fixes for B007

extensions-builtin/LDSR/ldsr_model_arch.py

@@ -88,7 +88,7 @@ class LDSR:
 
         x_t = None
         logs = None
-        for n in range(n_runs):
+        for _ in range(n_runs):
             if custom_shape is not None:
                 x_t = torch.randn(1, custom_shape[1], custom_shape[2], custom_shape[3]).to(model.device)
                 x_t = repeat(x_t, '1 c h w -> b c h w', b=custom_shape[0])

extensions-builtin/Lora/lora.py

@@ -418,7 +418,7 @@ def infotext_pasted(infotext, params):
 
     added = []
 
-    for k, v in params.items():
+    for k in params:
         if not k.startswith("AddNet Model "):
             continue
 

extensions-builtin/ScuNET/scripts/scunet_model.py

@@ -132,7 +132,7 @@ class UpscalerScuNET(modules.upscaler.Upscaler):
         model = net(in_nc=3, config=[4, 4, 4, 4, 4, 4, 4], dim=64)
         model.load_state_dict(torch.load(filename), strict=True)
         model.eval()
-        for k, v in model.named_parameters():
+        for _, v in model.named_parameters():
             v.requires_grad = False
         model = model.to(device)
 

extensions-builtin/SwinIR/swinir_model_arch.py

@@ -848,7 +848,7 @@ class SwinIR(nn.Module):
         H, W = self.patches_resolution
         flops += H * W * 3 * self.embed_dim * 9
         flops += self.patch_embed.flops()
-        for i, layer in enumerate(self.layers):
+        for layer in self.layers:
             flops += layer.flops()
         flops += H * W * 3 * self.embed_dim * self.embed_dim
         flops += self.upsample.flops()

extensions-builtin/SwinIR/swinir_model_arch_v2.py

@@ -1001,7 +1001,7 @@ class Swin2SR(nn.Module):
         H, W = self.patches_resolution
         flops += H * W * 3 * self.embed_dim * 9
         flops += self.patch_embed.flops()
-        for i, layer in enumerate(self.layers):
+        for layer in self.layers:
             flops += layer.flops()
         flops += H * W * 3 * self.embed_dim * self.embed_dim
         flops += self.upsample.flops()

modules/codeformer_model.py

@@ -94,7 +94,7 @@ def setup_model(dirname):
                 self.face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5)
                 self.face_helper.align_warp_face()
 
-                for idx, cropped_face in enumerate(self.face_helper.cropped_faces):
+                for cropped_face in self.face_helper.cropped_faces:
                     cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True)
                     normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
                     cropped_face_t = cropped_face_t.unsqueeze(0).to(devices.device_codeformer)

modules/esrgan_model.py

@@ -16,9 +16,7 @@ def mod2normal(state_dict):
     # this code is copied from https://github.com/victorca25/iNNfer
     if 'conv_first.weight' in state_dict:
         crt_net = {}
-        items = []
-        for k, v in state_dict.items():
-            items.append(k)
+        items = list(state_dict)
 
         crt_net['model.0.weight'] = state_dict['conv_first.weight']
         crt_net['model.0.bias'] = state_dict['conv_first.bias']
@@ -52,9 +50,7 @@ def resrgan2normal(state_dict, nb=23):
     if "conv_first.weight" in state_dict and "body.0.rdb1.conv1.weight" in state_dict:
         re8x = 0
         crt_net = {}
-        items = []
-        for k, v in state_dict.items():
-            items.append(k)
+        items = list(state_dict)
 
         crt_net['model.0.weight'] = state_dict['conv_first.weight']
         crt_net['model.0.bias'] = state_dict['conv_first.bias']

modules/extra_networks.py

@@ -91,7 +91,7 @@ def deactivate(p, extra_network_data):
     """call deactivate for extra networks in extra_network_data in specified order, then call
     deactivate for all remaining registered networks"""
 
-    for extra_network_name, extra_network_args in extra_network_data.items():
+    for extra_network_name in extra_network_data:
         extra_network = extra_network_registry.get(extra_network_name, None)
         if extra_network is None:
             continue

modules/generation_parameters_copypaste.py

@@ -247,7 +247,7 @@ Steps: 20, Sampler: Euler a, CFG scale: 7, Seed: 965400086, Size: 512x512, Model
         lines.append(lastline)
         lastline = ''
 
-    for i, line in enumerate(lines):
+    for line in lines:
         line = line.strip()
         if line.startswith("Negative prompt:"):
             done_with_prompt = True

modules/hypernetworks/hypernetwork.py

@@ -177,34 +177,34 @@ class Hypernetwork:
 
     def weights(self):
         res = []
-        for k, layers in self.layers.items():
+        for layers in self.layers.values():
             for layer in layers:
                 res += layer.parameters()
         return res
 
     def train(self, mode=True):
-        for k, layers in self.layers.items():
+        for layers in self.layers.values():
             for layer in layers:
                 layer.train(mode=mode)
                 for param in layer.parameters():
                     param.requires_grad = mode
 
     def to(self, device):
-        for k, layers in self.layers.items():
+        for layers in self.layers.values():
             for layer in layers:
                 layer.to(device)
 
         return self
 
     def set_multiplier(self, multiplier):
-        for k, layers in self.layers.items():
+        for layers in self.layers.values():
             for layer in layers:
                 layer.multiplier = multiplier
 
         return self
 
     def eval(self):
-        for k, layers in self.layers.items():
+        for layers in self.layers.values():
             for layer in layers:
                 layer.eval()
                 for param in layer.parameters():
@@ -619,7 +619,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
     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:

modules/images.py

@@ -149,7 +149,7 @@ def draw_grid_annotations(im, width, height, hor_texts, ver_texts, margin=0):
             return ImageFont.truetype(Roboto, fontsize)
 
     def draw_texts(drawing, draw_x, draw_y, lines, initial_fnt, initial_fontsize):
-        for i, line in enumerate(lines):
+        for line in lines:
             fnt = initial_fnt
             fontsize = initial_fontsize
             while drawing.multiline_textsize(line.text, font=fnt)[0] > line.allowed_width and fontsize > 0:

modules/interrogate.py

@@ -207,8 +207,8 @@ class InterrogateModels:
 
                 image_features /= image_features.norm(dim=-1, keepdim=True)
 
-                for name, topn, items in self.categories():
-                    matches = self.rank(image_features, items, top_count=topn)
+                for cat in self.categories():
+                    matches = self.rank(image_features, cat.items, top_count=cat.topn)
                     for match, score in matches:
                         if shared.opts.interrogate_return_ranks:
                             res += f", ({match}:{score/100:.3f})"

modules/prompt_parser.py

@@ -143,7 +143,7 @@ def get_learned_conditioning(model, prompts, steps):
         conds = model.get_learned_conditioning(texts)
 
         cond_schedule = []
-        for i, (end_at_step, text) in enumerate(prompt_schedule):
+        for i, (end_at_step, _) in enumerate(prompt_schedule):
             cond_schedule.append(ScheduledPromptConditioning(end_at_step, conds[i]))
 
         cache[prompt] = cond_schedule
@@ -219,8 +219,8 @@ def reconstruct_cond_batch(c: List[List[ScheduledPromptConditioning]], current_s
     res = torch.zeros((len(c),) + param.shape, device=param.device, dtype=param.dtype)
     for i, cond_schedule in enumerate(c):
         target_index = 0
-        for current, (end_at, cond) in enumerate(cond_schedule):
-            if current_step <= end_at:
+        for current, entry in enumerate(cond_schedule):
+            if current_step <= entry.end_at_step:
                 target_index = current
                 break
         res[i] = cond_schedule[target_index].cond
@@ -234,13 +234,13 @@ def reconstruct_multicond_batch(c: MulticondLearnedConditioning, current_step):
     tensors = []
     conds_list = []
 
-    for batch_no, composable_prompts in enumerate(c.batch):
+    for composable_prompts in c.batch:
         conds_for_batch = []
 
-        for cond_index, composable_prompt in enumerate(composable_prompts):
+        for composable_prompt in composable_prompts:
             target_index = 0
-            for current, (end_at, cond) in enumerate(composable_prompt.schedules):
-                if current_step <= end_at:
+            for current, entry in enumerate(composable_prompt.schedules):
+                if current_step <= entry.end_at_step:
                     target_index = current
                     break
 

modules/safe.py

@@ -95,11 +95,11 @@ def check_pt(filename, extra_handler):
 
     except zipfile.BadZipfile:
 
-        # if it's not a zip file, it's an olf pytorch format, with five objects written to pickle
+        # if it's not a zip file, it's an old pytorch format, with five objects written to pickle
         with open(filename, "rb") as file:
             unpickler = RestrictedUnpickler(file)
             unpickler.extra_handler = extra_handler
-            for i in range(5):
+            for _ in range(5):
                 unpickler.load()
 
 

modules/scripts.py

@@ -231,7 +231,7 @@ def load_scripts():
     syspath = sys.path
 
     def register_scripts_from_module(module):
-        for key, script_class in module.__dict__.items():
+        for script_class in module.__dict__.values():
             if type(script_class) != type:
                 continue
 
@@ -295,9 +295,9 @@ class ScriptRunner:
 
         auto_processing_scripts = scripts_auto_postprocessing.create_auto_preprocessing_script_data()
 
-        for script_class, path, basedir, script_module in auto_processing_scripts + scripts_data:
-            script = script_class()
-            script.filename = path
+        for script_data in auto_processing_scripts + scripts_data:
+            script = script_data.script_class()
+            script.filename = script_data.path
             script.is_txt2img = not is_img2img
             script.is_img2img = is_img2img
 
@@ -492,7 +492,7 @@ class ScriptRunner:
                 module = script_loading.load_module(script.filename)
                 cache[filename] = module
 
-            for key, script_class in module.__dict__.items():
+            for script_class in module.__dict__.values():
                 if type(script_class) == type and issubclass(script_class, Script):
                     self.scripts[si] = script_class()
                     self.scripts[si].filename = filename

modules/scripts_postprocessing.py

@@ -66,9 +66,9 @@ class ScriptPostprocessingRunner:
     def initialize_scripts(self, scripts_data):
         self.scripts = []
 
-        for script_class, path, basedir, script_module in scripts_data:
-            script: ScriptPostprocessing = script_class()
-            script.filename = path
+        for script_data in scripts_data:
+            script: ScriptPostprocessing = script_data.script_class()
+            script.filename = script_data.path
 
             if script.name == "Simple Upscale":
                 continue
@@ -124,7 +124,7 @@ class ScriptPostprocessingRunner:
             script_args = args[script.args_from:script.args_to]
 
             process_args = {}
-            for (name, component), value in zip(script.controls.items(), script_args):
+            for (name, component), value in zip(script.controls.items(), script_args):  # noqa B007
                 process_args[name] = value
 
             script.process(pp, **process_args)

modules/sd_hijack_clip.py

@@ -223,7 +223,7 @@ class FrozenCLIPEmbedderWithCustomWordsBase(torch.nn.Module):
             self.hijack.fixes = [x.fixes for x in batch_chunk]
 
             for fixes in self.hijack.fixes:
-                for position, embedding in fixes:
+                for position, embedding in fixes:  # noqa: B007
                     used_embeddings[embedding.name] = embedding
 
             z = self.process_tokens(tokens, multipliers)

modules/shared.py

@@ -211,7 +211,7 @@ class OptionInfo:
 
 
 def options_section(section_identifier, options_dict):
-    for k, v in options_dict.items():
+    for v in options_dict.values():
         v.section = section_identifier
 
     return options_dict
@@ -579,7 +579,7 @@ class Options:
 
         section_ids = {}
         settings_items = self.data_labels.items()
-        for k, item in settings_items:
+        for _, item in settings_items:
             if item.section not in section_ids:
                 section_ids[item.section] = len(section_ids)
 
@@ -740,7 +740,7 @@ def walk_files(path, allowed_extensions=None):
     if allowed_extensions is not None:
         allowed_extensions = set(allowed_extensions)
 
-    for root, dirs, files in os.walk(path):
+    for root, _, files in os.walk(path):
         for filename in files:
             if allowed_extensions is not None:
                 _, ext = os.path.splitext(filename)

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(':')

modules/textual_inversion/textual_inversion.py

@@ -29,7 +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)
 
@@ -198,7 +198,7 @@ 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)
@@ -215,7 +215,7 @@ 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
@@ -228,7 +228,7 @@ 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()
 
@@ -469,7 +469,7 @@ 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:

modules/ui.py

@@ -416,7 +416,7 @@ def create_sampler_and_steps_selection(choices, tabname):
 def ordered_ui_categories():
     user_order = {x.strip(): i * 2 + 1 for i, x in enumerate(shared.opts.ui_reorder.split(","))}
 
-    for i, category in sorted(enumerate(shared.ui_reorder_categories), key=lambda x: user_order.get(x[1], x[0] * 2 + 0)):
+    for _, category in sorted(enumerate(shared.ui_reorder_categories), key=lambda x: user_order.get(x[1], x[0] * 2 + 0)):
         yield category
 
 
@@ -1646,7 +1646,7 @@ def create_ui():
 
     with gr.Blocks(theme=shared.gradio_theme, analytics_enabled=False, title="Stable Diffusion") as demo:
         with gr.Row(elem_id="quicksettings", variant="compact"):
-            for i, k, item in sorted(quicksettings_list, key=lambda x: quicksettings_names.get(x[1], x[0])):
+            for _i, k, _item in sorted(quicksettings_list, key=lambda x: quicksettings_names.get(x[1], x[0])):
                 component = create_setting_component(k, is_quicksettings=True)
                 component_dict[k] = component
 
@@ -1673,7 +1673,7 @@ def create_ui():
             outputs=[text_settings, result],
         )
 
-        for i, k, item in quicksettings_list:
+        for _i, k, _item in quicksettings_list:
             component = component_dict[k]
             info = opts.data_labels[k]
 

modules/ui_extra_networks.py

@@ -90,7 +90,7 @@ class ExtraNetworksPage:
 
         subdirs = {}
         for parentdir in [os.path.abspath(x) for x in self.allowed_directories_for_previews()]:
-            for root, dirs, files in os.walk(parentdir):
+            for root, dirs, _ in os.walk(parentdir):
                 for dirname in dirs:
                     x = os.path.join(root, dirname)
 

modules/ui_tempdir.py

@@ -72,7 +72,7 @@ def cleanup_tmpdr():
     if temp_dir == "" or not os.path.isdir(temp_dir):
         return
 
-    for root, dirs, files in os.walk(temp_dir, topdown=False):
+    for root, _, files in os.walk(temp_dir, topdown=False):
         for name in files:
             _, extension = os.path.splitext(name)
             if extension != ".png":

modules/upscaler.py

@@ -55,7 +55,7 @@ class Upscaler:
         dest_w = int(img.width * scale)
         dest_h = int(img.height * scale)
 
-        for i in range(3):
+        for _ in range(3):
             shape = (img.width, img.height)
 
             img = self.do_upscale(img, selected_model)

pyproject.toml

@@ -20,7 +20,6 @@ ignore = [
 	"I001", # Import block is un-sorted or un-formatted
 	"C901", # Function is too complex
 	"C408", # Rewrite as a literal
-	"B007", # Loop control variable not used within loop body
 
 ]
 

scripts/prompts_from_file.py

@@ -156,7 +156,7 @@ class Script(scripts.Script):
         images = []
         all_prompts = []
         infotexts = []
-        for n, args in enumerate(jobs):
+        for args in jobs:
             state.job = f"{state.job_no + 1} out of {state.job_count}"
 
             copy_p = copy.copy(p)

scripts/sd_upscale.py

@@ -56,7 +56,7 @@ class Script(scripts.Script):
 
         work = []
 
-        for y, h, row in grid.tiles:
+        for _y, _h, row in grid.tiles:
             for tiledata in row:
                 work.append(tiledata[2])
 
@@ -85,7 +85,7 @@ class Script(scripts.Script):
                 work_results += processed.images
 
             image_index = 0
-            for y, h, row in grid.tiles:
+            for _y, _h, row in grid.tiles:
                 for tiledata in row:
                     tiledata[2] = work_results[image_index] if image_index < len(work_results) else Image.new("RGB", (p.width, p.height))
                     image_index += 1

scripts/xyz_grid.py

@@ -704,7 +704,7 @@ class Script(scripts.Script):
 
         if not include_sub_grids:
             # Done with sub-grids, drop all related information:
-            for sg in range(z_count):
+            for _ in range(z_count):
                 del processed.images[1]
                 del processed.all_prompts[1]
                 del processed.all_seeds[1]