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This commit is contained in:
sayakpaul
2025-10-03 20:14:45 +05:30
parent 128535cfcd
commit f8f27891c6
5 changed files with 20 additions and 45 deletions
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6
tests/lora/test_lora_layers_cogvideox.py
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@@ -131,13 +131,13 @@ class TestCogVideoXLoRA(PeftLoraLoaderMixinTests):
@pytest.mark.parametrize(
"offload_type, use_stream",
[("block_level", True), ("leaf_level", False), ("leaf_level", True)],
[("block_level", True), ("leaf_level", False)],
)
@require_torch_accelerator
def test_group_offloading_inference_denoiser(self, offload_type, use_stream, tmpdirname):
def test_group_offloading_inference_denoiser(self, offload_type, use_stream, tmpdirname, pipe):
# TODO: We don't run the (leaf_level, True) test here that is enabled for other models.
# The reason for this can be found here: https://github.com/huggingface/diffusers/pull/11804#issuecomment-3013325338
super()._test_group_offloading_inference_denoiser(offload_type, use_stream, tmpdirname)
super()._test_group_offloading_inference_denoiser(offload_type, use_stream, tmpdirname, pipe)
@pytest.mark.skip("Not supported in CogVideoX.")
def test_simple_inference_with_text_denoiser_block_scale(self):

6
tests/lora/test_lora_layers_cogview4.py
View File

@@ -117,13 +117,13 @@ class TestCogView4LoRA(PeftLoraLoaderMixinTests):
@pytest.mark.parametrize(
"offload_type, use_stream",
[("block_level", True), ("leaf_level", False), ("leaf_level", True)],
[("block_level", True), ("leaf_level", False)],
)
@require_torch_accelerator
def test_group_offloading_inference_denoiser(self, offload_type, use_stream, tmpdirname):
def test_group_offloading_inference_denoiser(self, offload_type, use_stream, tmpdirname, pipe):
# TODO: We don't run the (leaf_level, True) test here that is enabled for other models.
# The reason for this can be found here: https://github.com/huggingface/diffusers/pull/11804#issuecomment-3013325338
super()._test_group_offloading_inference_denoiser(offload_type, use_stream, tmpdirname)
super()._test_group_offloading_inference_denoiser(offload_type, use_stream, tmpdirname, pipe)
@pytest.mark.skip("Not supported in CogView4.")
def test_simple_inference_with_text_denoiser_block_scale(self):

40
tests/lora/test_lora_layers_flux.py
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@@ -111,11 +111,8 @@ class TestFluxLoRA(PeftLoraLoaderMixinTests):
return noise, input_ids, pipeline_inputs
def test_with_alpha_in_state_dict(self, tmpdirname):
components, _, denoiser_lora_config = self.get_dummy_components(FlowMatchEulerDiscreteScheduler)
pipe = self.pipeline_class(**components)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
def test_with_alpha_in_state_dict(self, tmpdirname, pipe):
_, _, denoiser_lora_config = self.get_dummy_components(FlowMatchEulerDiscreteScheduler)
_, _, inputs = self.get_dummy_inputs(with_generator=False)
pipe.transformer.add_adapter(denoiser_lora_config)
@@ -152,11 +149,8 @@ class TestFluxLoRA(PeftLoraLoaderMixinTests):
)
assert not np.allclose(images_lora_with_alpha, images_lora, atol=0.001, rtol=0.001)
def test_lora_expansion_works_for_absent_keys(self, base_pipe_output, tmpdirname):
components, _, denoiser_lora_config = self.get_dummy_components(FlowMatchEulerDiscreteScheduler)
pipe = self.pipeline_class(**components)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
def test_lora_expansion_works_for_absent_keys(self, base_pipe_output, tmpdirname, pipe):
_, _, denoiser_lora_config = self.get_dummy_components(FlowMatchEulerDiscreteScheduler)
_, _, inputs = self.get_dummy_inputs(with_generator=False)
# Modify the config to have a layer which won't be present in the second LoRA we will load.
@@ -192,11 +186,8 @@ class TestFluxLoRA(PeftLoraLoaderMixinTests):
"LoRA should lead to different results."
)
def test_lora_expansion_works_for_extra_keys(self, base_pipe_output, tmpdirname):
components, _, denoiser_lora_config = self.get_dummy_components(FlowMatchEulerDiscreteScheduler)
pipe = self.pipeline_class(**components)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
def test_lora_expansion_works_for_extra_keys(self, base_pipe_output, tmpdirname, pipe):
_, _, denoiser_lora_config = self.get_dummy_components(FlowMatchEulerDiscreteScheduler)
_, _, inputs = self.get_dummy_inputs(with_generator=False)
modified_denoiser_lora_config = copy.deepcopy(denoiser_lora_config)
@@ -312,12 +303,7 @@ class TestFluxControlLoRA(PeftLoraLoaderMixinTests):
return noise, input_ids, pipeline_inputs
def test_with_norm_in_state_dict(self):
components, _, denoiser_lora_config = self.get_dummy_components(FlowMatchEulerDiscreteScheduler)
pipe = self.pipeline_class(**components)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
def test_with_norm_in_state_dict(self, pipe):
_, _, inputs = self.get_dummy_inputs(with_generator=False)
logger = logging.get_logger("diffusers.loaders.lora_pipeline")
@@ -346,6 +332,7 @@ class TestFluxControlLoRA(PeftLoraLoaderMixinTests):
pipe.unload_lora_weights()
lora_unload_output = pipe(**inputs, generator=torch.manual_seed(0))[0]
assert pipe.transformer._transformer_norm_layers is None
assert np.allclose(original_output, lora_unload_output, atol=1e-05, rtol=1e-05)
assert not np.allclose(original_output, lora_load_output, atol=1e-06, rtol=1e-06), (
@@ -358,11 +345,8 @@ class TestFluxControlLoRA(PeftLoraLoaderMixinTests):
pipe.load_lora_weights(norm_state_dict)
assert "Unsupported keys found in state dict when trying to load normalization layers" in cap_logger.out
def test_lora_parameter_expanded_shapes(self):
def test_lora_parameter_expanded_shapes(self, pipe):
components, _, _ = self.get_dummy_components(FlowMatchEulerDiscreteScheduler)
pipe = self.pipeline_class(**components)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
_, _, inputs = self.get_dummy_inputs(with_generator=False)
original_out = pipe(**inputs, generator=torch.manual_seed(0))[0]
@@ -573,14 +557,10 @@ class TestFluxControlLoRA(PeftLoraLoaderMixinTests):
lora_output_4 = pipe(**inputs, generator=torch.manual_seed(0))[0]
assert np.allclose(lora_output_3, lora_output_4, atol=0.001, rtol=0.001)
def test_load_regular_lora(self, base_pipe_output):
def test_load_regular_lora(self, base_pipe_output, pipe):
# This test checks if a regular lora (think of one trained on Flux.1 Dev for example) can be loaded
# into the transformer with more input channels than Flux.1 Dev, for example. Some examples of those
# transformers include Flux Fill, Flux Control, etc.
components, _, _ = self.get_dummy_components(FlowMatchEulerDiscreteScheduler)
pipe = self.pipeline_class(**components)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
_, _, inputs = self.get_dummy_inputs(with_generator=False)
out_features, in_features = pipe.transformer.x_embedder.weight.shape

7
tests/lora/test_lora_layers_lumina2.py
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@@ -138,11 +138,8 @@ class TestLumina2LoRA(PeftLoraLoaderMixinTests):
reason="Test currently fails on CPU and PyTorch 2.5.1 but not on PyTorch 2.4.1.",
strict=False,
)
def test_lora_fuse_nan(self):
components, text_lora_config, denoiser_lora_config = self.get_dummy_components()
pipe = self.pipeline_class(**components)
pipe = pipe.to(torch_device)
pipe.set_progress_bar_config(disable=None)
def test_lora_fuse_nan(self, pipe):
_, text_lora_config, denoiser_lora_config = self.get_dummy_components()
_, _, inputs = self.get_dummy_inputs(with_generator=False)
if "text_encoder" in self.pipeline_class._lora_loadable_modules:

6
tests/lora/test_lora_layers_wanvace.py
View File

@@ -31,7 +31,6 @@ from ..testing_utils import (
require_peft_backend,
require_peft_version_greater,
skip_mps,
torch_device,
)
@@ -159,10 +158,9 @@ class TestWanVACELoRA(PeftLoraLoaderMixinTests):
pass
@require_peft_version_greater("0.13.2")
def test_lora_exclude_modules_wanvace(self, base_pipe_output, tmpdirname):
def test_lora_exclude_modules_wanvace(self, base_pipe_output, tmpdirname, pipe):
exclude_module_name = "vace_blocks.0.proj_out"
components, text_lora_config, denoiser_lora_config = self.get_dummy_components()
pipe = self.pipeline_class(**components).to(torch_device)
_, text_lora_config, denoiser_lora_config = self.get_dummy_components()
_, _, inputs = self.get_dummy_inputs(with_generator=False)
assert base_pipe_output.shape == self.output_shape