Merge branch 'comfyanonymous:master' into feature/settings

.gitignore

@@ -13,4 +13,5 @@ extra_model_paths.yaml
 venv/
 web/extensions/*
 !web/extensions/logging.js.example
-!web/extensions/core/
+!web/extensions/core/
+startup-scripts/

comfy/latent_formats.py

@@ -21,7 +21,7 @@ class SD15(LatentFormat):
 class SDXL(LatentFormat):
     def __init__(self):
         self.scale_factor = 0.13025
-        self.latent_rgb_factors = [ #TODO: these are the factors for SD1.5, need to estimate new ones for SDXL
+        self.latent_rgb_factors = [
                     #   R        G        B
                     [ 0.3920,  0.4054,  0.4549],
                     [-0.2634, -0.0196,  0.0653],

comfy/sd.py

@@ -359,10 +359,11 @@ class ModelPatcher:
 
             if len(v) == 1:
                 w1 = v[0]
-                if w1.shape != weight.shape:
-                    print("WARNING SHAPE MISMATCH {} WEIGHT NOT MERGED {} != {}".format(key, w1.shape, weight.shape))
-                else:
-                    weight += alpha * w1.type(weight.dtype).to(weight.device)
+                if alpha != 0.0:
+                    if w1.shape != weight.shape:
+                        print("WARNING SHAPE MISMATCH {} WEIGHT NOT MERGED {} != {}".format(key, w1.shape, weight.shape))
+                    else:
+                        weight += alpha * w1.type(weight.dtype).to(weight.device)
             elif len(v) == 4: #lora/locon
                 mat1 = v[0]
                 mat2 = v[1]

comfy/sd1_clip.py

@@ -233,7 +233,7 @@ def expand_directory_list(directories):
             dirs.add(root)
     return list(dirs)
 
-def load_embed(embedding_name, embedding_directory, embedding_size):
+def load_embed(embedding_name, embedding_directory, embedding_size, embed_key=None):
     if isinstance(embedding_directory, str):
         embedding_directory = [embedding_directory]
 
@@ -292,13 +292,15 @@ def load_embed(embedding_name, embedding_directory, embedding_size):
                         continue
                     out_list.append(t.reshape(-1, t.shape[-1]))
             embed_out = torch.cat(out_list, dim=0)
+        elif embed_key is not None and embed_key in embed:
+            embed_out = embed[embed_key]
         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, embedding_size=768):
+    def __init__(self, tokenizer_path=None, max_length=77, pad_with_end=True, embedding_directory=None, embedding_size=768, embedding_key='clip_l'):
         if tokenizer_path is None:
             tokenizer_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "sd1_tokenizer")
         self.tokenizer = CLIPTokenizer.from_pretrained(tokenizer_path)
@@ -315,17 +317,18 @@ class SD1Tokenizer:
         self.max_word_length = 8
         self.embedding_identifier = "embedding:"
         self.embedding_size = embedding_size
+        self.embedding_key = embedding_key
 
     def _try_get_embedding(self, embedding_name:str):
         '''
         Takes a potential embedding name and tries to retrieve it.
         Returns a Tuple consisting of the embedding and any leftover string, embedding can be None.
         '''
-        embed = load_embed(embedding_name, self.embedding_directory, self.embedding_size)
+        embed = load_embed(embedding_name, self.embedding_directory, self.embedding_size, self.embedding_key)
         if embed is None:
             stripped = embedding_name.strip(',')
             if len(stripped) < len(embedding_name):
-                embed = load_embed(stripped, self.embedding_directory, self.embedding_size)
+                embed = load_embed(stripped, self.embedding_directory, self.embedding_size, self.embedding_key)
                 return (embed, embedding_name[len(stripped):])
         return (embed, "")
 

comfy/sdxl_clip.py

@@ -41,7 +41,7 @@ class SDXLClipG(sd1_clip.SD1ClipModel):
 
 class SDXLClipGTokenizer(sd1_clip.SD1Tokenizer):
     def __init__(self, tokenizer_path=None, embedding_directory=None):
-        super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory, embedding_size=1280)
+        super().__init__(tokenizer_path, pad_with_end=False, embedding_directory=embedding_directory, embedding_size=1280, embedding_key='clip_g')
 
 
 class SDXLTokenizer(sd1_clip.SD1Tokenizer):

main.py

@@ -1,6 +1,38 @@
+import os
+import importlib.util
+import folder_paths
+
+
+def execute_prestartup_script():
+    def execute_script(script_path):
+        if os.path.exists(script_path):
+            module_name = os.path.splitext(script_path)[0]
+            try:
+                spec = importlib.util.spec_from_file_location(module_name, script_path)
+                module = importlib.util.module_from_spec(spec)
+                spec.loader.exec_module(module)
+            except Exception as e:
+                print(f"Failed to execute startup-script: {script_path} / {e}")
+
+    node_paths = folder_paths.get_folder_paths("custom_nodes")
+    for custom_node_path in node_paths:
+        possible_modules = os.listdir(custom_node_path)
+
+        for possible_module in possible_modules:
+            module_path = os.path.join(custom_node_path, possible_module)
+            if os.path.isfile(module_path) or module_path.endswith(".disabled") or module_path == "__pycache__":
+                continue
+
+            script_path = os.path.join(module_path, "prestartup_script.py")
+            execute_script(script_path)
+
+
+execute_prestartup_script()
+
+
+# Main code
 import asyncio
 import itertools
-import os
 import shutil
 import threading
 import gc
@@ -22,7 +54,6 @@ if __name__ == "__main__":
 import yaml
 
 import execution
-import folder_paths
 import server
 from server import BinaryEventTypes
 from nodes import init_custom_nodes