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Merge branch 'main' into remove-unittest-modelmixin

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This commit is contained in:
DN6
2025-09-24 09:16:37 +05:30
parent e4a1c036f1 9ef118509e
commit 361ca4cc95
18 changed files with 1049 additions and 1148 deletions
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1
tests/lora/test_lora_layers_auraflow.py
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@@ -43,7 +43,6 @@ from .utils import PeftLoraLoaderMixinTests # noqa: E402
class AuraFlowLoRATests(unittest.TestCase, PeftLoraLoaderMixinTests):
pipeline_class = AuraFlowPipeline
scheduler_cls = FlowMatchEulerDiscreteScheduler
scheduler_classes = [FlowMatchEulerDiscreteScheduler]
scheduler_kwargs = {}
transformer_kwargs = {

2
tests/lora/test_lora_layers_cogvideox.py
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@@ -21,7 +21,6 @@ from transformers import AutoTokenizer, T5EncoderModel
from diffusers import (
AutoencoderKLCogVideoX,
CogVideoXDDIMScheduler,
CogVideoXDPMScheduler,
CogVideoXPipeline,
CogVideoXTransformer3DModel,
@@ -44,7 +43,6 @@ class CogVideoXLoRATests(unittest.TestCase, PeftLoraLoaderMixinTests):
pipeline_class = CogVideoXPipeline
scheduler_cls = CogVideoXDPMScheduler
scheduler_kwargs = {"timestep_spacing": "trailing"}
scheduler_classes = [CogVideoXDDIMScheduler, CogVideoXDPMScheduler]
transformer_kwargs = {
"num_attention_heads": 4,

36
tests/lora/test_lora_layers_cogview4.py
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@@ -50,7 +50,6 @@ class TokenizerWrapper:
class CogView4LoRATests(unittest.TestCase, PeftLoraLoaderMixinTests):
pipeline_class = CogView4Pipeline
scheduler_cls = FlowMatchEulerDiscreteScheduler
scheduler_classes = [FlowMatchEulerDiscreteScheduler]
scheduler_kwargs = {}
transformer_kwargs = {
@@ -124,30 +123,29 @@ class CogView4LoRATests(unittest.TestCase, PeftLoraLoaderMixinTests):
"""
Tests a simple usecase where users could use saving utilities for LoRA through save_pretrained
"""
for scheduler_cls in self.scheduler_classes:
components, _, _ = self.get_dummy_components(scheduler_cls)
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)
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)
output_no_lora = pipe(**inputs, generator=torch.manual_seed(0))[0]
self.assertTrue(output_no_lora.shape == self.output_shape)
output_no_lora = pipe(**inputs, generator=torch.manual_seed(0))[0]
self.assertTrue(output_no_lora.shape == self.output_shape)
images_lora = pipe(**inputs, generator=torch.manual_seed(0))[0]
images_lora = pipe(**inputs, generator=torch.manual_seed(0))[0]
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(tmpdirname)
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(tmpdirname)
pipe_from_pretrained = self.pipeline_class.from_pretrained(tmpdirname)
pipe_from_pretrained.to(torch_device)
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]
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.",
)
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)])
@require_torch_accelerator

6
tests/lora/test_lora_layers_flux.py
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@@ -55,9 +55,8 @@ from .utils import PeftLoraLoaderMixinTests, check_if_lora_correctly_set # noqa
@require_peft_backend
class FluxLoRATests(unittest.TestCase, PeftLoraLoaderMixinTests):
pipeline_class = FluxPipeline
scheduler_cls = FlowMatchEulerDiscreteScheduler()
scheduler_cls = FlowMatchEulerDiscreteScheduler
scheduler_kwargs = {}
scheduler_classes = [FlowMatchEulerDiscreteScheduler]
transformer_kwargs = {
"patch_size": 1,
"in_channels": 4,
@@ -282,9 +281,8 @@ class FluxLoRATests(unittest.TestCase, PeftLoraLoaderMixinTests):
class FluxControlLoRATests(unittest.TestCase, PeftLoraLoaderMixinTests):
pipeline_class = FluxControlPipeline
scheduler_cls = FlowMatchEulerDiscreteScheduler()
scheduler_cls = FlowMatchEulerDiscreteScheduler
scheduler_kwargs = {}
scheduler_classes = [FlowMatchEulerDiscreteScheduler]
transformer_kwargs = {
"patch_size": 1,
"in_channels": 8,

1
tests/lora/test_lora_layers_hunyuanvideo.py
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@@ -51,7 +51,6 @@ from .utils import PeftLoraLoaderMixinTests # noqa: E402
class HunyuanVideoLoRATests(unittest.TestCase, PeftLoraLoaderMixinTests):
pipeline_class = HunyuanVideoPipeline
scheduler_cls = FlowMatchEulerDiscreteScheduler
scheduler_classes = [FlowMatchEulerDiscreteScheduler]
scheduler_kwargs = {}
transformer_kwargs = {

1
tests/lora/test_lora_layers_ltx_video.py
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@@ -37,7 +37,6 @@ from .utils import PeftLoraLoaderMixinTests # noqa: E402
class LTXVideoLoRATests(unittest.TestCase, PeftLoraLoaderMixinTests):
pipeline_class = LTXPipeline
scheduler_cls = FlowMatchEulerDiscreteScheduler
scheduler_classes = [FlowMatchEulerDiscreteScheduler]
scheduler_kwargs = {}
transformer_kwargs = {

46
tests/lora/test_lora_layers_lumina2.py
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@@ -39,7 +39,6 @@ from .utils import PeftLoraLoaderMixinTests, check_if_lora_correctly_set # noqa
class Lumina2LoRATests(unittest.TestCase, PeftLoraLoaderMixinTests):
pipeline_class = Lumina2Pipeline
scheduler_cls = FlowMatchEulerDiscreteScheduler
scheduler_classes = [FlowMatchEulerDiscreteScheduler]
scheduler_kwargs = {}
transformer_kwargs = {
@@ -141,33 +140,30 @@ class Lumina2LoRATests(unittest.TestCase, PeftLoraLoaderMixinTests):
strict=False,
)
def test_lora_fuse_nan(self):
for scheduler_cls in self.scheduler_classes:
components, text_lora_config, denoiser_lora_config = self.get_dummy_components(scheduler_cls)
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)
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)
_, _, inputs = self.get_dummy_inputs(with_generator=False)
if "text_encoder" in self.pipeline_class._lora_loadable_modules:
pipe.text_encoder.add_adapter(text_lora_config, "adapter-1")
self.assertTrue(
check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder"
)
if "text_encoder" in self.pipeline_class._lora_loadable_modules:
pipe.text_encoder.add_adapter(text_lora_config, "adapter-1")
self.assertTrue(check_if_lora_correctly_set(pipe.text_encoder), "Lora not correctly set in text encoder")
denoiser = pipe.transformer if self.unet_kwargs is None else pipe.unet
denoiser.add_adapter(denoiser_lora_config, "adapter-1")
self.assertTrue(check_if_lora_correctly_set(denoiser), "Lora not correctly set in denoiser.")
denoiser = pipe.transformer if self.unet_kwargs is None else pipe.unet
denoiser.add_adapter(denoiser_lora_config, "adapter-1")
self.assertTrue(check_if_lora_correctly_set(denoiser), "Lora not correctly set in denoiser.")
# corrupt one LoRA weight with `inf` values
with torch.no_grad():
pipe.transformer.layers[0].attn.to_q.lora_A["adapter-1"].weight += float("inf")
# corrupt one LoRA weight with `inf` values
with torch.no_grad():
pipe.transformer.layers[0].attn.to_q.lora_A["adapter-1"].weight += float("inf")
# with `safe_fusing=True` we should see an Error
with self.assertRaises(ValueError):
pipe.fuse_lora(components=self.pipeline_class._lora_loadable_modules, safe_fusing=True)
# with `safe_fusing=True` we should see an Error
with self.assertRaises(ValueError):
pipe.fuse_lora(components=self.pipeline_class._lora_loadable_modules, safe_fusing=True)
# without we should not see an error, but every image will be black
pipe.fuse_lora(components=self.pipeline_class._lora_loadable_modules, safe_fusing=False)
out = pipe(**inputs)[0]
# without we should not see an error, but every image will be black
pipe.fuse_lora(components=self.pipeline_class._lora_loadable_modules, safe_fusing=False)
out = pipe(**inputs)[0]
self.assertTrue(np.isnan(out).all())
self.assertTrue(np.isnan(out).all())

1
tests/lora/test_lora_layers_mochi.py
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@@ -37,7 +37,6 @@ from .utils import PeftLoraLoaderMixinTests # noqa: E402
class MochiLoRATests(unittest.TestCase, PeftLoraLoaderMixinTests):
pipeline_class = MochiPipeline
scheduler_cls = FlowMatchEulerDiscreteScheduler
scheduler_classes = [FlowMatchEulerDiscreteScheduler]
scheduler_kwargs = {}
transformer_kwargs = {

1
tests/lora/test_lora_layers_qwenimage.py
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@@ -37,7 +37,6 @@ from .utils import PeftLoraLoaderMixinTests # noqa: E402
class QwenImageLoRATests(unittest.TestCase, PeftLoraLoaderMixinTests):
pipeline_class = QwenImagePipeline
scheduler_cls = FlowMatchEulerDiscreteScheduler
scheduler_classes = [FlowMatchEulerDiscreteScheduler]
scheduler_kwargs = {}
transformer_kwargs = {

5
tests/lora/test_lora_layers_sana.py
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@@ -31,9 +31,8 @@ from .utils import PeftLoraLoaderMixinTests # noqa: E402
@require_peft_backend
class SanaLoRATests(unittest.TestCase, PeftLoraLoaderMixinTests):
pipeline_class = SanaPipeline
scheduler_cls = FlowMatchEulerDiscreteScheduler(shift=7.0)
scheduler_kwargs = {}
scheduler_classes = [FlowMatchEulerDiscreteScheduler]
scheduler_cls = FlowMatchEulerDiscreteScheduler
scheduler_kwargs = {"shift": 7.0}
transformer_kwargs = {
"patch_size": 1,
"in_channels": 4,

1
tests/lora/test_lora_layers_sd3.py
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@@ -55,7 +55,6 @@ class SD3LoRATests(unittest.TestCase, PeftLoraLoaderMixinTests):
pipeline_class = StableDiffusion3Pipeline
scheduler_cls = FlowMatchEulerDiscreteScheduler
scheduler_kwargs = {}
scheduler_classes = [FlowMatchEulerDiscreteScheduler]
transformer_kwargs = {
"sample_size": 32,
"patch_size": 1,

1
tests/lora/test_lora_layers_wan.py
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@@ -42,7 +42,6 @@ from .utils import PeftLoraLoaderMixinTests # noqa: E402
class WanLoRATests(unittest.TestCase, PeftLoraLoaderMixinTests):
pipeline_class = WanPipeline
scheduler_cls = FlowMatchEulerDiscreteScheduler
scheduler_classes = [FlowMatchEulerDiscreteScheduler]
scheduler_kwargs = {}
transformer_kwargs = {

4
tests/lora/test_lora_layers_wanvace.py
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@@ -50,7 +50,6 @@ from .utils import PeftLoraLoaderMixinTests # noqa: E402
class WanVACELoRATests(unittest.TestCase, PeftLoraLoaderMixinTests):
pipeline_class = WanVACEPipeline
scheduler_cls = FlowMatchEulerDiscreteScheduler
scheduler_classes = [FlowMatchEulerDiscreteScheduler]
scheduler_kwargs = {}
transformer_kwargs = {
@@ -165,9 +164,8 @@ class WanVACELoRATests(unittest.TestCase, PeftLoraLoaderMixinTests):
@require_peft_version_greater("0.13.2")
def test_lora_exclude_modules_wanvace(self):
scheduler_cls = self.scheduler_classes[0]
exclude_module_name = "vace_blocks.0.proj_out"
components, text_lora_config, denoiser_lora_config = self.get_dummy_components(scheduler_cls)
components, text_lora_config, denoiser_lora_config = self.get_dummy_components()
pipe = self.pipeline_class(**components).to(torch_device)
_, _, inputs = self.get_dummy_inputs(with_generator=False)

2083
tests/lora/utils.py
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1
tests/pipelines/easyanimate/test_easyanimate.py
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@@ -48,6 +48,7 @@ class EasyAnimatePipelineFastTests(PipelineTesterMixin, unittest.TestCase):
batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
image_params = TEXT_TO_IMAGE_IMAGE_PARAMS
image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS
test_xformers_attention = False
required_optional_params = frozenset(
[
"num_inference_steps",

2
tests/pipelines/hidream_image/test_pipeline_hidream.py
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@@ -47,8 +47,8 @@ class HiDreamImagePipelineFastTests(PipelineTesterMixin, unittest.TestCase):
batch_params = TEXT_TO_IMAGE_BATCH_PARAMS
image_params = TEXT_TO_IMAGE_IMAGE_PARAMS
image_latents_params = TEXT_TO_IMAGE_IMAGE_PARAMS
required_optional_params = PipelineTesterMixin.required_optional_params
test_xformers_attention = False
test_layerwise_casting = True
supports_dduf = False

2
tests/pipelines/omnigen/test_pipeline_omnigen.py
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@@ -22,7 +22,7 @@ class OmniGenPipelineFastTests(unittest.TestCase, PipelineTesterMixin):
pipeline_class = OmniGenPipeline
params = frozenset(["prompt", "guidance_scale"])
batch_params = frozenset(["prompt"])
test_xformers_attention = False
test_layerwise_casting = True
def get_dummy_components(self):

3
tests/pipelines/qwenimage/test_qwenimage_controlnet.py
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@@ -44,7 +44,6 @@ class QwenControlNetPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
batch_params = frozenset(["prompt", "negative_prompt", "control_image"])
image_params = frozenset(["control_image"])
image_latents_params = frozenset(["latents"])
required_optional_params = frozenset(
[
"num_inference_steps",
@@ -59,7 +58,7 @@ class QwenControlNetPipelineFastTests(PipelineTesterMixin, unittest.TestCase):
)
supports_dduf = False
test_xformers_attention = True
test_xformers_attention = False
test_layerwise_casting = True
test_group_offloading = True