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fix ci and bug #905

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Merged
gushiqiao merged 2 commits into from
Feb 27, 2026
Merged

fix ci and bug #905

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bb64811
fix ci and bug
Feb 27, 2026
1d32189
fix ci and bug
Feb 27, 2026
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2 changes: 1 addition & 1 deletion configs/z_image/z_image_turbo_t2i.json
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Original file line number Diff line number Diff line change
Expand Up @@ -6,4 +6,4 @@
"enable_cfg": false,
"sample_guide_scale": 0.0,
"patch_size": 2
}
}
8 changes: 4 additions & 4 deletions lightx2v/common/modules/weight_module.py
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Original file line number Diff line number Diff line change
@@ -1,4 +1,6 @@
from lightx2v_platform.base.global_var import AI_DEVICE


class WeightModule:
def __init__(self):
self._modules = {}
Expand Down Expand Up @@ -122,11 +124,10 @@ def to_cpu(self, non_blocking=False):

def to_cuda(self, non_blocking=False):
"""Move parameters to GPU device (supports cuda/intel xpu)"""
target_device = AI_DEVICE
for name, param in self._parameters.items():
if param is not None:
if hasattr(param, "cuda"):
self._parameters[name] = param.to(target_device, non_blocking=non_blocking)
self._parameters[name] = param.to(AI_DEVICE, non_blocking=non_blocking)
elif hasattr(param, "to_cuda"):
self._parameters[name].to_cuda()
setattr(self, name, self._parameters[name])
Expand Down Expand Up @@ -166,11 +167,10 @@ def to_cpu_async(self, non_blocking=True):
module.to_cpu(non_blocking=True)

def to_cuda_async(self, non_blocking=True):
target_device = AI_DEVICE
for name, param in self._parameters.items():
if param is not None:
if hasattr(param, "cuda"):
self._parameters[name] = param.to(target_device, non_blocking=non_blocking)
self._parameters[name] = param.to(AI_DEVICE, non_blocking=non_blocking)
elif hasattr(param, "to_cuda"):
self._parameters[name].to_cuda(non_blocking=True)
setattr(self, name, self._parameters[name])
Expand Down
24 changes: 19 additions & 5 deletions lightx2v/common/ops/conv/conv3d.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,7 +6,6 @@
from lightx2v.common.ops.utils import *
from lightx2v.utils.envs import *
from lightx2v.utils.registry_factory import CONV3D_WEIGHT_REGISTER
from lightx2v_platform.base.global_var import AI_DEVICE


class Conv3dWeightTemplate(metaclass=ABCMeta):
Expand Down Expand Up @@ -35,8 +34,23 @@ def _get_lora_attr_mapping(self):
"weight_diff": "weight_diff_name",
"bias_diff": "bias_diff_name",
}
self.weight_diff = torch.tensor(0.0, dtype=GET_DTYPE(), device=AI_DEVICE)
self.bias_diff = torch.tensor(0.0, dtype=GET_DTYPE(), device=AI_DEVICE)

def _get_actual_weight(self):
if not hasattr(self, "weight_diff"):
return self.weight
return self.weight + self.weight_diff

def _get_actual_bias(self, bias=None):
if bias is not None:
if not hasattr(self, "bias_diff"):
return bias
return bias + self.bias_diff
else:
if not hasattr(self, "bias") or self.bias is None:
return None
if not hasattr(self, "bias_diff"):
return self.bias
return self.bias + self.bias_diff
Comment on lines +43 to +53
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@gemini-code-assist gemini-code-assist bot Feb 27, 2026

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medium

The logic in _get_actual_bias can be simplified to improve readability and reduce duplication. The current implementation repeats the check for bias_diff. Consider refactoring to first determine the base bias and then apply the diff if it exists.

Suggested change
def _get_actual_bias(self, bias=None):
if bias is not None:
if not hasattr(self, "bias_diff"):
return bias
return bias + self.bias_diff
else:
if not hasattr(self, "bias") or self.bias is None:
return None
if not hasattr(self, "bias_diff"):
return self.bias
return self.bias + self.bias_diff
def _get_actual_bias(self, bias=None):
if bias is None:
if not hasattr(self, "bias") or self.bias is None:
return None
base_bias = self.bias
else:
base_bias = bias
if hasattr(self, "bias_diff"):
return base_bias + self.bias_diff
return base_bias


def register_diff(self, weight_dict):
if self.weight_diff_name in weight_dict:
Expand Down Expand Up @@ -74,8 +88,8 @@ def load(self, weight_dict):
def apply(self, input_tensor):
output_tensor = torch.nn.functional.conv3d(
input_tensor,
weight=self.weight + self.weight_diff,
bias=self.bias + self.bias_diff,
weight=self._get_actual_weight(),
bias=self._get_actual_bias(),
stride=self.stride,
padding=self.padding,
dilation=self.dilation,
Expand Down
2 changes: 0 additions & 2 deletions lightx2v/models/networks/wan/sf_model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,8 +11,6 @@
class WanSFModel(WanModel):
def __init__(self, model_path, config, device, lora_path=None, lora_strength=1.0):
super().__init__(model_path, config, device, lora_path=lora_path, lora_strength=lora_strength)
if config["model_cls"] not in ["wan2.1_sf_mtxg2"]:
self.to_cuda()

def _load_ckpt(self, unified_dtype, sensitive_layer):
sf_confg = self.config["sf_config"]
Expand Down
5 changes: 3 additions & 2 deletions lightx2v_platform/base/intel_xpu.py
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Original file line number Diff line number Diff line change
Expand Up @@ -8,7 +8,6 @@
"""

import torch
import torch.distributed as dist
from loguru import logger

from lightx2v_platform.registry_factory import PLATFORM_DEVICE_REGISTER
Expand Down Expand Up @@ -58,5 +57,7 @@ def get_device() -> str:
# """
# dist.init_process_group(backend="ccl")
# torch.xpu.set_device(dist.get_rank())


# Register alias "xpu" for backward compatibility
PLATFORM_DEVICE_REGISTER._dict["xpu"] = IntelXpuDevice
PLATFORM_DEVICE_REGISTER._dict["xpu"] = IntelXpuDevice