Merge branch 'master' into master

1 files changed, 2 insertions, 3 deletions

diff --git a/modules/hypernetworks/hypernetwork.py b/modules/hypernetworks/hypernetwork.py
index 082165f4..583ada31 100644
--- a/modules/hypernetworks/hypernetwork.py
+++ b/modules/hypernetworks/hypernetwork.py
@@ -244,7 +244,7 @@ def stack_conds(conds):
 

     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):

+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):

     assert hypernetwork_name, 'hypernetwork not selected'

 

     path = shared.hypernetworks.get(hypernetwork_name, None)

@@ -273,8 +273,7 @@ def train_hypernetwork(hypernetwork_name, learn_rate, batch_size, data_root, log
 

     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, batch_size=batch_size)

-        assert ds.length > 1, "Dataset should contain more than 1 images"

+        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=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)

         shared.sd_model.first_stage_model.to(devices.cpu)