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

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. Additionally, we find that our models can be fine-tuned to perform image inpainting, enabling powerful text-driven image editing.

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• 1 We explore diffusion models for the problem of text-conditional image synthesis and compare two different guidance strategies: CLIP guidance and classifier-free guidance.
• 2 We find that the latter is preferred by human evaluators for both photorealism and caption similarity, and often produces photorealistic samples.
• 3 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.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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