SD, ADM, LDM也都单卡跑吗？batchsize好小啊，lr， king也用一样的吗？ about diff-sampler

[YuChen-Liang]

通过与DPMv3的代码对照，LDM是不使用thresholding的，predict_x0=True效果会好一些。

[YuChen-Liang]

我未能复现LSUNbedroom LDM的结果。您能提供一下DPM++求解器的sample参数吗

[YuChen-Liang]

设置predict_x0=False反而work了，这很奇怪。看起来实现有一些问题

[zhyzhouu]

1.我们最多使用了四张A100
2.Batch size: 主要依照显存来设置。四卡下，SD/ImageNet256/Bedroom取batch_size=32，Bedroom_LDM/ImageNet64/FFHQ64取batch_size=64，CIFAR10取128。
3.Leaning rate: 统一取5e-3
4.�kimg: 出于时长考虑，SD取5；其他统一取10。
5.对于SD/LDM/ImageNet256，我们默认取predict_x0=False，lower_order_final=True。对NFE=4下的Bedroom_LDM，lower_order_final=False效果更好
6.等目前的项目处理完后，我会在文档中更新主要实验设置

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1.We use up to 4 A100 GPUs.
2.We set batch_size arroding to the graphics memory. Using 4 GPUs, we set 32 for SD/ImageNet256/Bedroom, 64 for Bedroom_LDM/ImageNet64/FFHQ64 and 128 for CIFAR10.
3.The learning rate is 5e-3 across all experiments.
4.kimg: mostly 10. Only for SD we use 5 considering the training time.
5.For SD/LDM/ImageNet256, we use predict_x0=False and lower_order_final=True by default. We find lower_order_final=False better for 4-NFE Bedroom_LDM.
6.Currenly working on other projects. Afterwards, I will update the main experimental settings in the document.