Update sd_hijack.py

modules/sd_hijack.py

@@ -57,57 +57,11 @@ def list_optimizers():
 
 
 def apply_optimizations(option=None):
-    global current_optimizer
-
-    undo_optimizations()
-
-    if len(optimizers) == 0:
-        # a script can access the model very early, and optimizations would not be filled by then
-        current_optimizer = None
-        return ''
-
-    ldm.modules.diffusionmodules.model.nonlinearity = silu
-    ldm.modules.diffusionmodules.openaimodel.th = sd_hijack_unet.th
-
-    sgm.modules.diffusionmodules.model.nonlinearity = silu
-    sgm.modules.diffusionmodules.openaimodel.th = sd_hijack_unet.th
-
-    if current_optimizer is not None:
-        current_optimizer.undo()
-        current_optimizer = None
-
-    selection = option or shared.opts.cross_attention_optimization
-    if selection == "Automatic" and len(optimizers) > 0:
-        matching_optimizer = next(iter([x for x in optimizers if x.cmd_opt and getattr(shared.cmd_opts, x.cmd_opt, False)]), optimizers[0])
-    else:
-        matching_optimizer = next(iter([x for x in optimizers if x.title() == selection]), None)
-
-    if selection == "None":
-        matching_optimizer = None
-    elif selection == "Automatic" and shared.cmd_opts.disable_opt_split_attention:
-        matching_optimizer = None
-    elif matching_optimizer is None:
-        matching_optimizer = optimizers[0]
-
-    if matching_optimizer is not None:
-        print(f"Applying attention optimization: {matching_optimizer.name}... ", end='')
-        matching_optimizer.apply()
-        print("done.")
-        current_optimizer = matching_optimizer
-        return current_optimizer.name
-    else:
-        print("Disabling attention optimization")
-        return ''
+    return
 
 
 def undo_optimizations():
-    ldm.modules.diffusionmodules.model.nonlinearity = diffusionmodules_model_nonlinearity
-    ldm.modules.attention.CrossAttention.forward = hypernetwork.attention_CrossAttention_forward
-    ldm.modules.diffusionmodules.model.AttnBlock.forward = diffusionmodules_model_AttnBlock_forward
-
-    sgm.modules.diffusionmodules.model.nonlinearity = diffusionmodules_model_nonlinearity
-    sgm.modules.attention.CrossAttention.forward = hypernetwork.attention_CrossAttention_forward
-    sgm.modules.diffusionmodules.model.AttnBlock.forward = diffusionmodules_model_AttnBlock_forward
+    return 
 
 
 def fix_checkpoint():