Merge branch 'dev' into meta_class

4 files changed, 34 insertions, 26 deletions

diff --git a/modules/textual_inversion/autocrop.py b/modules/textual_inversion/autocrop.py
index 8e667a4d..1675e39a 100644
--- a/modules/textual_inversion/autocrop.py
+++ b/modules/textual_inversion/autocrop.py
@@ -77,27 +77,27 @@ def focal_point(im, settings):
     pois = []

 

     weight_pref_total = 0

-    if len(corner_points) > 0:

+    if corner_points:

       weight_pref_total += settings.corner_points_weight

-    if len(entropy_points) > 0:

+    if entropy_points:

       weight_pref_total += settings.entropy_points_weight

-    if len(face_points) > 0:

+    if face_points:

       weight_pref_total += settings.face_points_weight

 

     corner_centroid = None

-    if len(corner_points) > 0:

+    if corner_points:

       corner_centroid = centroid(corner_points)

       corner_centroid.weight = settings.corner_points_weight / weight_pref_total

       pois.append(corner_centroid)

 

     entropy_centroid = None

-    if len(entropy_points) > 0:

+    if entropy_points:

       entropy_centroid = centroid(entropy_points)

       entropy_centroid.weight = settings.entropy_points_weight / weight_pref_total

       pois.append(entropy_centroid)

 

     face_centroid = None

-    if len(face_points) > 0:

+    if face_points:

       face_centroid = centroid(face_points)

       face_centroid.weight = settings.face_points_weight / weight_pref_total

       pois.append(face_centroid)

@@ -187,7 +187,7 @@ def image_face_points(im, settings):
         except Exception:

           continue

 

-        if len(faces) > 0:

+        if faces:

           rects = [[f[0], f[1], f[0] + f[2], f[1] + f[3]] for f in faces]

           return [PointOfInterest((r[0] +r[2]) // 2, (r[1] + r[3]) // 2, size=abs(r[0]-r[2]), weight=1/len(rects)) for r in rects]

     return []

@@ -298,8 +298,7 @@ def download_and_cache_models(dirname):
     download_url = 'https://github.com/opencv/opencv_zoo/blob/91fb0290f50896f38a0ab1e558b74b16bc009428/models/face_detection_yunet/face_detection_yunet_2022mar.onnx?raw=true'

     model_file_name = 'face_detection_yunet.onnx'

 

-    if not os.path.exists(dirname):

-        os.makedirs(dirname)

+    os.makedirs(dirname, exist_ok=True)

 

     cache_file = os.path.join(dirname, model_file_name)

     if not os.path.exists(cache_file):

diff --git a/modules/textual_inversion/dataset.py b/modules/textual_inversion/dataset.py
index b9621fc9..7ee05061 100644
--- a/modules/textual_inversion/dataset.py
+++ b/modules/textual_inversion/dataset.py
@@ -32,7 +32,7 @@ class DatasetEntry:
 

 class PersonalizedBase(Dataset):

     def __init__(self, data_root, width, height, repeats, flip_p=0.5, placeholder_token="*", model=None, cond_model=None, device=None, template_file=None, include_cond=False, batch_size=1, gradient_step=1, shuffle_tags=False, tag_drop_out=0, latent_sampling_method='once', varsize=False, use_weight=False):

-        re_word = re.compile(shared.opts.dataset_filename_word_regex) if len(shared.opts.dataset_filename_word_regex) > 0 else None

+        re_word = re.compile(shared.opts.dataset_filename_word_regex) if shared.opts.dataset_filename_word_regex else None

 

         self.placeholder_token = placeholder_token

 

diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index a009d8e8..0d4c3f84 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -47,7 +47,7 @@ def save_pic_with_caption(image, index, params: PreprocessParams, existing_capti
         caption += shared.interrogator.generate_caption(image)

 

     if params.process_caption_deepbooru:

-        if len(caption) > 0:

+        if caption:

             caption += ", "

         caption += deepbooru.model.tag_multi(image)

 

@@ -67,7 +67,7 @@ def save_pic_with_caption(image, index, params: PreprocessParams, existing_capti
 

     caption = caption.strip()

 

-    if len(caption) > 0:

+    if caption:

         with open(os.path.join(params.dstdir, f"{basename}.txt"), "w", encoding="utf8") as file:

             file.write(caption)

 

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index d489ed1e..bb6f211c 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -1,6 +1,4 @@
 import os

-import sys

-import traceback

 from collections import namedtuple

 

 import torch

@@ -14,7 +12,7 @@ import numpy as np
 from PIL import Image, PngImagePlugin

 from torch.utils.tensorboard import SummaryWriter

 

-from modules import shared, devices, sd_hijack, processing, sd_models, images, sd_samplers, sd_hijack_checkpoint

+from modules import shared, devices, sd_hijack, processing, sd_models, images, sd_samplers, sd_hijack_checkpoint, errors

 import modules.textual_inversion.dataset

 from modules.textual_inversion.learn_schedule import LearnRateScheduler

 

@@ -120,16 +118,29 @@ class EmbeddingDatabase:
         self.embedding_dirs.clear()

 

     def register_embedding(self, embedding, model):

-        self.word_embeddings[embedding.name] = embedding

-

-        ids = model.cond_stage_model.tokenize([embedding.name])[0]

+        return self.register_embedding_by_name(embedding, model, embedding.name)

 

+    def register_embedding_by_name(self, embedding, model, name):

+        ids = model.cond_stage_model.tokenize([name])[0]

         first_id = ids[0]

         if first_id not in self.ids_lookup:

             self.ids_lookup[first_id] = []

-

-        self.ids_lookup[first_id] = sorted(self.ids_lookup[first_id] + [(ids, embedding)], key=lambda x: len(x[0]), reverse=True)

-

+        if name in self.word_embeddings:

+            # remove old one from the lookup list

+            lookup = [x for x in self.ids_lookup[first_id] if x[1].name!=name]

+        else:

+            lookup = self.ids_lookup[first_id]

+        if embedding is not None:

+            lookup += [(ids, embedding)]

+        self.ids_lookup[first_id] = sorted(lookup, key=lambda x: len(x[0]), reverse=True)

+        if embedding is None:

+            # unregister embedding with specified name

+            if name in self.word_embeddings:

+                del self.word_embeddings[name]

+            if len(self.ids_lookup[first_id])==0:

+                del self.ids_lookup[first_id]

+            return None

+        self.word_embeddings[name] = embedding

         return embedding

 

     def get_expected_shape(self):

@@ -207,8 +218,7 @@ class EmbeddingDatabase:
 

                     self.load_from_file(fullfn, fn)

                 except Exception:

-                    print(f"Error loading embedding {fn}:", file=sys.stderr)

-                    print(traceback.format_exc(), file=sys.stderr)

+                    errors.report(f"Error loading embedding {fn}", exc_info=True)

                     continue

 

     def load_textual_inversion_embeddings(self, force_reload=False):

@@ -241,7 +251,7 @@ class EmbeddingDatabase:
         if self.previously_displayed_embeddings != displayed_embeddings:

             self.previously_displayed_embeddings = displayed_embeddings

             print(f"Textual inversion embeddings loaded({len(self.word_embeddings)}): {', '.join(self.word_embeddings.keys())}")

-            if len(self.skipped_embeddings) > 0:

+            if self.skipped_embeddings:

                 print(f"Textual inversion embeddings skipped({len(self.skipped_embeddings)}): {', '.join(self.skipped_embeddings.keys())}")

 

     def find_embedding_at_position(self, tokens, offset):

@@ -632,8 +642,7 @@ Last saved image: {html.escape(last_saved_image)}<br/>
         filename = os.path.join(shared.cmd_opts.embeddings_dir, f'{embedding_name}.pt')

         save_embedding(embedding, optimizer, checkpoint, embedding_name, filename, remove_cached_checksum=True)

     except Exception:

-        print(traceback.format_exc(), file=sys.stderr)

-        pass

+        errors.report("Error training embedding", exc_info=True)

     finally:

         pbar.leave = False

         pbar.close()