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xpu enabling for 4 cases #12345

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Merged
merged 4 commits into from
Sep 24, 2025
Merged

xpu enabling for 4 cases #12345

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814c3bb
xpu enabling for 4 cases
yao-matrix Sep 17, 2025
82ce1de
Merge branch 'main' into issue-522
yao-matrix Sep 18, 2025
3afd261
Merge branch 'main' into issue-522
yao-matrix Sep 22, 2025
b604e3f
Merge branch 'main' into issue-522
yao-matrix Sep 22, 2025
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13 changes: 10 additions & 3 deletions src/diffusers/modular_pipelines/components_manager.py
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Original file line number Diff line number Diff line change
Expand Up @@ -25,6 +25,7 @@
is_accelerate_available,
logging,
)
from ..utils.torch_utils import get_device


if is_accelerate_available():
Expand Down Expand Up @@ -161,7 +162,9 @@ def __call__(self, hooks, model_id, model, execution_device):

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 []
Expand Down Expand Up @@ -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")
Expand Down Expand Up @@ -490,6 +493,8 @@ def remove(self, component_id: str = None):
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(
Expand Down Expand Up @@ -678,7 +683,7 @@ def matches_pattern(component_id, pattern, exact_match=False):

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"):
"""
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as in #11288, i suggest make None as the default value of device, so in that case, if users run on other devices like XPU w/o specify the device argument, the code will auto detect the device and run, rather than crash. Thx

Enable automatic CPU offloading for all components.

Expand All @@ -704,6 +709,8 @@ def enable_auto_cpu_offload(self, device: Union[str, int, torch.device] = "cuda"

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}")
Expand Down
1 change: 1 addition & 0 deletions tests/lora/test_lora_layers_hunyuanvideo.py
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Original file line number Diff line number Diff line change
Expand Up @@ -254,6 +254,7 @@ def test_original_format_cseti(self):
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
Expand Down