Make HunyuanDiT2DModel and HunyuanDiT2DControlNetModel hidden states contiguous

[menglcai]

What does this PR do?

Similar to #14186

The non-contiguous tensor originates from PatchEmbed.forward — flatten(2).transpose(1, 2) (BCHW → BNC) produces a non-contiguous layout whose overhead accumulates across transformer blocks in HunyuanDiT2DModel and HunyuanDiT2DControlNetModel.
Unlike SD3 where the benefit is limited to ROCm, this patch improves performance on both ROCm and NVIDIA for HunyuanDiT, though the gain on NVIDIA is smaller.

Benchmarks (HunyuanDiT-v1.1-Diffusers, 512×512, 50 steps, fp16):

Platform	PyTorch	Default	+contiguous	Speedup
NVIDIA RTX 5090	2.12.1+cu132	3.16 it/s	3.29 it/s	+4.1%
AMD Radeon AI PRO R9700 (gfx1201)	2.12.0+rocm7.15.0a20260713	1.34 it/s	1.52 it/s	+13.4%
AMD Radeon(TM) 8060S Graphics (gfx1151)	2.12.0+rocm7.15.0a20260711	3.23 s/it	3.05 s/it	+5.8%

Benchmarks (HunyuanDiT-v1.2-Diffusers, 512×512, 50 steps, fp16):

Platform	PyTorch	Default	+contiguous	Speedup
NVIDIA RTX 5090	2.12.1+cu132	3.16 it/s	3.30 it/s	+4.4%
AMD Radeon AI PRO R9700 (gfx1201)	2.12.0+rocm7.15.0a20260713	1.35 it/s	1.53 it/s	+13.3%
AMD Radeon(TM) 8060S Graphics (gfx1151)	2.12.0+rocm7.15.0a20260711	3.23 s/it	3.05 s/it	+5.6%

Benchmarks (HunyuanDiT-v1.2-ControlNet-Diffusers-Canny + HunyuanDiT-v1.2-Diffusers-Distilled, 512×512, 50 steps, fp16):

Platform	PyTorch	Default	+contiguous	Speedup
NVIDIA RTX 5090	2.12.1+cu132	2.12 it/s	2.26 it/s	+6.6%
AMD Radeon AI PRO R9700 (gfx1201)	2.12.0+rocm7.15.0a20260713	1.13 s/it	1.04 it/s	+8.0%
AMD Radeon(TM) 8060S Graphics (gfx1151)	2.12.0+rocm7.15.0a20260711	4.73 s/it	3.79 s/it	+19.9%

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Who can review?

@sayakpaul @yiyixuxu

Anyone in the community is free to review the PR once the tests have passed. Feel free to tag
members/contributors who may be interested in your PR.

[sayakpaul]

Thanks for your PR. Instead of Hunyuan, do we want to treat another model that has popularity and more usage?

[github-actions]

Hi @menglcai, thanks for the PR! It does not appear to link an issue it fixes. If this PR addresses an existing issue, please add a closing keyword (e.g. Fixes #1234) to the PR description so the issue is linked. See the contribution guide for more details. If this PR intentionally does not fix a tracked issue, a maintainer can add the no-issue-needed label to silence this reminder.

[menglcai]

Thanks for your PR. Instead of Hunyuan, do we want to treat another model that has popularity and more usage?

Understood, I'll look into more popular or widely used models and open a follow-up PR if a similar fix applies.