[AutoRound] Support WAN2.2 W4A16 quantization model

[lvliang-intel]

PLEASE FILL IN THE PR DESCRIPTION HERE ENSURING ALL CHECKLIST ITEMS (AT THE BOTTOM) HAVE BEEN CONSIDERED.

Purpose

Add AutoRound W4A16 quantization support for Wan2.2 pipelines and transformer modules.
https://huggingface.co/Intel/Wan2.2-TI2V-5B-Diffusers-int4-AutoRound
https://huggingface.co/Intel/Wan2.2-I2V-A14B-Diffusers-int4-AutoRound
https://huggingface.co/Intel/Wan2.2-T2V-A14B-Diffusers-int4-AutoRound

Related: #1325, #1777, #2670

Test Plan

Run UT
Run VBench dataset accuracy test

Test Result

Raw Scores

Subject Consistency	Wan2.2-I2V-A14B-Diffusers	Wan2.2-I2V-A14B-Diffusers-Int4-AutoRound	Wan2.2-T2V-A14B-Diffusers	Wan2.2-T2V-A14B-Diffusers-Int4-AutoRound
Subject Consistency	0.9752	0.9741	0.9508	0.9578
Background Consistency	0.9704	0.9691	0.9449	0.9465
Aesthetic Quality	0.6241	0.6089	0.5730	0.5980
Imaging Quality	0.6832	0.6679	0.6623	0.6591

Aggregate by Category

Category	Wan2.2-I2V-A14B-Diffusers	Wan2.2-I2V-A14B-Diffusers-Int4-AutoRound	Wan2.2-T2V-A14B-Diffusers	Wan2.2-T2V-A14B-Diffusers-Int4-AutoRound
Consistency	0.9728	0.9716	0.9478	0.9522
Quality	0.6537	0.6384	0.6176	0.6286

Evaluated Dimension Average

Model	Dimensions Evaluated	Avg Score
Wan2.2-I2V-A14B-Diffusers	4	0.8132
Wan2.2-I2V-A14B-Diffusers-Int4-AutoRound	4	0.8050
Wan2.2-T2V-A14B-Diffusers	4	0.7827
Wan2.2-T2V-A14B-Diffusers-Int4-AutoRound	4	0.7904

Generation Statistics

Model	Success Rate	Avg Latency(s)	Avg Memory(MB)	Speedup vs Ref	Memory Ratio vs Ref
Wan2.2-I2V-A14B-Diffusers	100.0	377.31	76309.33	1.00x	1.00x
Wan2.2-I2V-A14B-Diffusers-Int4-AutoRound	100.0	439.68	36893.0	0.86x	0.48x
Wan2.2-T2V-A14B-Diffusers	100.0	736.09	76298.33	1.00x	1.00x
Wan2.2-T2V-A14B-Diffusers-Int4-AutoRound	100.0	863.49	36891.33	0.85x	0.48x

The test is mainly for accuracy purpose. For video generation at batch size 1, Int4 W4A16 primarily saves memory (0.48x as shown — great for fitting larger models / longer videos in VRAM) but does not necessarily improve latency because the workload is compute-bound and dequantization overhead is significant.

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[hsliuustc0106]

Comprehensive benchmarks and well-structured tests. Memory reduction to 0.48x is significant for VRAM-constrained deployments. Two notes: 1) Checklist items at the bottom are unchecked - confirm documentation was updated if required. 2) Latency impact (0.86x speedup) is expected for compute-bound workloads at batch size 1, but consider documenting guidance for optimal batch sizes where dequantization overhead is amortized.

[david6666666]

Merge conflicts need fixing before review. Thx.

[david6666666]

LGTM now

[david6666666]

@yenuo26 please check test