  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.
