From 339b5315700a469f4a9f0d5afc08ca2aca60c579 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Sat, 27 May 2023 15:47:33 +0300
Subject: custom unet support

---
 modules/sd_hijack.py | 20 ++++++++++++++------
 1 file changed, 14 insertions(+), 6 deletions(-)

(limited to 'modules/sd_hijack.py')

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index f93df0a6..487dfd60 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -3,7 +3,7 @@ from torch.nn.functional import silu
 from types import MethodType
 
 import modules.textual_inversion.textual_inversion
-from modules import devices, sd_hijack_optimizations, shared, script_callbacks, errors
+from modules import devices, sd_hijack_optimizations, shared, script_callbacks, errors, sd_unet
 from modules.hypernetworks import hypernetwork
 from modules.shared import cmd_opts
 from modules import sd_hijack_clip, sd_hijack_open_clip, sd_hijack_unet, sd_hijack_xlmr, xlmr
@@ -43,7 +43,7 @@ def list_optimizers():
     optimizers.extend(new_optimizers)
 
 
-def apply_optimizations():
+def apply_optimizations(option=None):
     global current_optimizer
 
     undo_optimizations()
@@ -60,7 +60,7 @@ def apply_optimizations():
         current_optimizer.undo()
         current_optimizer = None
 
-    selection = shared.opts.cross_attention_optimization
+    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:
@@ -72,12 +72,13 @@ def apply_optimizations():
         matching_optimizer = optimizers[0]
 
     if matching_optimizer is not None:
-        print(f"Applying optimization: {matching_optimizer.name}... ", end='')
+        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 ''
 
 
@@ -155,9 +156,9 @@ class StableDiffusionModelHijack:
     def __init__(self):
         self.embedding_db.add_embedding_dir(cmd_opts.embeddings_dir)
 
-    def apply_optimizations(self):
+    def apply_optimizations(self, option=None):
         try:
-            self.optimization_method = apply_optimizations()
+            self.optimization_method = apply_optimizations(option)
         except Exception as e:
             errors.display(e, "applying cross attention optimization")
             undo_optimizations()
@@ -194,6 +195,11 @@ class StableDiffusionModelHijack:
 
         self.layers = flatten(m)
 
+        if not hasattr(ldm.modules.diffusionmodules.openaimodel, 'copy_of_UNetModel_forward_for_webui'):
+            ldm.modules.diffusionmodules.openaimodel.copy_of_UNetModel_forward_for_webui = ldm.modules.diffusionmodules.openaimodel.UNetModel.forward
+
+        ldm.modules.diffusionmodules.openaimodel.UNetModel.forward = sd_unet.UNetModel_forward
+
     def undo_hijack(self, m):
         if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation:
             m.cond_stage_model = m.cond_stage_model.wrapped
@@ -215,6 +221,8 @@ class StableDiffusionModelHijack:
         self.layers = None
         self.clip = None
 
+        ldm.modules.diffusionmodules.openaimodel.UNetModel.forward = ldm.modules.diffusionmodules.openaimodel.copy_of_UNetModel_forward_for_webui
+
     def apply_circular(self, enable):
         if self.circular_enabled == enable:
             return
-- 
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