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Unified Multi-Modal Latent Diffusion for Joint Subject and Text Conditional Image Generation.

Computing Research Repository (CoRR)(2023)

Cited 27|Views93
Abstract
Language-guided image generation has achieved great success nowadays by using diffusion models. However, texts can be less detailed to describe highly-specific subjects such as a particular dog or a certain car, which makes pure text-to-image generation not accurate enough to satisfy user requirements. In this work, we present a novel Unified Multi-Modal Latent Diffusion (UMM-Diffusion) which takes joint texts and images containing specified subjects as input sequences and generates customized images with the subjects. To be more specific, both input texts and images are encoded into one unified multi-modal latent space, in which the input images are learned to be projected to pseudo word embedding and can be further combined with text to guide image generation. Besides, to eliminate the irrelevant parts of the input images such as background or illumination, we propose a novel sampling technique of diffusion models used by the image generator which fuses the results guided by multi-modal input and pure text input. By leveraging the large-scale pre-trained text-to-image generator and the designed image encoder, our method is able to generate high-quality images with complex semantics from both aspects of input texts and images.
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Image Inpainting,Image Captioning,Image Synthesis,Unsupervised Learning,Texture Synthesis
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要点】:本论文提出了一种名为UMM-Diffusion的新型统一多模态潜在扩散模型,通过将文本和包含特定主体的图像作为输入序列,实现了对定制图像的生成,创新点在于结合了图像和文本信息来提高文本到图像生成的准确性。

方法】:UMM-Diffusion模型将输入的文本和图像编码到一个统一的多模态潜在空间中,图像被学习映射到伪词嵌入,并与文本结合来指导图像生成。

实验】:该模型采用了一种新的扩散模型采样技术,消除了输入图像中的无关部分,如背景或光照。通过利用大规模预训练的文本到图像生成器和设计的图像编码器,该方法能够从输入文本和图像的两个方面生成具有复杂语义的高质量图像。实验结果显示,该方法在生成特定主题的图像时,比纯文本到图像生成的方法更具准确性和实用性。