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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>