diff options
Diffstat (limited to 'modules/textual_inversion/textual_inversion.py')
| -rw-r--r-- | modules/textual_inversion/textual_inversion.py | 119 |
1 files changed, 83 insertions, 36 deletions
diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py index 529ed3e2..e0babb46 100644 --- a/modules/textual_inversion/textual_inversion.py +++ b/modules/textual_inversion/textual_inversion.py @@ -10,7 +10,7 @@ import csv from PIL import Image, PngImagePlugin
-from modules import shared, devices, sd_hijack, processing, sd_models
+from modules import shared, devices, sd_hijack, processing, sd_models, images
import modules.textual_inversion.dataset
from modules.textual_inversion.learn_schedule import LearnRateScheduler
@@ -119,7 +119,7 @@ class EmbeddingDatabase: vec = emb.detach().to(devices.device, dtype=torch.float32)
embedding = Embedding(vec, name)
embedding.step = data.get('step', None)
- embedding.sd_checkpoint = data.get('hash', None)
+ embedding.sd_checkpoint = data.get('sd_checkpoint', None)
embedding.sd_checkpoint_name = data.get('sd_checkpoint_name', None)
self.register_embedding(embedding, shared.sd_model)
@@ -157,6 +157,9 @@ def create_embedding(name, num_vectors_per_token, overwrite_old, init_text='*'): cond_model = shared.sd_model.cond_stage_model
embedding_layer = cond_model.wrapped.transformer.text_model.embeddings
+ with devices.autocast():
+ cond_model([""]) # will send cond model to GPU if lowvram/medvram is active
+
ids = cond_model.tokenizer(init_text, max_length=num_vectors_per_token, return_tensors="pt", add_special_tokens=False)["input_ids"]
embedded = embedding_layer.token_embedding.wrapped(ids.to(devices.device)).squeeze(0)
vec = torch.zeros((num_vectors_per_token, embedded.shape[1]), device=devices.device)
@@ -164,6 +167,8 @@ def create_embedding(name, num_vectors_per_token, overwrite_old, init_text='*'): for i in range(num_vectors_per_token):
vec[i] = embedded[i * int(embedded.shape[0]) // num_vectors_per_token]
+ # Remove illegal characters from name.
+ name = "".join( x for x in name if (x.isalnum() or x in "._- "))
fn = os.path.join(shared.cmd_opts.embeddings_dir, f"{name}.pt")
if not overwrite_old:
assert not os.path.exists(fn), f"file {fn} already exists"
@@ -179,9 +184,8 @@ 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:
+ if (step + 1) % 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:
@@ -191,18 +195,39 @@ def write_loss(log_directory, filename, step, epoch_len, values): csv_writer.writeheader()
epoch = step // epoch_len
- epoch_step = step - epoch * epoch_len
+ epoch_step = step % epoch_len
csv_writer.writerow({
"step": step + 1,
- "epoch": epoch + 1,
+ "epoch": epoch,
"epoch_step": epoch_step + 1,
**values,
})
+def validate_train_inputs(model_name, learn_rate, batch_size, data_root, template_file, steps, save_model_every, create_image_every, log_directory, name="embedding"):
+ assert model_name, f"{name} not selected"
+ assert learn_rate, "Learning rate is empty or 0"
+ assert isinstance(batch_size, int), "Batch size must be integer"
+ assert batch_size > 0, "Batch size must be positive"
+ assert data_root, "Dataset directory is empty"
+ assert os.path.isdir(data_root), "Dataset directory doesn't exist"
+ assert os.listdir(data_root), "Dataset directory is empty"
+ assert template_file, "Prompt template file is empty"
+ assert os.path.isfile(template_file), "Prompt template file doesn't exist"
+ assert steps, "Max steps is empty or 0"
+ assert isinstance(steps, int), "Max steps must be integer"
+ assert steps > 0 , "Max steps must be positive"
+ assert isinstance(save_model_every, int), "Save {name} must be integer"
+ assert save_model_every >= 0 , "Save {name} must be positive or 0"
+ assert isinstance(create_image_every, int), "Create image must be integer"
+ assert create_image_every >= 0 , "Create image must be positive or 0"
+ if save_model_every or create_image_every:
+ assert log_directory, "Log directory is empty"
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'
+ save_embedding_every = save_embedding_every or 0
+ create_image_every = create_image_every or 0
+ validate_train_inputs(embedding_name, learn_rate, batch_size, data_root, template_file, steps, save_embedding_every, create_image_every, log_directory, name="embedding")
shared.state.textinfo = "Initializing textual inversion training..."
shared.state.job_count = steps
@@ -228,31 +253,36 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc os.makedirs(images_embeds_dir, exist_ok=True)
else:
images_embeds_dir = None
-
+
cond_model = shared.sd_model.cond_stage_model
+ hijack = sd_hijack.model_hijack
+
+ embedding = hijack.embedding_db.word_embeddings[embedding_name]
+ checkpoint = sd_models.select_checkpoint()
+
+ ititial_step = embedding.step or 0
+ if ititial_step >= steps:
+ shared.state.textinfo = f"Model has already been trained beyond specified max steps"
+ return embedding, filename
+
+ scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
+
+ # dataset loading may take a while, so input validations and early returns should be done before this
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, batch_size=batch_size)
- hijack = sd_hijack.model_hijack
-
- embedding = hijack.embedding_db.word_embeddings[embedding_name]
embedding.vec.requires_grad = True
+ optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate)
losses = torch.zeros((32,))
last_saved_file = "<none>"
last_saved_image = "<none>"
+ forced_filename = "<none>"
embedding_yet_to_be_embedded = False
- ititial_step = embedding.step or 0
- if ititial_step > steps:
- return embedding, filename
-
- scheduler = LearnRateScheduler(learn_rate, steps, ititial_step)
- optimizer = torch.optim.AdamW([embedding.vec], lr=scheduler.learn_rate)
-
pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)
for i, entries in pbar:
embedding.step = i + ititial_step
@@ -276,15 +306,18 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc loss.backward()
optimizer.step()
+ steps_done = embedding.step + 1
epoch_num = embedding.step // len(ds)
- epoch_step = embedding.step - (epoch_num * len(ds)) + 1
+ epoch_step = embedding.step % len(ds)
- pbar.set_description(f"[Epoch {epoch_num}: {epoch_step}/{len(ds)}]loss: {losses.mean():.7f}")
+ pbar.set_description(f"[Epoch {epoch_num}: {epoch_step+1}/{len(ds)}]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')
- embedding.save(last_saved_file)
+ if embedding_dir is not None and steps_done % save_embedding_every == 0:
+ # Before saving, change name to match current checkpoint.
+ embedding_name_every = f'{embedding_name}-{steps_done}'
+ last_saved_file = os.path.join(embedding_dir, f'{embedding_name_every}.pt')
+ save_embedding(embedding, checkpoint, embedding_name_every, last_saved_file, remove_cached_checksum=True)
embedding_yet_to_be_embedded = True
write_loss(log_directory, "textual_inversion_loss.csv", embedding.step, len(ds), {
@@ -292,9 +325,9 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc "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')
-
+ if images_dir is not None and steps_done % create_image_every == 0:
+ forced_filename = f'{embedding_name}-{steps_done}'
+ last_saved_image = os.path.join(images_dir, forced_filename)
p = processing.StableDiffusionProcessingTxt2Img(
sd_model=shared.sd_model,
do_not_save_grid=True,
@@ -326,7 +359,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc if save_image_with_stored_embedding and os.path.exists(last_saved_file) and embedding_yet_to_be_embedded:
- last_saved_image_chunks = os.path.join(images_embeds_dir, f'{embedding_name}-{embedding.step}.png')
+ last_saved_image_chunks = os.path.join(images_embeds_dir, f'{embedding_name}-{steps_done}.png')
info = PngImagePlugin.PngInfo()
data = torch.load(last_saved_file)
@@ -342,7 +375,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc checkpoint = sd_models.select_checkpoint()
footer_left = checkpoint.model_name
footer_mid = '[{}]'.format(checkpoint.hash)
- footer_right = '{}v {}s'.format(vectorSize, embedding.step)
+ footer_right = '{}v {}s'.format(vectorSize, steps_done)
captioned_image = caption_image_overlay(image, title, footer_left, footer_mid, footer_right)
captioned_image = insert_image_data_embed(captioned_image, data)
@@ -350,8 +383,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, data_root, log_direc captioned_image.save(last_saved_image_chunks, "PNG", pnginfo=info)
embedding_yet_to_be_embedded = False
- image.save(last_saved_image)
-
+ last_saved_image, last_text_info = images.save_image(image, images_dir, "", p.seed, p.prompt, shared.opts.samples_format, processed.infotexts[0], p=p, forced_filename=forced_filename, save_to_dirs=False)
last_saved_image += f", prompt: {preview_text}"
shared.state.job_no = embedding.step
@@ -366,11 +398,26 @@ Last saved image: {html.escape(last_saved_image)}<br/> </p>
"""
- checkpoint = sd_models.select_checkpoint()
-
- embedding.sd_checkpoint = checkpoint.hash
- embedding.sd_checkpoint_name = checkpoint.model_name
- embedding.cached_checksum = None
- embedding.save(filename)
+ filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')
+ save_embedding(embedding, checkpoint, embedding_name, filename, remove_cached_checksum=True)
return embedding, filename
+
+def save_embedding(embedding, checkpoint, embedding_name, filename, remove_cached_checksum=True):
+ old_embedding_name = embedding.name
+ old_sd_checkpoint = embedding.sd_checkpoint if hasattr(embedding, "sd_checkpoint") else None
+ old_sd_checkpoint_name = embedding.sd_checkpoint_name if hasattr(embedding, "sd_checkpoint_name") else None
+ old_cached_checksum = embedding.cached_checksum if hasattr(embedding, "cached_checksum") else None
+ try:
+ embedding.sd_checkpoint = checkpoint.hash
+ embedding.sd_checkpoint_name = checkpoint.model_name
+ if remove_cached_checksum:
+ embedding.cached_checksum = None
+ embedding.name = embedding_name
+ embedding.save(filename)
+ except:
+ embedding.sd_checkpoint = old_sd_checkpoint
+ embedding.sd_checkpoint_name = old_sd_checkpoint_name
+ embedding.name = old_embedding_name
+ embedding.cached_checksum = old_cached_checksum
+ raise
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