Comments (1)
ustcnewly
commented on July 17, 2024
Some functions support arbitrary resolution, like image harmonization (CDTNet, PCTNet). For other functions, you can use super-resolution to upscale the model output, or retrain the model using high-resolution images.
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Related Issues (20)
- Couldn't get it installed on Mac HOT 1
- 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
- Getting out of memory HOT 1
- 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
- Import error for trilinear package HOT 1
- Unexpected harmonization results HOT 1
- opa_score报错 HOT 1
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