diff options
Diffstat (limited to 'modules/hypernetworks/hypernetwork.py')
| -rw-r--r-- | modules/hypernetworks/hypernetwork.py | 57 |
1 files changed, 45 insertions, 12 deletions
diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index f1248bb7..a2b3bc0a 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -5,6 +5,7 @@ import os import sys
import traceback
import tqdm
+import csv
import torch
@@ -14,6 +15,7 @@ import torch from torch import einsum
from einops import rearrange, repeat
import modules.textual_inversion.dataset
+from modules.textual_inversion import textual_inversion
from modules.textual_inversion.learn_schedule import LearnRateScheduler
@@ -180,7 +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_image_prompt):
+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)
@@ -209,7 +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=1, 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)
@@ -233,7 +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)
@@ -244,11 +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()
@@ -262,23 +279,39 @@ def train_hypernetwork(hypernetwork_name, learn_rate, data_root, log_directory, last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name}-{hypernetwork.step}.pt')
hypernetwork.save(last_saved_file)
+ textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), {
+ "loss": f"{losses.mean():.7f}",
+ "learn_rate": scheduler.learn_rate
+ })
+
if hypernetwork.step > 0 and images_dir is not None and hypernetwork.step % create_image_every == 0:
last_saved_image = os.path.join(images_dir, f'{hypernetwork_name}-{hypernetwork.step}.png')
- preview_text = entry.cond_text if preview_image_prompt == "" else preview_image_prompt
-
optimizer.zero_grad()
shared.sd_model.cond_stage_model.to(devices.device)
shared.sd_model.first_stage_model.to(devices.device)
p = processing.StableDiffusionProcessingTxt2Img(
sd_model=shared.sd_model,
- prompt=preview_text,
- steps=20,
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
+
+ preview_text = p.prompt
+
processed = processing.process_images(p)
image = processed.images[0] if len(processed.images)>0 else None
@@ -297,7 +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>
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