

We can now infer the image by passing pixel values to the model.
thon
import torch
with torch.no_grad():
  outputs = model(pixel_values)
``
Output is an object of typeImageSuperResolutionOutput` that looks like below 👇 
(loss=None, reconstruction=tensor([[[[0.8270, 0.8269, 0.8275,  , 0.7463, 0.7446, 0.7453],
          [0.8287, 0.8278, 0.8283,  , 0.7451, 0.7448, 0.7457],
          [0.8280, 0.8273, 0.8269,  , 0.7447, 0.7446, 0.7452],
          ,
          [0.5923, 0.5933, 0.5924,  , 0.0697, 0.0695, 0.0706],
          [0.5926, 0.5932, 0.5926,  , 0.0673, 0.0687, 0.0705],
          [0.5927, 0.5914, 0.5922,  , 0.0664, 0.0694, 0.0718]]]],
       device='cuda:0'), hidden_states=None, attentions=None)
We need to get the reconstruction and post-process it for visualization. Let's see how it looks like.
thon
outputs.reconstruction.data.shape
torch.Size([1, 3, 880, 1072])