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

Alexander Nichol · Prafulla Dhariwal · Aditya Ramesh · Pranav Shyam · Pamela Mishkin · Bob McGrew · Ilya Sutskever · Mark Chen

Hall E #326

Keywords: [ DL: Generative Models and Autoencoders ]


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

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