Addressing Image Hallucination in Text-to-Image Generation through Factual Image Retrieval
This addresses a key issue for users of text-to-image models who need reliable and factually correct outputs, though it is incremental as it builds on existing methods.
The paper tackles the problem of image hallucination in text-to-image generation, where models produce factually inconsistent images, by using factual image retrieval and off-the-shelf editing tools to generate more accurate images.
Text-to-image generation has shown remarkable progress with the emergence of diffusion models. However, these models often generate factually inconsistent images, failing to accurately reflect the factual information and common sense conveyed by the input text prompts. We refer to this issue as Image hallucination. Drawing from studies on hallucinations in language models, we classify this problem into three types and propose a methodology that uses factual images retrieved from external sources to generate realistic images. Depending on the nature of the hallucination, we employ off-the-shelf image editing tools, either InstructPix2Pix or IP-Adapter, to leverage factual information from the retrieved image. This approach enables the generation of images that accurately reflect the facts and common sense.