diff options
Diffstat (limited to 'modules/hypernetworks')
| -rw-r--r-- | modules/hypernetworks/hypernetwork.py | 26 | ||||
| -rw-r--r-- | modules/hypernetworks/ui.py | 8 |
2 files changed, 24 insertions, 10 deletions
diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py index e493f366..b7a04038 100644 --- a/modules/hypernetworks/hypernetwork.py +++ b/modules/hypernetworks/hypernetwork.py @@ -41,12 +41,12 @@ class HypernetworkModule(torch.nn.Module): linears.append(torch.nn.Linear(int(dim * layer_structure[i]), int(dim * layer_structure[i+1])))
# Add an activation func
- if activation_func == "linear":
+ if activation_func == "linear" or activation_func is None:
pass
elif activation_func in self.activation_dict:
linears.append(self.activation_dict[activation_func]())
else:
- raise NotImplementedError(
+ raise RuntimeError(
"Valid activation funcs: 'linear', 'relu', 'leakyrelu', 'elu', 'swish'"
)
@@ -65,7 +65,7 @@ class HypernetworkModule(torch.nn.Module): self.load_state_dict(state_dict)
else:
for layer in self.linear:
- if isinstance(layer, torch.nn.Linear):
+ if type(layer) == torch.nn.Linear or type(layer) == torch.nn.LayerNorm:
layer.weight.data.normal_(mean=0.0, std=0.01)
layer.bias.data.zero_()
@@ -93,7 +93,7 @@ class HypernetworkModule(torch.nn.Module): def trainables(self):
layer_structure = []
for layer in self.linear:
- if isinstance(layer, torch.nn.Linear):
+ if type(layer) == torch.nn.Linear or type(layer) == torch.nn.LayerNorm:
layer_structure += [layer.weight, layer.bias]
return layer_structure
@@ -272,6 +272,9 @@ def stack_conds(conds): def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log_directory, training_width, training_height, 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):
+ # images allows training previews to have infotext. Importing it at the top causes a circular import problem.
+ from modules import images
+
assert hypernetwork_name, 'hypernetwork not selected'
path = shared.hypernetworks.get(hypernetwork_name, None)
@@ -314,6 +317,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log last_saved_file = "<none>"
last_saved_image = "<none>"
+ forced_filename = "<none>"
ititial_step = hypernetwork.step or 0
if ititial_step > steps:
@@ -353,7 +357,9 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log pbar.set_description(f"loss: {mean_loss:.7f}")
if hypernetwork.step > 0 and hypernetwork_dir is not None and hypernetwork.step % save_hypernetwork_every == 0:
- last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork_name}-{hypernetwork.step}.pt')
+ # Before saving, change name to match current checkpoint.
+ hypernetwork.name = f'{hypernetwork_name}-{hypernetwork.step}'
+ last_saved_file = os.path.join(hypernetwork_dir, f'{hypernetwork.name}.pt')
hypernetwork.save(last_saved_file)
textual_inversion.write_loss(log_directory, "hypernetwork_loss.csv", hypernetwork.step, len(ds), {
@@ -362,7 +368,8 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log })
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')
+ forced_filename = f'{hypernetwork_name}-{hypernetwork.step}'
+ last_saved_image = os.path.join(images_dir, forced_filename)
optimizer.zero_grad()
shared.sd_model.cond_stage_model.to(devices.device)
@@ -398,7 +405,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log if image is not None:
shared.state.current_image = image
- 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)
last_saved_image += f", prompt: {preview_text}"
shared.state.job_no = hypernetwork.step
@@ -408,7 +415,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log Loss: {mean_loss:.7f}<br/>
Step: {hypernetwork.step}<br/>
Last prompt: {html.escape(entries[0].cond_text)}<br/>
-Last saved embedding: {html.escape(last_saved_file)}<br/>
+Last saved hypernetwork: {html.escape(last_saved_file)}<br/>
Last saved image: {html.escape(last_saved_image)}<br/>
</p>
"""
@@ -417,6 +424,9 @@ Last saved image: {html.escape(last_saved_image)}<br/> hypernetwork.sd_checkpoint = checkpoint.hash
hypernetwork.sd_checkpoint_name = checkpoint.model_name
+ # Before saving for the last time, change name back to the base name (as opposed to the save_hypernetwork_every step-suffixed naming convention).
+ hypernetwork.name = hypernetwork_name
+ filename = os.path.join(shared.cmd_opts.hypernetwork_dir, f'{hypernetwork.name}.pt')
hypernetwork.save(filename)
return hypernetwork, filename
diff --git a/modules/hypernetworks/ui.py b/modules/hypernetworks/ui.py index 5f6f17b6..2b472d87 100644 --- a/modules/hypernetworks/ui.py +++ b/modules/hypernetworks/ui.py @@ -9,9 +9,13 @@ from modules import devices, sd_hijack, shared from modules.hypernetworks import hypernetwork
-def create_hypernetwork(name, enable_sizes, layer_structure=None, activation_func=None, add_layer_norm=False, use_dropout=False):
+def create_hypernetwork(name, enable_sizes, overwrite_old, layer_structure=None, activation_func=None, add_layer_norm=False, use_dropout=False):
+ # 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.hypernetwork_dir, f"{name}.pt")
- assert not os.path.exists(fn), f"file {fn} already exists"
+ if not overwrite_old:
+ assert not os.path.exists(fn), f"file {fn} already exists"
if type(layer_structure) == str:
layer_structure = [float(x.strip()) for x in layer_structure.split(",")]
|