  Text-to-image generative models often reflect the biases of the training
data, leading to unequal representations of underrepresented groups. This study
investigates inclusive text-to-image generative models that generate images
based on human-written prompts and ensure the resulting images are uniformly
distributed across attributes of interest. Unfortunately, directly expressing
the desired attributes in the prompt often leads to sub-optimal results due to
linguistic ambiguity or model misrepresentation. Hence, this paper proposes a
drastically different approach that adheres to the maxim that "a picture is
worth a thousand words". We show that, for some attributes, images can
represent concepts more expressively than text. For instance, categories of
skin tones are typically hard to specify by text but can be easily represented
by example images. Building upon these insights, we propose a novel approach,
ITI-GEN, that leverages readily available reference images for Inclusive
Text-to-Image GENeration. The key idea is learning a set of prompt embeddings
to generate images that can effectively represent all desired attribute
categories. More importantly, ITI-GEN requires no model fine-tuning, making it
computationally efficient to augment existing text-to-image models. Extensive
experiments demonstrate that ITI-GEN largely improves over state-of-the-art
models to generate inclusive images from a prompt. Project page:
https://czhang0528.github.io/iti-gen.
