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Official code implementation of " TextDiff: Mask-Guided Residual Diffusion Models for Scene Text Image "

Home Page: https://www.aibupt.com/

diffusion-models scene-text-image-super-resolution datasets deep-learning deep-neural-networks image-to-image image-translation img2img low-level-vision pytorch-implementation

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textdiff's Issues

关于扩散模型在训练和预测过程的输入

您好!我阅读了您的论文,但在某些部分我还有一些理解上的困惑。因此,我希望能够向您请教并解答一些疑问。非常感谢您的时间和帮助!
我看到您在扩散模型的训练过程中,给出的公式中xt是由xres加噪获取的图像,这里为什么可以将GT的信息(GT减去SR)作为模型训练的条件?在预测过程中,这部分依旧是xres吗,还是其他的内容?

Question

作者您好,
我在尝试自行复现的过程中遇到以下几点疑惑:

  1. 论文中报告DocDiff在TextZoom数据集的aster识别平均正确率只有56%,与我跑的结果相似,您认为是什么原因导致DocDiff在该数据集上表现不佳呢?
  2. 您是否尝试过在输入网络前对图片进行STN,该操作对识别率影响大嘛?
  3. 您是否尝试过在TextZoom上不使用第一阶段的CNN而是直接训练ddpm呢,效果是否有明显下降?

期待您的回复~

Code

Hi,

This is a nice work! Is there a plan to release the code?

Thanks

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