Internally the output array will be created via:
ndarr = grid.mul(255).add_(0.5).clamp_(0, 255).permute(1, 2, 0).to('cpu', torch.uint8).numpy()

so you don’t need to multiply your predictions with 255 and can pass it as a float tensor.
The error is thrown at the .add_(0.5) step.
The documentation doesn’t mention any required ranges, so you might open a GitHub issue and link to this topic.