refactor deepfloyd if img2img pipeline tests to the new mixin structure

[akshan-main]

What does this PR do?

Refactors the DeepFloyd IF img2img pipeline tests to the new mixin structure from #14113, same shape as the text-to-image port: an IFImg2ImgPipelineTesterConfig plus TestIFImg2ImgPipeline(PipelineTesterMixin) and TestIFImg2ImgPipelineMemory(PipelineOffloadTesterMixin), slow class ported to pytest, dummy components inlined from the shared package mixin, and a fresh CPU slice in test_inference.

The same verified choices as #14580 apply: @skip_mps kept (all-NaN output on mps), latents dropped from optional_input_params, the custom test_save_load_optional_components ported (prompt pre-encoded), the obsolete save_load_local override dropped, and the float16 1e-1 tolerance kept. The old test_float16_inference override maps to the framework's test_half_precision_inference_no_nan, which needs no tolerance. Follows #14580.

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 read our philosophy doc (important for complex PRs)?
• Was this discussed/approved via a GitHub issue or the forum? Discussed on Slack with @sayakpaul
• Did you make sure to update the documentation with your changes?
• Did you write any new necessary tests?

Who can review?

@sayakpaul

[sayakpaul]

I think if we load it using eval() it should be fine? Could we check?

[akshan-main]

You're right. The old base test ran the fresh components in train mode, so the tiny-random-t5 dropout was the "non-determinism"; the new get_pipeline() puts both sides in eval. Replicated the save/load flow on CPU fp32: eval both sides gives max_diff 1.7e-5, the train-mode first pipe gives 0.94. Dropped the override here and in #14580.

[sayakpaul]

Thanks. Left one comment.

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