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LayoutDiffuse: Adapting Foundational Diffusion Models for Layout-to-Image Generation
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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.
Forward citations
Cited by 2 Pith papers
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FICGen: Frequency-Inspired Contextual Disentanglement for Layout-driven Degraded Image Generation
A frequency-guided layout-to-image generation framework, FICGen, improves fidelity, layout alignment, and detector trainability on degraded scenes across five benchmarks.
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Why Settle for Mid: A Probabilistic Viewpoint to Spatial Relationship Alignment in Text-to-image Models
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.
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