From d682444ecc99319fbd2b142a12727501e2884ba7 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 11 Oct 2022 18:04:47 +0300
Subject: add option to select hypernetwork modules when creating

---
 modules/hypernetworks/hypernetwork.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

(limited to 'modules/hypernetworks/hypernetwork.py')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index aa701bda..b081f14e 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -42,7 +42,7 @@ class Hypernetwork:
     filename = None
     name = None
 
-    def __init__(self, name=None):
+    def __init__(self, name=None, enable_sizes=None):
         self.filename = None
         self.name = name
         self.layers = {}
@@ -50,7 +50,7 @@ class Hypernetwork:
         self.sd_checkpoint = None
         self.sd_checkpoint_name = None
 
-        for size in [320, 640, 768, 1280]:
+        for size in enable_sizes or [320, 640, 768, 1280]:
             self.layers[size] = (HypernetworkModule(size), HypernetworkModule(size))
 
     def weights(self):
-- 
cgit v1.2.3


From d4ea5f4d8631f778d11efcde397e4a5b8801d43b Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 11 Oct 2022 19:03:08 +0300
Subject: add an option to unload models during hypernetwork training to save
 VRAM

---
 modules/hypernetworks/hypernetwork.py          | 25 +++++++++++++++-------
 modules/hypernetworks/ui.py                    |  4 +++-
 modules/shared.py                              |  4 ++++
 modules/textual_inversion/dataset.py           | 29 ++++++++++++++++++--------
 modules/textual_inversion/textual_inversion.py |  2 +-
 5 files changed, 46 insertions(+), 18 deletions(-)

(limited to 'modules/hypernetworks/hypernetwork.py')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index b081f14e..4700e1ec 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -175,6 +175,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
     filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork_name}.pt')
 
     log_directory = os.path.join(log_directory, datetime.datetime.now().strftime("%Y-%m-%d"), hypernetwork_name)
+    unload = shared.opts.unload_models_when_training
 
     if save_hypernetwork_every > 0:
         hypernetwork_dir = os.path.join(log_directory, "hypernetworks")
@@ -188,11 +189,13 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
     else:
         images_dir = None
 
-    cond_model = shared.sd_model.cond_stage_model
-
     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=512, height=512, repeats=1, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file)
+        ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=512, height=512, repeats=1, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True)
+
+    if unload:
+        shared.sd_model.cond_stage_model.to(devices.cpu)
+        shared.sd_model.first_stage_model.to(devices.cpu)
 
     hypernetwork = shared.loaded_hypernetwork
     weights = hypernetwork.weights()
@@ -211,7 +214,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
         return hypernetwork, filename
 
     pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)
-    for i, (x, text) in pbar:
+    for i, (x, text, cond) in pbar:
         hypernetwork.step = i + ititial_step
 
         if hypernetwork.step > steps:
@@ -221,11 +224,11 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
             break
 
         with torch.autocast("cuda"):
-            c = cond_model([text])
-
+            cond = cond.to(devices.device)
             x = x.to(devices.device)
-            loss = shared.sd_model(x.unsqueeze(0), c)[0]
+            loss = shared.sd_model(x.unsqueeze(0), cond)[0]
             del x
+            del cond
 
             losses[hypernetwork.step % losses.shape[0]] = loss.item()
 
@@ -244,6 +247,10 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
 
             preview_text = text if preview_image_prompt == "" else preview_image_prompt
 
+            optimizer.zero_grad()
+            shared.sd_model.cond_stage_model.to(devices.device)
+            shared.sd_model.first_stage_model.to(devices.device)
+
             p = processing.StableDiffusionProcessingTxt2Img(
                 sd_model=shared.sd_model,
                 prompt=preview_text,
@@ -255,6 +262,10 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
             processed = processing.process_images(p)
             image = processed.images[0]
 
+            if unload:
+                shared.sd_model.cond_stage_model.to(devices.cpu)
+                shared.sd_model.first_stage_model.to(devices.cpu)
+
             shared.state.current_image = image
             image.save(last_saved_image)
 
diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py
index 3541a388..c67facbb 100644
--- a/modules/hypernetworks/ui.py
+++ b/modules/hypernetworks/ui.py
@@ -5,7 +5,7 @@ import gradio as gr
 
 import modules.textual_inversion.textual_inversion
 import modules.textual_inversion.preprocess
-from modules import sd_hijack, shared
+from modules import sd_hijack, shared, devices
 from modules.hypernetworks import hypernetwork
 
 
@@ -41,5 +41,7 @@ Hypernetwork saved to {html.escape(filename)}
         raise
     finally:
         shared.loaded_hypernetwork = initial_hypernetwork
+        shared.sd_model.cond_stage_model.to(devices.device)
+        shared.sd_model.first_stage_model.to(devices.device)
         sd_hijack.apply_optimizations()
 
diff --git a/modules/shared.py b/modules/shared.py
index 20b45f23..c1092ff7 100644
--- a/modules/shared.py
+++ b/modules/shared.py
@@ -228,6 +228,10 @@ options_templates.update(options_section(('system', "System"), {
     "multiple_tqdm": OptionInfo(True, "Add a second progress bar to the console that shows progress for an entire job."),
 }))
 
+options_templates.update(options_section(('training', "Training"), {
+    "unload_models_when_training": OptionInfo(False, "Unload VAE and CLIP form VRAM when training"),
+}))
+
 options_templates.update(options_section(('sd', "Stable Diffusion"), {
     "sd_model_checkpoint": OptionInfo(None, "Stable Diffusion checkpoint", gr.Dropdown, lambda: {"choices": modules.sd_models.checkpoint_tiles()}, show_on_main_page=True),
     "sd_hypernetwork": OptionInfo("None", "Stable Diffusion finetune hypernetwork", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}),
diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index 4d006366..f61f40d3 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -8,14 +8,14 @@ from torchvision import transforms
 
 import random
 import tqdm
-from modules import devices
+from modules import devices, shared
 import re
 
 re_tag = re.compile(r"[a-zA-Z][_\w\d()]+")
 
 
 class PersonalizedBase(Dataset):
-    def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None):
+    def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None, include_cond=False):
 
         self.placeholder_token = placeholder_token
 
@@ -32,6 +32,8 @@ class PersonalizedBase(Dataset):
 
         assert data_root, 'dataset directory not specified'
 
+        cond_model = shared.sd_model.cond_stage_model
+
         self.image_paths = [os.path.join(data_root, file_path) for file_path in os.listdir(data_root)]
         print("Preparing dataset...")
         for path in tqdm.tqdm(self.image_paths):
@@ -53,7 +55,13 @@ class PersonalizedBase(Dataset):
             init_latent = model.get_first_stage_encoding(model.encode_first_stage(torchdata.unsqueeze(dim=0))).squeeze()
             init_latent = init_latent.to(devices.cpu)
 
-            self.dataset.append((init_latent, filename_tokens))
+            if include_cond:
+                text = self.create_text(filename_tokens)
+                cond = cond_model([text]).to(devices.cpu)
+            else:
+                cond = None
+
+            self.dataset.append((init_latent, filename_tokens, cond))
 
         self.length = len(self.dataset) * repeats
 
@@ -64,6 +72,12 @@ class PersonalizedBase(Dataset):
     def shuffle(self):
         self.indexes = self.initial_indexes[torch.randperm(self.initial_indexes.shape[0])]
 
+    def create_text(self, filename_tokens):
+        text = random.choice(self.lines)
+        text = text.replace("[name]", self.placeholder_token)
+        text = text.replace("[filewords]", ' '.join(filename_tokens))
+        return text
+
     def __len__(self):
         return self.length
 
@@ -72,10 +86,7 @@ class PersonalizedBase(Dataset):
             self.shuffle()
 
         index = self.indexes[i % len(self.indexes)]
-        x, filename_tokens = self.dataset[index]
-
-        text = random.choice(self.lines)
-        text = text.replace("[name]", self.placeholder_token)
-        text = text.replace("[filewords]", ' '.join(filename_tokens))
+        x, filename_tokens, cond = self.dataset[index]
 
-        return x, text
+        text = self.create_text(filename_tokens)
+        return x, text, cond
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index bb05cdc6..35f4bd9e 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -201,7 +201,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
         return embedding, filename
 
     pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
-    for i, (x, text) in pbar:
+    for i, (x, text, _) in pbar:
         embedding.step = i + ititial_step
 
         if embedding.step > steps:
-- 
cgit v1.2.3


From 6a9ea5b41cf92cd9e980349bb5034439f4e7a58b Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 11 Oct 2022 19:22:30 +0300
Subject: prevent extra modules from being saved/loaded with hypernet

---
 modules/hypernetworks/hypernetwork.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

(limited to 'modules/hypernetworks/hypernetwork.py')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 4700e1ec..5608e799 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -50,7 +50,7 @@ class Hypernetwork:
         self.sd_checkpoint = None
         self.sd_checkpoint_name = None
 
-        for size in enable_sizes or [320, 640, 768, 1280]:
+        for size in enable_sizes or []:
             self.layers[size] = (HypernetworkModule(size), HypernetworkModule(size))
 
     def weights(self):
-- 
cgit v1.2.3


From d6fcc6b87bc00fcdecea276fe5b7c7945f7a8b14 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Tue, 11 Oct 2022 22:03:05 +0300
Subject: apply lr schedule to hypernets

---
 modules/hypernetworks/hypernetwork.py          | 19 ++++++++---
 modules/textual_inversion/learn_schedule.py    | 34 ++++++++++++++++++++
 modules/textual_inversion/textual_inversion.py | 44 +++-----------------------
 modules/ui.py                                  |  2 +-
 4 files changed, 54 insertions(+), 45 deletions(-)
 create mode 100644 modules/textual_inversion/learn_schedule.py

(limited to 'modules/hypernetworks/hypernetwork.py')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 5608e799..470659df 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -14,6 +14,7 @@ import torch
 from torch import einsum
 from einops import rearrange, repeat
 import modules.textual_inversion.dataset
+from modules.textual_inversion.learn_schedule import LearnSchedule
 
 
 class HypernetworkModule(torch.nn.Module):
@@ -202,8 +203,6 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
     for weight in weights:
         weight.requires_grad = True
 
-    optimizer = torch.optim.AdamW(weights, lr=learn_rate)
-
     losses = torch.zeros((32,))
 
     last_saved_file = "<none>"
@@ -213,12 +212,24 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
     if ititial_step > steps:
         return hypernetwork, filename
 
+    schedules = iter(LearnSchedule(learn_rate, steps, ititial_step))
+    (learn_rate, end_step) = next(schedules)
+    print(f'Training at rate of {learn_rate} until step {end_step}')
+
+    optimizer = torch.optim.AdamW(weights, lr=learn_rate)
+
     pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)
     for i, (x, text, cond) in pbar:
         hypernetwork.step = i + ititial_step
 
-        if hypernetwork.step > steps:
-            break
+        if hypernetwork.step > end_step:
+            try:
+                (learn_rate, end_step) = next(schedules)
+            except Exception:
+                break
+            tqdm.tqdm.write(f'Training at rate of {learn_rate} until step {end_step}')
+            for pg in optimizer.param_groups:
+                pg['lr'] = learn_rate
 
         if shared.state.interrupted:
             break
diff --git a/modules/textual_inversion/learn_schedule.py b/modules/textual_inversion/learn_schedule.py
new file mode 100644
index 00000000..db720271
--- /dev/null
+++ b/modules/textual_inversion/learn_schedule.py
@@ -0,0 +1,34 @@
+
+class LearnSchedule:
+    def __init__(self, learn_rate, max_steps, cur_step=0):
+        pairs = learn_rate.split(',')
+        self.rates = []
+        self.it = 0
+        self.maxit = 0
+        for i, pair in enumerate(pairs):
+            tmp = pair.split(':')
+            if len(tmp) == 2:
+                step = int(tmp[1])
+                if step > cur_step:
+                    self.rates.append((float(tmp[0]), min(step, max_steps)))
+                    self.maxit += 1
+                    if step > max_steps:
+                        return
+                elif step == -1:
+                    self.rates.append((float(tmp[0]), max_steps))
+                    self.maxit += 1
+                    return
+            else:
+                self.rates.append((float(tmp[0]), max_steps))
+                self.maxit += 1
+                return
+
+    def __iter__(self):
+        return self
+
+    def __next__(self):
+        if self.it < self.maxit:
+            self.it += 1
+            return self.rates[self.it - 1]
+        else:
+            raise StopIteration
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 47a27faf..7717837d 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -10,6 +10,7 @@ import datetime
 
 from modules import shared, devices, sd_hijack, processing, sd_models
 import modules.textual_inversion.dataset
+from modules.textual_inversion.learn_schedule import LearnSchedule
 
 
 class Embedding:
@@ -198,11 +199,8 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
     if ititial_step > steps:
         return embedding, filename
 
-    tr_img_len = len([os.path.join(data_root, file_path) for file_path in os.listdir(data_root)])
-    epoch_len = (tr_img_len * num_repeats) + tr_img_len
-
-    scheduleIter = iter(LearnSchedule(learn_rate, steps, ititial_step))
-    (learn_rate, end_step) = next(scheduleIter)
+    schedules = iter(LearnSchedule(learn_rate, steps, ititial_step))
+    (learn_rate, end_step) = next(schedules)
     print(f'Training at rate of {learn_rate} until step {end_step}')
 
     optimizer = torch.optim.AdamW([embedding.vec], lr=learn_rate)
@@ -213,7 +211,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
 
         if embedding.step > end_step:
             try:
-                (learn_rate, end_step) = next(scheduleIter)
+                (learn_rate, end_step) = next(schedules)
             except:
                 break
             tqdm.tqdm.write(f'Training at rate of {learn_rate} until step {end_step}')
@@ -288,37 +286,3 @@ Last saved image: {html.escape(last_saved_image)}<br/>
     embedding.save(filename)
 
     return embedding, filename
-
-class LearnSchedule:
-    def __init__(self, learn_rate, max_steps, cur_step=0):
-        pairs = learn_rate.split(',')
-        self.rates = []
-        self.it = 0
-        self.maxit = 0
-        for i, pair in enumerate(pairs):
-            tmp = pair.split(':')
-            if len(tmp) == 2:
-                step = int(tmp[1])
-                if step > cur_step:
-                    self.rates.append((float(tmp[0]), min(step, max_steps)))
-                    self.maxit += 1
-                    if step > max_steps:
-                        return
-                elif step == -1:
-                    self.rates.append((float(tmp[0]), max_steps))
-                    self.maxit += 1
-                    return
-            else:
-                self.rates.append((float(tmp[0]), max_steps))
-                self.maxit += 1
-                return
-
-    def __iter__(self):
-        return self
-
-    def __next__(self):
-        if self.it < self.maxit:
-            self.it += 1
-            return self.rates[self.it - 1]
-        else:
-            raise StopIteration
diff --git a/modules/ui.py b/modules/ui.py
index 2b688e32..1204eef7 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1070,7 +1070,7 @@ def create_ui(wrap_gradio_gpu_call):
                     gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding; must specify a directory with a set of 1:1 ratio images</p>")
                     train_embedding_name = gr.Dropdown(label='Embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
                     train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', choices=[x for x in shared.hypernetworks.keys()])
-                    learn_rate = gr.Textbox(label='Learning rate', placeholder="Learning rate", value = "5.0e-03")
+                    learn_rate = gr.Textbox(label='Learning rate', placeholder="Learning rate", value="0.005")
                     dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
                     log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
                     template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"))
-- 
cgit v1.2.3


From 2d006ce16cd95d587533656c3ac4991495e96f23 Mon Sep 17 00:00:00 2001
From: Milly <milly.ca@gmail.com>
Date: Mon, 10 Oct 2022 00:56:36 +0900
Subject: xy_grid: Find hypernetwork by closest name

---
 modules/hypernetworks/hypernetwork.py | 11 +++++++++++
 scripts/xy_grid.py                    |  6 +++++-
 2 files changed, 16 insertions(+), 1 deletion(-)

(limited to 'modules/hypernetworks/hypernetwork.py')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 470659df..8f2192e2 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -120,6 +120,17 @@ def load_hypernetwork(filename):
         shared.loaded_hypernetwork = None
 
 
+def find_closest_hypernetwork_name(search: str):
+    if not search:
+        return None
+    search = search.lower()
+    applicable = [name for name in shared.hypernetworks if search in name.lower()]
+    if not applicable:
+        return None
+    applicable = sorted(applicable, key=lambda name: len(name))
+    return applicable[0]
+
+
 def apply_hypernetwork(hypernetwork, context, layer=None):
     hypernetwork_layers = (hypernetwork.layers if hypernetwork is not None else {}).get(context.shape[2], None)
 
diff --git a/scripts/xy_grid.py b/scripts/xy_grid.py
index ef431105..6f4217ec 100644
--- a/scripts/xy_grid.py
+++ b/scripts/xy_grid.py
@@ -84,7 +84,11 @@ def apply_checkpoint(p, x, xs):
 
 
 def apply_hypernetwork(p, x, xs):
-    hypernetwork.load_hypernetwork(x)
+    if x.lower() in ["", "none"]:
+        name = None
+    else:
+        name = hypernetwork.find_closest_hypernetwork_name(x)
+    hypernetwork.load_hypernetwork(name)
 
 
 def apply_clip_skip(p, x, xs):
-- 
cgit v1.2.3


From ee015a1af66a94a75c914659fa0d321e702a0a87 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 12 Oct 2022 11:05:57 +0300
Subject: change textual inversion tab to train remake train interface to use
 tabs

---
 modules/hypernetworks/hypernetwork.py |  2 +-
 modules/ui.py                         | 22 +++++++++-------------
 2 files changed, 10 insertions(+), 14 deletions(-)

(limited to 'modules/hypernetworks/hypernetwork.py')

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 8f2192e2..8314450a 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -175,7 +175,7 @@ def attention_CrossAttention_forward(self, x, context=None, mask=None):
 
 
 def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, template_file, preview_image_prompt):
-    assert hypernetwork_name, 'embedding not selected'
+    assert hypernetwork_name, 'hypernetwork not selected'
 
     path = shared.hypernetworks.get(hypernetwork_name, None)
     shared.loaded_hypernetwork = Hypernetwork()
diff --git a/modules/ui.py b/modules/ui.py
index 4bfdd275..86a2da6c 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1035,14 +1035,14 @@ def create_ui(wrap_gradio_gpu_call):
 
     sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings()
 
-    with gr.Blocks() as textual_inversion_interface:
+    with gr.Blocks() as train_interface:
         with gr.Row().style(equal_height=False):
-            with gr.Column():
-                with gr.Group():
-                    gr.HTML(value="<p style='margin-bottom: 0.7em'>See <b><a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\">wiki</a></b> for detailed explanation.</p>")
+            gr.HTML(value="<p style='margin-bottom: 0.7em'>See <b><a href=\"https://github.com/AUTOMATIC1111/stable-diffusion-webui/wiki/Textual-Inversion\">wiki</a></b> for detailed explanation.</p>")
 
-                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Create a new embedding</p>")
+        with gr.Row().style(equal_height=False):
+            with gr.Tabs(elem_id="train_tabs"):
 
+                with gr.Tab(label="Create embedding"):
                     new_embedding_name = gr.Textbox(label="Name")
                     initialization_text = gr.Textbox(label="Initialization text", value="*")
                     nvpt = gr.Slider(label="Number of vectors per token", minimum=1, maximum=75, step=1, value=1)
@@ -1054,9 +1054,7 @@ def create_ui(wrap_gradio_gpu_call):
                         with gr.Column():
                             create_embedding = gr.Button(value="Create embedding", variant='primary')
 
-                with gr.Group():
-                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Create a new hypernetwork</p>")
-
+                with gr.Tab(label="Create hypernetwork"):
                     new_hypernetwork_name = gr.Textbox(label="Name")
                     new_hypernetwork_sizes = gr.CheckboxGroup(label="Modules", value=["768", "320", "640", "1280"], choices=["768", "320", "640", "1280"])
 
@@ -1067,9 +1065,7 @@ def create_ui(wrap_gradio_gpu_call):
                         with gr.Column():
                             create_hypernetwork = gr.Button(value="Create hypernetwork", variant='primary')
 
-                with gr.Group():
-                    gr.HTML(value="<p style='margin-bottom: 0.7em'>Preprocess images</p>")
-
+                with gr.Tab(label="Preprocess images"):
                     process_src = gr.Textbox(label='Source directory')
                     process_dst = gr.Textbox(label='Destination directory')
                     process_width = gr.Slider(minimum=64, maximum=2048, step=64, label="Width", value=512)
@@ -1091,7 +1087,7 @@ def create_ui(wrap_gradio_gpu_call):
                         with gr.Column():
                             run_preprocess = gr.Button(value="Preprocess", variant='primary')
 
-                with gr.Group():
+                with gr.Tab(label="Train"):
                     gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding; must specify a directory with a set of 1:1 ratio images</p>")
                     train_embedding_name = gr.Dropdown(label='Embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
                     train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', choices=[x for x in shared.hypernetworks.keys()])
@@ -1388,7 +1384,7 @@ Requested path was: {f}
         (extras_interface, "Extras", "extras"),
         (pnginfo_interface, "PNG Info", "pnginfo"),
         (modelmerger_interface, "Checkpoint Merger", "modelmerger"),
-        (textual_inversion_interface, "Textual inversion", "ti"),
+        (train_interface, "Train", "ti"),
         (settings_interface, "Settings", "settings"),
     ]
 
-- 
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