[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 <[email protected]>
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 <[email protected]>
…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 <[email protected]>
…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 <[email protected]>
…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 <[email protected]>
sayakpaul
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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
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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
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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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@DN6 a gentle ping. |
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@dg845 a gentle ping. |
This was referenced Jun 9, 2026
dg845
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Thanks for the PR! Can you fix the merge conflicts?
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* refactor unet2d condition model tests. * fix tests * up * fix * Revert "fix" This reverts commit 46d44b7. * up * recompile limit * [tests] refactor test_models_unet_1d.py to use modular testing mixins 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 <[email protected]> * [tests] refactor test_models_unet_2d.py to use modular testing mixins 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 <[email protected]> * [tests] refactor test_models_unet_3d_condition.py to use modular testing 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 <[email protected]> * [tests] refactor test_models_unet_controlnetxs.py to use modular testing 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 <[email protected]> * [tests] refactor test_models_unet_spatiotemporal.py to use modular testing 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 <[email protected]> * remove test suites that are passed. * fix consistencydecodervae tests * Revert "fix consistencydecodervae tests" This reverts commit 41b036b. --------- Co-authored-by: Claude Opus 4.6 <[email protected]> Co-authored-by: dg845 <[email protected]>
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* update * update * update * update * [CI] Refactor SD3 Transformer Test (#13340) * update * update --------- Co-authored-by: Sayak Paul <[email protected]> * refactor unet tests (3d_condition, motion, controlnetxs) (#13897) * refactor unet_3d_condition tests * refactor unet_motion tests * refactor unet_controlnetxs tests * refactor unet_1d tests (#13898) * refactor unet_1d tests * use per-sample output_shape for unet_1d tests --------- Co-authored-by: Sayak Paul <[email protected]> * refactor unet_2d tests (#13901) Co-authored-by: Sayak Paul <[email protected]> * [chore] log quant config to the user_agent (#13850) log quant config to the user_agent * Integrate AutoRound into Diffusers (#13552) * support auto_round Signed-off-by: Xin He <[email protected]> * add document and unit tests Signed-off-by: Xin He <[email protected]> * fix CI Signed-off-by: Xin He <[email protected]> * Apply suggestions from code review Co-authored-by: Steven Liu <[email protected]> * update document and overwrite the default quantization_config with specified backend. Signed-off-by: Xin He <[email protected]> * add UT and fix bug Signed-off-by: Xin He <[email protected]> * update per comments Signed-off-by: Xin He <[email protected]> * update per comments Signed-off-by: Xin He <[email protected]> * fix compile error in doc Signed-off-by: Xin He <[email protected]> * Apply style fixes * small nits * Add auto_round dependency to the versions table Signed-off-by: Xin He <[email protected]> * fix make deps_table_check_updated Signed-off-by: Xin He <[email protected]> * fix CI Signed-off-by: Xin He <[email protected]> --------- Signed-off-by: Xin He <[email protected]> Co-authored-by: Steven Liu <[email protected]> Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> Co-authored-by: Sayak Paul <[email protected]> * [tests] refactor UNet model tests to align with the new pattern (#13153) * refactor unet2d condition model tests. * fix tests * up * fix * Revert "fix" This reverts commit 46d44b7. * up * recompile limit * [tests] refactor test_models_unet_1d.py to use modular testing mixins 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 <[email protected]> * [tests] refactor test_models_unet_2d.py to use modular testing mixins 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 <[email protected]> * [tests] refactor test_models_unet_3d_condition.py to use modular testing 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 <[email protected]> * [tests] refactor test_models_unet_controlnetxs.py to use modular testing 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 <[email protected]> * [tests] refactor test_models_unet_spatiotemporal.py to use modular testing 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 <[email protected]> * remove test suites that are passed. * fix consistencydecodervae tests * Revert "fix consistencydecodervae tests" This reverts commit 41b036b. --------- Co-authored-by: Claude Opus 4.6 <[email protected]> Co-authored-by: dg845 <[email protected]> * [tests] fix vidtok tests (#13894) * fix vidtok tests * style * Update tests/models/autoencoders/test_models_autoencoder_vidtok.py Co-authored-by: dg845 <[email protected]> * Apply style fixes --------- Co-authored-by: dg845 <[email protected]> Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> * clean up --------- Signed-off-by: Xin He <[email protected]> Co-authored-by: Sayak Paul <[email protected]> Co-authored-by: Akshan Krithick <[email protected]> Co-authored-by: Xin He <[email protected]> Co-authored-by: Steven Liu <[email protected]> Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> Co-authored-by: Claude Opus 4.6 <[email protected]> Co-authored-by: dg845 <[email protected]>
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* refactor unet2d condition model tests. * fix tests * up * fix * Revert "fix" This reverts commit 46d44b7. * up * recompile limit * [tests] refactor test_models_unet_1d.py to use modular testing mixins 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 <[email protected]> * [tests] refactor test_models_unet_2d.py to use modular testing mixins 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 <[email protected]> * [tests] refactor test_models_unet_3d_condition.py to use modular testing 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 <[email protected]> * [tests] refactor test_models_unet_controlnetxs.py to use modular testing 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 <[email protected]> * [tests] refactor test_models_unet_spatiotemporal.py to use modular testing 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 <[email protected]> * remove test suites that are passed. * fix consistencydecodervae tests * Revert "fix consistencydecodervae tests" This reverts commit 41b036b. --------- Co-authored-by: Claude Opus 4.6 <[email protected]> Co-authored-by: dg845 <[email protected]>
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* update * update * update * update * [CI] Refactor SD3 Transformer Test (#13340) * update * update --------- Co-authored-by: Sayak Paul <[email protected]> * refactor unet tests (3d_condition, motion, controlnetxs) (#13897) * refactor unet_3d_condition tests * refactor unet_motion tests * refactor unet_controlnetxs tests * refactor unet_1d tests (#13898) * refactor unet_1d tests * use per-sample output_shape for unet_1d tests --------- Co-authored-by: Sayak Paul <[email protected]> * refactor unet_2d tests (#13901) Co-authored-by: Sayak Paul <[email protected]> * [chore] log quant config to the user_agent (#13850) log quant config to the user_agent * Integrate AutoRound into Diffusers (#13552) * support auto_round Signed-off-by: Xin He <[email protected]> * add document and unit tests Signed-off-by: Xin He <[email protected]> * fix CI Signed-off-by: Xin He <[email protected]> * Apply suggestions from code review Co-authored-by: Steven Liu <[email protected]> * update document and overwrite the default quantization_config with specified backend. Signed-off-by: Xin He <[email protected]> * add UT and fix bug Signed-off-by: Xin He <[email protected]> * update per comments Signed-off-by: Xin He <[email protected]> * update per comments Signed-off-by: Xin He <[email protected]> * fix compile error in doc Signed-off-by: Xin He <[email protected]> * Apply style fixes * small nits * Add auto_round dependency to the versions table Signed-off-by: Xin He <[email protected]> * fix make deps_table_check_updated Signed-off-by: Xin He <[email protected]> * fix CI Signed-off-by: Xin He <[email protected]> --------- Signed-off-by: Xin He <[email protected]> Co-authored-by: Steven Liu <[email protected]> Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> Co-authored-by: Sayak Paul <[email protected]> * [tests] refactor UNet model tests to align with the new pattern (#13153) * refactor unet2d condition model tests. * fix tests * up * fix * Revert "fix" This reverts commit 46d44b7. * up * recompile limit * [tests] refactor test_models_unet_1d.py to use modular testing mixins 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 <[email protected]> * [tests] refactor test_models_unet_2d.py to use modular testing mixins 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 <[email protected]> * [tests] refactor test_models_unet_3d_condition.py to use modular testing 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 <[email protected]> * [tests] refactor test_models_unet_controlnetxs.py to use modular testing 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 <[email protected]> * [tests] refactor test_models_unet_spatiotemporal.py to use modular testing 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 <[email protected]> * remove test suites that are passed. * fix consistencydecodervae tests * Revert "fix consistencydecodervae tests" This reverts commit 41b036b. --------- Co-authored-by: Claude Opus 4.6 <[email protected]> Co-authored-by: dg845 <[email protected]> * [tests] fix vidtok tests (#13894) * fix vidtok tests * style * Update tests/models/autoencoders/test_models_autoencoder_vidtok.py Co-authored-by: dg845 <[email protected]> * Apply style fixes --------- Co-authored-by: dg845 <[email protected]> Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> * clean up --------- Signed-off-by: Xin He <[email protected]> Co-authored-by: Sayak Paul <[email protected]> Co-authored-by: Akshan Krithick <[email protected]> Co-authored-by: Xin He <[email protected]> Co-authored-by: Steven Liu <[email protected]> Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> Co-authored-by: Claude Opus 4.6 <[email protected]> Co-authored-by: dg845 <[email protected]>
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What does this PR do?
Some comments in-line. I have run the tests locally and all of them pass.