image embedding data cache (#16556)

modules/shared_options.py

@@ -291,6 +291,7 @@
     "textual_inversion_print_at_load": OptionInfo(False, "Print a list of Textual Inversion embeddings when loading model"),
     "textual_inversion_add_hashes_to_infotext": OptionInfo(True, "Add Textual Inversion hashes to infotext"),
     "sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, lambda: {"choices": ["None", *shared.hypernetworks]}, refresh=shared_items.reload_hypernetworks),
+    "textual_inversion_image_embedding_data_cache": OptionInfo(False, 'Cache the data of image embeddings').info('potentially increase TI load time at the cost some disk space'),
 }))
 
 options_templates.update(options_section(('ui_prompt_editing', "Prompt editing", "ui"), {

modules/textual_inversion/textual_inversion.py

@@ -12,7 +12,7 @@
 import numpy as np
 from PIL import Image, PngImagePlugin
 
-from modules import shared, devices, sd_hijack, sd_models, images, sd_samplers, sd_hijack_checkpoint, errors, hashes
+from modules import shared, devices, sd_hijack, sd_models, images, sd_samplers, sd_hijack_checkpoint, errors, hashes, cache
 import modules.textual_inversion.dataset
 from modules.textual_inversion.learn_schedule import LearnRateScheduler
 
@@ -116,6 +116,7 @@ def __init__(self):
         self.expected_shape = -1
         self.embedding_dirs = {}
         self.previously_displayed_embeddings = ()
+        self.image_embedding_cache = cache.cache('image-embedding')
 
     def add_embedding_dir(self, path):
         self.embedding_dirs[path] = DirWithTextualInversionEmbeddings(path)
@@ -154,6 +155,31 @@ def get_expected_shape(self):
         vec = shared.sd_model.cond_stage_model.encode_embedding_init_text(",", 1)
         return vec.shape[1]
 
+    def read_embedding_from_image(self, path, name):
+        try:
+            ondisk_mtime = os.path.getmtime(path)
+
+            if (cache_embedding := self.image_embedding_cache.get(path)) and ondisk_mtime == cache_embedding.get('mtime', 0):
+                # cache will only be used if the file has not been modified time matches
+                return cache_embedding.get('data', None), cache_embedding.get('name', None)
+
+            embed_image = Image.open(path)
+            if hasattr(embed_image, 'text') and 'sd-ti-embedding' in embed_image.text:
+                data = embedding_from_b64(embed_image.text['sd-ti-embedding'])
+                name = data.get('name', name)
+            elif data := extract_image_data_embed(embed_image):
+                name = data.get('name', name)
+
+            if data is None or shared.opts.textual_inversion_image_embedding_data_cache:
+                # data of image embeddings only will be cached if the option textual_inversion_image_embedding_data_cache is enabled
+                # results of images that are not embeddings will allways be cached to reduce unnecessary future disk reads
+                self.image_embedding_cache[path] = {'data': data, 'name': None if data is None else name, 'mtime': ondisk_mtime}
+
+            return data, name
+        except Exception:
+            errors.report(f"Error loading embedding {path}", exc_info=True)
+        return None, None
+
     def load_from_file(self, path, filename):
         name, ext = os.path.splitext(filename)
         ext = ext.upper()
@@ -163,17 +189,10 @@ def load_from_file(self, path, filename):
             if second_ext.upper() == '.PREVIEW':
                 return
 
-            embed_image = Image.open(path)
-            if hasattr(embed_image, 'text') and 'sd-ti-embedding' in embed_image.text:
-                data = embedding_from_b64(embed_image.text['sd-ti-embedding'])
-                name = data.get('name', name)
-            else:
-                data = extract_image_data_embed(embed_image)
-                if data:
-                    name = data.get('name', name)
-                else:
-                    # if data is None, means this is not an embedding, just a preview image
-                    return
+            data, name = self.read_embedding_from_image(path, name)
+            if data is None:
+                return
+
         elif ext in ['.BIN', '.PT']:
             data = torch.load(path, map_location="cpu")
         elif ext in ['.SAFETENSORS']:
@@ -191,7 +210,6 @@ def load_from_file(self, path, filename):
         else:
             print(f"Unable to load Textual inversion embedding due to data issue: '{name}'.")
 
-
     def load_from_dir(self, embdir):
         if not os.path.isdir(embdir.path):
             return