lycoris, strong linting, model keyword, circular imports

modules/sd_hijack.py

@@ -1,15 +1,7 @@
+from types import MethodType
+from rich import print
 import torch
 from torch.nn.functional import silu
-from types import MethodType
-
-import modules.textual_inversion.textual_inversion
-from modules import devices, sd_hijack_optimizations, shared, sd_hijack_checkpoint
-from modules.hypernetworks import hypernetwork
-from modules.shared import cmd_opts, opts
-from modules.shared_items import list_crossattention
-from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet, sd_hijack_xlmr, xlmr
-from rich import print
-
 import ldm.modules.attention
 import ldm.modules.diffusionmodules.model
 import ldm.modules.diffusionmodules.openaimodel
@@ -17,6 +9,12 @@ import ldm.models.diffusion.ddim
 import ldm.models.diffusion.plms
 import ldm.modules.encoders.modules
 
+import modules.textual_inversion.textual_inversion
+from modules import devices, sd_hijack_optimizations, shared
+from modules.hypernetworks import hypernetwork
+from modules.shared import opts
+from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet, sd_hijack_xlmr, xlmr
+
 attention_CrossAttention_forward = ldm.modules.attention.CrossAttention.forward
 diffusionmodules_model_nonlinearity = ldm.modules.diffusionmodules.model.nonlinearity
 diffusionmodules_model_AttnBlock_forward = ldm.modules.diffusionmodules.model.AttnBlock.forward
@@ -36,7 +34,7 @@ def apply_optimizations():
 
     ldm.modules.diffusionmodules.model.nonlinearity = silu
     ldm.modules.diffusionmodules.openaimodel.th = sd_hijack_unet.th
-    
+
     optimization_method = None
 
     can_use_sdp = hasattr(torch.nn.functional, "scaled_dot_product_attention") and callable(getattr(torch.nn.functional, "scaled_dot_product_attention"))
@@ -97,12 +95,12 @@ def fix_checkpoint():
 def weighted_loss(sd_model, pred, target, mean=True):
     #Calculate the weight normally, but ignore the mean
     loss = sd_model._old_get_loss(pred, target, mean=False)
-    
+
     #Check if we have weights available
     weight = getattr(sd_model, '_custom_loss_weight', None)
     if weight is not None:
         loss *= weight
-    
+
     #Return the loss, as mean if specified
     return loss.mean() if mean else loss
 
@@ -110,7 +108,7 @@ def weighted_forward(sd_model, x, c, w, *args, **kwargs):
     try:
         #Temporarily append weights to a place accessible during loss calc
         sd_model._custom_loss_weight = w
-        
+
         #Replace 'get_loss' with a weight-aware one. Otherwise we need to reimplement 'forward' completely
         #Keep 'get_loss', but don't overwrite the previous old_get_loss if it's already set
         if not hasattr(sd_model, '_old_get_loss'):
@@ -123,9 +121,9 @@ def weighted_forward(sd_model, x, c, w, *args, **kwargs):
         try:
             #Delete temporary weights if appended
             del sd_model._custom_loss_weight
-        except AttributeError as e:
+        except AttributeError:
             pass
-            
+
         #If we have an old loss function, reset the loss function to the original one
         if hasattr(sd_model, '_old_get_loss'):
             sd_model.get_loss = sd_model._old_get_loss
@@ -138,7 +136,7 @@ def apply_weighted_forward(sd_model):
 def undo_weighted_forward(sd_model):
     try:
         del sd_model.weighted_forward
-    except AttributeError as e:
+    except AttributeError:
         pass
 
 
@@ -200,7 +198,7 @@ class StableDiffusionModelHijack:
 
     def undo_hijack(self, m):
         if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation:
-            m.cond_stage_model = m.cond_stage_model.wrapped 
+            m.cond_stage_model = m.cond_stage_model.wrapped
 
         elif type(m.cond_stage_model) == sd_hijack_clip.FrozenCLIPEmbedderWithCustomWords:
             m.cond_stage_model = m.cond_stage_model.wrapped