Comments (5)
Hi,you can upscale the face by bicubic interpolation to specified size like code in data/prepare.py
first, then validate the pretrain model.
from image-super-resolution-via-iterative-refinement.
I do not understand, I have a blurred face, how do I make it hd?
from image-super-resolution-via-iterative-refinement.
Hi,you can find it in Inference Alone
part.
from image-super-resolution-via-iterative-refinement.
help infer.py asks me for 3 images sr hr lr, but I only have one image.
the description says "HR (high resolution vanilla images), SR (images need to be processed)"
what is hr? Is it the same image in high resolution? It doesn't make sense if my goal is to go from low resolution to high resolution.
from image-super-resolution-via-iterative-refinement.
You can upscale the face by yourself to get the SR first. HR is the ground truth image, you can replace it with SR if you did not have ground truth image.
from image-super-resolution-via-iterative-refinement.
Related Issues (20)
- How to speed up Training?
- Using latest faster samplers such as DPM++2M / DPM++3M
- Output shape should be same as the input shape in conditional image generation HOT 1
- About model convergence
- in test phase, it doesn't load the trained net models and states HOT 1
- Could you explain the q_sample ? HOT 2
- Question of pretrained model HOT 1
- Using pretrained model
- A little question about val.py HOT 6
- prepare_data TypeError: cannot pickle 'Environment' object HOT 1
- Question about your inference code
- About "finetune_norm" HOT 1
- Distributed Training
- What is requirement to run this project?
- Why is there 1 more res block in up module than in down's?
- val.log中的PSNR值时高时低?
- 通过prepare.py得到的HR会极大地压缩质量? HOT 2
- How to obtain interpolated images?
- 应该怎么更改呢
- 为什么会报错呢?24-07-31 10:06:39.557 - INFO: Initial Model Finished 段错误 (核心已转储)
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