testing error

[lanlinxxs]

Hello, I encountered an error during prediction. Can you tell me why this happened?
Traceback (most recent call last):
File "/root/autodl-tmp/scripts/segmentation_sample.py", line 130, in
main()
File "/root/autodl-tmp/scripts/segmentation_sample.py", line 97, in main
sample, x_noisy, org = sample_fn(
File "/root/autodl-tmp/guided_diffusion/gaussian_diffusion.py", line 520, in p_sample_loop_known
for sample in self.p_sample_loop_progressive(
File "/root/autodl-tmp/guided_diffusion/gaussian_diffusion.py", line 586, in p_sample_loop_progressive
out = self.p_sample(
File "/root/autodl-tmp/guided_diffusion/gaussian_diffusion.py", line 433, in p_sample
out = self.p_mean_variance(
File "/root/autodl-tmp/guided_diffusion/respace.py", line 96, in p_mean_variance
return super().p_mean_variance(self._wrap_model(model), *args, **kwargs)
File "/root/autodl-tmp/guided_diffusion/gaussian_diffusion.py", line 316, in p_mean_variance
self._predict_xstart_from_eps(x_t=x, t=t, eps=model_output)
File "/root/autodl-tmp/guided_diffusion/gaussian_diffusion.py", line 337, in _predict_xstart_from_eps
assert x_t.shape == eps.shape
AssertionError

[wildailurpodan]

x_t shape: torch.Size([1, 1, 224, 224])
eps shape: torch.Size([1, 2, 224, 224])
i met the same error as you do. It looks like the expected output has two layers, is the original and the generated mask put together? Grayscale images should have only one layer