Safely load pickled embeds that don't load with weights_only=True.

comfy/sd1_clip.py

@@ -3,6 +3,7 @@ import os
 from transformers import CLIPTokenizer, CLIPTextModel, CLIPTextConfig
 import torch
 import traceback
+import zipfile
 
 class ClipTokenWeightEncoder:
     def encode_token_weights(self, token_weight_pairs):
@@ -171,6 +172,26 @@ def unescape_important(text):
     text = text.replace("\0\2", "(")
     return text
 
+def safe_load_embed_zip(embed_path):
+    with zipfile.ZipFile(embed_path) as myzip:
+        names = list(filter(lambda a: "data/" in a, myzip.namelist()))
+        names.reverse()
+        for n in names:
+            with myzip.open(n) as myfile:
+                data = myfile.read()
+                number = len(data) // 4
+                length_embed = 1024 #sd2.x
+                if number < 768:
+                    continue
+                if number % 768 == 0:
+                    length_embed = 768 #sd1.x
+                num_embeds = number // length_embed
+                embed = torch.frombuffer(data, dtype=torch.float)
+                out = embed.reshape((num_embeds, length_embed)).clone()
+                del embed
+                return out
+
+
 def load_embed(embedding_name, embedding_directory):
     if isinstance(embedding_directory, str):
         embedding_directory = [embedding_directory]
@@ -195,13 +216,18 @@ def load_embed(embedding_name, embedding_directory):
 
     embed_path = valid_file
 
+    embed_out = None
+
     try:
         if embed_path.lower().endswith(".safetensors"):
             import safetensors.torch
             embed = safetensors.torch.load_file(embed_path, device="cpu")
         else:
             if 'weights_only' in torch.load.__code__.co_varnames:
-                embed = torch.load(embed_path, weights_only=True, map_location="cpu")
+                try:
+                    embed = torch.load(embed_path, weights_only=True, map_location="cpu")
+                except:
+                    embed_out = safe_load_embed_zip(embed_path)
             else:
                 embed = torch.load(embed_path, map_location="cpu")
     except Exception as e:
@@ -210,11 +236,13 @@ def load_embed(embedding_name, embedding_directory):
         print("error loading embedding, skipping loading:", embedding_name)
         return None
 
-    if 'string_to_param' in embed:
-        values = embed['string_to_param'].values()
-    else:
-        values = embed.values()
-    return next(iter(values))
+    if embed_out is None:
+        if 'string_to_param' in embed:
+            values = embed['string_to_param'].values()
+        else:
+            values = embed.values()
+        embed_out = next(iter(values))
+    return embed_out
 
 class SD1Tokenizer:
     def __init__(self, tokenizer_path=None, max_length=77, pad_with_end=True, embedding_directory=None):