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Diffusion models have recently been shown to generate high-quality syntheticimages, especially when paired with a guidance technique to trade off diversityfor fidelity. We explore diffusion models for the problem of text-conditionalimage synthesis and compare two different guidance strategies: CLIP guidanceand classifier-free guidance. We find that the latter is preferred by humanevaluators for both photorealism and caption similarity, and often producesphotorealistic samples. Samples from a 3.5 billion parameter text-conditionaldiffusion model using classifier-free guidance are favored by human evaluatorsto those from DALL-E, even when the latter uses expensive CLIP reranking.Additionally, we find that our models can be fine-tuned to perform imageinpainting, enabling powerful text-driven image editing. We train a smallermodel on a filtered dataset and release the code and weights athttps://github.com/openai/glide-text2im.

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