-conditional image synthesis and compare two different guidance strategies: CLIP guidance and classifier-free 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 text-conditional diffusion model using classifier-free guidance are favored by human evaluators to those from DALL-E, even when the latter uses expensive CLIP reranking. Additionally, we find that our models can be fine-tuned to perform image inpainting, enabling powerful text-driven 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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