diff options
Diffstat (limited to 'modules/textual_inversion')
| -rw-r--r-- | modules/textual_inversion/dataset.py | 32 | ||||
| -rw-r--r-- | modules/textual_inversion/textual_inversion.py | 70 |
2 files changed, 76 insertions, 26 deletions
diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py index 67e90afe..23bb4b6a 100644 --- a/modules/textual_inversion/dataset.py +++ b/modules/textual_inversion/dataset.py @@ -24,11 +24,12 @@ class DatasetEntry: class PersonalizedBase(Dataset):
- def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, device=None, template_file=None, include_cond=False):
- re_word = re.compile(shared.opts.dataset_filename_word_regex) if len(shared.opts.dataset_filename_word_regex)>0 else 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, batch_size=1):
+ re_word = re.compile(shared.opts.dataset_filename_word_regex) if len(shared.opts.dataset_filename_word_regex) > 0 else None
self.placeholder_token = placeholder_token
+ self.batch_size = batch_size
self.width = width
self.height = height
self.flip = transforms.RandomHorizontalFlip(p=flip_p)
@@ -78,13 +79,14 @@ class PersonalizedBase(Dataset): if include_cond:
entry.cond_text = self.create_text(filename_text)
- entry.cond = cond_model([entry.cond_text]).to(devices.cpu)
+ entry.cond = cond_model([entry.cond_text]).to(devices.cpu).squeeze(0)
self.dataset.append(entry)
- self.length = len(self.dataset) * repeats
+ assert len(self.dataset) > 1, "No images have been found in the dataset."
+ self.length = len(self.dataset) * repeats // batch_size
- self.initial_indexes = np.arange(self.length) % len(self.dataset)
+ self.initial_indexes = np.arange(len(self.dataset))
self.indexes = None
self.shuffle()
@@ -101,13 +103,19 @@ class PersonalizedBase(Dataset): return self.length
def __getitem__(self, i):
- if i % len(self.dataset) == 0:
- self.shuffle()
+ res = []
- index = self.indexes[i % len(self.indexes)]
- entry = self.dataset[index]
+ for j in range(self.batch_size):
+ position = i * self.batch_size + j
+ if position % len(self.indexes) == 0:
+ self.shuffle()
- if entry.cond is None:
- entry.cond_text = self.create_text(entry.filename_text)
+ index = self.indexes[position % len(self.indexes)]
+ entry = self.dataset[index]
- return entry
+ if entry.cond is None:
+ entry.cond_text = self.create_text(entry.filename_text)
+
+ res.append(entry)
+
+ return res
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index fa0e33a2..e754747e 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 csv
from PIL import Image, PngImagePlugin
@@ -172,7 +173,33 @@ def create_embedding(name, num_vectors_per_token, init_text='*'): return fn
-def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding, preview_image_prompt):
+def write_loss(log_directory, filename, step, epoch_len, values):
+ if shared.opts.training_write_csv_every == 0:
+ return
+
+ 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
+
+ with open(os.path.join(log_directory, filename), "a+", newline='') as fout:
+ csv_writer = csv.DictWriter(fout, fieldnames=["step", "epoch", "epoch_step", *(values.keys())])
+
+ if write_csv_header:
+ csv_writer.writeheader()
+
+ epoch = step // epoch_len
+ epoch_step = step - epoch * epoch_len
+
+ csv_writer.writerow({
+ "step": step + 1,
+ "epoch": epoch + 1,
+ "epoch_step": epoch_step + 1,
+ **values,
+ })
+
+
+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):
assert embedding_name, 'embedding not selected'
shared.state.textinfo = "Initializing textual inversion training..."
@@ -204,7 +231,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini 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)
+ 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)
hijack = sd_hijack.model_hijack
@@ -224,7 +251,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate)
pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
- for i, entry in pbar:
+ for i, entries in pbar:
embedding.step = i + ititial_step
scheduler.apply(optimizer, embedding.step)
@@ -235,10 +262,9 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini break
with torch.autocast("cuda"):
- c = cond_model([entry.cond_text])
-
- x = entry.latent.to(devices.device)
- loss = shared.sd_model(x.unsqueeze(0), c)[0]
+ c = cond_model([entry.cond_text for entry in entries])
+ x = torch.stack([entry.latent for entry in entries]).to(devices.device)
+ loss = shared.sd_model(x, c)[0]
del x
losses[embedding.step % losses.shape[0]] = loss.item()
@@ -256,21 +282,37 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini last_saved_file = os.path.join(embedding_dir, f'{embedding_name}-{embedding.step}.pt')
embedding.save(last_saved_file)
+ write_loss(log_directory, "textual_inversion_loss.csv", embedding.step, len(ds), {
+ "loss": f"{losses.mean():.7f}",
+ "learn_rate": scheduler.learn_rate
+ })
+
if embedding.step > 0 and images_dir is not None and embedding.step % create_image_every == 0:
last_saved_image = os.path.join(images_dir, f'{embedding_name}-{embedding.step}.png')
- preview_text = entry.cond_text if preview_image_prompt == "" else preview_image_prompt
-
p = processing.StableDiffusionProcessingTxt2Img(
sd_model=shared.sd_model,
- prompt=preview_text,
- steps=20,
- height=training_height,
- width=training_width,
do_not_save_grid=True,
do_not_save_samples=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]
@@ -305,7 +347,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini <p>
Loss: {losses.mean():.7f}<br/>
Step: {embedding.step}<br/>
-Last prompt: {html.escape(entry.cond_text)}<br/>
+Last prompt: {html.escape(entries[0].cond_text)}<br/>
Last saved embedding: {html.escape(last_saved_file)}<br/>
Last saved image: {html.escape(last_saved_image)}<br/>
</p>
|