Layout-to-Image Generation
18 papers with code • 7 benchmarks • 4 datasets
Layout-to-image generation its the task to generate a scene based on the given layout. The layout describes the location of the objects to be included in the output image. In this section, you can find state-of-the-art leaderboards for Layout-to-image generation.
Benchmarks
These leaderboards are used to track progress in Layout-to-Image Generation
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MS COCO
Visual Genome
COCO-Stuff
LayoutBench
Latest papers with no code
ObjBlur: A Curriculum Learning Approach With Progressive Object-Level Blurring for Improved Layout-to-Image Generation
We present ObjBlur, a novel curriculum learning approach to improve layout-to-image generation models, where the task is to produce realistic images from layouts composed of boxes and labels.
Layout-to-Image Generation with Localized Descriptions using ControlNet with Cross-Attention Control
While text-to-image diffusion models can generate highquality images from textual descriptions, they generally lack fine-grained control over the visual composition of the generated images.
Divide and Conquer: Language Models can Plan and Self-Correct for Compositional Text-to-Image Generation
Given a complex text prompt containing multiple concepts including objects, attributes, and relationships, the LLM agent initially decomposes it, which entails the extraction of individual objects, their associated attributes, and the prediction of a coherent scene layout.
SmartMask: Context Aware High-Fidelity Mask Generation for Fine-grained Object Insertion and Layout Control
To overcome these limitations, we introduce SmartMask, which allows any novice user to create detailed masks for precise object insertion.
SSMG: Spatial-Semantic Map Guided Diffusion Model for Free-form Layout-to-Image Generation
Despite significant progress in Text-to-Image (T2I) generative models, even lengthy and complex text descriptions still struggle to convey detailed controls.
LAW-Diffusion: Complex Scene Generation by Diffusion with Layouts
Thanks to the rapid development of diffusion models, unprecedented progress has been witnessed in image synthesis.
GeoDiffusion: Text-Prompted Geometric Control for Object Detection Data Generation
Diffusion models have attracted significant attention due to the remarkable ability to create content and generate data for tasks like image classification.
Guided Image Synthesis via Initial Image Editing in Diffusion Model
Diffusion models have the ability to generate high quality images by denoising pure Gaussian noise images.
LayoutDiffuse: Adapting Foundational Diffusion Models for Layout-to-Image Generation
Layout-to-image generation refers to the task of synthesizing photo-realistic images based on semantic layouts.
Object-Centric Image Generation from Layouts
In this paper, we start with the idea that a model must be able to understand individual objects and relationships between objects in order to generate complex scenes well.
