mlsanityData to 7 Oct 2026

Addressing Image Hallucination in Text-to-Image Generation through Factual Image Retrieval

arXiv 2407.10683 ↗First posted 15 Jul 2024cs.CV cs.AIcited by 6

On the map: Diffusion models → Medical imaging and segmentation

Abstract (arXiv)

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.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. Builds on 14 of its 18 references that are in the map. First posted on arXiv 15 Jul 2024.

Builds on

14 of 18 references.

References · 14

  1. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  2. 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,322
  3. 2021TruthfulQA: Measuring How Models Mimic Human Falsehoodscited by 2,919
  4. 2022InstructPix2Pix: Learning to Follow Image Editing Instructionscited by 2,591
  5. 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questionscited by 1,582
  6. 2023IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Modelscited by 1,439
  7. 2021Improving language models by retrieving from trillions of tokenscited by 1,278
  8. 2022Paint by Example: Exemplar-based Image Editing with Diffusion Modelscited by 489
  9. 2022MuRAG: Multimodal Retrieval-Augmented Generator for Open Question Answering over Images and Textcited by 216
  10. 2022Re-Imagen: Retrieval-Augmented Text-to-Image Generatorcited by 208
  11. 2022Retrieval-Augmented Multimodal Language Modelingcited by 125
  12. 2023Self-correcting LLM-controlled Diffusion Modelscited by 112
2 more
  1. 2023Improving Language Models via Plug-and-Play Retrieval Feedbackcited by 87
  2. 2023Exposing Attention Glitches with Flip-Flop Language Modelingcited by 74

Built on by

Cited by year

Citing papers per year: 2024: 3; 2025: 3.

6 papers.

  1. 2025Can Diffusion Models Learn Hidden Inter-Feature Rules Behind Images?cited by 15
  2. 2024Trustworthy Text-to-Image Diffusion Models: A Timely and Focused Surveycited by 6
  3. 2025Trustworthy Medical Imaging with Large Language Models: A Study of Hallucinations Across Modalitiescited by 6
  4. 2024KITTEN: A Knowledge-Intensive Evaluation of Image Generation on Visual Entitiescited by 4
  5. 2024Automatic Scene Generation: State-of-the-Art Techniques, Models, Datasets, Challenges, and Future Prospectscited by 2
  6. 2025Taming the Tri-Space Tension: ARC-Guided Hallucination Modeling and Control for Text-to-Image Generationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Hallucination as an Upper Bound: A New Perspective on Text-to-Image Evaluation
  2. 2025A Survey of Multimodal Hallucination Evaluation and Detection
  3. 2026Reducing Object Hallucination in LVLMs via Emphasizing Image-negative Tokens
  4. 2025GHOST: Hallucination-Inducing Image Generation for Multimodal LLMs
  5. 2025MIHBench: Benchmarking and Mitigating Multi-Image Hallucinations in Multimodal Large Language Models
  6. 2024ConVis: Contrastive Decoding with Hallucination Visualization for Mitigating Hallucinations in Multimodal Large Language Models
  7. 2025Enhancing Visual Reliance in Text Generation: A Bayesian Perspective on Mitigating Hallucination in Large Vision-Language Models
  8. 2024Pensieve: Retrospect-then-Compare Mitigates Visual Hallucination