1. Introduction Layout-to-image synthesis (LIS) is one of the prevailing topics in the research of conditional image generation. It aims to generate complex scenes where a user requires fine controls over the objects appearing in a scene. There are different types of layouts including bboxes+...
While previous research has primarily focused on improving the control of image generation through adjusting the denoising process, we propose a novel direction of manipulating the initial noise to control the generated image. Through experiments on stable diffusion, we show that blocks of pixels in ...
Layout-to-Image Synthesis (LIS) To generate images under the traditional LIS setting, run: python scripts/LIS.py --batch_size 8 --config /path/to/config --ckpt /path/to/trained_model --dataset <dataset name> --outdir /path/to/output --txt_file /path/to/dataset/with/val.txt --data...
Paper:[1801.05091] Inferring Semantic Layout for Hierarchical Text-to-Image Synthesis内容来自:通过推测语义布局,层级形式文本到图像的合成《Inferring Semantic Layout for Hierarchical Text-to-image Synthesis》 Introduction 论文对于text-to-image synthesis这件事,提出了一个新的方法:通过推断语义布局分层实现text-...
Recent breakthroughs in text-to-image diffusion models have significantly advanced the generation of high-fidelity, photo-realistic images from textual descriptions. Yet, these models often struggle with interpreting spatial arrangements from text, hindering their ability to produce images with precise ...
Inferring Semantic Layout for Hierarchical Text-to-Image Synthesis 摘要 本文提出了一种基于语义布局的层次化文本图像合成方法。该算法不是学习从文本到图像的直接映射,而是将生成过程分解为多个步骤,首先通过布局生成器从文本中构造语义布局,然后通过图像生成器将布局转换为图像。所提出的布局生成器通过生成对象边界框并...
Advancements on text-to-image synthesis generate remarkable images from textual descriptions. However, these methods are designed to generate only one object with varying attributes. They face difficulties with complex descriptions having multiple arbitrary objects since it would require information on the ...
Figure 1. Scene-to-image synthesis by Grid2Im [1] vs. PLGAN 针对上述问题,引入全景分割[3]的概念,提出了基于全景布局(Panoptic Layout)的图像合成方法。在全景分割问题中[3],将物体类别分为了可数类(things)和不可数类(stuff),其中可数类(things)指有特定形状的前景类别,不可数类(stuff)指没有特定形状...
内容提示: Learning to Predict Layout-to-image ConditionalConvolutions for Semantic Image SynthesisXihui LiuThe Chinese University of Hong Kongxihuiliu@ee.cuhk.edu.hkGuojun YinUniversity of Science and Technology of Chinagjyin91@gmail.comJing ShaoSenseTime Researchshaojing@sensetime.comXiaogang WangThe...
Semantic Image Synthesis with DPGAN Layout-to-Image Translation with Double Pooling Generative Adversarial Networks Hao Tang1,Nicu Sebe2. 1ETH Zurich, Switzerland,2University of Trento, Italy. InTIP 2021. The repository offers the official implementation of our paper in PyTorch. ...