Text-to-image synthesis (T2I) focuses on generating high-quality images from textual descriptions In this survey, we review 490 papers on T2I spanning the past decade, with representative advances covered through 2026. We start by briefly introducing how GANs, autoregressive models, and diffusion models have been used for image generation, and we additionally examine the shared components whose evolution has driven recent progress. Building on this foundation, we discuss the development of these models for T2I and organize cutting-edge research by problem rather than by chronology alone, covering controllability, personalized and identity-preserving generation, instruction-based image editing, preference alignment and post-training with human and AI feedback, efficiency, safety, and consistency in content and spatial relationships. We further review unified multimodal understanding-and-generation models, summarize the datasets and evaluation metrics commonly used in T2I research, and provide a comparative discussion of the three paradigms together with their respective trade-offs. Finally, we discuss the potential applications of T2I within AIGC, along with the challenges and forward-looking research opportunities in this field.
Figures & tables
Figure 1: Supplementary survey timeline of representative T2I milestones from 2015 to 2026. The lanes indicate historical emphasis rather than mutually exclusive model families; hybrid systems can combine different representations, objectives, backbones, and tasks. This figure is a qualitative synthesis and is not a performance ranking.
Figure 2: The text-conditional convolutional GAN architecture Reed et al. (2016) . Text encoding p(t) is used by both generator and discriminator. It is projected to lower dimensions and depth concatenated with image feature maps for further stages of convolutional processing.
Figure 3: The training of DALL-E Ramesh et al. (2021) is divided into two stages. The first stage trains the codebook of VQ-VAE, while the second stage trains the Transformer, corresponding to Stage One and Stage Two indicated in the figure.
Figure 4: The framework of CogView Ding et al. (2021) . [ROI1], [BASE1], etc., are seperator tokens.
Figure 5: DDPM Ho et al. (2020) generates new images by adding noise and then denoising.
Figure 6: Selected samples from GLIDE Nichol et al. (2021) using classifier-free guidance. We observe that the model can produce photorealistic images with shadows and reflections, can compose multiple concepts in the correct way, and can produce artistic renderings of novel concepts.
Figure 7: The condition LDM Rombach et al. (2022) either via concatenation or by a more general cross-attention mechanism.
Figure 8: The Diffusion Transformer (DiT) architecture Peebles and Xie (2023) . The convolutional U-Net denoiser is replaced by a Transformer that operates on a sequence of noised latent patches, with conditioning such as timestep and class or text embeddings injected through adaptive layer normalization.
Aspect
GAN
Autoregressive (AR)
Diffusion / Flow
Training objective
Adversarial min–max game Goodfellow et al. (2014)
Likelihood over image tokens Ramesh et al. (2021)
Denoising score matching Ho et al. (2020) or flow matching Lipman et al. (2022)
Image representation
Continuous pixels/latents
Discrete (or continuous) tokens
Continuous latents (or pixels)
Sampling
Single forward pass
Sequential token decoding
Iterative denoising / ODE integration
Main inference cost
Low (one step)
Grows with sequence length
Grows with number of steps
Typical risk
Training instability, mode collapse
Exposure bias, slow decoding
High sampling cost (mitigated by distillation / few-step flow)
Table 1: Qualitative comparison of the three T2I paradigms. Entries describe typical designs and tendencies, not absolute guarantees, and are not a performance ranking.
Year
Source
Model and Description
2016
ICML
Text-conditional GAN Reed et al. (2016) : The first GAN used for T2I tasks.
2017
arXiv
TAC-GAN Dash et al. (2017) : Incorporated auxiliary classification tasks into the discriminator.
ICCV
StackGAN Zhang et al. (2017) : 1. Generate Stage-I low-resolution images; 2. Generate high-resolution images using text descriptions.
TPAMI
StackGAN++ Zhang et al. (2018a) : Composed of multiple generators and discriminators in a tree-like structure.
2018
ECCV
Fused GAN Bodla et al. (2018) : Two generators for conditional and unconditional image synthesis.
CVPR
HDGAN Zhang et al. (2018b) : Accompanying hierarchical-nested adversarial objectives inside the network hierarchies.
Table 2: Overview of GAN models in T2I by year.
Year
Source
Model and Description
2021
ICML
DALL-E Ramesh et al. (2021) : The first to use AR for generation in T2I.
NeurIPS
CogView Ding et al. (2021) : A 4-billion-parameter Transformer with a VQ-VAE tokenizer.
arXiv
M6 Lin et al. (2021) : The largest dataset for Chinese multimodal pre-training.
2022
TMLR
Parti Yu et al. () : Uses image token sequences as the target output instead of text tokens.
NeurIPS
CogView2 Ding et al. (2022) : A solution based on hierarchical transformers and local parallel autoregressive generation.
ECCV
Make-A-Scene Gafni et al. (2022) : Control mechanism complementary to text in the form of a scene.
Table 3: Overview of AR models in T2I by year.
Year
Source
Model and Description
2021
arXiv
GLIDE Nichol et al. (2021) : The first diffusion work in T2I.
2022
CVPR
VQ-Diffusion Gu et al. (2022) : Based on a vector quantization diffusion model.
NeurIPS
Imagen Saharia et al. (2022) : Combines Transformer language models and diffusion processes.
CVPR
Stable Diffusion Rombach et al. (2022) : Applies the diffusion model to the latent space of a powerful pretrained autoencoder.
arXiv
DALLE-2 Ramesh et al. (2022) : A two-stage model generating CLIP image embeddings based on text.
CVPR
Blended Diffusion Avrahami et al. (2022) : Using natural language guidance to produce realistic and diverse images.
Table 4: Overview of DM models in T2I by year.
Model
Visual representation / sharing
Image-generation objective
Main survey interpretation
Unified-IO Lu et al. (2022)
Unified task/token interface
Token generation
Connects image generation with a broad set of vision-language tasks.
CM3Leon Yu et al. (2023)
Token interface, LM recipe
Autoregressive tokens
Shows a text-LM training recipe transfers to multimodal generation.
Janus Wu et al. (2025b)
Decoupled visual encoders
Autoregressive generation
A unified framework need not share one visual encoder.
Show-o Xie et al. (2024)
Unified Transformer
Discrete diffusion
Image-side discrete diffusion differs from continuous latent diffusion.
Transfusion Zhou et al. (2024a)
Mixed-modal shared model
Continuous diffusion
Language and image prediction use different objectives in one model.
BAGEL Deng et al. (2025)
Mixture of Transformers
Flow-based generation
Joint task support must be separated from evidence of cross-task benefit.
Table 5: Representative unified understanding-and-generation models. The table records architecture choices, not a cross-paper performance ranking obtained under different protocols.
Research Area
Model
Sec. 4.1 : Alignment and Human Feedback
RAHF Liang et al. (2024) , DifffAgent Zhao et al. (2024a) , HPS Wu et al. (2023e) , ImageReward Xu et al. (2024a) , Multi-dimensional Human
Preference (MHP) Zhang et al. (2024e) , MPS Zhang et al. (2024e)
Sec. 4.2 : Personalized Generation
Textual Inversion Gal et al. (2022) , PIA Zhang et al. (2024h) , Prompt-Free Diffusion Xu et al. (2024b) , ADI Huang et al. (2024c) , HyperDreamBooth Ruiz et al. (2024) ,
DreamBooth Ruiz et al. (2023) , Personalized Residuals Ham et al. (2024) , PALP Arar et al. (2024) , PhotoMaker Li et al. (2024m) , DreamTurner Hua et al. (2023) ,
ELITE Wei et al. (2023) , Tailored Visions Chen et al. (2024f) , RealCustom Huang et al. (2024a) , SAG Chan et al. (2024) , CoRe Wu et al. (2024a) , Imagine Yourself He et al. (2024c) ,
FastComposer Xiao et al. (2024a) , InstantID Wang et al. (2024e) , Specialist Diffusion Lu et al. (2023) , ControlStyle Chen et al. (2023a) , UniPortrait He et al. (2024a) ,
Table 6: Overview of T2I research directions.
Figure 9: With just a few images (typically 3-5) of a subject (left), DreamBooth Ruiz et al. (2023) , the AI-powered photo booth, can generate a myriad of images of the subject in different contexts (right), using the guidance of a text prompt. The results exhibit natural interactions with the environment, as well as novel articulations and variation in lighting conditions, all while maintaining high fidelity to the key visual features of the subject.
Figure 10: Fine-tuning of DreamBooth Ruiz et al. (2023) . Given approximately 3-5 images of a subject, we fine-tune a text-to-image diffusion model with the input images paired with a text prompt containing a unique identifier and the name of the class the subject belongs to (e.g., “A [V] dog”). In parallel, we apply a class-specific prior preservation loss, which leverages the semantic prior that the model has on the class and encourages it to generate diverse instances belonging to the subject’s class using the class name in a text prompt (e.g., “A dog”).
Figure 11: Controlling Stable Diffusion Rombach et al. (2022) with Learned Conditions. ControlNet Zhang et al. (2023c) allows users to add conditions like Canny edges (top), human pose (bottom), etc., to control the image generation of large pretrained diffusion models. The default results use the prompt “a high-quality, detailed, and professional image”. Users can optionally give prompts like “chef in kitchen”.
Figure 12: Stable Diffusion Rombach et al. (2022) ’s U-net architecture is connected with a ControlNet Zhang et al. (2023c) on the encoder blocks and middle block. The locked, gray blocks show the structure of Stable Diffusion V1.5 (or V2.1, as they use the same U-net architecture). The trainable blue blocks and the white zero convolution layers are added to build a ControlNet.
Figure 13: Style transfer results using the proposed method. Given only a single input painting, InST Zhang et al. (2023d) can accurately transfer the style attributes such as semantics, material, object shape, brushstrokes and colors of the references to a natural image with a very simple learned textual description “[C]”.
Figure 14: Different target texts applied to the same image. Imagic Kawar et al. (2023) edits the same image differently depending on the input text.
Figure 15: SVDiff Han et al. (2023) proposes the Cut-Mix-Unmix data augmentation technique to enhance the quality of multi-subject image generation, which is a simple text-based image editing framework.
Figure 16: Images shown in each group share the same source image. AdvDm Liang et al. (2023) use textual inversion Gal et al. (2022) to extract the style of training samples from WikiArt, shown in a separate column. For each group, the top row shows the generated images based on the style extracted from the clean examples. The bottom row shows the generated images based on the style extracted from the adversarial examples. Strength is a hyper-parameter that indicates how much the style of the source image is covered by the target style. LDM fails to capture the style from adversarial examples, compared to clean images.
Figure 17: Given a pretrained text-to-image diffusion model (e.g., Stable Diffusion Rombach et al. (2022) ) method in GSN Chefer et al. (2023) , Attend-and-Excite, guides the generative model to modify the cross-attention values during the image synthesis process to generate images that more faithfully depict the input text prompt. Stable Diffusion alone (top row) struggles to generate multiple objects (e.g., a horse and a dog). However, by incorporating Attend-and-Excite (bottom row) to strengthen the subject tokens (marked in blue), we achieve images that are more semantically faithful with respect to the input text prompts.
Figure 18: The existing vision-language datasets do not capture spatial relationships well. To alleviate this shortcoming, we synthetically re-caption 6M images with a spatial focus, and create the SPRIGHT (SPatially RIGHT) Chatterjee et al. (2025) dataset. Shown above are samples from the COCO Validation Set, where text in denotes ground-truth captions and text in are corresponding captions from SPRIGHT.
Figure 19: Emotional Image Content Generation (EICG). Given an emotion category, The network EmoGen Yang et al. (2024b) produces images that exhibit unambiguous meanings (semantic-clear), reflect the intended emotion (emotion-faithful) and incorporate varied semantics (semantic-diverse).
Figure 20: Colors and contrast. The top row is SDXL Podell et al. (2023) , bottom row is Playground v2.5. The model Playground v2.5 Li et al. (2024a) can generate samples with more vibrant colors and contrast.
Figure 21: Overview of MLLM-Guided Image Editing (MGIE) Fu et al. (2023b) , which leverages MLLMs to enhance instruction-based image editing. MGIE learns to derive concise and expressive instructions while providing explicit visual-related guidance for the intended goal. The diffusion model jointly trains and achieves image editing with latent imagination through the edit head in an end-to-end manner, showing that the module is trainable or frozen, respectively.
Dataset
Year
Size
Source
Oxford 102 Flower Dataset Nilsback and Zisserman (2008)
2008
8,189
University of Oxford
UIUC Pascal Sentence Rashtchian et al. (2010)
2010
1,000
VOC2008
SBU Captions Ordonez et al. (2011)
2011
1,000,000
Flickr.com
Flickr8K Hodosh et al. (2013)
2013
8,092
Flickr.com
COCO Lin et al. (2014)
2014
330,000+
Microsoft
ABSTRACT-50S Vedantam et al. (2015)
2015
500
ASD
Table 7: Datasets in the T2I task. The “Size” column follows the unit reported by each resource (images, image-text pairs, prompts, or QA items) and is not directly comparable across rows.
Metric
Year
Human Evaluated
Positive Correlation
SSIM Wang et al. (2004)
2004
✓
✓
PSNR Hore and Ziou (2010)
2010
✓
✓
CIDEr Vedantam et al. (2015)
2015
✓
✓
IS Salimans et al. (2016)
2016
×
✓
C2ST Lopez-Paz and Oquab (2016)
2016
×
✓
FID Heusel et al. (2017)
2017
×
×
Table 8: Evaluation metrics in the T2I task. “Human Evaluated” and “Positive Correlation” are coarse descriptors taken from each metric’s own reporting, not a unified re-evaluation.
Benchmark
What it evaluates
Scoring
Main limitation
MME-Unify Xie et al. (2025)
Unified understanding, generation, and mixed-modality interaction
Multi-task, model-based
Mixed-task protocols vary; interaction gains hard to isolate
KontextBench Labs et al. (2025)
In-context generation and single-/multi-turn editing (1,026 image-prompt pairs)
Human and automatic
Introduced with a specific model family
LongT2IBench Yang et al. (2025)
Long-prompt generation with graph-structured annotations
Structured, automatic
Focused on long-prompt setting
WISE Niu et al. (2025)
World-knowledge-informed semantic correctness
Knowledge-grounded checks
Depends on knowledge coverage
GenExam Wang et al. (2025c)
Multidisciplinary “exam-style” prompts
Rubric/answer-based
Subject coverage is finite
Qwen-Image-Bench Li et al. (2026)
Creator-oriented real-world fidelity and creative generation
Human and model-based
Creator pool and judge may share source bias
Table 9: Representative 2025–2026 T2I benchmarks, organized by evaluation target. Scores across these resources are not directly comparable because inputs, judges, and model pools differ.
Figure 22: Example storytelling images generated by the method AutoStory Wang et al. (2023b) . it can generate text-aligned, identity-consistent, and high-quality story images from user-input stories and characters (the dog and cat on the left, specified by about 5 images per character), without additional inputs like sketches.
Figure 23: Example images generated by Kolors Team (2024) , with the text description “refreshing summer juice drink”. Kolors can produce a variety of clear image results.
Figure 24: Given Kolors Team (2024) the content “explain the process of photosynthesis”, it can generate a series of images related to photosynthesis to enhance educational significance.
Figure 25: Given the text description “a futuristic city”, a series of images can be generated to capture users’ attention. This example was generated by Kolors Team (2024) .
Appendix figures & tables4 assets
Supplementary material from the paper’s appendix.
Appendix
Figure 26: The model, GigaGAN [ 157 ] , shows GAN frameworks can also be scaled up for general T2I synthesis and superresolution tasks, generating a 512px output at an interactive speed of 0.13s, and 4096px within 3.7s. Selected examples at 2K resolution.
Figure 27: Sampling is derived from Stable Diffusion 3 [ 82 ] , generating fantastical pictures.
Figure 28: Hunyuan-DiT [ 208 ] can generate images containing Chinese elements; the examples shown are generated directly from Chinese prompts.
Figure 29: RAHF [ 213 ] region inpainting with Muse [ 32 ] generative model. From left to right, original images with artifacts from Muse, implausibility heatmaps predicted by RAHF, masks by processing (thresholding, dilating) the heatmaps, and new images from Muse region inpainting with the mask, respectively.
Text-to-image generation creates images from natural-language prompts using generative models, with research focused on fidelity, control, efficiency, and safety.
Modern text-to-image (T2I) systems use internet-scale language pretraining for text-conditioned image generation. We ask whether language pretraining is necessary for strong T2I generation. We introduce Halcyon, a family of early-fusion multimodal autoregressive models pretrained entirely from scratch on GPIC, a dataset of 100M image-text pairs. Halcyon-0.52B outperforms LlamaGen-XL/8 (48.0 vs. 63.3 gFD) with 18.2× less pretraining compute. Further, Halcyon-1.6B outperforms JiT-T2I-XXL/8++ (35.3 vs. 41.0 gFD) with 275× less pretraining compute. Controlled experiments show that text-only pretraining does not improve T2I generation. Halcyon also learns image captioning by replacing caption dropout with I2T supervision, maintaining competitive T2I generation quality under the same pretraining budget. We show that strong T2I systems can be trained from scratch without internet-scale language pretraining. Code and models are available at https://cs.stanford.edu/~keshik/halcyon
Keshigeyan Chandrasegaran, Yue Zhao, Armin W. Thomas +5
Recent text-to-image (T2I) diffusion models achieve remarkable realism, yet faithful prompt-image alignment remains challenging, particularly for complex prompts with multiple objects, relations, and fine-grained attributes. Existing training-free inference-time scaling methods rely on fixed iteration budgets that cannot adapt to prompt difficulty, while reflection-tuned models require carefully curated reflection datasets and extensive joint fine-tuning of diffusion and vision-language models, often overfitting to reflection paths data and lacking transferability across models. We introduce RAISE (Requirement-Adaptive Self-Improving Evolution), a training-free, requirement-driven evolutionary framework for adaptive T2I generation. RAISE formulates image generation as a requirement-driven adaptive scaling process, evolving a population of candidates at inference time through a diverse set of refinement actions-including prompt rewriting, noise resampling, and instructional editing. Each generation is verified against a structured checklist of requirements, enabling the system to dynamically identify unsatisfied items and allocate further computation only where needed. This achieves adaptive test-time scaling that aligns computational effort with semantic query complexity. On GenEval and DrawBench, RAISE attains state-of-the-art alignment (0.94 overall GenEval) while incurring fewer generated samples (reduced by 30-40%) and VLM calls (reduced by 80%) than prior scaling and reflection-tuned baselines, demonstrating efficient, generalizable, and model-agnostic multi-round self-improvement. Code is available at https://github.com/LiyaoJiang1998/RAISE.
Liyao Jiang, Ruichen Chen, Chao Gao +1
Department of ECE, University of Alberta, Canada · Huawei Technologies, Canada
Recent text-to-image (T2I) diffusion models produce visually stunning images and demonstrate excellent prompt following. But do they perform well as synthetic vision data generators? In this work, we revisit the promise of synthetic data as a scalable substitute for real training sets and uncover a surprising performance regression. We generate large-scale synthetic datasets using state-of-the-art T2I models released between 2022 and 2025, train standard classifiers solely on this synthetic data, and evaluate them on real test data. Despite observable advances in visual fidelity and prompt adherence, classification accuracy on real test data consistently declines with newer T2I models as training data generators. Our analysis reveals a hidden trend: These models collapse to a narrow, aesthetic-centric distribution that undermines diversity and real data distribution coverage. Overall, our findings challenge a growing assumption in vision research, namely that progress in generative realism implies progress in data realism. We thus highlight an urgent need to rethink the capabilities of modern T2I models as reliable training data generators.
Krzysztof Adamkiewicz, Brian Bernhard Moser, Stanislav Frolov +3
1RPTU University Kaiserslautern-Landau, Kaiserslautern, Germany · 2German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany