diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index 7c44ea5be..acc4ce597 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -15,13 +15,13 @@ re_tag = re.compile(r"[a-zA-Z][_\w\d()]+")
class PersonalizedBase(Dataset):
- def __init__(self, data_root, size=None, repeats=100, flip_p=0.5, placeholder_token="*", width=512, height=512, model=None, device=None, template_file=None):
+ def __init__(self, data_root, size, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None):
self.placeholder_token = placeholder_token
self.size = size
- self.width = width
- self.height = height
+ self.width = size
+ self.height = size
self.flip = transforms.RandomHorizontalFlip(p=flip_p)
self.dataset = []
diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index f1c002a2b..b3de6fd7e 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -7,8 +7,8 @@ import tqdm
from modules import shared, images
-def preprocess(process_src, process_dst, process_flip, process_split, process_caption):
- size = 512
+def preprocess(process_src, process_dst, process_size, process_flip, process_split, process_caption):
+ size = process_size
src = os.path.abspath(process_src)
dst = os.path.abspath(process_dst)
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index cd9f34984..e34dc2e81 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -6,6 +6,7 @@ import torch
import tqdm
import html
import datetime
+import math
from modules import shared, devices, sd_hijack, processing, sd_models
@@ -156,7 +157,7 @@ def create_embedding(name, num_vectors_per_token, init_text='*'):
return fn
-def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps, create_image_every, save_embedding_every, template_file):
+def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_size, steps, num_repeats, create_image_every, save_embedding_every, template_file):
assert embedding_name, 'embedding not selected'
shared.state.textinfo = "Initializing textual inversion training..."
@@ -182,7 +183,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
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, size=512, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file)
+ ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, size=training_size, repeats=num_repeats, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file)
hijack = sd_hijack.model_hijack
@@ -200,6 +201,9 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
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
+
pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
for i, (x, text) in pbar:
embedding.step = i + ititial_step
@@ -223,7 +227,10 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
loss.backward()
optimizer.step()
- pbar.set_description(f"loss: {losses.mean():.7f}")
+ epoch_num = math.floor(embedding.step / epoch_len)
+ epoch_step = embedding.step - (epoch_num * epoch_len)
+
+ pbar.set_description(f"[Epoch {epoch_num}: {epoch_step}/{epoch_len}]loss: {losses.mean():.7f}")
if embedding.step > 0 and embedding_dir is not None and embedding.step % save_embedding_every == 0:
last_saved_file = os.path.join(embedding_dir, f'{embedding_name}-{embedding.step}.pt')
@@ -236,6 +243,8 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
sd_model=shared.sd_model,
prompt=text,
steps=20,
+ height=training_size,
+ width=training_size,
do_not_save_grid=True,
do_not_save_samples=True,
)
diff --git a/modules/ui.py b/modules/ui.py
index 2231a8ed8..f821fd8db 100644
--- a/modules/ui.py
+++ b/modules/ui.py
@@ -1029,6 +1029,7 @@ def create_ui(wrap_gradio_gpu_call):
process_src = gr.Textbox(label='Source directory')
process_dst = gr.Textbox(label='Destination directory')
+ process_size = gr.Slider(minimum=64, maximum=2048, step=64, label="Size (width and height)", value=512)
with gr.Row():
process_flip = gr.Checkbox(label='Create flipped copies')
@@ -1043,13 +1044,15 @@ def create_ui(wrap_gradio_gpu_call):
run_preprocess = gr.Button(value="Preprocess", variant='primary')
with gr.Group():
- gr.HTML(value="Train an embedding; must specify a directory with a set of 512x512 images
")
+ gr.HTML(value="Train an embedding; must specify a directory with a set of 1:1 ratio images
")
train_embedding_name = gr.Dropdown(label='Embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
learn_rate = gr.Number(label='Learning rate', value=5.0e-03)
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"))
+ training_size = gr.Slider(minimum=64, maximum=2048, step=64, label="Size (width and height)", value=512)
steps = gr.Number(label='Max steps', value=100000, precision=0)
+ num_repeats = gr.Number(label='Number of repeats for a single input image per epoch', value=100, precision=0)
create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
@@ -1092,6 +1095,7 @@ def create_ui(wrap_gradio_gpu_call):
inputs=[
process_src,
process_dst,
+ process_size,
process_flip,
process_split,
process_caption,
@@ -1110,7 +1114,9 @@ def create_ui(wrap_gradio_gpu_call):
learn_rate,
dataset_directory,
log_directory,
+ training_size,
steps,
+ num_repeats,
create_image_every,
save_embedding_every,
template_file,