Comments (3)
你好,非常感谢你对我们工作的认可。
这个过程是先从 Xt_min 估计 t_min 时刻的噪声,再通过估计的噪声和 Xt_min 来估计 t_min 时刻的 X0。
最后, 直接输出t_min 时刻的 X0作为 diffusion 的最终结果,将该结果送入 VAE decoder 做进一步的增强。
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大概明白了。谢谢你!
P.S 我觉得文章的另外一个很大的卖点就是加速呀,毕竟已经在15步完成,非常的吸引人。
另外还有一个小问题:在Table2中,StableSR的Params是1409.11M,但是在StableSR的论文Table4中,为149.91M呀~这个是怎么回事?
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你好!我们后面的消融实验发现:更少的步数,以及不同的 t_max 和 t_min 的组合会带来不同的超分效果。这个部分还是有一些可以挖掘的地方,欢迎一起交流讨论。
我们在 Table2 给出的是所有模型的参数量。StableSR 的 Table4 是可训练模型的参数量。因为在训练的时候有相当一部分的模型参数都是冻住的,所以这两个参数量相差比较大。
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