Merge branch 'AUTOMATIC1111:master' into master

1 files changed, 13 insertions, 4 deletions

diff --git a/modules/sd_models_xl.py b/modules/sd_models_xl.py
index 40559208..01123321 100644
--- a/modules/sd_models_xl.py
+++ b/modules/sd_models_xl.py
@@ -56,6 +56,14 @@ def encode_embedding_init_text(self: sgm.modules.GeneralConditioner, init_text,
     return torch.cat(res, dim=1)

 

 

+def tokenize(self: sgm.modules.GeneralConditioner, texts):

+    for embedder in [embedder for embedder in self.embedders if hasattr(embedder, 'tokenize')]:

+        return embedder.tokenize(texts)

+

+    raise AssertionError('no tokenizer available')

+

+

+

 def process_texts(self, texts):

     for embedder in [embedder for embedder in self.embedders if hasattr(embedder, 'process_texts')]:

         return embedder.process_texts(texts)

@@ -68,6 +76,7 @@ def get_target_prompt_token_count(self, token_count):
 

 # those additions to GeneralConditioner make it possible to use it as model.cond_stage_model from SD1.5 in exist

 sgm.modules.GeneralConditioner.encode_embedding_init_text = encode_embedding_init_text

+sgm.modules.GeneralConditioner.tokenize = tokenize

 sgm.modules.GeneralConditioner.process_texts = process_texts

 sgm.modules.GeneralConditioner.get_target_prompt_token_count = get_target_prompt_token_count

 

@@ -89,10 +98,10 @@ def extend_sdxl(model):
     model.conditioner.wrapped = torch.nn.Module()

 

 

-sgm.modules.attention.print = lambda *args: None

-sgm.modules.diffusionmodules.model.print = lambda *args: None

-sgm.modules.diffusionmodules.openaimodel.print = lambda *args: None

-sgm.modules.encoders.modules.print = lambda *args: None

+sgm.modules.attention.print = shared.ldm_print

+sgm.modules.diffusionmodules.model.print = shared.ldm_print

+sgm.modules.diffusionmodules.openaimodel.print = shared.ldm_print

+sgm.modules.encoders.modules.print = shared.ldm_print

 

 # this gets the code to load the vanilla attention that we override

 sgm.modules.attention.SDP_IS_AVAILABLE = True