mlsanityData to 14 Oct 2026

A Review on Generative AI For Text-To-Image and Image-To-Image Generation and Implications To Scientific Images

arXiv 2502.21151 ↗First posted 28 Feb 2025cs.CVcited by 8

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Abstract (arXiv)

This review surveys the state-of-the-art in text-to-image and image-to-image generation within the scope of generative AI. We provide a comparative analysis of three prominent architectures: Variational Autoencoders, Generative Adversarial Networks and Diffusion Models. For each, we elucidate core concepts, architectural innovations, and practical strengths and limitations, particularly for scientific image understanding. Finally, we discuss critical open challenges and potential future research directions in this rapidly evolving field.

Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). Builds on 26 of its 37 references that are in the map. First posted on arXiv 28 Feb 2025.

Builds on

26 of 37 references.

References · 26

  1. 2017Attention Is All You Needcited by 60,361
  2. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,744
  3. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  4. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  5. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,122
  6. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,255
  7. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
  8. 2022Scalable Diffusion Models with Transformerscited by 6,054
  9. 2022Classifier-Free Diffusion Guidancecited by 5,847
  10. 2021Zero-Shot Text-to-Image Generationcited by 4,288
  11. 2021Improved Denoising Diffusion Probabilistic Modelscited by 3,761
  12. 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,588
14 more
  1. 2014Conditional Generative Adversarial Netscited by 2,162
  2. 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,054
  3. 2017AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networkscited by 664
  4. 2016StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networkscited by 577
  5. 2020Reliable Fidelity and Diversity Metrics for Generative Modelscited by 434
  6. 2023Emu Edit: Precise Image Editing via Recognition and Generation Taskscited by 324
  7. 2023Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystackcited by 279
  8. 2022Brain Imaging Generation with Latent Diffusion Modelscited by 268
  9. 2024NVLM: Open Frontier-Class Multimodal LLMscited by 125
  10. 2023Conditional Diffusion Models for Semantic 3D Brain MRI Synthesiscited by 86
  11. 2022A Morphology Focused Diffusion Probabilistic Model for Synthesis of Histopathology Imagescited by 74
  12. 2024AI Hallucinations: A Misnomer Worth Clarifyingcited by 34
  13. 2021SDRBench: Scientific Data Reduction Benchmark for Lossy Compressorscited by 18
  14. 2022Few-shot Image Generation via Masked Discriminationcited by 12

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 5; 2026: 3.

8 papers.

  1. 2026AtelierEval: Agentic Evaluation of Humans & LLMs as Text-to-Image Prompterscited by 8
  2. 2025FaSTA^*: Fast-Slow Toolpath Agent with Subroutine Mining for Efficient Multi-turn Image Editingcited by 4
  3. 2025Exposing Blindspots: Cultural Bias Evaluation in Generative Image Modelscited by 4
  4. 2025Generative AI as a Pillar for Predicting 2D and 3D Wildfire Spread: Beyond Physics-Based Models and Traditional Deep Learningcited by 1
  5. 2026The critical slowing down in training diffusion modelscited by 1
  6. 2025Ascribe New Dimensions to Scientific Data Visualization with VRcited by 0
  7. 2025VAGUEGAN: Stealthy Poisoning and Backdoor Attacks on Image Generative Pipelinescited by 0
  8. 2026Text to Automata Diagrams: Comparing TikZ Code Generation with Direct Image Synthesiscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Generative AI for Vision: A Comprehensive Study of Frameworks and Applications
  2. 2023Text-to-image Diffusion Models in Generative AI: A Survey
  3. 2023RenAIssance: A Survey into AI Text-to-Image Generation in the Era of Large Model
  4. 2022Implementing and Experimenting with Diffusion Models for Text-to-Image Generation
  5. 2024Generative AI in Vision: A Survey on Models, Metrics and Applications
  6. 2025Generative AI in Depth: A Survey of Recent Advances, Model Variants, and Real-World Applications
  7. 2022Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translation
  8. 2024Text-to-Image Synthesis: A Decade Survey