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LayoutDiffuse: Adapting Foundational Diffusion Models for Layout-to-Image Generation

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arxiv 2302.08908 v1 pith:SKE6U545 submitted 2023-02-16 cs.CV

Jiaxin Cheng , Xiao Liang , Xingjian Shi , Tong He , Tianjun Xiao , Mu Li This is my paper · ORCID
classification cs.CV
keywords diffusiongenerationlayout-to-imagemodelsdatasetsfoundationalimageslayout
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Layout-to-image generation refers to the task of synthesizing photo-realistic images based on semantic layouts. In this paper, we propose LayoutDiffuse that adapts a foundational diffusion model pretrained on large-scale image or text-image datasets for layout-to-image generation. By adopting a novel neural adaptor based on layout attention and task-aware prompts, our method trains efficiently, generates images with both high perceptual quality and layout alignment, and needs less data. Experiments on three datasets show that our method significantly outperforms other 10 generative models based on GANs, VQ-VAE, and diffusion models.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. FICGen: Frequency-Inspired Contextual Disentanglement for Layout-driven Degraded Image Generation

    cs.CV 2025-09 conditional novelty 6.0 of 10

    A frequency-guided layout-to-image generation framework, FICGen, improves fidelity, layout alignment, and detector trainability on degraded scenes across five benchmarks.

  2. DIVE: Inverting Conditional Diffusion Models for Discriminative Tasks

    cs.CV 2025-04 conditional novelty 6.0 of 10

    A frozen conditional diffusion model can be inverted via gradient-based discrete optimization, plus a learned layout prior, to perform object detection and faster classification without training a discriminative head.

  3. ComposeAnyone: Controllable Layout-to-Human Generation with Decoupled Multimodal Conditions

    cs.CV 2025-01 reject novelty 6.0 of 10

    ComposeAnyone generates human images by conditioning a diffusion model on hand-drawn color-block layouts together with decoupled text or reference-image descriptions for each body part.

  4. Why Settle for Mid: A Probabilistic Viewpoint to Spatial Relationship Alignment in Text-to-image Models

    cs.CV 2025-06 conditional novelty 5.0 of 10

    A probabilistic overlap measure for object positions yields a human-aligned spatial relationship metric and a training-free generation guidance method for text-to-image models.

  5. Dense-Face: Personalized Face Generation Model via Dense Annotation Prediction

    cs.CV 2024-12 conditional novelty 4.0 of 10

    Dense-Face is a personalized face generation model that adds a pose-controllable adapter and dense face annotation prediction to Stable Diffusion, improving identity preservation and text alignment.

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