Merge branch 'master' into inpaint_textual_inversion

1 files changed, 244 insertions, 160 deletions

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 2630c7c9..5421a758 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -10,7 +10,7 @@ import csv
 

 from PIL import Image, PngImagePlugin

 

-from modules import shared, devices, sd_hijack, processing, sd_models, images

+from modules import shared, devices, sd_hijack, processing, sd_models, images, sd_samplers

 import modules.textual_inversion.dataset

 from modules.textual_inversion.learn_schedule import LearnRateScheduler

 

@@ -23,9 +23,12 @@ class Embedding:
         self.vec = vec

         self.name = name

         self.step = step

+        self.shape = None

+        self.vectors = 0

         self.cached_checksum = None

         self.sd_checkpoint = None

         self.sd_checkpoint_name = None

+        self.optimizer_state_dict = None

 

     def save(self, filename):

         embedding_data = {

@@ -39,6 +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

@@ -57,14 +67,17 @@ class EmbeddingDatabase:
     def __init__(self, embeddings_dir):

         self.ids_lookup = {}

         self.word_embeddings = {}

+        self.skipped_embeddings = {}

         self.dir_mtime = None

         self.embeddings_dir = embeddings_dir

+        self.expected_shape = -1

 

     def register_embedding(self, embedding, model):

 

         self.word_embeddings[embedding.name] = embedding

 

-        ids = model.cond_stage_model.tokenizer([embedding.name], add_special_tokens=False)['input_ids'][0]

+        # TODO changing between clip and open clip changes tokenization, which will cause embeddings to stop working

+        ids = model.cond_stage_model.tokenize([embedding.name])[0]

 

         first_id = ids[0]

         if first_id not in self.ids_lookup:

@@ -74,21 +87,26 @@ class EmbeddingDatabase:
 

         return embedding

 

-    def load_textual_inversion_embeddings(self):

+    def get_expected_shape(self):

+        vec = shared.sd_model.cond_stage_model.encode_embedding_init_text(",", 1)

+        return vec.shape[1]

+

+    def load_textual_inversion_embeddings(self, force_reload = False):

         mt = os.path.getmtime(self.embeddings_dir)

-        if self.dir_mtime is not None and mt <= self.dir_mtime:

+        if not force_reload and self.dir_mtime is not None and mt <= self.dir_mtime:

             return

 

         self.dir_mtime = mt

         self.ids_lookup.clear()

         self.word_embeddings.clear()

+        self.skipped_embeddings.clear()

+        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()

 

-            data = []

-

-            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'])

@@ -96,8 +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:

@@ -121,7 +141,13 @@ class EmbeddingDatabase:
             embedding.step = data.get('step', None)

             embedding.sd_checkpoint = data.get('sd_checkpoint', None)

             embedding.sd_checkpoint_name = data.get('sd_checkpoint_name', None)

-            self.register_embedding(embedding, shared.sd_model)

+            embedding.vectors = vec.shape[0]

+            embedding.shape = vec.shape[-1]

+

+            if self.expected_shape == -1 or self.expected_shape == embedding.shape:

+                self.register_embedding(embedding, shared.sd_model)

+            else:

+                self.skipped_embeddings[name] = embedding

 

         for fn in os.listdir(self.embeddings_dir):

             try:

@@ -132,12 +158,13 @@ class EmbeddingDatabase:
 

                 process_file(fullfn, fn)

             except Exception:

-                print(f"Error loading emedding {fn}:", file=sys.stderr)

+                print(f"Error loading embedding {fn}:", file=sys.stderr)

                 print(traceback.format_exc(), file=sys.stderr)

                 continue

 

-        print(f"Loaded a total of {len(self.word_embeddings)} textual inversion embeddings.")

-        print("Embeddings:", ', '.join(self.word_embeddings.keys()))

+        print(f"Textual inversion embeddings loaded({len(self.word_embeddings)}): {', '.join(self.word_embeddings.keys())}")

+        if len(self.skipped_embeddings) > 0:

+            print(f"Textual inversion embeddings skipped({len(self.skipped_embeddings)}): {', '.join(self.skipped_embeddings.keys())}")

 

     def find_embedding_at_position(self, tokens, offset):

         token = tokens[offset]

@@ -155,13 +182,11 @@ class EmbeddingDatabase:
 

 def create_embedding(name, num_vectors_per_token, overwrite_old, init_text='*'):

     cond_model = shared.sd_model.cond_stage_model

-    embedding_layer = cond_model.wrapped.transformer.text_model.embeddings

 

     with devices.autocast():

         cond_model([""])  # will send cond model to GPU if lowvram/medvram is active

 

-    ids = cond_model.tokenizer(init_text, max_length=num_vectors_per_token, return_tensors="pt", add_special_tokens=False)["input_ids"]

-    embedded = embedding_layer.token_embedding.wrapped(ids.to(devices.device)).squeeze(0)

+    embedded = cond_model.encode_embedding_init_text(init_text, num_vectors_per_token)

     vec = torch.zeros((num_vectors_per_token, embedded.shape[1]), device=devices.device)

 

     for i in range(num_vectors_per_token):

@@ -184,7 +209,7 @@ def write_loss(log_directory, filename, step, epoch_len, values):
     if shared.opts.training_write_csv_every == 0:

         return

 

-    if (step + 1) % shared.opts.training_write_csv_every != 0:

+    if step % shared.opts.training_write_csv_every != 0:

         return

     write_csv_header = False if os.path.exists(os.path.join(log_directory, filename)) else True

 

@@ -194,21 +219,23 @@ def write_loss(log_directory, filename, step, epoch_len, values):
         if write_csv_header:

             csv_writer.writeheader()

 

-        epoch = step // epoch_len

-        epoch_step = step % epoch_len 

+        epoch = (step - 1) // epoch_len

+        epoch_step = (step - 1) % epoch_len

 

         csv_writer.writerow({

-            "step": step + 1,

+            "step": step,

             "epoch": epoch,

-            "epoch_step": epoch_step + 1,

+            "epoch_step": epoch_step,

             **values,

         })

 

-def validate_train_inputs(model_name, learn_rate, batch_size, data_root, template_file, steps, save_model_every, create_image_every, log_directory, name="embedding"):

+def validate_train_inputs(model_name, learn_rate, batch_size, gradient_step, data_root, template_file, steps, save_model_every, create_image_every, log_directory, name="embedding"):

     assert model_name, f"{name} not selected"

     assert learn_rate, "Learning rate is empty or 0"

     assert isinstance(batch_size, int), "Batch size must be integer"

     assert batch_size > 0, "Batch size must be positive"

+    assert isinstance(gradient_step, int), "Gradient accumulation step must be integer"

+    assert gradient_step > 0, "Gradient accumulation step must be positive"

     assert data_root, "Dataset directory is empty"

     assert os.path.isdir(data_root), "Dataset directory doesn't exist"

     assert os.listdir(data_root), "Dataset directory is empty"

@@ -244,11 +271,12 @@ def create_dummy_mask(x, width=None, height=None):
     return image_conditioning

 

 

-def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):

+def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_root, log_directory, training_width, training_height, steps, shuffle_tags, tag_drop_out, latent_sampling_method, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):

     save_embedding_every = save_embedding_every or 0

     create_image_every = create_image_every or 0

-    validate_train_inputs(embedding_name, learn_rate, batch_size, data_root, template_file, steps, save_embedding_every, create_image_every, log_directory, name="embedding")

+    validate_train_inputs(embedding_name, learn_rate, batch_size, gradient_step, data_root, template_file, steps, save_embedding_every, create_image_every, log_directory, name="embedding")

 

+    shared.state.job = "train-embedding"

     shared.state.textinfo = "Initializing textual inversion training..."

     shared.state.job_count = steps

 

@@ -275,31 +303,58 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
     else:

         images_embeds_dir = None

 

-    cond_model = shared.sd_model.cond_stage_model

-

     hijack = sd_hijack.model_hijack

 

     embedding = hijack.embedding_db.word_embeddings[embedding_name]

     checkpoint = sd_models.select_checkpoint()

 

-    ititial_step = embedding.step or 0

-    if ititial_step >= steps:

-        shared.state.textinfo = f"Model has already been trained beyond specified max steps"

+    initial_step = embedding.step or 0

+    if initial_step >= steps:

+        shared.state.textinfo = "Model has already been trained beyond specified max steps"

         return embedding, filename

-

-    scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)

+    scheduler = LearnRateScheduler(learn_rate, steps, initial_step)

 

     # dataset loading may take a while, so input validations and early returns should be done before this

     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."

-    with torch.autocast("cuda"):

-        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file, batch_size=batch_size)

+    old_parallel_processing_allowed = shared.parallel_processing_allowed

+

+    pin_memory = shared.opts.pin_memory

+

+    ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=training_width, height=training_height, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=embedding_name, model=shared.sd_model, cond_model=shared.sd_model.cond_stage_model, device=devices.device, template_file=template_file, batch_size=batch_size, gradient_step=gradient_step, shuffle_tags=shuffle_tags, tag_drop_out=tag_drop_out, latent_sampling_method=latent_sampling_method)

+

+    latent_sampling_method = ds.latent_sampling_method

+

+    dl = modules.textual_inversion.dataset.PersonalizedDataLoader(ds, latent_sampling_method=latent_sampling_method, batch_size=ds.batch_size, pin_memory=pin_memory)

+

     if unload:

+        shared.parallel_processing_allowed = False

         shared.sd_model.first_stage_model.to(devices.cpu)

 

     embedding.vec.requires_grad = True

-    optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate)

-

-    losses = torch.zeros((32,))

+    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

+    gradient_step = ds.gradient_step

+    # n steps = batch_size * gradient_step * n image processed

+    steps_per_epoch = len(ds) // batch_size // gradient_step

+    max_steps_per_epoch = len(ds) // batch_size - (len(ds) // batch_size) % gradient_step

+    loss_step = 0

+    _loss_step = 0 #internal

 

     last_saved_file = "<none>"

     last_saved_image = "<none>"

@@ -307,138 +362,166 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc
     embedding_yet_to_be_embedded = False

 

     img_c = None

-    pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)

-    for i, entries in pbar:

-        embedding.step = i + ititial_step

-

-        scheduler.apply(optimizer, embedding.step)

-        if scheduler.finished:

-            break

-

-        if shared.state.interrupted:

-            break

-

-        with torch.autocast("cuda"):

-            c = cond_model([entry.cond_text for entry in entries])

-            if img_c is None:

-                img_c = create_dummy_mask(c, training_width, training_height)

-

-            x = torch.stack([entry.latent for entry in entries]).to(devices.device)

-            cond = {"c_concat": [img_c], "c_crossattn": [c]}

-            loss = shared.sd_model(x, cond)[0]

-            del x

-

-            losses[embedding.step % losses.shape[0]] = loss.item()

-

-            optimizer.zero_grad()

-            loss.backward()

-            optimizer.step()

-

-        steps_done = embedding.step + 1

-

-        epoch_num = embedding.step // len(ds)

-        epoch_step = embedding.step % len(ds)

-

-        pbar.set_description(f"[Epoch {epoch_num}: {epoch_step+1}/{len(ds)}]loss: {losses.mean():.7f}")

-

-        if embedding_dir is not None and steps_done % save_embedding_every == 0:

-            # 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')

-            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, len(ds), {

-            "loss": f"{losses.mean():.7f}",

-            "learn_rate": scheduler.learn_rate

-        })

-

-        if images_dir is not None and steps_done % create_image_every == 0:

-            forced_filename = f'{embedding_name}-{steps_done}'

-            last_saved_image = os.path.join(images_dir, forced_filename)

-

-            shared.sd_model.first_stage_model.to(devices.device)

-

-            p = processing.StableDiffusionProcessingTxt2Img(

-                sd_model=shared.sd_model,

-                do_not_save_grid=True,

-                do_not_save_samples=True,

-                do_not_reload_embeddings=True,

-            )

-

-            if preview_from_txt2img:

-                p.prompt = preview_prompt

-                p.negative_prompt = preview_negative_prompt

-                p.steps = preview_steps

-                p.sampler_index = preview_sampler_index

-                p.cfg_scale = preview_cfg_scale

-                p.seed = preview_seed

-                p.width = preview_width

-                p.height = preview_height

-            else:

-                p.prompt = entries[0].cond_text

-                p.steps = 20

-                p.width = training_width

-                p.height = training_height

-

-            preview_text = p.prompt

-

-            processed = processing.process_images(p)

-            image = processed.images[0]

-

-            if unload:

-                shared.sd_model.first_stage_model.to(devices.cpu)

-

-            shared.state.current_image = image

-

-            if save_image_with_stored_embedding and os.path.exists(last_saved_file) and embedding_yet_to_be_embedded:

-

-                last_saved_image_chunks = os.path.join(images_embeds_dir, f'{embedding_name}-{steps_done}.png')

-

-                info = PngImagePlugin.PngInfo()

-                data = torch.load(last_saved_file)

-                info.add_text("sd-ti-embedding", embedding_to_b64(data))

-

-                title = "<{}>".format(data.get('name', '???'))

-

-                try:

-                    vectorSize = list(data['string_to_param'].values())[0].shape[0]

-                except Exception as e:

-                    vectorSize = '?'

-

-                checkpoint = sd_models.select_checkpoint()

-                footer_left = checkpoint.model_name

-                footer_mid = '[{}]'.format(checkpoint.hash)

-                footer_right = '{}v {}s'.format(vectorSize, steps_done)

-

-                captioned_image = caption_image_overlay(image, title, footer_left, footer_mid, footer_right)

-                captioned_image = insert_image_data_embed(captioned_image, data)

-

-                captioned_image.save(last_saved_image_chunks, "PNG", pnginfo=info)

-                embedding_yet_to_be_embedded = False

-

-            last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False)

-            last_saved_image += f", prompt: {preview_text}"

-

-        shared.state.job_no = embedding.step

-

-        shared.state.textinfo = f"""

+    pbar = tqdm.tqdm(total=steps - initial_step)

+    try:

+        for i in range((steps-initial_step) * gradient_step):

+            if scheduler.finished:

+                break

+            if shared.state.interrupted:

+                break

+            for j, batch in enumerate(dl):

+                # works as a drop_last=True for gradient accumulation

+                if j == max_steps_per_epoch:

+                    break

+                scheduler.apply(optimizer, embedding.step)

+                if scheduler.finished:

+                    break

+                if shared.state.interrupted:

+                    break

+

+                with devices.autocast():

+                    # c = stack_conds(batch.cond).to(devices.device)

+                    # mask = torch.tensor(batch.emb_index).to(devices.device, non_blocking=pin_memory)

+                    # print(mask)

+                    # c[:, 1:1+embedding.vec.shape[0]] = embedding.vec.to(devices.device, non_blocking=pin_memory)

+                    

+                    

+                    if img_c is None:

+                        img_c = create_dummy_mask(c, training_width, training_height)

+                    

+                    x = batch.latent_sample.to(devices.device, non_blocking=pin_memory)

+                    c = shared.sd_model.cond_stage_model(batch.cond_text)

+                    cond = {"c_concat": [img_c], "c_crossattn": [c]}

+                    loss = shared.sd_model(x, cond)[0] / gradient_step

+                    del x

+

+                    _loss_step += loss.item()

+                scaler.scale(loss).backward()

+

+                # go back until we reach gradient accumulation steps

+                if (j + 1) % gradient_step != 0:

+                    continue

+                scaler.step(optimizer)

+                scaler.update()

+                embedding.step += 1

+                pbar.update()

+                optimizer.zero_grad(set_to_none=True)

+                loss_step = _loss_step

+                _loss_step = 0

+

+                steps_done = embedding.step + 1

+

+                epoch_num = embedding.step // steps_per_epoch

+                epoch_step = embedding.step % steps_per_epoch

+

+                pbar.set_description(f"[Epoch {epoch_num}: {epoch_step+1}/{steps_per_epoch}]loss: {loss_step:.7f}")

+                if embedding_dir is not None and steps_done % save_embedding_every == 0:

+                    # 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')

+                    save_embedding(embedding, optimizer, 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, {

+                    "loss": f"{loss_step:.7f}",

+                    "learn_rate": scheduler.learn_rate

+                })

+

+                if images_dir is not None and steps_done % create_image_every == 0:

+                    forced_filename = f'{embedding_name}-{steps_done}'

+                    last_saved_image = os.path.join(images_dir, forced_filename)

+

+                    shared.sd_model.first_stage_model.to(devices.device)

+

+                    p = processing.StableDiffusionProcessingTxt2Img(

+                        sd_model=shared.sd_model,

+                        do_not_save_grid=True,

+                        do_not_save_samples=True,

+                        do_not_reload_embeddings=True,

+                    )

+

+                    if preview_from_txt2img:

+                        p.prompt = preview_prompt

+                        p.negative_prompt = preview_negative_prompt

+                        p.steps = preview_steps

+                        p.sampler_name = sd_samplers.samplers[preview_sampler_index].name

+                        p.cfg_scale = preview_cfg_scale

+                        p.seed = preview_seed

+                        p.width = preview_width

+                        p.height = preview_height

+                    else:

+                        p.prompt = batch.cond_text[0]

+                        p.steps = 20

+                        p.width = training_width

+                        p.height = training_height

+

+                    preview_text = p.prompt

+

+                    processed = processing.process_images(p)

+                    image = processed.images[0] if len(processed.images) > 0 else None

+

+                    if unload:

+                        shared.sd_model.first_stage_model.to(devices.cpu)

+

+                    if image is not None:

+                        shared.state.current_image = image

+                        last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False)

+                        last_saved_image += f", prompt: {preview_text}"

+

+                    if save_image_with_stored_embedding and os.path.exists(last_saved_file) and embedding_yet_to_be_embedded:

+

+                        last_saved_image_chunks = os.path.join(images_embeds_dir, f'{embedding_name}-{steps_done}.png')

+

+                        info = PngImagePlugin.PngInfo()

+                        data = torch.load(last_saved_file)

+                        info.add_text("sd-ti-embedding", embedding_to_b64(data))

+

+                        title = "<{}>".format(data.get('name', '???'))

+

+                        try:

+                            vectorSize = list(data['string_to_param'].values())[0].shape[0]

+                        except Exception as e:

+                            vectorSize = '?'

+

+                        checkpoint = sd_models.select_checkpoint()

+                        footer_left = checkpoint.model_name

+                        footer_mid = '[{}]'.format(checkpoint.hash)

+                        footer_right = '{}v {}s'.format(vectorSize, steps_done)

+

+                        captioned_image = caption_image_overlay(image, title, footer_left, footer_mid, footer_right)

+                        captioned_image = insert_image_data_embed(captioned_image, data)

+

+                        captioned_image.save(last_saved_image_chunks, "PNG", pnginfo=info)

+                        embedding_yet_to_be_embedded = False

+

+                    last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False)

+                    last_saved_image += f", prompt: {preview_text}"

+

+                shared.state.job_no = embedding.step

+

+                shared.state.textinfo = f"""

 <p>

-Loss: {losses.mean():.7f}<br/>

-Step: {embedding.step}<br/>

-Last prompt: {html.escape(entries[0].cond_text)}<br/>

+Loss: {loss_step:.7f}<br/>

+Step: {steps_done}<br/>

+Last prompt: {html.escape(batch.cond_text[0])}<br/>

 Last saved embedding: {html.escape(last_saved_file)}<br/>

 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)

-    shared.sd_model.first_stage_model.to(devices.device)

+        filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')

+        save_embedding(embedding, optimizer, checkpoint, embedding_name, filename, remove_cached_checksum=True)

+    except Exception:

+        print(traceback.format_exc(), file=sys.stderr)

+        pass

+    finally:

+        pbar.leave = False

+        pbar.close()

+        shared.sd_model.first_stage_model.to(devices.device)

+        shared.parallel_processing_allowed = old_parallel_processing_allowed

 

     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

@@ -449,6 +532,7 @@ 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