v1.0.2 Newer torch version, improved support for dt argument

The model is re-exported with torch 2.1.1

Includes minor fixes:

• Fix UserWarning: Using padding='same' with even kernel lengths and odd dilation may require a zero-padded copy of the input be created
• Now it doesn't ignore dt argument thanks to @niqodea (however it seems that authors of the model recommend to stick with .5)
• Supports batched inference

v1.0 - Initial release

v1.0 - Initial release

Initially exported models in TorchScript compiled format for easy use in all supported environments with a couple of lines of code.

How to use

Download a compiled model and specify the path to the file in the following snippet:

import torch

device = torch.device('cuda')
precision = torch.float16

model = torch.jit.load(model_path, map_location='cpu')
model.eval().to(device=device, dtype=precision)

img1 = torch.rand(1, 3, 720, 1080).to(precision).to(device)
img3 = torch.rand(1, 3, 720, 1080).to(precision).to(device)
dt = img1.new_full((1, 1), .5)

with torch.no_grad():
    img2 = model(img1, img3, dt)   # Will be of the same shape as inputs (1, 3, 720, 1080)