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Merge branch 'main' into vae-tests-mixin

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This commit is contained in:
Sayak Paul
2025-09-24 09:43:58 +05:30
committed by GitHub
parent 6a01c4681c 08c29020dd
commit 9a6ecfbcc4
3 changed files with 32 additions and 12 deletions
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13
src/diffusers/modular_pipelines/components_manager.py
View File

@@ -25,6 +25,7 @@ from ..utils import (
is_accelerate_available,
logging,
)
from ..utils.torch_utils import get_device
if is_accelerate_available():
@@ -161,7 +162,9 @@ class AutoOffloadStrategy:
current_module_size = model.get_memory_footprint()
mem_on_device = torch.cuda.mem_get_info(execution_device.index)[0]
device_type = execution_device.type
device_module = getattr(torch, device_type, torch.cuda)
mem_on_device = device_module.mem_get_info(execution_device.index)[0]
mem_on_device = mem_on_device - self.memory_reserve_margin
if current_module_size < mem_on_device:
return []
@@ -301,7 +304,7 @@ class ComponentsManager:
cm.add("vae", vae_model, collection="sdxl")
# Enable auto offloading
cm.enable_auto_cpu_offload(device="cuda")
cm.enable_auto_cpu_offload()
# Retrieve components
unet = cm.get_one(name="unet", collection="sdxl")
@@ -490,6 +493,8 @@ class ComponentsManager:
gc.collect()
if torch.cuda.is_available():
torch.cuda.empty_cache()
if torch.xpu.is_available():
torch.xpu.empty_cache()
# YiYi TODO: rename to search_components for now, may remove this method
def search_components(
@@ -678,7 +683,7 @@ class ComponentsManager:
return get_return_dict(matches, return_dict_with_names)
def enable_auto_cpu_offload(self, device: Union[str, int, torch.device] = "cuda", memory_reserve_margin="3GB"):
def enable_auto_cpu_offload(self, device: Union[str, int, torch.device] = None, memory_reserve_margin="3GB"):
"""
Enable automatic CPU offloading for all components.
@@ -704,6 +709,8 @@ class ComponentsManager:
self.disable_auto_cpu_offload()
offload_strategy = AutoOffloadStrategy(memory_reserve_margin=memory_reserve_margin)
if device is None:
device = get_device()
device = torch.device(device)
if device.index is None:
device = torch.device(f"{device.type}:{0}")

1
tests/lora/test_lora_layers_hunyuanvideo.py
View File

@@ -253,6 +253,7 @@ class HunyuanVideoLoRAIntegrationTests(unittest.TestCase):
expected_slices = Expectations(
{
("cuda", 7): np.array([0.1013, 0.1924, 0.0078, 0.1021, 0.1929, 0.0078, 0.1023, 0.1919, 0.7402, 0.104, 0.4482, 0.7354, 0.0925, 0.4382, 0.7275, 0.0815]),
("xpu", 3): np.array([0.1013, 0.1924, 0.0078, 0.1021, 0.1929, 0.0078, 0.1023, 0.1919, 0.7402, 0.104, 0.4482, 0.7354, 0.0925, 0.4382, 0.7275, 0.0815]),
}
)
# fmt: on

30
tests/pipelines/marigold/test_marigold_depth.py
View File

@@ -33,6 +33,7 @@ from diffusers import (
)
from ...testing_utils import (
Expectations,
backend_empty_cache,
enable_full_determinism,
floats_tensor,
@@ -356,7 +357,7 @@ class MarigoldDepthPipelineIntegrationTests(unittest.TestCase):
match_input_resolution=True,
)
def test_marigold_depth_einstein_f32_cuda_G0_S1_P768_E1_B1_M1(self):
def test_marigold_depth_einstein_f32_accelerator_G0_S1_P768_E1_B1_M1(self):
self._test_marigold_depth(
is_fp16=False,
device=torch_device,
@@ -369,7 +370,7 @@ class MarigoldDepthPipelineIntegrationTests(unittest.TestCase):
match_input_resolution=True,
)
def test_marigold_depth_einstein_f16_cuda_G0_S1_P768_E1_B1_M1(self):
def test_marigold_depth_einstein_f16_accelerator_G0_S1_P768_E1_B1_M1(self):
self._test_marigold_depth(
is_fp16=True,
device=torch_device,
@@ -382,7 +383,7 @@ class MarigoldDepthPipelineIntegrationTests(unittest.TestCase):
match_input_resolution=True,
)
def test_marigold_depth_einstein_f16_cuda_G2024_S1_P768_E1_B1_M1(self):
def test_marigold_depth_einstein_f16_accelerator_G2024_S1_P768_E1_B1_M1(self):
self._test_marigold_depth(
is_fp16=True,
device=torch_device,
@@ -395,12 +396,23 @@ class MarigoldDepthPipelineIntegrationTests(unittest.TestCase):
match_input_resolution=True,
)
def test_marigold_depth_einstein_f16_cuda_G0_S2_P768_E1_B1_M1(self):
def test_marigold_depth_einstein_f16_accelerator_G0_S2_P768_E1_B1_M1(self):
# fmt: off
expected_slices = Expectations(
{
("cuda", 7): np.array([0.1085, 0.1098, 0.1110, 0.1081, 0.1085, 0.1082, 0.1085, 0.1057, 0.0996]),
("xpu", 3): np.array([0.1084, 0.1096, 0.1108, 0.1080, 0.1083, 0.1080,
0.1085, 0.1057, 0.0996]),
}
)
expected_slice = expected_slices.get_expectation()
# fmt: on
self._test_marigold_depth(
is_fp16=True,
device=torch_device,
generator_seed=0,
expected_slice=np.array([0.1085, 0.1098, 0.1110, 0.1081, 0.1085, 0.1082, 0.1085, 0.1057, 0.0996]),
expected_slice=expected_slice,
num_inference_steps=2,
processing_resolution=768,
ensemble_size=1,
@@ -408,7 +420,7 @@ class MarigoldDepthPipelineIntegrationTests(unittest.TestCase):
match_input_resolution=True,
)
def test_marigold_depth_einstein_f16_cuda_G0_S1_P512_E1_B1_M1(self):
def test_marigold_depth_einstein_f16_accelerator_G0_S1_P512_E1_B1_M1(self):
self._test_marigold_depth(
is_fp16=True,
device=torch_device,
@@ -421,7 +433,7 @@ class MarigoldDepthPipelineIntegrationTests(unittest.TestCase):
match_input_resolution=True,
)
def test_marigold_depth_einstein_f16_cuda_G0_S1_P768_E3_B1_M1(self):
def test_marigold_depth_einstein_f16_accelerator_G0_S1_P768_E3_B1_M1(self):
self._test_marigold_depth(
is_fp16=True,
device=torch_device,
@@ -435,7 +447,7 @@ class MarigoldDepthPipelineIntegrationTests(unittest.TestCase):
match_input_resolution=True,
)
def test_marigold_depth_einstein_f16_cuda_G0_S1_P768_E4_B2_M1(self):
def test_marigold_depth_einstein_f16_accelerator_G0_S1_P768_E4_B2_M1(self):
self._test_marigold_depth(
is_fp16=True,
device=torch_device,
@@ -449,7 +461,7 @@ class MarigoldDepthPipelineIntegrationTests(unittest.TestCase):
match_input_resolution=True,
)
def test_marigold_depth_einstein_f16_cuda_G0_S1_P512_E1_B1_M0(self):
def test_marigold_depth_einstein_f16_accelerator_G0_S1_P512_E1_B1_M0(self):
self._test_marigold_depth(
is_fp16=True,
device=torch_device,