Textual Inversion: Added custom training image size and number of repeats per input image in a single epoch

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.width = size
-        self.height = height
+        self.height = size
         self.flip = transforms.RandomHorizontalFlip(p=flip_p)
 
         self.dataset = []

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):
+def preprocess(process_src, process_dst, process_size, process_flip, process_split, process_caption):
-    size = 512
+    size = process_size
     src = os.path.abspath(process_src)
     dst = os.path.abspath(process_dst)
 

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,
             )

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="<p style='margin-bottom: 0.7em'>Train an embedding; must specify a directory with a set of 512x512 images</p>")
+                    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()))
                     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,