Save Optimizer next to TI embedding

Also add check to load only .PT and .BIN files as embeddings. (since we add .optim files in the same directory)

modules/shared.py

@@ -355,7 +355,7 @@ options_templates.update(options_section(('system', "System"), {
 options_templates.update(options_section(('training', "Training"), {
     "unload_models_when_training": OptionInfo(False, "Move VAE and CLIP to RAM when training if possible. Saves VRAM."),
     "pin_memory": OptionInfo(False, "Turn on pin_memory for DataLoader. Makes training slightly faster but can increase memory usage."),
-    "save_optimizer_state": OptionInfo(False, "Saves Optimizer state as separate *.optim file. Training can be resumed with HN itself and matching optim file."),
+    "save_optimizer_state": OptionInfo(False, "Saves Optimizer state as separate *.optim file. Training of embedding or HN can be resumed with the matching optim file."),
     "dataset_filename_word_regex": OptionInfo("", "Filename word regex"),
     "dataset_filename_join_string": OptionInfo(" ", "Filename join string"),
     "training_image_repeats_per_epoch": OptionInfo(1, "Number of repeats for a single input image per epoch; used only for displaying epoch number", gr.Number, {"precision": 0}),

modules/textual_inversion/textual_inversion.py

@@ -28,6 +28,7 @@ class Embedding:
         self.cached_checksum = None
         self.sd_checkpoint = None
         self.sd_checkpoint_name = None
+        self.optimizer_state_dict = None
 
     def save(self, filename):
         embedding_data = {
@@ -41,6 +42,13 @@ class Embedding:
 
         torch.save(embedding_data, filename)
 
+        if shared.opts.save_optimizer_state and self.optimizer_state_dict is not None:
+            optimizer_saved_dict = {
+                'hash': self.checksum(),
+                'optimizer_state_dict': self.optimizer_state_dict,
+            }
+            torch.save(optimizer_saved_dict, filename + '.optim')
+
     def checksum(self):
         if self.cached_checksum is not None:
             return self.cached_checksum
@@ -95,9 +103,10 @@ class EmbeddingDatabase:
         self.expected_shape = self.get_expected_shape()
 
         def process_file(path, filename):
-            name = os.path.splitext(filename)[0]
+            name, ext = os.path.splitext(filename)
+            ext = ext.upper()
 
-            if os.path.splitext(filename.upper())[-1] in ['.PNG', '.WEBP', '.JXL', '.AVIF']:
+            if ext in ['.PNG', '.WEBP', '.JXL', '.AVIF']:
                 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'])
@@ -105,8 +114,10 @@ class EmbeddingDatabase:
                 else:
                     data = extract_image_data_embed(embed_image)
                     name = data.get('name', name)
-            else:
+            elif ext in ['.BIN', '.PT']:
                 data = torch.load(path, map_location="cpu")
+            else:
+                return
 
             # textual inversion embeddings
             if 'string_to_param' in data:
@@ -300,6 +311,20 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
 
     embedding.vec.requires_grad = True
     optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate, weight_decay=0.0)
+    if shared.opts.save_optimizer_state:
+        optimizer_state_dict = None
+        if os.path.exists(filename + '.optim'):
+            optimizer_saved_dict = torch.load(filename + '.optim', map_location='cpu')
+            if embedding.checksum() == optimizer_saved_dict.get('hash', None):
+                optimizer_state_dict = optimizer_saved_dict.get('optimizer_state_dict', None)
+    
+        if optimizer_state_dict is not None:
+            optimizer.load_state_dict(optimizer_state_dict)
+            print("Loaded existing optimizer from checkpoint")
+        else:
+            print("No saved optimizer exists in checkpoint")
+
+
     scaler = torch.cuda.amp.GradScaler()
 
     batch_size = ds.batch_size
@@ -366,9 +391,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
                     # Before saving, change name to match current checkpoint.
                     embedding_name_every = f'{embedding_name}-{steps_done}'
                     last_saved_file = os.path.join(embedding_dir, f'{embedding_name_every}.pt')
-                    #if shared.opts.save_optimizer_state:
+                    save_embedding(embedding, optimizer, checkpoint, embedding_name_every, last_saved_file, remove_cached_checksum=True)
-                        #embedding.optimizer_state_dict = optimizer.state_dict()
-                    save_embedding(embedding, checkpoint, embedding_name_every, last_saved_file, remove_cached_checksum=True)
                     embedding_yet_to_be_embedded = True
 
                 write_loss(log_directory, "textual_inversion_loss.csv", embedding.step, steps_per_epoch, {
@@ -458,7 +481,7 @@ Last saved image: {html.escape(last_saved_image)}<br/>
 </p>
 """
         filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')
-        save_embedding(embedding, checkpoint, embedding_name, filename, remove_cached_checksum=True)
+        save_embedding(embedding, optimizer, checkpoint, embedding_name, filename, remove_cached_checksum=True)
     except Exception:
         print(traceback.format_exc(), file=sys.stderr)
         pass
@@ -470,7 +493,7 @@ Last saved image: {html.escape(last_saved_image)}<br/>
 
     return embedding, filename
 
-def save_embedding(embedding, checkpoint, embedding_name, filename, remove_cached_checksum=True):
+def save_embedding(embedding, optimizer, checkpoint, embedding_name, filename, remove_cached_checksum=True):
     old_embedding_name = embedding.name
     old_sd_checkpoint = embedding.sd_checkpoint if hasattr(embedding, "sd_checkpoint") else None
     old_sd_checkpoint_name = embedding.sd_checkpoint_name if hasattr(embedding, "sd_checkpoint_name") else None
@@ -481,6 +504,7 @@ def save_embedding(embedding, checkpoint, embedding_name, filename, remove_cache
         if remove_cached_checksum:
             embedding.cached_checksum = None
         embedding.name = embedding_name
+        embedding.optimizer_state_dict = optimizer.state_dict()
         embedding.save(filename)
     except:
         embedding.sd_checkpoint = old_sd_checkpoint