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Fix T5GemmaEncoder loading for transformers 5.x composite T5GemmaConfig #13143

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Fix T5GemmaEncoder loading for transformers 5.x composite T5GemmaConfig #13143

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17 changes: 14 additions & 3 deletions src/diffusers/pipelines/prx/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -24,14 +24,25 @@
else:
_import_structure["pipeline_prx"] = ["PRXPipeline"]

# Import T5GemmaEncoder for pipeline loading compatibility
# Wrap T5GemmaEncoder to pass config.encoder (T5GemmaModuleConfig) instead of the
# composite T5GemmaConfig, which lacks flat attributes expected by T5GemmaEncoder.__init__.
try:
if is_transformers_available():
import transformers
from transformers.models.t5gemma.modeling_t5gemma import T5GemmaEncoder
from transformers.models.t5gemma.modeling_t5gemma import T5GemmaEncoder as _T5GemmaEncoder

class T5GemmaEncoder(_T5GemmaEncoder):
@classmethod
def from_pretrained(cls, pretrained_model_name_or_path, *args, **kwargs):
if "config" not in kwargs:
from transformers.models.t5gemma.configuration_t5gemma import T5GemmaConfig

config = T5GemmaConfig.from_pretrained(pretrained_model_name_or_path)
if hasattr(config, "encoder"):
kwargs["config"] = config.encoder
return super().from_pretrained(pretrained_model_name_or_path, *args, **kwargs)

_additional_imports["T5GemmaEncoder"] = T5GemmaEncoder
# Patch transformers module directly for serialization
if not hasattr(transformers, "T5GemmaEncoder"):
transformers.T5GemmaEncoder = T5GemmaEncoder
except ImportError:
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