[tests] refactor c* pipeline tests. - #14626
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Migrate the remaining `tests/pipelines/` directories starting with C to the config class + composable pytest mixins introduced in #14113: chroma, chronoedit, cogview3, consisid, consistency_models, controlnet, controlnet_flux, controlnet_hunyuandit, controlnet_sd3 and cosmos. cogvideo and cogview4 were already migrated. Notable points beyond the mechanical conversion: - Expected slices switch from the `"np"` layout (`[0, -3:, -3:, -1]`) to the `"pt"` one (`[0, -1, -3:, -3:]`); the values themselves are unchanged. - The Cosmos `safety_checker` overrides (save/load, variants, dtype dict, encode_prompt) were duplicated across five files and now live in a shared `CosmosSafetyCheckerTesterMixin` in `tests/pipelines/cosmos/testing_utils.py`. - chroma and controlnet_flux import `FluxIPAdapterTesterMixin` from `..flux.testing_utils` rather than the old `..test_pipelines_common` one. - `PipelineTesterMixin.test_save_load_float16` now passes the non-deprecated `dtype=` to `from_pretrained` instead of `torch_dtype=`, which the Cosmos2.5 `from_pretrained` wrappers read when casting the safety checker they inject. - Dropped along the way, matching #14619: `PipelineLatentTesterMixin`, `PipelineKarrasSchedulerTesterMixin`, the attention-slicing and xformers tests, and the `test_ip_adapter` overrides that only supplied an `expected_pipe_slice`. `pytest tests/pipelines/{chroma,chronoedit,cogview3,consisid,consistency_models,controlnet,controlnet_flux,controlnet_hunyuandit,controlnet_sd3,cosmos}` gives 14 failed, 1014 passed, 82 skipped. All 14 failures are in cosmos and reproduce unchanged on main: `DummyCosmosSafetyChecker` is a parameter-less `ModelMixin` with a single non-persistent buffer, so accelerate does not hook it for sequential offload and a reloaded pipeline gets a fresh fp32 one. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
sayakpaul
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Aug 27, 2026
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| pipe.save_pretrained(tmp_path) | ||
| pipe_loaded = self.pipeline_class.from_pretrained(tmp_path, torch_dtype=torch.float16) | ||
| pipe_loaded = self.pipeline_class.from_pretrained(tmp_path, dtype=torch.float16) |
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Just promote the use of dtype rather than torch_type.
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