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Modified altdiffusion pipline to support altdiffusion-m18 (#2993)

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* Modified altdiffusion pipline to support altdiffusion-m18

* Modified altdiffusion pipline to support altdiffusion-m18

* Modified altdiffusion pipline to support altdiffusion-m18

* Modified altdiffusion pipline to support altdiffusion-m18

* Modified altdiffusion pipline to support altdiffusion-m18

* Modified altdiffusion pipline to support altdiffusion-m18

* Modified altdiffusion pipline to support altdiffusion-m18

---------

Co-authored-by: root <fulong_ye@163.com>
This commit is contained in:
superhero-7
2023-04-20 01:00:29 +08:00
committed by GitHub
parent 3becd368b1
commit a4c91be73b
1 changed files with 24 additions and 9 deletions
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33
src/diffusers/pipelines/alt_diffusion/modeling_roberta_series.py
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@@ -56,7 +56,7 @@ class RobertaSeriesConfig(XLMRobertaConfig):
class RobertaSeriesModelWithTransformation(RobertaPreTrainedModel):
_keys_to_ignore_on_load_unexpected = [r"pooler"]
_keys_to_ignore_on_load_unexpected = [r"pooler", r"logit_scale"]
_keys_to_ignore_on_load_missing = [r"position_ids", r"predictions.decoder.bias"]
base_model_prefix = "roberta"
config_class = RobertaSeriesConfig
@@ -65,6 +65,10 @@ class RobertaSeriesModelWithTransformation(RobertaPreTrainedModel):
super().__init__(config)
self.roberta = XLMRobertaModel(config)
self.transformation = nn.Linear(config.hidden_size, config.project_dim)
self.has_pre_transformation = getattr(config, "has_pre_transformation", False)
if self.has_pre_transformation:
self.transformation_pre = nn.Linear(config.hidden_size, config.project_dim)
self.pre_LN = nn.LayerNorm(config.hidden_size, eps=config.layer_norm_eps)
self.post_init()
def forward(
@@ -95,15 +99,26 @@ class RobertaSeriesModelWithTransformation(RobertaPreTrainedModel):
encoder_hidden_states=encoder_hidden_states,
encoder_attention_mask=encoder_attention_mask,
output_attentions=output_attentions,
output_hidden_states=output_hidden_states,
output_hidden_states=True if self.has_pre_transformation else output_hidden_states,
return_dict=return_dict,
)
projection_state = self.transformation(outputs.last_hidden_state)
if self.has_pre_transformation:
sequence_output2 = outputs["hidden_states"][-2]
sequence_output2 = self.pre_LN(sequence_output2)
projection_state2 = self.transformation_pre(sequence_output2)
return TransformationModelOutput(
projection_state=projection_state,
last_hidden_state=outputs.last_hidden_state,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
return TransformationModelOutput(
projection_state=projection_state2,
last_hidden_state=outputs.last_hidden_state,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)
else:
projection_state = self.transformation(outputs.last_hidden_state)
return TransformationModelOutput(
projection_state=projection_state,
last_hidden_state=outputs.last_hidden_state,
hidden_states=outputs.hidden_states,
attentions=outputs.attentions,
)