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add option to use batch size for training

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AUTOMATIC 2022-10-15 09:24:59 +03:00
parent acedbe67d2
commit c7a86f7fe9
4 changed files with 54 additions and 30 deletions
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33
modules/hypernetworks/hypernetwork.py
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@ -182,7 +182,21 @@ def attention_CrossAttention_forward(self, x, context=None, mask=None):
return self.to_out(out)
def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
def stack_conds(conds):
if len(conds) == 1:
return torch.stack(conds)
# same as in reconstruct_multicond_batch
token_count = max([x.shape[0] for x in conds])
for i in range(len(conds)):
if conds[i].shape[0] != token_count:
last_vector = conds[i][-1:]
last_vector_repeated = last_vector.repeat([token_count - conds[i].shape[0], 1])
conds[i] = torch.vstack([conds[i], last_vector_repeated])
return torch.stack(conds)
def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, steps, create_image_every, save_hypernetwork_every, template_file, preview_from_txt2img, preview_prompt, preview_negative_prompt, preview_steps, preview_sampler_index, preview_cfg_scale, preview_seed, preview_width, preview_height):
assert hypernetwork_name, 'hypernetwork not selected'
path = shared.hypernetworks.get(hypernetwork_name, None)
@ -211,7 +225,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
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=512, height=512, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True)
ds = modules.textual_inversion.dataset.PersonalizedBase(data_root=data_root, width=512, height=512, repeats=shared.opts.training_image_repeats_per_epoch, placeholder_token=hypernetwork_name, model=shared.sd_model, device=devices.device, template_file=template_file, include_cond=True, batch_size=batch_size)
if unload:
shared.sd_model.cond_stage_model.to(devices.cpu)
@ -235,7 +249,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
optimizer = torch.optim.AdamW(weights, lr=scheduler.learn_rate)
pbar = tqdm.tqdm(enumerate(ds), total=steps - ititial_step)
for i, entry in pbar:
for i, entries in pbar:
hypernetwork.step = i + ititial_step
scheduler.apply(optimizer, hypernetwork.step)
@ -246,11 +260,12 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
break
with torch.autocast("cuda"):
cond = entry.cond.to(devices.device)
x = entry.latent.to(devices.device)
loss = shared.sd_model(x.unsqueeze(0), cond)[0]
c = stack_conds([entry.cond for entry in entries]).to(devices.device)
# c = torch.vstack([entry.cond for entry in entries]).to(devices.device)
x = torch.stack([entry.latent for entry in entries]).to(devices.device)
loss = shared.sd_model(x, c)[0]
del x
del cond
del c
losses[hypernetwork.step % losses.shape[0]] = loss.item()
@ -292,7 +307,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
p.width = preview_width
p.height = preview_height
else:
p.prompt = entry.cond_text
p.prompt = entries[0].cond_text
p.steps = 20
preview_text = p.prompt
@ -315,7 +330,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory,
<p>
Loss: {losses.mean():.7f}<br/>
Step: {hypernetwork.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>

21
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):
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:
res = []
for j in range(self.batch_size):
position = i * self.batch_size + j
if position % len(self.indexes) == 0:
self.shuffle()
index = self.indexes[i % len(self.indexes)]
index = self.indexes[position % len(self.indexes)]
entry = self.dataset[index]
if entry.cond is None:
entry.cond_text = self.create_text(entry.filename_text)
return entry
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>

3
modules/ui.py
View File

@ -1166,6 +1166,7 @@ def create_ui(wrap_gradio_gpu_call):
train_embedding_name = gr.Dropdown(label='Embedding', choices=sorted(sd_hijack.model_hijack.embedding_db.word_embeddings.keys()))
train_hypernetwork_name = gr.Dropdown(label='Hypernetwork', choices=[x for x in shared.hypernetworks.keys()])
learn_rate = gr.Textbox(label='Learning rate', placeholder="Learning rate", value="0.005")
batch_size = gr.Number(label='Batch size', value=1, precision=0)
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"))
@ -1244,6 +1245,7 @@ def create_ui(wrap_gradio_gpu_call):
inputs=[
train_embedding_name,
learn_rate,
batch_size,
dataset_directory,
log_directory,
training_width,
@ -1268,6 +1270,7 @@ def create_ui(wrap_gradio_gpu_call):
inputs=[
train_hypernetwork_name,
learn_rate,
batch_size,
dataset_directory,
log_directory,
steps,