Paper

GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models

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

2022

Overview

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 https://github.com/openai/glide-text2im.

• Publisher: Cornell University Library, arXiv.org
• Subject: Classifiers / Diffusion / Editing / Evaluation / Image processing / Image quality