Cuda out of memory #68
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Hello! Typically 1x models will consume more VRAM than 4x models if the same configuration is used (specially batch size and crop size). If using the standard ESRGAN architecture, the recommendation here is to reduce either the batch size, the crop size or both, so the tensors fit in memory. Alternatively, you can try to use the pixel-unshuffle wrapper, but that depends on how you plan to use the model after it's trained and if it that code supports using the models with the wrappers. |
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Thanks to Victoria also gives her Train_sr.yml maybe you will come up with an idea what can be improved. Thanks again!
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Hello! Typically 1x models will consume more VRAM than 4x models if the same configuration is used (specially batch size and crop size).
If using the standard ESRGAN architecture, the recommendation here is to reduce either the batch size, the crop size or both, so the tensors fit in memory.
Alternatively, you can try to use the pixel-unshuffle wrapper, but that depends on how you plan to use the model after it's trained and if it that code supports using the models with the wrappers.