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Artykuły w czasopismach na temat „Text-to-Image generation”

Autor: Grafiati

Data publikacji: 2 czerwca 2025

Data aktualizacji: 31 lipca 2025

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1

V, Pavithra, Rosy S, Srinishanthini R. B, and Prinslin L. "Text-To-Image Generation Using AI." International Journal of Research Publication and Reviews 4, no. 4 (2023): 4932–37. http://dx.doi.org/10.55248/gengpi.234.4.38568.

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Prasad, Timande Sahil Dhawale Shreyas Waghmare Sanket Dhumane Rushikesh Pimple Kshitij Madpuwar Dr. S. W. Mohod. "Text-to-Image Generation Using Deep Learning." International Journal of Advanced Innovative Technology in Engineering 10, no. 2 (2025): 113–15. https://doi.org/10.5281/zenodo.15407889.

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The Text-to-Image generation tasks has stand out as an innovation in the field of computer vision (CV). Recent innovations and advancements in generative models have led to the development of various text-to-image generation techniques. This paper presents a comprehensive implementation of a text to image generation system leveraging Stable Diffusion, a diffusion based generative model that can produce high-quality images with fine-grained details. The system is integrated with a Flask-based web application, providing users with a user-friendly interface to generate images from textual prompts

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Chaddha, Mahima, Sneha Kashid, and Snehal Bhosale Prof Radha Deoghare. "Deep Learning for X-ray Image to Text Generation." International Journal of Trend in Scientific Research and Development Volume-3, Issue-3 (2019): 1679–82. http://dx.doi.org/10.31142/ijtsrd23168.

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Avhad, Pranjali. "WordCanvas: Text-to-Image Generation." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem32152.

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This project investigates the novel use of stable dif- fusion techniques to generate high-quality images from detailed text descriptions. The combination of natural language under- standing and computer vision in text-to-image conversion opens up new possibilities for content creation and communication. Using cutting-edge stable diffusion models, our project builds a solid foundation for the generation process, which includes tokenization, pre-processing, specialized architecture design, and post-processing techniques. The advantages include eye-catching images, increased user engagement, cont

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Konarieva, I., D. Pydorenko, and O. Turuta. "A SURVEY OF METHODS OF TEXT-TO-IMAGE TRANSLATION." Bionics of Intelligence 2, no. 93 (2019): 64–68. http://dx.doi.org/10.30837/bi.2019.2(93).11.

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The given work considers the existing methods of text compression (finding keywords or creating summary) using RAKE, Lex Rank, Luhn, LSA, Text Rank algorithms; image generation; text-to-image and image-to-image translation including GANs (generative adversarial networks). Different types of GANs were described such as StyleGAN, GauGAN, Pix2Pix, CycleGAN, BigGAN, AttnGAN. This work aims to show ways to create illustrations for the text. First, key information should be obtained from the text. Second, this key information should be transformed into images. There were proposed several ways to tra

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Li, Jia, Lijie Hu, Jingfeng Zhang, Tianhang Zheng, Hua Zhang, and Di Wang. "Fair Text-to-Image Diffusion via Fair Mapping." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 25 (2025): 26256–64. https://doi.org/10.1609/aaai.v39i25.34823.

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In this paper, we address the limitations of existing text-to-image diffusion models in generating demographically fair results when given human-related descriptions. These models often struggle to disentangle the target language context from sociocultural biases, resulting in biased image generation. To overcome this challenge, we propose Fair Mapping, a flexible, model-agnostic, and lightweight approach that modifies a pre-trained text-to-image diffusion model by controlling the prompt to achieve fair image generation. One key advantage of our approach is its high efficiency. It only require

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S A, Priyanka. "Dynamic Visual Creation: Implementing Text to Image Generation." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48652.

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Abstract—Generative Adversarial Network (GANs) has become one of the most interesting ideas in the last years in Machine Learning. Generative Adversarial Network is a very exciting area and that’s why researchers are so excited about building generative models as they are set to vary what machines can do for humans. This paper proposes the generation of realistic images according to their semantics based on text description using a Knowledge Graph alongside Knowledge Guided Generative Adversarial Network (KG-GAN) that comes with the embeddings generated from the Knowledge Graph (KG) into GAN.

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Tewel, Yoad, Omri Kaduri, Rinon Gal, et al. "Training-Free Consistent Text-to-Image Generation." ACM Transactions on Graphics 43, no. 4 (2024): 1–18. http://dx.doi.org/10.1145/3658157.

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Text-to-image models offer a new level of creative flexibility by allowing users to guide the image generation process through natural language. However, using these models to consistently portray the same subject across diverse prompts remains challenging. Existing approaches fine-tune the model to teach it new words that describe specific user-provided subjects or add image conditioning to the model. These methods require lengthy persubject optimization or large-scale pre-training. Moreover, they struggle to align generated images with text prompts and face difficulties in portraying multipl

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Liao, Jiayi, Xu Chen, Qiang Fu, et al. "Text-to-Image Generation for Abstract Concepts." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 4 (2024): 3360–68. http://dx.doi.org/10.1609/aaai.v38i4.28122.

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Recent years have witnessed the substantial progress of large-scale models across various domains, such as natural language processing and computer vision, facilitating the expression of concrete concepts. Unlike concrete concepts that are usually directly associated with physical objects, expressing abstract concepts through natural language requires considerable effort since they are characterized by intricate semantics and connotations. An alternative approach is to leverage images to convey rich visual information as a supplement. Nevertheless, existing Text-to-Image (T2I) models are prima

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Liu, An-An, Zefang Sun, Ning Xu, et al. "Prior knowledge guided text to image generation." Pattern Recognition Letters 177 (January 2024): 89–95. http://dx.doi.org/10.1016/j.patrec.2023.12.003.

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Li, Yang, HuaQiang Jiang, and YangKai Wu. "Semantic Draw Engineering for Text-to-Image Creation." Journal of advances in information science and technology 1, no. 1 (2023): 1–6. https://doi.org/10.5281/zenodo.10500576.

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Text-to-image generation is conducted through Generative Adversarial Networks (GANs) or transformer models. However, the current challenge lies in accurately generating images based on textual descriptions, especially in scenarios where the content and theme of the target image are ambiguous. In this paper, we propose a method that utilizes artificial intelligence models for thematic creativity, followed by a classification modeling of the actual painting process. The method involves converting all visual elements into quantifiable data structures before creating images. We evaluate the effect

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Razan, Bayoumi, Alfonse Marco, Roushdy Mohamed, and M. Salem Abdel-Badeeh. "Text-to-image generation based on AttnDM-GAN and DMAttnGAN: applications and challenges." Bulletin of Electrical Engineering and Informatics 12, no. 2 (2023): 1180~1188. https://doi.org/10.11591/eei.v12i2.4199.

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The deep fake faces generation using generative adversarial networks (GANs) has reached an incredible level of realism where people can’t differentiate the real from the fake. Text-to-face is a very challenging task compared to other text-to-image syntheses because of the detailed, precise, and complex nature of the human faces in addition to the textual description details. Providing an accurate realistic text-to-image model can be useful for many applications such as criminal identification where the model will be acting as the forensic artist. This paper presents text-to-image generat

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Wang, Qianqian, Xiaoguang Ma, Xiaoyu Jiang, Jianmin Ji, and Honghu Pan. "Towards unified bijective image–text generation for text-to-image person re-identification." Knowledge-Based Systems 325 (September 2025): 114014. https://doi.org/10.1016/j.knosys.2025.114014.

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He, Yuze, Yushi Bai, Matthieu Lin, et al. "Text-image conditioned diffusion for consistent text-to-3D generation." Computer Aided Geometric Design 111 (June 2024): 102292. http://dx.doi.org/10.1016/j.cagd.2024.102292.

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Dandekar, Anushree, Rohini Malladi, Payal Gore, and Dr Vipul Dalal. "Text to Image Synthesis using Generative Adversarial Networks." International Journal for Research in Applied Science and Engineering Technology 11, no. 4 (2023): 2723–30. http://dx.doi.org/10.22214/ijraset.2023.50584.

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Abstract: Image generation has been a significant field of research in computer vision and machine learning for several years. It involves generating new images that resemble real-world images based on a given input or set of inputs. This process has a wide range of applications, including video games, computer graphics, and image editing. With the advancements in deep learning, the development of generative models has revolutionized the field of image generation. Generative models such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) have demonstrated remarkable s

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Golait, Dr Snehal. "Implementation of Text to Image using Diffusion Model." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem34583.

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Text-to-image generation is a transformative field in artificial intelligence, aiming to bridge the semantic gap between textual descriptions and visual representations. This presents a comprehensive approach to tackle this challenging task. Leveraging the advancements in deep learning, natural language processing (NLP), and computer vision, this proposes a cutting-edge model for generating high-fidelity images from textual prompts. Trained on a vast and varied dataset of written descriptions and related images, this model combines an image decoder and a text encoder within a hierarchical fram

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R, Prof Seema. "STABLE DIFFUSION TEXT TO IMAGE USING AI." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem33350.

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The Stable Diffusion Text-to-Image Generation Project is an innovative endeavor in the field of generative adversarial networks (GANs) and natural language processing (NLP). This project aims to bridge the semantic gap between textual descriptions and visual content by utilizing the Stable Diffusion training framework to generate highly realistic and coherent images from text prompts. The project leverages recent advancements in deep learning techniques to tackle the challenging task of text-to image synthesis. The project introduces an innovative approach at the crossroads of generative adver

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Wu, Feize, Yun Pang, Junyi Zhang, et al. "CoRe: Context-Regularized Text Embedding Learning for Text-to-Image Personalization." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 8 (2025): 8377–85. https://doi.org/10.1609/aaai.v39i8.32904.

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Recent advances in text-to-image personalization have enabled high-quality and controllable image synthesis for user-provided concepts. However, existing methods still struggle to balance identity preservation with text alignment. Our approach is based on the fact that generating prompt-aligned images requires a precise semantic understanding of the prompt, which involves accurately processing the interactions between the new concept and its surrounding context tokens within the CLIP text encoder. To address this, we aim to embed the new concept properly into the input embedding space of the t

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Kavitha Soppari, Bhanu Vangapally, Syed Sameer Sohail, and Harish Dubba. "Text to image generation using BERT and GAN." International Journal of Science and Research Archive 14, no. 1 (2025): 720–25. https://doi.org/10.30574/ijsra.2025.14.1.0137.

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Generating text to images is a difficult task that combines natural language processing and computer vision. Currently available generative adversarial network (GAN)-based models usually employ text encoders that have already been trained on image-text pairs. Nevertheless, these encoders frequently fall short in capturing the semantic complexity of unread text during pre-training, which makes it challenging to produce images that accurately correspond with the written descriptions supplied.Using BERT, a very successful pre-trained language model in natural language processing, we present a nov

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VANISHREE, A. TWINKLE, and PINNAMRAJU T. S. . PRIYA. "Text-To-Image Generator Using Deeping Learning." International Scientific Journal of Engineering and Management 04, no. 07 (2025): 1–9. https://doi.org/10.55041/isjem04863.

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Text-to-image generation is a transformative field in artificial intelligence that focuses on synthesizing realistic images from natural language descriptions. This paper explores the integration of diffusion models and transformer-based architectures to achieve high-quality, semantically aligned image generation from textual prompts. Diffusion models, known for their superior generative capabilities, gradually transform noise into images through a learned denoising process. Meanwhile, transformers, particularly pre-trained language and vision-language models like CLIP, are employed to underst

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Khushboo Patel. "Semantic-aware Mapping for Text-to-Image Synthesis." Journal of Information Systems Engineering and Management 10, no. 2 (2025): 746–54. https://doi.org/10.52783/jisem.v10i2.3135.

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This study explores the fast-progressing domain of Text-to-Image (T2I) synthesis, which aims to bridge the gap between language and visual comprehension. The main emphasis is on the crucial significance of Generative Adversarial Networks (GANs), which have transformed the process of image formation, with a specific emphasis on the impact of conditional GANs. The conditional models enable controlled image generation, and their influence on the production of high-quality images is extensively analyzed. We propose a novel method of generating semantically aware embeddings from the input text desc

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Tan, Zhaorui, Xi Yang, Zihan Ye, et al. "Semantic Similarity Distance: Towards better text-image consistency metric in text-to-image generation." Pattern Recognition 144 (December 2023): 109883. http://dx.doi.org/10.1016/j.patcog.2023.109883.

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GOWDA S,, RUDRESH. "Text-to-Image Generator with Consistory." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem45536.

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Artificial Intelligence (AI) and computer vision technologies have evolved rapidly, enabling new creative possibilities. One of the most exciting developments is text-to-image generation, where a system creates realistic images based on a natural language description. However, traditional models often struggle with subject consistency, making it hard to generate images that represent the same character across different scenes. Our project, "Text-to-Image Generator with Consistory," addresses this problem using NVIDIA's Consistency Model, known as Consistory. This model enables consistent subje

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Xu, Yun. "Investigation related to application of Generative Adversarial Networks in text-to-image synthesis." Applied and Computational Engineering 55, no. 1 (2024): 8–13. http://dx.doi.org/10.54254/2755-2721/55/20241100.

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Recent research attention has been captivated by the advent of Generative Adversarial Networks (GANs) in the realm of generating visuals from textual descriptions. Within a GAN framework, the interplay between the discriminator and generator components facilitates the production of lifelike visuals. This method proves to be versatile and user-friendly, allowing for the generation of authentic, diverse, and semantically faithful conditional images. However, the field still has to solve two issues: the development of high-resolution images with multiple elements and the construction of proper ev

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Ramesh, J. "Image Generator: Harnessing Stable Diffusion." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48827.

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Abstract - Recent advancements in artificial intelligence have significantly improved the ability to create realistic images from text descriptions. This project presents "Stable Diffusion," a cutting-edge text-to-image generation model that produces photorealistic visuals through a distinctive iterative refinement technique. The model begins with a noise-filled image and progressively adjusts it to match the given textual prompt. This method, known as text-to-image synthesis, automates the creation of images based on input text. The process iterates until the output converges, ultimately gene

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Zhou, Yuan, Peng Wang, Lei Xiang, and Haofeng Zhang. "Feature-Grounded Single-Stage Text-to-Image Generation." Tsinghua Science and Technology 29, no. 2 (2024): 469–80. http://dx.doi.org/10.26599/tst.2023.9010023.

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Liu, Jingyi. "How to Imagine the World with Text? From Text-to-image Generation View." Highlights in Science, Engineering and Technology 39 (April 1, 2023): 644–50. http://dx.doi.org/10.54097/hset.v39i.6619.

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Words are an effective and convenient way to describe the world, but sometimes what the texts convey may be misunderstood by readers. The expression of pictures is more vivid, easy to understand and has no borders, but creating a painting often takes a long time. Text-to-image makes the two expressions complement each other: It makes every ordinary person a “painter”, so that they can feel the world, express themselves, and create more whimsy through many rich pictures. For this vision, technologists are trying their best to improve image generation models, which enables computers to generate

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Qi, Yujuan, and Debao Guo. "Enhancing Text-to-Image Generation with Diversity Regularization and Fine-Grained Supervision." Highlights in Science, Engineering and Technology 122 (December 15, 2024): 1–9. https://doi.org/10.54097/42m6by18.

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Generating high-quality and realistic objects in the field of generation poses a significant challenge in artificial intelligence. Text-to-image generation technology is one of the focal points of research in this cutting-edge area. Currently, Generative Adversarial Networks (GANs) for text-to-image generation face two major issues: the mode collapse problem in conditional GANs is increasingly severe, and some existing models rely solely on sentence-level features, resulting in a lack of detailed features in the generated images. To address these problems, we propose a concise and efficient ge

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Lim, Youngsun, Hojun Choi, and Hyunjung Shim. "Evaluating Image Hallucination in Text-to-Image Generation with Question-Answering." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 25 (2025): 26290–98. https://doi.org/10.1609/aaai.v39i25.34827.

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Despite the impressive success of text-to-image (TTI) models, existing studies overlook the issue of whether these models accurately convey factual information. In this paper, we focus on the problem of image hallucination, where images created by TTI models fail to faithfully depict factual content. To address this, we introduce I-HallA (Image Hallucination evaluation with Question Answering), a novel automated evaluation metric that measures the factuality of generated images through visual question answering (VQA). We also introduce I-HallA v1.0, a curated benchmark dataset for this purpose

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Wu, Yixuan, and Zhaonan Zhou. "Enhancing DF-GAN for Text-to-Image Synthesis: Improved Text-Encoding and Network Structure." Applied and Computational Engineering 107, no. 1 (2024): 83–91. http://dx.doi.org/10.54254/2755-2721/107/20241098.

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Abstract. Text-to-image synthesis is one of the most challenging and popular tasks in machine learning, with many models developed to improve performance in this area. Deep Fusion Generative Adversarial Networks (DF-GAN) is a straightforward but efficient model for image generation, but it has three key limitations. First, it only supports sentence-level textual descriptions, restricting its ability to extract fine-grained features from word-level inputs. Second, the structure of the residual layers and blocks, along with key parameters, could be optimized for better performance. Third, existi

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Meng, Chutian, Fan Ma, Jiaxu Miao, Chi Zhang, Yi Yang, and Yueting Zhuang. "Image Regeneration: Evaluating Text-to-Image Model via Generating Identical Image with Multimodal Large Language Models." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 6 (2025): 6090–98. https://doi.org/10.1609/aaai.v39i6.32651.

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Diffusion models have revitalized the image generation domain, playing crucial roles in both academic research and artistic expression. With the emergence of new diffusion models, assessing the performance of text-to-image models has become increasingly important. Current metrics focus on directly matching the input text with the generated image, but due to cross-modal information asymmetry, this leads to unreliable or incomplete assessment results. Motivated by this, we introduce the Image Regeneration task in this study to assess text-to-image models by tasking the T2I model with generating

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Zhang, Lingjun, Xinyuan Chen, Yaohui Wang, Yue Lu, and Yu Qiao. "Brush Your Text: Synthesize Any Scene Text on Images via Diffusion Model." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 7 (2024): 7215–23. http://dx.doi.org/10.1609/aaai.v38i7.28550.

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Recently, diffusion-based image generation methods are credited for their remarkable text-to-image generation capabilities, while still facing challenges in accurately generating multilingual scene text images. To tackle this problem, we propose Diff-Text, which is a training-free scene text generation framework for any language. Our model outputs a photo-realistic image given a text of any language along with a textual description of a scene. The model leverages rendered sketch images as priors, thus arousing the potential multilingual-generation ability of the pre-trained Stable Diffusion. B

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Zakraoui, Jezia, Moutaz Saleh, Somaya Al-Maadeed, and Jihad Mohammed Jaam. "Improving text-to-image generation with object layout guidance." Multimedia Tools and Applications 80, no. 18 (2021): 27423–43. http://dx.doi.org/10.1007/s11042-021-11038-0.

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AbstractThe automatic generation of realistic images directly from a story text is a very challenging problem, as it cannot be addressed using a single image generation approach due mainly to the semantic complexity of the story text constituents. In this work, we propose a new approach that decomposes the task of story visualization into three phases: semantic text understanding, object layout prediction, and image generation and refinement. We start by simplifying the text using a scene graph triple notation that encodes semantic relationships between the story objects. We then introduce an

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Mahima, Chaddha, Kashid Sneha, and Bhosale |. Prof. Radha Deoghare Snehal. "Deep Learning for X ray Image to Text Generation." International Journal of Trend in Scientific Research and Development 3, no. 3 (2019): 1679–82. https://doi.org/10.31142/ijtsrd23168.

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Motivated by the recent success of supervised and weakly supervised common object discovery, in this work we move forward one step further to tackle common object discovery in a fully unsupervised way. Mainly, object co localization aims at simultaneously localizing the objects of the same class across a group of images. Traditional object localization detection usually trains the specific object detectors which require bounding box annotations of object instances, or at least image level labels to indicate the presence absence of objects in an image. Given a collection of images without any a

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Ibrahim, Dina M., and Amal A. Al-Shargabi. "Recognizing geographical locations using a GAN-based text-to-image approach." Indonesian Journal of Electrical Engineering and Computer Science 37, no. 2 (2025): 1168. http://dx.doi.org/10.11591/ijeecs.v37.i2.pp1168-1182.

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Generating photo-realistic images that align with the text descriptions is the goal of the text-to-image generation (T2I) model. They can assist in visualizing the descriptions thanks to advancements in machine learning algorithms. Using text as a source, generative adversarial networks (GANs) can generate a series of pictures that serve as descriptions. Recent GANs have allowed oldest T2I models to achieve remarkable gains. However, they have some limitations. The main target of this study is to address these limitations to enhance the text-to-image generation models to enhance location servi

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Gad, Raghad Ahmed, Salma Saad Abdelshakour, and Ahmed Abdelhafeez. "Text to Image using Deep Learning: A Survey." SciNexuses 1 (December 30, 2024): 184–202. https://doi.org/10.61356/j.scin.2024.1518.

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Text-to-image synthesis is an exciting marriage of natural language processing and computer vision for image synthesis from textual descriptions. This survey explores the discussed accomplishment of value in an industry that is rapidly evolving. Various attention mechanisms proposed by models such as AttnGAN have been discovered to improve fine-grained text-visual correspondences and hence deliver higher quality outputs. Comprehensive reviews of the text generation neural network have provided the base upon which various architectures and applications would be identified and investigated. Cond

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Dina, M. Ibrahim Amal A. Al-Shargabi. "Recognizing geographical locations using a GAN-based text-to-image approach." Indonesian Journal of Electrical Engineering and Computer Science 37, no. 2 (2025): 1168–82. https://doi.org/10.11591/ijeecs.v37.i2.pp1168-1182.

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Generating photo-realistic images that align with the text descriptions is the goal of the text-to-image generation (T2I) model. They can assist in visualizing the descriptions thanks to advancements in machine learning algorithms. Using text as a source, generative adversarial networks (GANs) can generate a series of pictures that serve as descriptions. Recent GANs have allowed oldest T2I models to achieve remarkable gains. However, they have some limitations. The main target of this study is to address these limitations to enhance the text-to-image generation models to enhance location servi

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Padmanandam, Kayal, Yeshasvi Mogula, and Nikitha Pitla. "TEXT DESCRIPTION TO IMAGE GENERATION USING GENERATIVE ADVERSARIAL NETWORK." Proceedings on Engineering Sciences 6, no. 4 (2024): 1829–36. https://doi.org/10.24874/pes.si.25.03b.015.

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Yu, Kai, Yi Bin, Ziqiang Zheng, and Yang Yang. "Text-to-image Generation Based on Conditional Semantic Augmentation." International Journal of Software and Informatics 14, no. 2 (2024): 185–204. http://dx.doi.org/10.21655/ijsi.1673-7288.00326.

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Srivastava, Ananya. "Advancements in Text-to-Image Generation through Generative AI." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem33569.

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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 sop

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Levin, A. O., and Yu S. Belov. "APPLICATION OF GENERATIVE-ADVERSARIAL NETWORKS TO TEXT TO IMAGE GENERATION." Научное обозрение. Технические науки (Scientific Review. Technical Sciences), no. 2 2023 (2023): 11–15. http://dx.doi.org/10.17513/srts.1427.

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Thorat, Ms Madhuri. "From Words to Wonders: AI-Generated Multimedia for Poetry Learning." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 3382–94. https://doi.org/10.22214/ijraset.2025.70946.

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The rise of Generative AI has led to the development of various tools that present new opportunities for businesses and professionals engaged in content creation. The education sector is undergoing a significant transformation in the methods of content development and delivery. AI models and tools facilitate the creation of customized learning materials and effective visuals that enhance and simplify the educational experience. The advent of Large Language Models (LLMs) such as GPT and Text-to-Image models like Stable Diffusion, Flux-Schnell has fundamentally changed and expedited the content

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Mathew, Rose Mary, Sujesh P. Lal, and Gowri Ganesh. "A unified deep learning model for image captioning and text-‎to-image synthesis." International Journal of Basic and Applied Sciences 14, no. 1 (2025): 334–38. https://doi.org/10.14419/m9njft54.

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Deep learning models have significantly advanced various artificial intelligence tasks, including text-to-image generation and image caption‎ing. However, there remains a semantic gap between textual descriptions and visual representations, which affects the accuracy and coherence of generated images and captions. This paper proposes a novel deep learning model that integrates Stable Diffusion, Convolutional ‎Neural Networks (CNN), and Long Short-Term Memory (LSTM) networks to enhance both text-to-image generation and image captioning ‎tasks. The model employs CNN-LSTM architecture for feature

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Wang, Runguo. "A Comparative Analysis of StackGAN and AttnGAN in Text-to-Image Generation." Applied and Computational Engineering 105, no. 1 (2024): 9–15. http://dx.doi.org/10.54254/2755-2721/105/2024tj0055.

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Abstract. This research looks at text-to-image generation as a whole, comparing two popular modelsStacked Generative Adversarial Networks (StackGAN) and Attentional Generative Adversarial Networks (AttnGAN)and their respective strengths and weaknesses. Text-to-image generation has seen significant advancements with the introduction of GAN-based models, and this paper aims to explore how these models perform in terms of image quality, realism, and alignment with textual descriptions. Using the Caltech-UCSD Birds (CUB)-200-2011 dataset, which consists of bird images, extensive experiments were c

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Jung, Sunghoon, and Yong Seok Heo. "Temporal Adaptive Attention Map Guidance for Text-to-Image Diffusion Models." Electronics 14, no. 3 (2025): 412. https://doi.org/10.3390/electronics14030412.

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Text-to-image generation aims to create visually compelling images aligned with input prompts, but challenges such as subject mixing and subject neglect, often caused by semantic leakage during the generation process, remain, particularly in multi-subject scenarios. To mitigate this, existing methods optimize attention maps in diffusion models, using static loss functions at each time step, often leading to suboptimal results due to insufficient consideration of varying characteristics across diffusion stages. To address this problem, we propose a novel framework that adaptively guides the att

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Liu, Mushui, Yuhang Ma, Zhen Yang, et al. "LLM4GEN: Leveraging Semantic Representation of LLMs for Text-to-Image Generation." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 5 (2025): 5523–31. https://doi.org/10.1609/aaai.v39i5.32588.

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Diffusion models have exhibited substantial success in text-to-image generation. However, they often encounter challenges when dealing with complex and dense prompts involving multiple objects, attribute binding, and long descriptions. In this paper, we propose a novel framework called LLM4GEN, which enhances the semantic understanding of text-to-image diffusion models by leveraging the representation of Large Language Models (LLMs). It can be seamlessly incorporated into various diffusion models as a plug-and-play component. A specially designed Cross-Adapter Module (CAM) integrates the origi

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Ivezić, Dora, and Marina Bagić Babac. "Trends and Challenges of Text-to-Image Generation: Sustainability Perspective." Croatian Regional Development Journal 4, no. 1 (2023): 56–77. http://dx.doi.org/10.2478/crdj-2023-0004.

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Abstract Text-to-image generation is a rapidly growing field that aims to generate images from textual descriptions. This paper provides a comprehensive overview of the latest trends and developments, highlighting their importance and relevance in various domains, such as art, photography, marketing, and learning. The paper describes and compares various text-to-image models and discusses the challenges and limitations of this field. The findings of this paper demonstrate that recent advancements in deep learning and computer vision have led to significant progress in text-to-image models, ena

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Gao, Xin, Yao Fu, Xiaonan Jiang, et al. "RSVQ-Diffusion Model for Text-to-Remote-Sensing Image Generation." Applied Sciences 15, no. 3 (2025): 1121. https://doi.org/10.3390/app15031121.

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Despite significant challenges, the text-guided remote sensing image generation method shows great potential in many practical applications such as generative adversarial networks in remote sensing tasks; generated images still face challenges such as low realism, face challenges, and unclear details. Moreover, the inherent spatial complexity of remote sensing images and the limited scale of publicly available datasets make it particularly challenging to generate high-quality remote sensing images from text descriptions. To address these challenges, this paper proposes the RSVQ-Diffusion model

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P, Sai Gyaneshwar Chary, Rahul Pasha Shaik, Sandeep Sirra, and Vishwa Shanthi M. "TEXT TO IMAGE GENERATION IN PYTHON USING IMAGEN MODEL AND STREAMLIT." TEXT TO IMAGE GENERATION IN PYTHON USING IMAGEN MODEL AND STREAMLIT 2 2, M. Vishwa Shanthi (2023): 54. https://doi.org/10.5281/zenodo.7868137.

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Text-to-image generation is a fascinating application of computer vision and natural language processing, where the goal is to generate realistic and diverse images based on textual descriptions. In this project, we propose a text-to-image generation system using Python programming language and two main libraries, Imagen and Streamlit. The system consists of a generative adversarial network (GAN) model trained on a large dataset of images and their corresponding captions, and a text processing and generation module. 

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Wang, Zeyu, Xiaoyu Liang, and Cheng Wang. "Controllable Text-to-Image Generation with Enhanced Text Encoder and Edge-Preserving Embedding." Journal of Physics: Conference Series 1856, no. 1 (2021): 012003. http://dx.doi.org/10.1088/1742-6596/1856/1/012003.

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