Crossref DOIMemberCiteFactoropen accessissn
SJIF Impact Factor 8.659 | ISSN: 2582-3930
IJSREM Logo

International Journal of Scientific Research in Engineering and Management

(Gateway to Global Scholars - An International, Scholarly, Multi-Disciplinary, Open Access Journal)

Peer-Reviewed Journal Designed to Meet UGC's Quality Parameters

HomeCurrent IssuePast IssuesPay OnlineConferencesEditorial BoardAbout UsContact Us
IJSREM

Loading article...

Follow Us

  • Google Scholar
  • ResearchGate
  • Academia.edu
  • Mendeley
  • LinkedIn
  • Facebook
  • Instagram

Journal Policies

  • Peer Review Policy
  • Publication Ethics
  • Copyright Infringement
  • Refund & Cancellation Policy
  • Terms and Conditions
  • Privacy Policy

Journal Identifiers & Licenses

  • ISSN Verified: 2582-3930ISSN Verified: 2582-3930
  • Crossref DOI: 10.55041Crossref DOI: 10.55041
  • SJIF Ranking: 08.659SJIF Ranking: 08.659
  • Licensed under CC BY 4.0Licensed under CC BY 4.0
  • ISO Certification: 9001:2015ISO Certification: 9001:2015
  • Indexed & Cited In: Google ScholarIndexed & Cited In: Google Scholar

Contact Us

International Journal of Scientific Research in Engineering and Management (IJSREM)

#62/1, New No 7, 1st Cross, 2nd Main, Ganganagar, R T Nagar, Bangalore North, Bangalore, Karnataka, India – 560032
[email protected]+91 93911 67991
logos
logos
Working Hours

Mon – Sat: 9:00 AM – 6:00 PM

Sunday: 10:00 AM – 4:00 PM

+91 93911 67991

© 2026 IJSREM – International Journal of Scientific Research in Engineering and Management. This is an open-access journal. Articles are published under the Creative Commons Attribution (CC BY 4.0) License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

DisclaimerPrivacy PolicyTerms and Conditions
ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM34403

From Words to Pictures: A Survey of Text-to-Image Generation Techniques

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM34403

Volume:

8 |

5 |

May 2024

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

Share this article:

Authors

MPS

Mr Puneet Shukla

AS

Ananya Srivastava

AG

Anshika Gupta

KS

Khyati Srivastava

AV

Anjali Vishwakarma

Author Services & Downloads

Full Articles

Abstract

Text-to-image generation, a fascinating intersection of natural language processing and computer vision, has witnessed remarkable progress in recent years. This research paper provides a comprehensive review of the state-of-the-art techniques, challenges, and applications in the field of text-to-image generation. The paper aims to analyze various approaches, discuss their strengths and limitations, and highlight potential directions for future research. Generative Artificial Intelligence (Generative AI) has revolutionized the fusion of textual information and visual content, giving rise to sophisticated Text-to-Image generators. In this context we will discuss dynamic landscape of Generative AI-driven text-to-image synthesis, exploring the state-of- the-art models, underlying architectures, and the impact of training strategies on the quality of generated images. The paper provides a comprehensive overview of Training strategies, encompassing dataset selection and fine-tuning approaches, are scrutinized for their impact on model performance. Common challenges, such as handling ambiguous textual descriptions and ensuring the avoidance of mode collapse, are addressed, offering insights into potential avenues for improvement. Training strategies, encompassing dataset selection and fine-tuning approaches, are scrutinized for their impact on model performance. Common challenges, such as handling ambiguous textual descriptions and ensuring the avoidance of mode collapse, are addressed, offering insights into potential avenues for improvement. Applications of Generative AI text-to-image generators are explored, ranging from content creation to virtual environment design, highlighting their versatility and real-world utility. Ethical considerations surrounding potential misuse, including the creation of deepfakes, are examined to foster a balanced understanding of the technology’s societal implications. The research paper concludes with a forward-looking exploration of fu

KEYWORDS

Cite This Article

Mr Puneet Shukla, Ananya Srivastava, Anshika Gupta, Khyati Srivastava, Anjali Vishwakarma, "From Words to Pictures: A Survey of Text-to-Image Generation Techniques," International Journal of Scientific Research in Engineering and Management, vol. 8, no. 5, May 2024.

Full Article/Conclusion

References

Editorial & Review Transparency

Publication Date

12/31/45431

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

Documents

Review Report (Confidential)
Copyright Form (Office)

Article Metrics & Impact

Article Views

0

Total views since publication

PDF Downloads

0

Total downloads since publication

Geographic Reach

5

Indexing, Archiving & Preservation

Google Scholar
Academia
ResearchGate
Crossref
SSRN
Semantic Scholar
Thomson Reuters
WorldCat
Mendeley
OpenAlex
EBSCO
Zenodo

Digital Preservation: This article is permanently archived on Zenodo and complies with all data preservation standards across major indexing platforms.

Ethics, Compliance & Transparency

Plagiarism Screening

This manuscript has been checked for plagiarism using TurnitinPlagiarismChecker software.

Conflict of Interest

The authors declare no conflict of interest related to this research work.

Funding Information

No funding information available for this research.

Similar & Recommended Articles

Similar Articles by Topic

Related research in the same subject area

Loading similar articles...