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Research map / Image generation / Text-to-image diffusion

Text-driven synthesis and semantic guidance

Control text-to-image models for size, semantics, and LLM-guided correction.

137 accepted papers at ICML, NeurIPS, ICLR, CVPR and AAAI (2023–2026) · an approach in Text-to-image diffusion, keyword cluster “synthesis · layout · object”

State History Tried Checklist Preprints Who Export

Where it stands

Not enough benchmark data Fewer than four papers report the same benchmark, so there is no record to follow.

60 papers in 2023–24 and 77 in 2025–26 (at ICML, NeurIPS, ICLR, CVPR and AAAI); its share of the topic shrank, from 15.3% to 9.0%.

How this is decided

A benchmark is tracked when at least four papers of this approach report their own method on the same dataset and metric in at least two different years. Its record is the best number reported up to 2024 compared with the best reported in 2025–26: still improving means at least 1% better, flat means at most 0.2% better with at least two papers from 2025–26 trying. Numbers on a different scale from the rest (a fraction among percentages) and far outliers are left out. Results come from different papers with different setups, so they are signals, not head-to-head comparisons. The share of papers is counted at ICML, NeurIPS, ICLR, CVPR and AAAI.

How it got here

2016: 0162017: 0172018: 0182019: 0192020: 0202021: 0212022: 0222023: 17232024: 43242025: 50252026: 2726

Most cited paper of each year

Earliest papers here

What has been tried

108 papers here report 1,167 measured changes (a component added, removed or swapped, compared with a baseline): helped 71%, no clear effect 5%, hurt 20%, mixed 4%. Papers mostly publish changes that helped, so "helped" is over-represented.

Ablations in recent papers

What was changedMeasured onResultEffectPaper
Use a temporal Transformer instead of 1D temporal convolutionVideo motion modeling · Motion incorporationHelpedAnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning (ICLR 2024 · 85 citations)
Use SeeCoder-PA instead of SeeCoderImage-Variation, no structural conditioning · image qualityHelpedPrompt-Free Diffusion: Taking "Text" out of Text-to-Image Diffusion Models (CVPR 2024 · 52 citations)
add reward feedbackSix styles · FID84.04 -> 83.21HelpedSVGDreamer: Text Guided SVG Generation with Diffusion Model (CVPR 2024 · 41 citations)
Remove optimization and directly inject Gaussian-falloff activations into directed-word and trailing attention mapsObject placement and scene generation · placement and scene qualityMixedDirected Diffusion: Direct Control of Object Placement through Attention Guidance (AAAI 2024 · 40 citations)
Add one round of SLD self-correctionLMD benchmark — Average · accuracy+ 26.0HelpedSelf-correcting LLM-controlled Diffusion Models (CVPR 2024 · 38 citations)
add nonlinear mapping network FEmotional image generation · FID57.54 -> 41.60HelpedEmoGen: Emotional Image Content Generation with Text-to-Image Diffusion Models (CVPR 2024 · 36 citations)
Add latent scaling to the encoded imagePascal VOC2012 segmentation · mIoUss82.34 -> 83.06HelpedText-Image Alignment for Diffusion-Based Perception (CVPR 2024 · 33 citations)
Add global structure guidance via SPM to RM, predicting image xTII-ST · accuracy69.40 -> 71.73HelpedText Image Inpainting via Global Structure-Guided Diffusion Models (AAAI 2024 · 29 citations)
Add InToken interaction tokensHICO-DET test, Default, Full · mAP25.23 -> 28.73HelpedInteractDiffusion: Interaction Control in Text-to-Image Diffusion Models (CVPR 2024 · 26 citations)
Add the LoRA teacher during student trainingCOCO 2014 5K subset · FIDHelpedSwiftBrush: One-Step Text-to-Image Diffusion Model with Variational Score Distillation (CVPR 2024 · 25 citations)
use explicit tile overlap instead of no overlapMM-CelebA-HQ · PSNR26.89 -> 27.49HelpedAny-Size-Diffusion: Toward Efficient Text-Driven Synthesis for Any-Size HD Images (AAAI 2024 · 17 citations)
Disable unconditional guidance trainingCXR generation · FID_XRV10.0 -> 16.4HurtTrade-Offs in Fine-Tuned Diffusion Models between Accuracy and Interpretability (AAAI 2024 · 14 citations)

What reviewers will expect

Datasets and baselines named in the 52 papers of this approach from 2025–2026 that have a full-text summary. Leaving out the common ones is a frequent review question.

Datasets

  • T2I-CompBench · 4 of 52 papers
  • LAION-5B · 3 of 52 papers
  • LAION · 3 of 52 papers
  • DreamBench · 3 of 52 papers
  • MultiGen-20M · 3 of 52 papers
  • WikiArt · 3 of 52 papers
  • MS-COCO · 3 of 52 papers
  • JourneyDB · 3 of 52 papers

Baselines

  • ControlNet · 7 of 52 papers
  • Textual Inversion · 7 of 52 papers
  • SDXL · 5 of 52 papers
  • DreamBooth · 4 of 52 papers
  • IP-Adapter · 4 of 52 papers
  • LoRA · 3 of 52 papers
  • InstructPix2Pix · 3 of 52 papers
  • BLIP-Diffusion · 3 of 52 papers

Recent preprints

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    Who publishes on it

    Institutions on the most papers of this approach since 2023 (of 137 papers; from OpenAlex and the papers' affiliations).

    Use it

    Download BibTeX of 32 key papersOpen the topic in the interactive map

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    Same keyword cluster

    Elsewhere in the topic

    Benchmark numbers, ablations and baseline numbers are extracted by AI from each paper's full text and can be wrong; the paper is the authority. Data: results index of 2026-11-02, citation counts from OpenAlex (2026-10-09).

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