Training-Free Layout Control with Cross-Attention Guidance
Not on the map. Why?
Cited by 185 in Diffusion models, but placed by its own abstract, which didn’t fit.
Abstract (arXiv)
Recent diffusion-based generators can produce high-quality images from textual prompts. However, they often disregard textual instructions that specify the spatial layout of the composition. We propose a simple approach that achieves robust layout control without the need for training or fine-tuning of the image generator. Our technique manipulates the cross-attention layers that the model uses to interface textual and visual information and steers the generation in the desired direction given, e.g., a user-specified layout. To determine how to best guide attention, we study the role of attention maps and explore two alternative strategies, forward and backward guidance. We thoroughly evaluate our approach on three benchmarks and provide several qualitative examples and a comparative analysis of the two strategies that demonstrate the superiority of backward guidance compared to forward guidance, as well as prior work. We further demonstrate the versatility of layout guidance by extending it to applications such as editing the layout and context of real images.
Key facts. Cited by 312 papers in the map (data through 7 Oct 2026). Builds on 55 of its 63 references that are in the map. First posted on arXiv 6 Apr 2023.
Builds on
55 of 63 references.
References · 55
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- 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
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- 2022DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generationcited by 3,286
- 2022Prompt-to-Prompt Image Editing with Cross Attention Controlcited by 2,409
- 2022An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversioncited by 2,231
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- 2022Scaling Autoregressive Models for Content-Rich Text-to-Image Generationcited by 1,287
- 2022Imagic: Text-Based Real Image Editing with Diffusion Modelscited by 1,172
- 2022eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoiserscited by 921
- 2023GLIGEN: Open-Set Grounded Text-to-Image Generationcited by 774
- 2021Vector Quantized Diffusion Model for Text-to-Image Synthesiscited by 773
- 2016Generative Adversarial Text to Image Synthesiscited by 735
- 2023Attend-and-Excite: Attention-Based Semantic Guidance for Text-to-Image Diffusion Modelscited by 696
- 2021CogView: Mastering Text-to-Image Generation via Transformerscited by 691
- 2017AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networkscited by 664
- 2022DiffEdit: Diffusion-based semantic image editing with mask guidancecited by 643
- 2022Compositional Visual Generation with Composable Diffusion Modelscited by 619
- 2022Detecting Twenty-thousand Classes using Image-level Supervisioncited by 613
- 2016StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networkscited by 575
- 2023MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generationcited by 529
- 2022Make-A-Scene: Scene-Based Text-to-Image Generation with Human Priorscited by 487
- 2023Universal Guidance for Diffusion Modelscited by 426
- 2022Training-Free Structured Diffusion Guidance for Compositional Text-to-Image Synthesiscited by 386
- 2018Image Generation from Scene Graphscited by 324
- 2022CogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformerscited by 305
- 2022Simple Open-Vocabulary Object Detection with Vision Transformerscited by 303
- 2023Composer: Creative and Controllable Image Synthesis with Composable Conditionscited by 303
- 2021Cross-Modal Contrastive Learning for Text-to-Image Generationcited by 259
- 2017StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networkscited by 229
- 2022SpaText: Spatio-Textual Representation for Controllable Image Generationcited by 217
- 2022ReCo: Region-Controlled Text-to-Image Generationcited by 177
- 2020DF-GAN: A Simple and Effective Baseline for Text-to-Image Synthesiscited by 161
- 2018Image Generation from Layoutcited by 105
- 2018Inferring Semantic Layout for Hierarchical Text-to-Image Synthesiscited by 101
- 2022Benchmarking Spatial Relationships in Text-to-Image Generationcited by 96
- 2019Image Synthesis From Reconfigurable Layout and Stylecited by 85
- 2021Multimodal Conditional Image Synthesis with Product-of-Experts GANscited by 75
- 2022Frido: Feature Pyramid Diffusion for Complex Scene Image Synthesiscited by 75
50 most cited of 55 All 55 (JSON)
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Citing papers per year: 2023: 47; 2024: 126; 2025: 95; 2026: 44.
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