diffusion_rs_common 0.1.0

Blazingly fast inference of diffusion models.
Documentation 
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import torch
from collections import OrderedDict

# Write a trivial tensor to a pt file
a = torch.tensor([[1, 2, 3, 4], [5, 6, 7, 8]])
o = OrderedDict()
o["test"] = a

# Write a trivial tensor to a pt file
torch.save(o, "test.pt")

############################################################################################################
# Write a trivial tensor to a pt file with a key
torch.save({"model_state_dict": o}, "test_with_key.pt")

############################################################################################################
# Create a tensor with fortran contiguous memory layout
import numpy as np

# Step 1: Create a 3D NumPy array with Fortran order using a range of numbers
# For example, creating a 2x3x4 array
array_fortran = np.asfortranarray(np.arange(1, 2 * 3 * 4 + 1).reshape(2, 3, 4))

# Verify the memory order
print(
    "Is Fortran contiguous (F order):", array_fortran.flags["F_CONTIGUOUS"]
)  # Should be True
print(
    "Is C contiguous (C order):", array_fortran.flags["C_CONTIGUOUS"]
)  # Should be False

# Step 2: Convert the NumPy array to a PyTorch tensor
tensor_fortran = torch.from_numpy(array_fortran)

# Verify the tensor layout
print(
    "Tensor stride:", tensor_fortran.stride()
)  # Stride will reflect the Fortran memory layout

# Step 3: Save the PyTorch tensor to a .pth file
torch.save({"tensor_fortran": tensor_fortran}, "fortran_tensor_3d.pth")

print("3D Tensor saved with Fortran layout.")