Merge pull request #10259 from AUTOMATIC1111/ruff

Ruff

5 files changed, 14 insertions, 22 deletions

diff --git a/modules/textual_inversion/autocrop.py b/modules/textual_inversion/autocrop.py
index ba1bdcd4..7770d22f 100644
--- a/modules/textual_inversion/autocrop.py
+++ b/modules/textual_inversion/autocrop.py
@@ -1,10 +1,8 @@
 import cv2

 import requests

 import os

-from collections import defaultdict

-from math import log, sqrt

 import numpy as np

-from PIL import Image, ImageDraw

+from PIL import ImageDraw

 

 GREEN = "#0F0"

 BLUE = "#00F"

@@ -185,7 +183,7 @@ def image_face_points(im, settings):
         try:

           faces = classifier.detectMultiScale(gray, scaleFactor=1.1,

             minNeighbors=7, minSize=(minsize, minsize), flags=cv2.CASCADE_SCALE_IMAGE)

-        except:

+        except Exception:

           continue

 

         if len(faces) > 0:

diff --git a/modules/textual_inversion/image_embedding.py b/modules/textual_inversion/image_embedding.py
index 5593f88c..d85a4888 100644
--- a/modules/textual_inversion/image_embedding.py
+++ b/modules/textual_inversion/image_embedding.py
@@ -2,7 +2,7 @@ import base64
 import json

 import numpy as np

 import zlib

-from PIL import Image, PngImagePlugin, ImageDraw, ImageFont

+from PIL import Image, ImageDraw, ImageFont

 from fonts.ttf import Roboto

 import torch

 from modules.shared import opts

@@ -17,7 +17,7 @@ class EmbeddingEncoder(json.JSONEncoder):
 

 class EmbeddingDecoder(json.JSONDecoder):

     def __init__(self, *args, **kwargs):

-        json.JSONDecoder.__init__(self, object_hook=self.object_hook, *args, **kwargs)

+        json.JSONDecoder.__init__(self, *args, object_hook=self.object_hook, **kwargs)

 

     def object_hook(self, d):

         if 'TORCHTENSOR' in d:

diff --git a/modules/textual_inversion/learn_schedule.py b/modules/textual_inversion/learn_schedule.py
index f63fc72f..c56bea45 100644
--- a/modules/textual_inversion/learn_schedule.py
+++ b/modules/textual_inversion/learn_schedule.py
@@ -12,7 +12,7 @@ class LearnScheduleIterator:
         self.it = 0

         self.maxit = 0

         try:

-            for i, pair in enumerate(pairs):

+            for pair in pairs:

                 if not pair.strip():

                     continue

                 tmp = pair.split(':')

@@ -32,8 +32,8 @@ class LearnScheduleIterator:
                     self.maxit += 1

                     return

             assert self.rates

-        except (ValueError, AssertionError):

-            raise Exception('Invalid learning rate schedule. It should be a number or, for example, like "0.001:100, 0.00001:1000, 1e-5:10000" to have lr of 0.001 until step 100, 0.00001 until 1000, and 1e-5 until 10000.')

+        except (ValueError, AssertionError) as e:

+            raise Exception('Invalid learning rate schedule. It should be a number or, for example, like "0.001:100, 0.00001:1000, 1e-5:10000" to have lr of 0.001 until step 100, 0.00001 until 1000, and 1e-5 until 10000.') from e

 

 

     def __iter__(self):

diff --git a/modules/textual_inversion/preprocess.py b/modules/textual_inversion/preprocess.py
index da0bcb26..d0cad09e 100644
--- a/modules/textual_inversion/preprocess.py
+++ b/modules/textual_inversion/preprocess.py
@@ -1,13 +1,9 @@
 import os

 from PIL import Image, ImageOps

 import math

-import platform

-import sys

 import tqdm

-import time

 

 from modules import paths, shared, images, deepbooru

-from modules.shared import opts, cmd_opts

 from modules.textual_inversion import autocrop

 

 

diff --git a/modules/textual_inversion/textual_inversion.py b/modules/textual_inversion/textual_inversion.py
index 4368eb63..9e1b2b9a 100644
--- a/modules/textual_inversion/textual_inversion.py
+++ b/modules/textual_inversion/textual_inversion.py
@@ -1,7 +1,6 @@
 import os

 import sys

 import traceback

-import inspect

 from collections import namedtuple

 

 import torch

@@ -30,7 +29,7 @@ textual_inversion_templates = {}
 def list_textual_inversion_templates():

     textual_inversion_templates.clear()

 

-    for root, dirs, fns in os.walk(shared.cmd_opts.textual_inversion_templates_dir):

+    for root, _, fns in os.walk(shared.cmd_opts.textual_inversion_templates_dir):

         for fn in fns:

             path = os.path.join(root, fn)

 

@@ -167,8 +166,7 @@ class EmbeddingDatabase:
         # textual inversion embeddings

         if 'string_to_param' in data:

             param_dict = data['string_to_param']

-            if hasattr(param_dict, '_parameters'):

-                param_dict = getattr(param_dict, '_parameters')  # fix for torch 1.12.1 loading saved file from torch 1.11

+            param_dict = getattr(param_dict, '_parameters', param_dict)  # fix for torch 1.12.1 loading saved file from torch 1.11

             assert len(param_dict) == 1, 'embedding file has multiple terms in it'

             emb = next(iter(param_dict.items()))[1]

         # diffuser concepts

@@ -199,7 +197,7 @@ class EmbeddingDatabase:
         if not os.path.isdir(embdir.path):

             return

 

-        for root, dirs, fns in os.walk(embdir.path, followlinks=True):

+        for root, _, fns in os.walk(embdir.path, followlinks=True):

             for fn in fns:

                 try:

                     fullfn = os.path.join(root, fn)

@@ -216,7 +214,7 @@ class EmbeddingDatabase:
     def load_textual_inversion_embeddings(self, force_reload=False):

         if not force_reload:

             need_reload = False

-            for path, embdir in self.embedding_dirs.items():

+            for embdir in self.embedding_dirs.values():

                 if embdir.has_changed():

                     need_reload = True

                     break

@@ -229,7 +227,7 @@ class EmbeddingDatabase:
         self.skipped_embeddings.clear()

         self.expected_shape = self.get_expected_shape()

 

-        for path, embdir in self.embedding_dirs.items():

+        for embdir in self.embedding_dirs.values():

             self.load_from_dir(embdir)

             embdir.update()

 

@@ -470,7 +468,7 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
     try:

         sd_hijack_checkpoint.add()

 

-        for i in range((steps-initial_step) * gradient_step):

+        for _ in range((steps-initial_step) * gradient_step):

             if scheduler.finished:

                 break

             if shared.state.interrupted:

@@ -603,7 +601,7 @@ def train_embedding(id_task, embedding_name, learn_rate, batch_size, gradient_st
 

                         try:

                             vectorSize = list(data['string_to_param'].values())[0].shape[0]

-                        except Exception as e:

+                        except Exception:

                             vectorSize = '?'

 

                         checkpoint = sd_models.select_checkpoint()