hi，I tried to reason with my own photo when it showed this.

[webe998]

E:\DiffPIR>python main_ddpir_sisr.py
LogHandlers setup!
23-07-30 10:47:38.869 : model_name:diffusion_ffhq_10m, sr_mode:blur, image sigma:0.050, model sigma:0.050
23-07-30 10:47:38.871 : eta:0.000, zeta:0.100, lambda:1.000, guidance_scale:1.00
23-07-30 10:47:38.871 : start step:999, skip_type:quad, skip interval:10, skipstep analytic steps:0
23-07-30 10:47:38.871 : analytic iter num:1, gamma:0.01
23-07-30 10:47:38.871 : Model path: model_zoo\diffusion_ffhq_10m.pt
23-07-30 10:47:38.871 : C:\Users\weber\Desktop\my_lq
Setting up [LPIPS] perceptual loss: trunk [vgg], v[0.1], spatial [off]
C:\Users\weber\AppData\Local\Programs\Python\Python310\lib\site-packages\torchvision\models_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.
warnings.warn(
C:\Users\weber\AppData\Local\Programs\Python\Python310\lib\site-packages\torchvision\models_utils.py:223: UserWarning: Arguments other than a weight enum or None for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing weights=VGG16_Weights.IMAGENET1K_V1. You can also use weights=VGG16_Weights.DEFAULT to get the most up-to-date weights.
warnings.warn(msg)
Loading model from: C:\Users\weber\AppData\Local\Programs\Python\Python310\lib\site-packages\lpips\weights\v0.1\vgg.pth
23-07-30 10:47:39.953 : --------- sf:4 --k: 0 ---------
23-07-30 10:47:39.955 : eta:0.000, zeta:0.250, lambda:2.000, inIter:1.000, gamma:0.010, guidance_scale:1.00
Traceback (most recent call last):
File "E:\DiffPIR\main_ddpir_sisr.py", line 502, in
main()
File "E:\DiffPIR\main_ddpir_sisr.py", line 485, in main
test_results_ave = test_rho(lambda_, zeta=zeta_i, model_output_type=model_output_type)
File "E:\DiffPIR\main_ddpir_sisr.py", line 298, in test_rho
x0 = utils_model.model_fn(x, noise_level=curr_sigma*255, model_out_type=model_out_type,
File "E:\DiffPIR\utils\utils_model.py", line 221, in model_fn
out = diffusion.p_sample(
File "E:\DiffPIR\guided_diffusion\gaussian_diffusion.py", line 422, in p_sample
out = self.p_mean_variance(
File "E:\DiffPIR\guided_diffusion\respace.py", line 91, in p_mean_variance
return super().p_mean_variance(self._wrap_model(model), *args, **kwargs)
File "E:\DiffPIR\guided_diffusion\gaussian_diffusion.py", line 260, in p_mean_variance
model_output = model(x, self._scale_timesteps(t), **model_kwargs)
File "E:\DiffPIR\guided_diffusion\respace.py", line 128, in call
return self.model(x, new_ts, **kwargs)
File "C:\Users\weber\AppData\Local\Programs\Python\Python310\lib\site-packages\torch\nn\modules\module.py", line 1190, in _call_impl
return forward_call(*input, **kwargs)
File "E:\DiffPIR\guided_diffusion\unet.py", line 660, in forward
h = th.cat([h, hs.pop()], dim=1)
RuntimeError: Sizes of tensors must match except in dimension 1. Expected size 24 but got size 25 for tensor number 1 in the list.

[yuanzhi-zhu]

@webe998
I think this error comes from the incorrect size of the input image.

Due to the limit of the pre-trained models we use, the images should better have a size 256 * 256.

Best,