Add z image turbo warmup script

[Musisoul]

No description provided.

[gemini-code-assist]

Summary of Changes

Hello @Musisoul, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request introduces a new benchmark script for the Z-Image-Turbo model, designed to include a warmup phase for performance evaluation. Concurrently, it refactors existing benchmark scripts by removing redundant os imports and explicit profiling environment variable settings, streamlining their execution. A profiling decorator has also been added to the Z-Image runner's pipeline method, enhancing performance monitoring capabilities.

Highlights

• New Warmup Script: A new script, run_lightx2v_z_image_turbo_with_warmup.py, has been added to facilitate benchmarking and performance evaluation for the Z-Image-Turbo model, including a dedicated warmup phase.
• Profiling Integration: The run_pipeline method within the z_image_runner.py now includes a @ProfilingContext4DebugL1 decorator, enabling more granular performance monitoring.
• Script Cleanup: Existing benchmark scripts (run_lightx2v_qwen_2512_with_warmup.py, run_lightx2v_qwen_edit_2511_with_warmup.py, run_lightx2v_wan22_t2v_8gpu_with_warmup.py) have been cleaned up by removing redundant import os statements and explicit PROFILING_DEBUG_LEVEL environment variable settings.

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[gemini-code-assist]

Code Review

This pull request adds a new warmup script for z_image_turbo and cleans up existing benchmark scripts by removing redundant profiling level settings. The changes are straightforward and improve the codebase. I've suggested a small refactoring in the new script to reduce code duplication and improve clarity.

[gemini-code-assist]

There are a couple of improvements that can be made here:

1. The arguments passed to pipe.generate are almost identical for the warmup and the actual generation call, leading to code duplication. To improve maintainability, you can store the common arguments in a dictionary and unpack them during the calls.
2. The comment on line 46, # Generate video, is misleading since this is a text-to-image (t2i) task that generates a .png file. It should be changed to # Generate image.

Here's a suggestion that addresses both points:

generate_args = {
    "seed": seed,
    "prompt": prompt,
    "target_shape": target_shape,
    "negative_prompt": negative_prompt,
    "save_result_path": save_result_path,
}

# warmup
pipe.generate(**generate_args)

# Generate image
pipe.generate(**generate_args, return_result_tensor=True)