hoelee/stable-diffusion-webui
1
0
Fork 0
mirror of https://github.com/AUTOMATIC1111/stable-diffusion-webui.git synced 2025-08-08 21:30:27 +00:00
Code Issues Actions Packages Projects Releases Wiki Activity

add option to use batch size for training

Browse Source
This commit is contained in:
AUTOMATIC
2022-10-15 09:24:59 +03:00
parent acedbe67d2
commit c7a86f7fe9
4 changed files with 54 additions and 30 deletions
Download Patch File Download Diff File Expand all files Collapse all files

31
modules/textual_inversion/dataset.py
View File

@@ -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 +79,13 @@ 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
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 +102,19 @@ 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

17
modules/textual_inversion/textual_inversion.py
View File

@@ -199,7 +199,7 @@ def write_loss(log_directory, filename, step, epoch_len, values):
})
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_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
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..."
@@ -231,7 +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
@@ -251,7 +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)
@@ -262,10 +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()
@@ -307,7 +306,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, traini
p.width = preview_width
p.height = preview_height
else:
p.prompt = entry.cond_text
p.prompt = entries[0].cond_text
p.steps = 20
p.width = training_width
p.height = training_height
@@ -348,7 +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>