Comments (1)
1)The memory space required for running different models varies, and it is recommended to test in an environment with 64GB of RAM or more whenever possible.
2)Try changing the map_location
parameter in the torch.load
function from cpu
to cuda:0
. This may reduce memory consumption. Refer to this link for more information.
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Related Issues (20)
- How could i do image harmonization like demo_image_composition? HOT 1
- 处理后的图像,细节部分处理不行,如何改善? HOT 1
- Failed to produce desired output HOT 1
- Training for specific use cases HOT 3
- Pretrained model AutoDownload error HOT 3
- pip install problem HOT 3
- 'Segmentation fault (core dumped)' in image_harmonization HOT 5
- training HOT 1
- pip install libcom error HOT 1
- CudaCheckError when applying CDTNet for image harmonization HOT 1
- 您好,controllable_composition生成的图像分辨率是512*512,看起来比较模糊,请问有办法生成分辨率更大或更清晰的图片吗? HOT 1
- Pip Installation Error: opencv_python==4.1.2.30: No Matching Distribution Found HOT 1
- 阴影生成模型SGDiffusion HOT 3
- pip install libcom fails HOT 1
- Resolution is too low HOT 1
- Import error for trilinear package HOT 1
- Unexpected harmonization results HOT 1
- opa_score报错 HOT 1
- high CPU consumption when using PCTNet HOT 1
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