cs.AI updates on arXiv.org 07月23日 12:03
LSSGen: Leveraging Latent Space Scaling in Flow and Diffusion for Efficient Text to Image Generation
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本文介绍了一种名为LSSGen的文本到图像生成框架,通过在潜在空间直接进行分辨率缩放,解决了传统方法在图像生成中的分辨率缩放问题,显著提升了生成图像的效率和视觉质量。

arXiv:2507.16154v1 Announce Type: cross Abstract: Flow matching and diffusion models have shown impressive results in text-to-image generation, producing photorealistic images through an iterative denoising process. A common strategy to speed up synthesis is to perform early denoising at lower resolutions. However, traditional methods that downscale and upscale in pixel space often introduce artifacts and distortions. These issues arise when the upscaled images are re-encoded into the latent space, leading to degraded final image quality. To address this, we propose {\bf Latent Space Scaling Generation (LSSGen)}, a framework that performs resolution scaling directly in the latent space using a lightweight latent upsampler. Without altering the Transformer or U-Net architecture, LSSGen improves both efficiency and visual quality while supporting flexible multi-resolution generation. Our comprehensive evaluation covering text-image alignment and perceptual quality shows that LSSGen significantly outperforms conventional scaling approaches. When generating $1024^2$ images at similar speeds, it achieves up to 246\% TOPIQ score improvement.

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文本到图像生成 LSSGen框架 分辨率缩放
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