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

2021/12/20 by Alex Nichol, Prafulla Dhariwal, Nichol, Alex +13 · 1 voice · 288 citations
Computer Science · #Generative Adversarial Networks and Image Synthesis #Multimodal Machine Learning Applications #Image Retrieval and Classification Techniques

paper · pdf · doi:10.48550/arxiv.2112.10741

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

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