An error occurs when using a custom image

[823863429]

Thank you for your outstanding work. When I use the image I uploaded and execute sample_i2v.yaml, I will be prompted：
loading video from input/i2v/rocket1.png
loading the input image
Traceback (most recent call last):
File "/content/SEINE/sample_scripts/with_mask_sample.py", line 243, in
main(omega_conf)
File "/content/SEINE/sample_scripts/with_mask_sample.py", line 226, in main
video_input, researve_frames = get_input(args) # f,c,h,w
File "/content/SEINE/sample_scripts/with_mask_sample.py", line 105, in get_input
video_frames = transform_video(video_frames)
File "/usr/local/lib/python3.10/dist-packages/torchvision/transforms/transforms.py", line 95, in call
img = t(img)
File "/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py", line 1501, in call_impl
return forward_call(*args, **kwargs)
File "/usr/local/lib/python3.10/dist-packages/torchvision/transforms/transforms.py", line 277, in forward
return F.normalize(tensor, self.mean, self.std, self.inplace)
File "/usr/local/lib/python3.10/dist-packages/torchvision/transforms/functional.py", line 363, in normalize
return F_t.normalize(tensor, mean=mean, std=std, inplace=inplace)
File "/usr/local/lib/python3.10/dist-packages/torchvision/transforms/functional_tensor.py", line 928, in normalize
return tensor.sub(mean).div(std)
RuntimeError: The size of tensor a (4) must match the size of tensor b (3) at non-singleton dimension 1

Is there any way to fix this error ??

[ExponentialML]

Hey. You can either re-save it as a jpg (your image has an alpha / transparency channel), or create a function in the main sample code by adding:

def get_img(img_path_or_tensor):
    img = torch.as_tensor(
        np.array(
            Image.open(img_path_or_tensor), 
            dtype=np.uint8, copy=True)
        ).unsqueeze(0)
    
    return img[:, :, :, :3]

Then replace all instances like from here with:

first_frame = get_img(first_frame_path)

[823863429]

Hey. You can either re-save it as a jpg (your image has an alpha / transparency channel), or create a function in the main sample code by adding:

def get_img(img_path_or_tensor):
    img = torch.as_tensor(
        np.array(
            Image.open(img_path_or_tensor), 
            dtype=np.uint8, copy=True)
        ).unsqueeze(0)
    
    return img[:, :, :, :3]

Then replace all instances like from here with:

first_frame = get_img(first_frame_path)

it work， thanks