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

• URL: http://arxiv.org/abs/2112.10741v2
• Date: Wed, 22 Dec 2021 18:39:39 GMT
• Title: GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models
• Authors: Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, Mark Chen
• Abstract summary: We explore diffusion models for the problem of text-conditional image synthesis and compare two different guidance strategies. We find that the latter is preferred by human evaluators for both photorealism and caption similarity, and often produces photorealistic samples. Our models can be fine-tuned to perform image inpainting, enabling powerful text-driven image editing.
• Score: 16.786221846896108
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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