Research map / Image generation
Text-to-image diffusion: research map
1,310 accepted papers on Text-to-image diffusion in Image generation, from ICML, NeurIPS, ICLR, CVPR and AAAI (2016–2026), grouped into 3 clusters and 12 approaches. The busiest year so far is 2026.
Within Image generation, its share grew from 18.8% in 2023–24 to 20.8% in 2025–26 (391 → 851 papers at ICML, NeurIPS, CVPR and AAAI, the venues with data for all four years).
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Approaches and key papers
synthesis · layout · object · 778 papers
Approaches in this cluster:
- Compositional text-to-image guidance (194 papers)
Improve object composition in text-to-image diffusion through attention guidance and token-level supervision. - Text-driven synthesis and semantic guidance (137 papers)
Control text-to-image models for size, semantics, and LLM-guided correction. - Personalized and region-controlled generation (136 papers)
Generate subject-driven images with personalization and region control, often without fine-tuning. - Layout-to-image control (164 papers)
Control spatial arrangement with layouts, instance maps, and spatio-textual representations. - Multi-subject personalization (82 papers)
Preserve identity and disentangle attributes for personalized multi-subject generation. - Text generation and structured prediction (65 papers)
Generate text or images with structured prediction and controlled decoding.
Most cited and most cited since 2024:
- DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation (CVPR 2023 · 2,183 citations)
- AttnGAN: Fine-Grained Text to Image Generation With Attentional Generative Adversarial Networks (CVPR 2018 · 1,935 citations)
- SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis (ICLR 2024 · 322 citations)
- Style Aligned Image Generation via Shared Attention (CVPR 2024 · 131 citations)
t2i · preference · alignment · 400 papers
Approaches in this cluster:
- Inference-time T2I improvement (117 papers)
Improve fidelity, safety, and diversity of text-to-image models through noise optimization and guidance. - T2I evaluation and controllable adapters (149 papers)
Benchmark text-to-image models with human feedback and add controllable adapters. - Preference optimization for diffusion (82 papers)
Align diffusion models with human preferences using direct preference optimization. - Fairness and bias in generative models (52 papers)
Measure and mitigate bias in text-to-image generation.
Most cited and most cited since 2024:
- Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding (NeurIPS 2022 · 2,090 citations)
- T2I-Adapter: Learning Adapters to Dig Out More Controllable Ability for Text-to-Image Diffusion Models (AAAI 2024 · 865 citations)
- Rethinking FID: Towards a Better Evaluation Metric for Image Generation (CVPR 2024 · 200 citations)
- Rich Human Feedback for Text-to-Image Generation (CVPR 2024 · 63 citations)
concept · erasure · unlearning · 132 papers
Approaches in this cluster:
- Concept erasure in diffusion models (76 papers)
Remove unwanted concepts from diffusion models via editing, adapters, and unlearning. - Multi-concept composition (56 papers)
Customize and compose multiple concepts in text-to-image diffusion models.
Most cited and most cited since 2024:
- Multi-Concept Customization of Text-to-Image Diffusion (CVPR 2023 · 627 citations)
- MACE: Mass Concept Erasure in Diffusion Models (CVPR 2024 · 73 citations)
- Self-Discovering Interpretable Diffusion Latent Directions for Responsible Text-to-Image Generation (CVPR 2024 · 30 citations)
- One-dimensional Adapter to Rule Them All: Concepts Diffusion Models and Erasing Applications (CVPR 2024 · 28 citations)
Related topics in Image generation
- Autoregressive and latent generation (1,747)
- GANs (515)
- Super-resolution (429)
- Editing and style transfer (988)
- Fast diffusion sampling (1,866)
- Image restoration (1,184)
