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fix diffuser save_lora_weights crash #1633

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fix diffuser save_lora_weights crash #1633

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4 changes: 0 additions & 4 deletions optimum/habana/diffusers/pipelines/pipeline_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -381,7 +381,6 @@ def save_lora_weights(
save_directory: Union[str, os.PathLike],
unet_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,
text_encoder_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,
text_encoder_2_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,
is_main_process: bool = True,
weight_name: str = None,
save_function: Callable = None,
Expand All @@ -392,13 +391,10 @@ def save_lora_weights(
unet_lora_layers = to_device_dtype(unet_lora_layers, target_device=torch.device("cpu"))
if text_encoder_lora_layers:
text_encoder_lora_layers = to_device_dtype(text_encoder_lora_layers, target_device=torch.device("cpu"))
if text_encoder_2_lora_layers:
text_encoder_2_lora_layers = to_device_dtype(text_encoder_2_lora_layers, target_device=torch.device("cpu"))
return super().save_lora_weights(
save_directory,
unet_lora_layers,
text_encoder_lora_layers,
text_encoder_2_lora_layers,
is_main_process,
weight_name,
save_function,
Expand Down
32 changes: 32 additions & 0 deletions optimum/habana/diffusers/pipelines/stable_diffusion_xl/pipeline_stable_diffusion_xl.py
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Original file line number Diff line number Diff line change
Expand Up @@ -11,6 +11,7 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import time
from dataclasses import dataclass
from math import ceil
Expand All @@ -33,6 +34,7 @@
CLIPVisionModelWithProjection,
)

from optimum.habana.utils import to_device_dtype
from optimum.utils import logging

from ....transformers.gaudi_configuration import GaudiConfig
Expand Down Expand Up @@ -142,6 +144,36 @@ def __init__(

self.to(self._device)

@classmethod
def save_lora_weights(
cls,
save_directory: Union[str, os.PathLike],
unet_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,
text_encoder_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,
text_encoder_2_lora_layers: Dict[str, Union[torch.nn.Module, torch.Tensor]] = None,
is_main_process: bool = True,
weight_name: str = None,
save_function: Callable = None,
safe_serialization: bool = True,
):
# Move the state dict from HPU to CPU before saving
if unet_lora_layers:
unet_lora_layers = to_device_dtype(unet_lora_layers, target_device=torch.device("cpu"))
if text_encoder_lora_layers:
text_encoder_lora_layers = to_device_dtype(text_encoder_lora_layers, target_device=torch.device("cpu"))
if text_encoder_2_lora_layers:
text_encoder_2_lora_layers = to_device_dtype(text_encoder_2_lora_layers, target_device=torch.device("cpu"))
return StableDiffusionXLPipeline.save_lora_weights(
save_directory,
unet_lora_layers,
text_encoder_lora_layers,
text_encoder_2_lora_layers,
is_main_process,
weight_name,
save_function,
safe_serialization,
)

def prepare_latents(self, num_images, num_channels_latents, height, width, dtype, device, generator, latents=None):
shape = (num_images, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
if isinstance(generator, list) and len(generator) != num_images:
Expand Down