Fix tokenizer cloning

comfy/model_patcher.py

@@ -416,10 +416,10 @@ class ModelPatcher(ModelManageable):
                     logging.debug("lowvram: loaded module regularly {} {}".format(n, m))
 
         if lowvram_counter > 0:
-            logging.info("loaded partially {} {} {}".format(lowvram_model_memory / (1024 * 1024), mem_counter / (1024 * 1024), patch_counter))
+            logging.debug("loaded partially {} {} {}".format(lowvram_model_memory / (1024 * 1024), mem_counter / (1024 * 1024), patch_counter))
             self._memory_measurements.model_lowvram = True
         else:
-            logging.info("loaded completely {} {}".format(lowvram_model_memory / (1024 * 1024), mem_counter / (1024 * 1024)))
+            logging.debug("loaded completely {} {}".format(lowvram_model_memory / (1024 * 1024), mem_counter / (1024 * 1024)))
             self._memory_measurements.model_lowvram = False
         self._memory_measurements.lowvram_patch_counter += patch_counter
         self._memory_measurements.model_loaded_weight_memory = mem_counter

comfy/text_encoders/flux.py

@@ -1,3 +1,5 @@
+import copy
+
 import torch
 from transformers import T5TokenizerFast
 
@@ -21,7 +23,9 @@ class T5XXLTokenizer(sd1_clip.SDTokenizer):
 
 
 class FluxTokenizer:
-    def __init__(self, embedding_directory=None, tokenizer_data={}):
+    def __init__(self, embedding_directory=None, tokenizer_data=None):
+        if tokenizer_data is None:
+            tokenizer_data = dict()
         self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory)
         self.t5xxl = T5XXLTokenizer(embedding_directory=embedding_directory)
 
@@ -37,6 +41,9 @@ class FluxTokenizer:
     def state_dict(self):
         return {}
 
+    def clone(self):
+        return copy.copy(self)
+
 
 class FluxClipModel(torch.nn.Module):
     def __init__(self, dtype_t5=None, device="cpu", dtype=None):

comfy/text_encoders/hydit.py

@@ -59,6 +59,9 @@ class HyditTokenizer:
     def state_dict(self):
         return {"mt5xl.spiece_model": self.mt5xl.state_dict()["spiece_model"]}
 
+    def clone(self):
+        return copy.copy(self)
+
 
 class HyditModel(torch.nn.Module):
     def __init__(self, device="cpu", dtype=None):

comfy/text_encoders/sd3_clip.py

@@ -1,3 +1,4 @@
+import copy
 import logging
 
 import torch
@@ -24,7 +25,9 @@ class T5XXLTokenizer(sd1_clip.SDTokenizer):
 
 
 class SD3Tokenizer:
-    def __init__(self, embedding_directory=None, tokenizer_data={}):
+    def __init__(self, embedding_directory=None, tokenizer_data=None):
+        if tokenizer_data is None:
+            tokenizer_data = dict()
         self.clip_l = sd1_clip.SDTokenizer(embedding_directory=embedding_directory)
         self.clip_g = sdxl_clip.SDXLClipGTokenizer(embedding_directory=embedding_directory)
         self.t5xxl = T5XXLTokenizer(embedding_directory=embedding_directory)
@@ -42,6 +45,9 @@ class SD3Tokenizer:
     def state_dict(self):
         return dict()
 
+    def clone(self):
+        return copy.copy(self)
+
 
 class SD3ClipModel(torch.nn.Module):
     def __init__(self, clip_l=True, clip_g=True, t5=True, dtype_t5=None, device="cpu", dtype=None):

comfy/text_encoders/spiece_tokenizer.py

@@ -1,3 +1,5 @@
+import copy
+
 import sentencepiece
 import torch
 
@@ -36,3 +38,7 @@ class SPieceTokenizer:
 
     def serialize_model(self):
         return torch.ByteTensor(list(self.tokenizer.serialized_model_proto()))
+
+    def clone(self):
+        return copy.copy(self)
+