fix: correct qwen image tile mask weights

[akaitsuki-ii]

No description provided.

[gemini-code-assist]

Code Review

This pull request corrects the indexing and weight calculations for the bottom and right borders in the _build_tile_mask method of the Qwen image autoencoder. The reviewer suggests vectorizing the loops for both border calculations using torch.arange and broadcasting to eliminate Python loop overhead and improve PyTorch performance.

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[gemini-code-assist]

Using a Python loop to perform element-wise multiplication on PyTorch tensors is inefficient because it incurs Python loop overhead and launches multiple small CUDA kernels. We can vectorize this operation using torch.arange and broadcasting, which is much faster and more idiomatic.

Note: The is_top and is_left blocks can also be vectorized in a similar manner.

Suggested change

	for y in range(border_h):
	mask[:, :, :, h - 1 - y, :] *= y / border_h
	mask[:, :, :, h - border_h + y, :] *= 1 - y / border_h
	weights = 1.0 - torch.arange(border_h, device=mask.device, dtype=mask.dtype) / border_h
	mask[:, :, :, h - border_h :, :] *= weights.view(1, 1, 1, border_h, 1)

[gemini-code-assist]

Similarly, this loop can be vectorized to avoid Python loop overhead and multiple CUDA kernel launches, improving performance.

Suggested change

	for x in range(border_w):
	mask[:, :, :, :, w - 1 - x] *= x / border_w
	mask[:, :, :, :, w - border_w + x] *= 1 - x / border_w
	weights = 1.0 - torch.arange(border_w, device=mask.device, dtype=mask.dtype) / border_w
	mask[:, :, :, :, w - border_w :] *= weights.view(1, 1, 1, 1, border_w)