possible fix for empty list of optimizations #10605

1 files changed, 15 insertions, 6 deletions

diff --git a/modules/sd_hijack.py b/modules/sd_hijack.py
index 08d31080..f93df0a6 100644
--- a/modules/sd_hijack.py
+++ b/modules/sd_hijack.py
@@ -48,6 +48,11 @@ def apply_optimizations():
 

     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

 

@@ -67,8 +72,9 @@ def apply_optimizations():
         matching_optimizer = optimizers[0]

 

     if matching_optimizer is not None:

-        print(f"Applying optimization: {matching_optimizer.name}")

+        print(f"Applying optimization: {matching_optimizer.name}... ", end='')

         matching_optimizer.apply()

+        print("done.")

         current_optimizer = matching_optimizer

         return current_optimizer.name

     else:

@@ -149,6 +155,13 @@ class StableDiffusionModelHijack:
     def __init__(self):

         self.embedding_db.add_embedding_dir(cmd_opts.embeddings_dir)

 

+    def apply_optimizations(self):

+        try:

+            self.optimization_method = apply_optimizations()

+        except Exception as e:

+            errors.display(e, "applying cross attention optimization")

+            undo_optimizations()

+

     def hijack(self, m):

         if type(m.cond_stage_model) == xlmr.BertSeriesModelWithTransformation:

             model_embeddings = m.cond_stage_model.roberta.embeddings

@@ -168,11 +181,7 @@ class StableDiffusionModelHijack:
         if m.cond_stage_key == "edit":

             sd_hijack_unet.hijack_ddpm_edit()

 

-        try:

-            self.optimization_method = apply_optimizations()

-        except Exception as e:

-            errors.display(e, "applying cross attention optimization")

-            undo_optimizations()

+        self.apply_optimizations()

 

         self.clip = m.cond_stage_model