Merge branch 'AUTOMATIC1111:master' into master

3 files changed, 9 insertions, 6 deletions

diff --git a/modules/textual_inversion/autocrop.py b/modules/textual_inversion/autocrop.py
index 9859974a..68e1103c 100644
--- a/modules/textual_inversion/autocrop.py
+++ b/modules/textual_inversion/autocrop.py
@@ -276,8 +276,8 @@ def poi_average(pois, settings):
         weight += poi.weight

         x += poi.x * poi.weight

         y += poi.y * poi.weight

-    avg_x = round(x / weight)

-    avg_y = round(y / weight)

+    avg_x = round(weight and x / weight)

+    avg_y = round(weight and y / weight)

 

     return PointOfInterest(avg_x, avg_y)

 

@@ -338,4 +338,4 @@ class Settings:
     self.face_points_weight = face_points_weight

     self.annotate_image = annotate_image

     self.destop_view_image = False

-    self.dnn_model_path = dnn_model_path
\ No newline at end of file
+    self.dnn_model_path = dnn_model_path

diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index e5725f33..2dc64c3c 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -82,7 +82,7 @@ class PersonalizedBase(Dataset):
             torchdata = torch.from_numpy(npimage).permute(2, 0, 1).to(device=device, dtype=torch.float32)

             latent_sample = None

 

-            with torch.autocast("cuda"):

+            with devices.autocast():

                 latent_dist = model.encode_first_stage(torchdata.unsqueeze(dim=0))

 

             if latent_sampling_method == "once" or (latent_sampling_method == "deterministic" and not isinstance(latent_dist, DiagonalGaussianDistribution)):

@@ -101,7 +101,7 @@ class PersonalizedBase(Dataset):
                 entry.cond_text = self.create_text(filename_text)

 

             if include_cond and not (self.tag_drop_out != 0 or self.shuffle_tags):

-                with torch.autocast("cuda"):

+                with devices.autocast():

                     entry.cond = cond_model([entry.cond_text]).to(devices.cpu).squeeze(0)

 

             self.dataset.append(entry)

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 4eb75cb5..e28c357a 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -269,6 +269,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
 

    # dataset loading may take a while, so input validations and early returns should be done before this

     shared.state.textinfo = f"Preparing dataset from {html.escape(data_root)}..."

+    old_parallel_processing_allowed = shared.parallel_processing_allowed

     

     pin_memory = shared.opts.pin_memory

     

@@ -279,6 +280,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
     dl = modules.textual_inversion.dataset.PersonalizedDataLoader(ds, latent_sampling_method=latent_sampling_method, batch_size=ds.batch_size, pin_memory=pin_memory)

 

     if unload:

+        shared.parallel_processing_allowed = False

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

 

     embedding.vec.requires_grad = True

@@ -316,7 +318,7 @@ def train_embedding(embedding_name, learn_rate, batch_size, gradient_step, data_
                 if shared.state.interrupted:

                     break

 

-                with torch.autocast("cuda"):

+                with devices.autocast():

                     # c = stack_conds(batch.cond).to(devices.device)

                     # mask = torch.tensor(batch.emb_index).to(devices.device, non_blocking=pin_memory)

                     # print(mask)

@@ -450,6 +452,7 @@ Last saved image: {html.escape(last_saved_image)}<br/>
         pbar.leave = False

         pbar.close()

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

+        shared.parallel_processing_allowed = old_parallel_processing_allowed

 

     return embedding, filename