Textual Inversion: Added custom training image size and number of repeats per input image in a single epoch
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alg-wiki
parent
8acc901ba3
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3110f895b2
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@ -15,13 +15,13 @@ re_tag = re.compile(r"[a-zA-Z][_\w\d()]+")
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class PersonalizedBase(Dataset):
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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):
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def __init__(self, data_root, size, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None):
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self.placeholder_token = placeholder_token
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self.size = size
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self.width = width
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self.height = height
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self.width = size
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self.height = size
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self.flip = transforms.RandomHorizontalFlip(p=flip_p)
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self.dataset = []
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@ -7,8 +7,8 @@ import tqdm
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from modules import shared, images
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def preprocess(process_src, process_dst, process_flip, process_split, process_caption):
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size = 512
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def preprocess(process_src, process_dst, process_size, process_flip, process_split, process_caption):
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size = process_size
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src = os.path.abspath(process_src)
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dst = os.path.abspath(process_dst)
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@ -6,6 +6,7 @@ import torch
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import tqdm
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import html
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import datetime
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import math
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from modules import shared, devices, sd_hijack, processing, sd_models
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@ -156,7 +157,7 @@ def create_embedding(name, num_vectors_per_token, init_text='*'):
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return fn
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def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps, create_image_every, save_embedding_every, template_file):
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def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_size, steps, num_repeats, create_image_every, save_embedding_every, template_file):
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assert embedding_name, 'embedding not selected'
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shared.state.textinfo = "Initializing textual inversion training..."
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@ -182,7 +183,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
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shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."
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with torch.autocast("cuda"):
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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)
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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)
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hijack = sd_hijack.model_hijack
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@ -200,6 +201,9 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
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if ititial_step > steps:
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return embedding, filename
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tr_img_len = len([os.path.join(data_root, file_path) for file_path in os.listdir(data_root)])
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epoch_len = (tr_img_len * num_repeats) + tr_img_len
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pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
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for i, (x, text) in pbar:
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embedding.step = i + ititial_step
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@ -223,7 +227,10 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
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loss.backward()
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optimizer.step()
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pbar.set_description(f"loss: {losses.mean():.7f}")
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epoch_num = math.floor(embedding.step / epoch_len)
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epoch_step = embedding.step - (epoch_num * epoch_len)
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pbar.set_description(f"[Epoch {epoch_num}: {epoch_step}/{epoch_len}]loss: {losses.mean():.7f}")
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if embedding.step > 0 and embedding_dir is not None and embedding.step % save_embedding_every == 0:
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last_saved_file = os.path.join(embedding_dir, f'{embedding_name}-{embedding.step}.pt')
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@ -236,6 +243,8 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
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sd_model=shared.sd_model,
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prompt=text,
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steps=20,
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height=training_size,
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width=training_size,
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do_not_save_grid=True,
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do_not_save_samples=True,
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)
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@ -1029,6 +1029,7 @@ def create_ui(wrap_gradio_gpu_call):
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process_src = gr.Textbox(label='Source directory')
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process_dst = gr.Textbox(label='Destination directory')
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process_size = gr.Slider(minimum=64, maximum=2048, step=64, label="Size (width and height)", value=512)
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with gr.Row():
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process_flip = gr.Checkbox(label='Create flipped copies')
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@ -1043,13 +1044,15 @@ def create_ui(wrap_gradio_gpu_call):
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run_preprocess = gr.Button(value="Preprocess", variant='primary')
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with gr.Group():
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gr.HTML(value="<p style='margin-bottom: 0.7em'>Train an embedding; must specify a directory with a set of 512x512 images</p>")
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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>")
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train_embedding_name = gr.Dropdown(label='Embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
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learn_rate = gr.Number(label='Learning rate', value=5.0e-03)
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dataset_directory = gr.Textbox(label='Dataset directory', placeholder="Path to directory with input images")
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log_directory = gr.Textbox(label='Log directory', placeholder="Path to directory where to write outputs", value="textual_inversion")
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template_file = gr.Textbox(label='Prompt template file', value=os.path.join(script_path, "textual_inversion_templates", "style_filewords.txt"))
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training_size = gr.Slider(minimum=64, maximum=2048, step=64, label="Size (width and height)", value=512)
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steps = gr.Number(label='Max steps', value=100000, precision=0)
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num_repeats = gr.Number(label='Number of repeats for a single input image per epoch', value=100, precision=0)
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create_image_every = gr.Number(label='Save an image to log directory every N steps, 0 to disable', value=500, precision=0)
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save_embedding_every = gr.Number(label='Save a copy of embedding to log directory every N steps, 0 to disable', value=500, precision=0)
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@ -1092,6 +1095,7 @@ def create_ui(wrap_gradio_gpu_call):
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inputs=[
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process_src,
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process_dst,
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process_size,
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process_flip,
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process_split,
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process_caption,
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@ -1110,7 +1114,9 @@ def create_ui(wrap_gradio_gpu_call):
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learn_rate,
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dataset_directory,
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log_directory,
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training_size,
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steps,
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num_repeats,
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create_image_every,
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save_embedding_every,
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template_file,
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