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Migrate lora pipeline tests to use pytest #12426

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6c6cade
migrate lora pipeline tests to pytest
sayakpaul Oct 3, 2025
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sayakpaul Oct 3, 2025
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sayakpaul Oct 3, 2025
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sayakpaul Oct 3, 2025
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tempfile is now a fixture.
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sayakpaul Oct 3, 2025
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sayakpaul Oct 3, 2025
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change flux lora integration tests to use pytest
sayakpaul Oct 3, 2025
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sayakpaul Oct 3, 2025
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sayakpaul Oct 3, 2025
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more fixtures.
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sayakpaul Oct 3, 2025
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Merge branch 'main' into migrate-lora-pytest
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Merge branch 'main' into migrate-lora-pytest
sayakpaul Oct 17, 2025
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Merge branch 'main' into migrate-lora-pytest
sayakpaul Oct 17, 2025
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Merge branch 'main' into migrate-lora-pytest
sayakpaul Oct 24, 2025
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Merge branch 'main' into migrate-lora-pytest
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Merge branch 'main' into migrate-lora-pytest
sayakpaul Nov 6, 2025
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Merge branch 'main' into migrate-lora-pytest
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sayakpaul Nov 20, 2025
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Merge branch 'main' into migrate-lora-pytest
sayakpaul Nov 20, 2025
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Merge branch 'main' into migrate-lora-pytest
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resolve conflicts.
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Merge branch 'main' into migrate-lora-pytest
sayakpaul Dec 10, 2025
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Merge branch 'main' into migrate-lora-pytest
sayakpaul Dec 15, 2025
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26 changes: 11 additions & 15 deletions tests/lora/test_lora_layers_auraflow.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,16 +13,12 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import sys
import unittest

import pytest
import torch
from transformers import AutoTokenizer, UMT5EncoderModel

from diffusers import (
AuraFlowPipeline,
AuraFlowTransformer2DModel,
FlowMatchEulerDiscreteScheduler,
)
from diffusers import AuraFlowPipeline, AuraFlowTransformer2DModel, FlowMatchEulerDiscreteScheduler

from ..testing_utils import (
floats_tensor,
Expand All @@ -40,7 +36,7 @@


@require_peft_backend
class AuraFlowLoRATests(unittest.TestCase, PeftLoraLoaderMixinTests):
class TestAuraFlowLoRA(PeftLoraLoaderMixinTests):
pipeline_class = AuraFlowPipeline
scheduler_cls = FlowMatchEulerDiscreteScheduler
scheduler_kwargs = {}
Expand Down Expand Up @@ -103,34 +99,34 @@ def get_dummy_inputs(self, with_generator=True):

return noise, input_ids, pipeline_inputs

@unittest.skip("Not supported in AuraFlow.")
@pytest.mark.skip("Not supported in AuraFlow.")
def test_simple_inference_with_text_denoiser_block_scale(self):
pass

@unittest.skip("Not supported in AuraFlow.")
@pytest.mark.skip("Not supported in AuraFlow.")
def test_simple_inference_with_text_denoiser_block_scale_for_all_dict_options(self):
pass

@unittest.skip("Not supported in AuraFlow.")
@pytest.mark.skip("Not supported in AuraFlow.")
def test_modify_padding_mode(self):
pass

@unittest.skip("Text encoder LoRA is not supported in AuraFlow.")
@pytest.mark.skip("Text encoder LoRA is not supported in AuraFlow.")
def test_simple_inference_with_partial_text_lora(self):
pass

@unittest.skip("Text encoder LoRA is not supported in AuraFlow.")
@pytest.mark.skip("Text encoder LoRA is not supported in AuraFlow.")
def test_simple_inference_with_text_lora(self):
pass

@unittest.skip("Text encoder LoRA is not supported in AuraFlow.")
@pytest.mark.skip("Text encoder LoRA is not supported in AuraFlow.")
def test_simple_inference_with_text_lora_and_scale(self):
pass

@unittest.skip("Text encoder LoRA is not supported in AuraFlow.")
@pytest.mark.skip("Text encoder LoRA is not supported in AuraFlow.")
def test_simple_inference_with_text_lora_fused(self):
pass

@unittest.skip("Text encoder LoRA is not supported in AuraFlow.")
@pytest.mark.skip("Text encoder LoRA is not supported in AuraFlow.")
def test_simple_inference_with_text_lora_save_load(self):
pass
46 changes: 25 additions & 21 deletions tests/lora/test_lora_layers_cogvideox.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,10 +13,9 @@
# limitations under the License.

import sys
import unittest

import pytest
import torch
from parameterized import parameterized
from transformers import AutoTokenizer, T5EncoderModel

from diffusers import (
Expand All @@ -39,7 +38,7 @@


@require_peft_backend
class CogVideoXLoRATests(unittest.TestCase, PeftLoraLoaderMixinTests):
class TestCogVideoXLoRA(PeftLoraLoaderMixinTests):
pipeline_class = CogVideoXPipeline
scheduler_cls = CogVideoXDPMScheduler
scheduler_kwargs = {"timestep_spacing": "trailing"}
Expand Down Expand Up @@ -119,54 +118,59 @@ def get_dummy_inputs(self, with_generator=True):

return noise, input_ids, pipeline_inputs

def test_simple_inference_with_text_lora_denoiser_fused_multi(self):
super().test_simple_inference_with_text_lora_denoiser_fused_multi(expected_atol=9e-3)
def test_simple_inference_with_text_lora_denoiser_fused_multi(self, pipe):
super().test_simple_inference_with_text_lora_denoiser_fused_multi(expected_atol=9e-3, pipe=pipe)

def test_simple_inference_with_text_denoiser_lora_unfused(self):
super().test_simple_inference_with_text_denoiser_lora_unfused(expected_atol=9e-3)
def test_simple_inference_with_text_denoiser_lora_unfused(self, pipe):
super().test_simple_inference_with_text_denoiser_lora_unfused(pipe=pipe, expected_atol=9e-3)

def test_lora_scale_kwargs_match_fusion(self):
super().test_lora_scale_kwargs_match_fusion(expected_atol=9e-3, expected_rtol=9e-3)
def test_lora_scale_kwargs_match_fusion(self, base_pipe_output):
super().test_lora_scale_kwargs_match_fusion(
base_pipe_output=base_pipe_output, expected_atol=9e-3, expected_rtol=9e-3
)

@parameterized.expand([("block_level", True), ("leaf_level", False)])
@pytest.mark.parametrize(
"offload_type, use_stream",
[("block_level", True), ("leaf_level", False)],
)
@require_torch_accelerator
def test_group_offloading_inference_denoiser(self, offload_type, use_stream):
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)
super()._test_group_offloading_inference_denoiser(offload_type, use_stream, tmpdirname, pipe)

@unittest.skip("Not supported in CogVideoX.")
@pytest.mark.skip("Not supported in CogVideoX.")
def test_simple_inference_with_text_denoiser_block_scale(self):
pass

@unittest.skip("Not supported in CogVideoX.")
@pytest.mark.skip("Not supported in CogVideoX.")
def test_simple_inference_with_text_denoiser_block_scale_for_all_dict_options(self):
pass

@unittest.skip("Not supported in CogVideoX.")
@pytest.mark.skip("Not supported in CogVideoX.")
def test_modify_padding_mode(self):
pass

@unittest.skip("Text encoder LoRA is not supported in CogVideoX.")
@pytest.mark.skip("Text encoder LoRA is not supported in CogVideoX.")
def test_simple_inference_with_partial_text_lora(self):
pass

@unittest.skip("Text encoder LoRA is not supported in CogVideoX.")
@pytest.mark.skip("Text encoder LoRA is not supported in CogVideoX.")
def test_simple_inference_with_text_lora(self):
pass

@unittest.skip("Text encoder LoRA is not supported in CogVideoX.")
@pytest.mark.skip("Text encoder LoRA is not supported in CogVideoX.")
def test_simple_inference_with_text_lora_and_scale(self):
pass

@unittest.skip("Text encoder LoRA is not supported in CogVideoX.")
@pytest.mark.skip("Text encoder LoRA is not supported in CogVideoX.")
def test_simple_inference_with_text_lora_fused(self):
pass

@unittest.skip("Text encoder LoRA is not supported in CogVideoX.")
@pytest.mark.skip("Text encoder LoRA is not supported in CogVideoX.")
def test_simple_inference_with_text_lora_save_load(self):
pass

@unittest.skip("Not supported in CogVideoX.")
@pytest.mark.skip("Not supported in CogVideoX.")
def test_simple_inference_with_text_denoiser_multi_adapter_block_lora(self):
pass
66 changes: 20 additions & 46 deletions tests/lora/test_lora_layers_cogview4.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,12 +13,9 @@
# limitations under the License.

import sys
import tempfile
import unittest

import numpy as np
import pytest
import torch
from parameterized import parameterized
from transformers import AutoTokenizer, GlmModel

from diffusers import AutoencoderKL, CogView4Pipeline, CogView4Transformer2DModel, FlowMatchEulerDiscreteScheduler
Expand All @@ -28,7 +25,6 @@
require_peft_backend,
require_torch_accelerator,
skip_mps,
torch_device,
)


Expand All @@ -47,7 +43,7 @@ def from_pretrained(*args, **kwargs):

@require_peft_backend
@skip_mps
class CogView4LoRATests(unittest.TestCase, PeftLoraLoaderMixinTests):
class TestCogView4LoRA(PeftLoraLoaderMixinTests):
pipeline_class = CogView4Pipeline
scheduler_cls = FlowMatchEulerDiscreteScheduler
scheduler_kwargs = {}
Expand Down Expand Up @@ -113,72 +109,50 @@ def get_dummy_inputs(self, with_generator=True):

return noise, input_ids, pipeline_inputs

def test_simple_inference_with_text_lora_denoiser_fused_multi(self):
super().test_simple_inference_with_text_lora_denoiser_fused_multi(expected_atol=9e-3)
def test_simple_inference_with_text_lora_denoiser_fused_multi(self, pipe):
super().test_simple_inference_with_text_lora_denoiser_fused_multi(pipe=pipe, expected_atol=9e-3)

def test_simple_inference_with_text_denoiser_lora_unfused(self):
super().test_simple_inference_with_text_denoiser_lora_unfused(expected_atol=9e-3)
def test_simple_inference_with_text_denoiser_lora_unfused(self, pipe):
super().test_simple_inference_with_text_denoiser_lora_unfused(pipe=pipe, expected_atol=9e-3)

def test_simple_inference_save_pretrained(self):
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@sayakpaul sayakpaul Oct 3, 2025

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It's not testing any LoRA-related stuff. So, let's remove.

"""
Tests a simple usecase where users could use saving utilities for LoRA through save_pretrained
"""
components, _, _ = self.get_dummy_components()
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)

images_lora = pipe(**inputs, generator=torch.manual_seed(0))[0]

with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(tmpdirname)

pipe_from_pretrained = self.pipeline_class.from_pretrained(tmpdirname)
pipe_from_pretrained.to(torch_device)

images_lora_save_pretrained = pipe_from_pretrained(**inputs, generator=torch.manual_seed(0))[0]

self.assertTrue(
np.allclose(images_lora, images_lora_save_pretrained, atol=1e-3, rtol=1e-3),
"Loading from saved checkpoints should give same results.",
)

@parameterized.expand([("block_level", True), ("leaf_level", False)])
@pytest.mark.parametrize(
"offload_type, use_stream",
[("block_level", True), ("leaf_level", False)],
)
@require_torch_accelerator
def test_group_offloading_inference_denoiser(self, offload_type, use_stream):
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)
super()._test_group_offloading_inference_denoiser(offload_type, use_stream, tmpdirname, pipe)

@unittest.skip("Not supported in CogView4.")
@pytest.mark.skip("Not supported in CogView4.")
def test_simple_inference_with_text_denoiser_block_scale(self):
pass

@unittest.skip("Not supported in CogView4.")
@pytest.mark.skip("Not supported in CogView4.")
def test_simple_inference_with_text_denoiser_block_scale_for_all_dict_options(self):
pass

@unittest.skip("Not supported in CogView4.")
@pytest.mark.skip("Not supported in CogView4.")
def test_modify_padding_mode(self):
pass

@unittest.skip("Text encoder LoRA is not supported in CogView4.")
@pytest.mark.skip("Text encoder LoRA is not supported in CogView4.")
def test_simple_inference_with_partial_text_lora(self):
pass

@unittest.skip("Text encoder LoRA is not supported in CogView4.")
@pytest.mark.skip("Text encoder LoRA is not supported in CogView4.")
def test_simple_inference_with_text_lora(self):
pass

@unittest.skip("Text encoder LoRA is not supported in CogView4.")
@pytest.mark.skip("Text encoder LoRA is not supported in CogView4.")
def test_simple_inference_with_text_lora_and_scale(self):
pass

@unittest.skip("Text encoder LoRA is not supported in CogView4.")
@pytest.mark.skip("Text encoder LoRA is not supported in CogView4.")
def test_simple_inference_with_text_lora_fused(self):
pass

@unittest.skip("Text encoder LoRA is not supported in CogView4.")
@pytest.mark.skip("Text encoder LoRA is not supported in CogView4.")
def test_simple_inference_with_text_lora_save_load(self):
pass
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