Merge branch 'disable_initialization'

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

@@ -0,0 +1,95 @@
+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
+    - 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
+
+    When it leaves the block, it reverts everything to how it was before.
+
+    Use it like this:
+    ```
+    with DisableInitialization():
+        do_things()
+    ```
+    """
+
+    def __enter__(self):
+        def do_nothing(*args, **kwargs):
+            pass
+
+        def create_model_and_transforms_without_pretrained(*args, pretrained=None, **kwargs):
+            return self.create_model_and_transforms(*args, pretrained=None, **kwargs)
+
+        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_modeling_utils_load_pretrained_model(*args, **kwargs):
+            args = args[0:3] + ('/', ) + args[4:]  # resolved_archive_file; must set it to something to prevent what seems to be a bug
+            return self.transformers_modeling_utils_load_pretrained_model(*args, **kwargs)
+
+        def transformers_utils_hub_get_file_from_cache(original, url, *args, **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 original(url, *args, local_files_only=True, **kwargs)
+            except Exception as e:
+                return original(url, *args, local_files_only=False, **kwargs)
+
+        def transformers_utils_hub_get_from_cache(url, *args, local_files_only=False, **kwargs):
+            return transformers_utils_hub_get_file_from_cache(self.transformers_utils_hub_get_from_cache, url, *args, **kwargs)
+
+        def transformers_tokenization_utils_base_cached_file(url, *args, local_files_only=False, **kwargs):
+            return transformers_utils_hub_get_file_from_cache(self.transformers_tokenization_utils_base_cached_file, url, *args, **kwargs)
+
+        def transformers_configuration_utils_cached_file(url, *args, local_files_only=False, **kwargs):
+            return transformers_utils_hub_get_file_from_cache(self.transformers_configuration_utils_cached_file, url, *args, **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_modeling_utils_load_pretrained_model = getattr(transformers.modeling_utils.PreTrainedModel, '_load_pretrained_model', None)
+        self.transformers_tokenization_utils_base_cached_file = getattr(transformers.tokenization_utils_base, 'cached_file', None)
+        self.transformers_configuration_utils_cached_file = getattr(transformers.configuration_utils, 'cached_file', None)
+        self.transformers_utils_hub_get_from_cache = getattr(transformers.utils.hub, 'get_from_cache', None)
+
+        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
+        if self.transformers_modeling_utils_load_pretrained_model is not None:
+            transformers.modeling_utils.PreTrainedModel._load_pretrained_model = transformers_modeling_utils_load_pretrained_model
+        if self.transformers_tokenization_utils_base_cached_file is not None:
+            transformers.tokenization_utils_base.cached_file = transformers_tokenization_utils_base_cached_file
+        if self.transformers_configuration_utils_cached_file is not None:
+            transformers.configuration_utils.cached_file = transformers_configuration_utils_cached_file
+        if self.transformers_utils_hub_get_from_cache is not None:
+            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
+        if self.transformers_modeling_utils_load_pretrained_model is not None:
+            transformers.modeling_utils.PreTrainedModel._load_pretrained_model = self.transformers_modeling_utils_load_pretrained_model
+        if self.transformers_tokenization_utils_base_cached_file is not None:
+            transformers.utils.hub.cached_file = self.transformers_tokenization_utils_base_cached_file
+        if self.transformers_configuration_utils_cached_file is not None:
+            transformers.utils.hub.cached_file = self.transformers_configuration_utils_cached_file
+        if self.transformers_utils_hub_get_from_cache is not None:
+            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
@@ -13,7 +14,7 @@ import ldm.modules.midas as midas
 
 from ldm.util import instantiate_from_config
 
-from modules import shared, modelloader, devices, script_callbacks, sd_vae
+from modules import shared, modelloader, devices, script_callbacks, sd_vae, sd_disable_initialization, errors
 from modules.paths import models_path
 from modules.sd_hijack_inpainting import do_inpainting_hijack, should_hijack_inpainting
 
@@ -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()
@@ -319,10 +331,21 @@ def load_model(checkpoint_info=None):
     if shared.cmd_opts.no_half:
         sd_config.model.params.unet_config.params.use_fp16 = False
 
+    timer = Timer()
+
+    try:
+        with sd_disable_initialization.DisableInitialization():
+            sd_model = instantiate_from_config(sd_config.model)
+    except Exception as e:
+        print('Failed to create model quickly; will retry using slow method.', file=sys.stderr)
         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:
@@ -337,7 +360,9 @@ 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
 
@@ -370,6 +395,8 @@ 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:
@@ -383,6 +410,8 @@ 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