Merge branch 'master' into embed-embeddings-in-images

1 files changed, 11 insertions, 3 deletions

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 9a18ee5c..7a24192e 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -190,7 +190,7 @@ def create_embedding(name, num_vectors_per_token, init_text='*'):
     return fn

 

 

-def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps, create_image_every, save_embedding_every, template_file, save_image_with_stored_embedding):

+def train_embedding(embedding_name, learn_rate, data_root, log_directory, training_width, training_height, steps, num_repeats, create_image_every, save_embedding_every, template_file):

     assert embedding_name, 'embedding not selected'

 

     shared.state.textinfo = "Initializing textual inversion training..."

@@ -222,7 +222,7 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
 

     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, size=512, 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=num_repeats, placeholder_token=embedding_name, model=shared.sd_model, device=devices.device, template_file=template_file)

 

     hijack = sd_hijack.model_hijack

 

@@ -240,6 +240,9 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
     if ititial_step > steps:

         return embedding, filename

 

+    tr_img_len = len([os.path.join(data_root, file_path) for file_path in os.listdir(data_root)])

+    epoch_len = (tr_img_len * num_repeats) + tr_img_len

+

     pbar = tqdm.tqdm(enumerate(ds), total=steps-ititial_step)

     for i, (x, text) in pbar:

         embedding.step = i + ititial_step

@@ -263,7 +266,10 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
             loss.backward()

             optimizer.step()

 

-        pbar.set_description(f"loss: {losses.mean():.7f}")

+        epoch_num = embedding.step // epoch_len

+        epoch_step = embedding.step - (epoch_num * epoch_len) + 1

+

+        pbar.set_description(f"[Epoch {epoch_num}: {epoch_step}/{epoch_len}]loss: {losses.mean():.7f}")

 

         if embedding.step > 0 and embedding_dir is not None and embedding.step % save_embedding_every == 0:

             last_saved_file = os.path.join(embedding_dir, f'{embedding_name}-{embedding.step}.pt')

@@ -276,6 +282,8 @@ def train_embedding(embedding_name, learn_rate, data_root, log_directory, steps,
                 sd_model=shared.sd_model,

                 prompt=text,

                 steps=20,

+				height=training_height,

+				width=training_width,

                 do_not_save_grid=True,

                 do_not_save_samples=True,

             )