[tests] refactor UNet model tests to align with the new pattern#13153
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[tests] refactor UNet model tests to align with the new pattern#13153
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Refactor UNet1D model tests to follow the modern testing pattern using BaseModelTesterConfig and focused mixin classes (ModelTesterMixin, MemoryTesterMixin, TrainingTesterMixin, LoraTesterMixin). Both UNet1D standard and RL variants now have separate config classes and dedicated test classes organized by concern (core, memory, training, LoRA, hub loading). Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
Refactor UNet2D model tests (standard, LDM, NCSN++) to follow the modern testing pattern. Each variant gets its own config class and dedicated test classes organized by concern (core, memory, training, LoRA, hub loading). Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
…ing mixins Refactor UNet3DConditionModel tests to follow the modern testing pattern with separate classes for core, attention, memory, training, and LoRA. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
…ing mixins Refactor UNetControlNetXSModel tests to follow the modern testing pattern with separate classes for core, memory, training, and LoRA. Specialized tests (from_unet, freeze_unet, forward_no_control, time_embedding_mixing) remain in the core test class. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
…sting mixins Refactored the spatiotemporal UNet test file to follow the modern modular testing pattern with BaseModelTesterConfig and focused test classes: - UNetSpatioTemporalTesterConfig: Base configuration with model setup - TestUNetSpatioTemporal: Core model tests (ModelTesterMixin, UNetTesterMixin) - TestUNetSpatioTemporalAttention: Attention-related tests (AttentionTesterMixin) - TestUNetSpatioTemporalMemory: Memory/offloading tests (MemoryTesterMixin) - TestUNetSpatioTemporalTraining: Training tests (TrainingTesterMixin) - TestUNetSpatioTemporalLoRA: LoRA adapter tests (LoraTesterMixin) Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
sayakpaul
commented
Feb 16, 2026
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| image = model(**self.get_dummy_inputs(), return_dict=False)[0] | ||
| new_image = new_model(**self.get_dummy_inputs(), return_dict=False)[0] | ||
| inputs_dict = self.get_dummy_inputs() | ||
| image = model(**inputs_dict, return_dict=False)[0] | ||
| new_image = new_model(**inputs_dict, return_dict=False)[0] |
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To ensure reproducibility.
sayakpaul
commented
Feb 16, 2026
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| if self.model_class.__name__ == "UNet2DConditionModel": | ||
| recompile_limit = 2 | ||
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Not needed as we pass recompile_limit explicitly now.
sayakpaul
commented
Feb 16, 2026
| with pytest.raises(RuntimeError, match=msg): | ||
| model.enable_lora_hotswap(target_rank=32) | ||
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| def test_enable_lora_hotswap_called_after_adapter_added_warning(self, caplog): |
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It was needed because caplog doesn't capture these warnings properly.
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What does this PR do?
Some comments in-line. I have run the tests locally and all of them pass.