feat(diffusers): add examples/diffusers/tests

[Cui-yshoho]

What does this PR do?

Fixes # (issue)

Adds # (feature)

Before submitting

• This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case).
• Did you read the contributor guideline?
• Did you make sure to update the documentation with your changes? E.g. record bug fixes or new features in What's New. Here are the
  documentation guidelines
• Did you build and run the code without any errors?
• Did you report the running environment (NPU type/MS version) and performance in the doc? (better record it for data loading, model inference, or training tasks)
• Did you write any new necessary tests?

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[gemini-code-assist]

Summary of Changes

Hello @Cui-yshoho, 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 significantly enhances the testing infrastructure for diffusers examples by adding dedicated test files for ControlNet, DreamBooth, Text-to-Image, Textual Inversion, and Unconditional Image Generation. It also refines the checkpointing mechanism in training scripts by adopting the safetensors format and save_pretrained() method for UNet models, alongside minor documentation updates to improve user experience.

Highlights

• New Test Suites: Introduced comprehensive test files for various diffusers examples, including ControlNet, DreamBooth (and its LoRA variants), Text-to-Image (and its LoRA variants), Textual Inversion, and Unconditional Image Generation. These tests cover basic functionality, checkpointing, and specific features like text encoder training and latent caching.
• Checkpoint Handling Improvement: Updated existing training scripts across ControlNet, DreamBooth, Text-to-Image, and Textual Inversion examples to standardize checkpoint loading and saving. The changes involve migrating from the .ckpt format to the more secure and efficient .safetensors format and utilizing unet.save_pretrained() for saving UNet models.
• Documentation Path Corrections: Minor adjustments were made to README.md files in cogvideox_factory, cogview, and dreambooth examples to correct cd commands and installation tips, ensuring accurate setup instructions.
• Test Utility Introduction: A new utility file, test_examples_utils.py, was added to provide common functionalities for running and asserting example tests, promoting consistency and reusability across the new test suites.

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[gemini-code-assist]

Code Review

This pull request introduces a comprehensive test suite for various diffusers examples, enhancing the project's robustness. It also refactors the checkpointing mechanism in several training scripts to consistently use the .safetensors format and the save_pretrained method, which is a good improvement for model serialization. My review identifies a few critical issues in the new test files and one of the updated training scripts that need to be addressed.

[gemini-code-assist]

The usage of ms.load_checkpoint as a context manager is incorrect, as it returns a dictionary and does not support the with statement. To read metadata from a .safetensors file, safetensors.safe_open should be used. You will need to add from safetensors import safe_open to the file imports.

Suggested change

	with ms.load_checkpoint(state_dict_file, format="safetensors") as f:
	metadata = f.metadata() or {}
	from safetensors import safe_open
	with safe_open(state_dict_file, framework="np") as f:
	metadata = f.metadata() or {}

[gemini-code-assist]

The ms.load_checkpoint function is being called to load a .safetensors file, but the format argument is missing. Without format="safetensors", MindSpore will try to load it as a standard checkpoint file, which will fail.

Suggested change

	ms.load_param_into_net(unet, ms.load_checkpoint(input_model_file), strict_load=True)
	ms.load_param_into_net(unet, ms.load_checkpoint(input_model_file, format="safetensors"), strict_load=True)

[gemini-code-assist]

The test method test_controlnet_sd3 in class ControlNetSD35 has a duplicated name with the test method in class ControlNetSD3. Test method names within a test class and its inherited test cases should be unique. This will cause one of the tests to be skipped by the test runner. Please rename it to reflect the test case, for example, test_controlnet_sd35.

Suggested change

	def test_controlnet_sd3(self):
	def test_controlnet_sd35(self):