add more stuff to ignore when creating model from config

prevent .vae.safetensors files from being listed as stable diffusion models

modules/modelloader.py

@@ -10,7 +10,7 @@ from modules.upscaler import Upscaler
 from modules.paths import script_path, models_path
 
 
-def load_models(model_path: str, model_url: str = None, command_path: str = None, ext_filter=None, download_name=None) -> list:
+def load_models(model_path: str, model_url: str = None, command_path: str = None, ext_filter=None, download_name=None, ext_blacklist=None) -> list:
     """
     A one-and done loader to try finding the desired models in specified directories.
 
@@ -45,6 +45,8 @@ def load_models(model_path: str, model_url: str = None, command_path: str = None
                     full_path = file
                     if os.path.isdir(full_path):
                         continue
+                    if ext_blacklist is not None and any([full_path.endswith(x) for x in ext_blacklist]):
+                        continue
                     if len(ext_filter) != 0:
                         model_name, extension = os.path.splitext(file)
                         if extension not in ext_filter:

modules/sd_disable_initialization.py

 import ldm.modules.encoders.modules
 import open_clip
 import torch
+import transformers.utils.hub
 
 
 class DisableInitialization:
     """
-    When an object of this class enters a `with` block, it starts preventing torch's layer initialization
-    functions from working, and changes CLIP and OpenCLIP to not download model weights. When it leaves,
-    reverts everything to how it was.
+    When an object of this class enters a `with` block, it starts:
+    - preventing torch's layer initialization functions from working
+    - changes CLIP and OpenCLIP to not download model weights
+    - changes CLIP to not make requests to check if there is a new version of a file you already have
 
-    Use like this:
+    When it leaves the block, it reverts everything to how it was before.
+
+    Use it like this:
     ```
     with DisableInitialization():
         do_things()
@@ -26,19 +30,36 @@ class DisableInitialization:
         def CLIPTextModel_from_pretrained(pretrained_model_name_or_path, *model_args, **kwargs):
             return self.CLIPTextModel_from_pretrained(None, *model_args, config=pretrained_model_name_or_path, state_dict={}, **kwargs)
 
+        def transformers_utils_hub_get_from_cache(url, *args, local_files_only=False, **kwargs):
+
+            # this file is always 404, prevent making request
+            if url == 'https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/added_tokens.json':
+                raise transformers.utils.hub.EntryNotFoundError
+
+            try:
+                return self.transformers_utils_hub_get_from_cache(url, *args, local_files_only=True, **kwargs)
+            except Exception as e:
+                return self.transformers_utils_hub_get_from_cache(url, *args, local_files_only=False, **kwargs)
+
         self.init_kaiming_uniform = torch.nn.init.kaiming_uniform_
         self.init_no_grad_normal = torch.nn.init._no_grad_normal_
+        self.init_no_grad_uniform_ = torch.nn.init._no_grad_uniform_
         self.create_model_and_transforms = open_clip.create_model_and_transforms
         self.CLIPTextModel_from_pretrained = ldm.modules.encoders.modules.CLIPTextModel.from_pretrained
+        self.transformers_utils_hub_get_from_cache = transformers.utils.hub.get_from_cache
 
         torch.nn.init.kaiming_uniform_ = do_nothing
         torch.nn.init._no_grad_normal_ = do_nothing
+        torch.nn.init._no_grad_uniform_ = do_nothing
         open_clip.create_model_and_transforms = create_model_and_transforms_without_pretrained
         ldm.modules.encoders.modules.CLIPTextModel.from_pretrained = CLIPTextModel_from_pretrained
+        transformers.utils.hub.get_from_cache = transformers_utils_hub_get_from_cache
 
     def __exit__(self, exc_type, exc_val, exc_tb):
         torch.nn.init.kaiming_uniform_ = self.init_kaiming_uniform
         torch.nn.init._no_grad_normal_ = self.init_no_grad_normal
+        torch.nn.init._no_grad_uniform_ = self.init_no_grad_uniform_
         open_clip.create_model_and_transforms = self.create_model_and_transforms
         ldm.modules.encoders.modules.CLIPTextModel.from_pretrained = self.CLIPTextModel_from_pretrained
+        transformers.utils.hub.get_from_cache = self.transformers_utils_hub_get_from_cache
 

modules/sd_models.py

@@ -2,6 +2,7 @@ import collections
 import os.path
 import sys
 import gc
+import time
 from collections import namedtuple
 import torch
 import re
@@ -61,7 +62,7 @@ def find_checkpoint_config(info):
 
 def list_models():
     checkpoints_list.clear()
-    model_list = modelloader.load_models(model_path=model_path, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt", ".safetensors"])
+    model_list = modelloader.load_models(model_path=model_path, command_path=shared.cmd_opts.ckpt_dir, ext_filter=[".ckpt", ".safetensors"], ext_blacklist=[".vae.safetensors"])
 
     def modeltitle(path, shorthash):
         abspath = os.path.abspath(path)
@@ -288,6 +289,17 @@ def enable_midas_autodownload():
     midas.api.load_model = load_model_wrapper
 
 
+class Timer:
+    def __init__(self):
+        self.start = time.time()
+
+    def elapsed(self):
+        end = time.time()
+        res = end - self.start
+        self.start = end
+        return res
+
+
 def load_model(checkpoint_info=None):
     from modules import lowvram, sd_hijack
     checkpoint_info = checkpoint_info or select_checkpoint()
@@ -319,11 +331,17 @@ def load_model(checkpoint_info=None):
     if shared.cmd_opts.no_half:
         sd_config.model.params.unet_config.params.use_fp16 = False
 
+    timer = Timer()
+
     with sd_disable_initialization.DisableInitialization():
         sd_model = instantiate_from_config(sd_config.model)
 
+    elapsed_create = timer.elapsed()
+
     load_model_weights(sd_model, checkpoint_info)
 
+    elapsed_load_weights = timer.elapsed()
+
     if shared.cmd_opts.lowvram or shared.cmd_opts.medvram:
         lowvram.setup_for_low_vram(sd_model, shared.cmd_opts.medvram)
     else:
@@ -338,7 +356,9 @@ def load_model(checkpoint_info=None):
 
     script_callbacks.model_loaded_callback(sd_model)
 
-    print("Model loaded.")
+    elapsed_the_rest = timer.elapsed()
+
+    print(f"Model loaded in {elapsed_create + elapsed_load_weights + elapsed_the_rest:.1f}s ({elapsed_create:.1f}s create model, {elapsed_load_weights:.1f}s load weights).")
 
     return sd_model
 
@@ -349,7 +369,7 @@ def reload_model_weights(sd_model=None, info=None):
 
     if not sd_model:
         sd_model = shared.sd_model
-    if sd_model is None: # previous model load failed
+    if sd_model is None:  # previous model load failed
         current_checkpoint_info = None
     else:
         current_checkpoint_info = sd_model.sd_checkpoint_info
@@ -371,6 +391,8 @@ def reload_model_weights(sd_model=None, info=None):
 
     sd_hijack.model_hijack.undo_hijack(sd_model)
 
+    timer = Timer()
+
     try:
         load_model_weights(sd_model, checkpoint_info)
     except Exception as e:
@@ -384,6 +406,8 @@ def reload_model_weights(sd_model=None, info=None):
         if not shared.cmd_opts.lowvram and not shared.cmd_opts.medvram:
             sd_model.to(devices.device)
 
-    print("Weights loaded.")
+    elapsed = timer.elapsed()
+
+    print(f"Weights loaded in {elapsed:.1f}s.")
 
     return sd_model