fix linter issues

modules/sd_hijack.py

@@ -212,7 +212,7 @@ class StableDiffusionModelHijack:
             model_embeddings = m.cond_stage_model.roberta.embeddings
             model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.word_embeddings, self)
             m.cond_stage_model = sd_hijack_xlmr.FrozenXLMREmbedderWithCustomWords(m.cond_stage_model, self)
-            
+
         elif type(m.cond_stage_model) == ldm.modules.encoders.modules.FrozenCLIPEmbedder:
             model_embeddings = m.cond_stage_model.transformer.text_model.embeddings
             model_embeddings.token_embedding = EmbeddingsWithFixes(model_embeddings.token_embedding, self)
@@ -258,7 +258,7 @@ class StableDiffusionModelHijack:
 
             if hasattr(m, 'cond_stage_model'):
                 delattr(m, 'cond_stage_model')
-                
+
         elif type(m.cond_stage_model) == sd_hijack_xlmr.FrozenXLMREmbedderWithCustomWords:
             m.cond_stage_model = m.cond_stage_model.wrapped
 

modules/sd_models_config.py

@@ -95,8 +95,7 @@ def guess_model_config_from_state_dict(sd, filename):
         if diffusion_model_input.shape[1] == 8:
             return config_instruct_pix2pix
 
-    
-    # import pdb; pdb.set_trace()
+
     if sd.get('cond_stage_model.roberta.embeddings.word_embeddings.weight', None) is not None:
         if sd.get('cond_stage_model.transformation.weight').size()[0] == 1024:
             return config_alt_diffusion_m18

modules/xlmr_m18.py

-from transformers import BertPreTrainedModel,BertModel,BertConfig
+from transformers import BertPreTrainedModel,BertConfig
 import torch.nn as nn
 import torch
 from transformers.models.xlm_roberta.configuration_xlm_roberta import XLMRobertaConfig
@@ -28,7 +28,7 @@ class BertSeriesModelWithTransformation(BertPreTrainedModel):
     config_class = BertSeriesConfig
 
     def __init__(self, config=None, **kargs):
-        # modify initialization for autoloading 
+        # modify initialization for autoloading
         if config is None:
             config = XLMRobertaConfig()
             config.attention_probs_dropout_prob= 0.1
@@ -80,7 +80,7 @@ class BertSeriesModelWithTransformation(BertPreTrainedModel):
         text["attention_mask"] = torch.tensor(
             text['attention_mask']).to(device)
         features = self(**text)
-        return features['projection_state'] 
+        return features['projection_state']
 
     def forward(
         self,
@@ -147,8 +147,8 @@ class BertSeriesModelWithTransformation(BertPreTrainedModel):
                 "hidden_states": outputs.hidden_states,
                 "attentions": outputs.attentions,
             }
-            
-        
+
+
         # return {
         #     'pooler_output':pooler_output,
         #     'last_hidden_state':outputs.last_hidden_state,
@@ -161,4 +161,4 @@ class BertSeriesModelWithTransformation(BertPreTrainedModel):
 
 class RobertaSeriesModelWithTransformation(BertSeriesModelWithTransformation):
     base_model_prefix = 'roberta'
-    config_class= RobertaSeriesConfig
+    config_class= RobertaSeriesConfig
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