autofixes from ruff

extensions-builtin/LDSR/ldsr_model_arch.py

@@ -110,7 +110,6 @@ def super_resolution(self, image, steps=100, target_scale=2, half_attention=Fals
         diffusion_steps = int(steps)
         eta = 1.0
 
-        down_sample_method = 'Lanczos'
 
         gc.collect()
         if torch.cuda.is_available:

extensions-builtin/LDSR/sd_hijack_autoencoder.py

@@ -165,7 +165,7 @@ def training_step(self, batch, batch_idx, optimizer_idx):
     def validation_step(self, batch, batch_idx):
         log_dict = self._validation_step(batch, batch_idx)
         with self.ema_scope():
-            log_dict_ema = self._validation_step(batch, batch_idx, suffix="_ema")
+            self._validation_step(batch, batch_idx, suffix="_ema")
         return log_dict
 
     def _validation_step(self, batch, batch_idx, suffix=""):

modules/api/api.py

@@ -60,7 +60,7 @@ def decode_base64_to_image(encoding):
     try:
         image = Image.open(BytesIO(base64.b64decode(encoding)))
         return image
-    except Exception as err:
+    except Exception:
         raise HTTPException(status_code=500, detail="Invalid encoded image")
 
 def encode_pil_to_base64(image):
@@ -264,11 +264,11 @@ def init_script_args(self, request, default_script_args, selectable_scripts, sel
         if request.alwayson_scripts and (len(request.alwayson_scripts) > 0):
             for alwayson_script_name in request.alwayson_scripts.keys():
                 alwayson_script = self.get_script(alwayson_script_name, script_runner)
-                if alwayson_script == None:
+                if alwayson_script is None:
                     raise HTTPException(status_code=422, detail=f"always on script {alwayson_script_name} not found")
                 # Selectable script in always on script param check
-                if alwayson_script.alwayson == False:
-                    raise HTTPException(status_code=422, detail=f"Cannot have a selectable script in the always on scripts params")
+                if alwayson_script.alwayson is False:
+                    raise HTTPException(status_code=422, detail="Cannot have a selectable script in the always on scripts params")
                 # always on script with no arg should always run so you don't really need to add them to the requests
                 if "args" in request.alwayson_scripts[alwayson_script_name]:
                     # min between arg length in scriptrunner and arg length in the request
@@ -310,7 +310,7 @@ def text2imgapi(self, txt2imgreq: StableDiffusionTxt2ImgProcessingAPI):
             p.outpath_samples = opts.outdir_txt2img_samples
 
             shared.state.begin()
-            if selectable_scripts != None:
+            if selectable_scripts is not None:
                 p.script_args = script_args
                 processed = scripts.scripts_txt2img.run(p, *p.script_args) # Need to pass args as list here
             else:
@@ -367,7 +367,7 @@ def img2imgapi(self, img2imgreq: StableDiffusionImg2ImgProcessingAPI):
             p.outpath_samples = opts.outdir_img2img_samples
 
             shared.state.begin()
-            if selectable_scripts != None:
+            if selectable_scripts is not None:
                 p.script_args = script_args
                 processed = scripts.scripts_img2img.run(p, *p.script_args) # Need to pass args as list here
             else:
@@ -642,7 +642,7 @@ def train_hypernetwork(self, args: dict):
                     sd_hijack.apply_optimizations()
                 shared.state.end()
             return TrainResponse(info=f"train embedding complete: filename: {filename} error: {error}")
-        except AssertionError as msg:
+        except AssertionError:
             shared.state.end()
             return TrainResponse(info=f"train embedding error: {error}")
 

modules/extras.py

@@ -136,14 +136,14 @@ def filename_nothing():
     result_is_instruct_pix2pix_model = False
 
     if theta_func2:
-        shared.state.textinfo = f"Loading B"
+        shared.state.textinfo = "Loading B"
         print(f"Loading {secondary_model_info.filename}...")
         theta_1 = sd_models.read_state_dict(secondary_model_info.filename, map_location='cpu')
     else:
         theta_1 = None
 
     if theta_func1:
-        shared.state.textinfo = f"Loading C"
+        shared.state.textinfo = "Loading C"
         print(f"Loading {tertiary_model_info.filename}...")
         theta_2 = sd_models.read_state_dict(tertiary_model_info.filename, map_location='cpu')
 

modules/images.py

@@ -409,13 +409,13 @@ def datetime(self, *args):
         time_format = args[0] if len(args) > 0 and args[0] != "" else self.default_time_format
         try:
             time_zone = pytz.timezone(args[1]) if len(args) > 1 else None
-        except pytz.exceptions.UnknownTimeZoneError as _:
+        except pytz.exceptions.UnknownTimeZoneError:
             time_zone = None
 
         time_zone_time = time_datetime.astimezone(time_zone)
         try:
             formatted_time = time_zone_time.strftime(time_format)
-        except (ValueError, TypeError) as _:
+        except (ValueError, TypeError):
             formatted_time = time_zone_time.strftime(self.default_time_format)
 
         return sanitize_filename_part(formatted_time, replace_spaces=False)

modules/img2img.py

@@ -59,7 +59,7 @@ def process_batch(p, input_dir, output_dir, inpaint_mask_dir, args):
             # try to find corresponding mask for an image using simple filename matching
             mask_image_path = os.path.join(inpaint_mask_dir, os.path.basename(image))
             # if not found use first one ("same mask for all images" use-case)
-            if not mask_image_path in inpaint_masks:
+            if mask_image_path not in inpaint_masks:
                 mask_image_path = inpaint_masks[0]
             mask_image = Image.open(mask_image_path)
             p.image_mask = mask_image

modules/prompt_parser.py

@@ -92,7 +92,7 @@ def __default__(self, data, children, meta):
     def get_schedule(prompt):
         try:
             tree = schedule_parser.parse(prompt)
-        except lark.exceptions.LarkError as e:
+        except lark.exceptions.LarkError:
             if 0:
                 import traceback
                 traceback.print_exc()

modules/realesrgan_model.py

@@ -134,6 +134,6 @@ def get_realesrgan_models(scaler):
             ),
         ]
         return models
-    except Exception as e:
+    except Exception:
         print("Error making Real-ESRGAN models list:", file=sys.stderr)
         print(traceback.format_exc(), file=sys.stderr)

modules/sd_disable_initialization.py

@@ -61,7 +61,7 @@ def transformers_utils_hub_get_file_from_cache(original, url, *args, **kwargs):
                 if res is None:
                     res = original(url, *args, local_files_only=False, **kwargs)
                 return res
-            except Exception as e:
+            except Exception:
                 return original(url, *args, local_files_only=False, **kwargs)
 
         def transformers_utils_hub_get_from_cache(url, *args, local_files_only=False, **kwargs):

modules/sd_hijack.py

@@ -118,7 +118,7 @@ def weighted_forward(sd_model, x, c, w, *args, **kwargs):
         try:
             #Delete temporary weights if appended
             del sd_model._custom_loss_weight
-        except AttributeError as e:
+        except AttributeError:
             pass
 
         #If we have an old loss function, reset the loss function to the original one
@@ -133,7 +133,7 @@ def apply_weighted_forward(sd_model):
 def undo_weighted_forward(sd_model):
     try:
         del sd_model.weighted_forward
-    except AttributeError as e:
+    except AttributeError:
         pass
 
 

modules/sd_hijack_ip2p.py

@@ -10,4 +10,4 @@ def should_hijack_ip2p(checkpoint_info):
     ckpt_basename = os.path.basename(checkpoint_info.filename).lower()
     cfg_basename = os.path.basename(sd_models_config.find_checkpoint_config_near_filename(checkpoint_info)).lower()
 
-    return "pix2pix" in ckpt_basename and not "pix2pix" in cfg_basename
+    return "pix2pix" in ckpt_basename and "pix2pix" not in cfg_basename

modules/sd_hijack_optimizations.py

@@ -296,7 +296,6 @@ def sub_quad_attention(q, k, v, q_chunk_size=1024, kv_chunk_size=None, kv_chunk_
     if chunk_threshold_bytes is not None and qk_matmul_size_bytes <= chunk_threshold_bytes:
         # the big matmul fits into our memory limit; do everything in 1 chunk,
         # i.e. send it down the unchunked fast-path
-        query_chunk_size = q_tokens
         kv_chunk_size = k_tokens
 
     with devices.without_autocast(disable=q.dtype == v.dtype):

modules/sd_models.py

@@ -239,7 +239,7 @@ def read_metadata_from_safetensors(filename):
             if isinstance(v, str) and v[0:1] == '{':
                 try:
                     res[k] = json.loads(v)
-                except Exception as e:
+                except Exception:
                     pass
 
         return res
@@ -467,7 +467,7 @@ def load_model(checkpoint_info=None, already_loaded_state_dict=None):
     try:
         with sd_disable_initialization.DisableInitialization(disable_clip=clip_is_included_into_sd):
             sd_model = instantiate_from_config(sd_config.model)
-    except Exception as e:
+    except Exception:
         pass
 
     if sd_model is None:
@@ -544,7 +544,7 @@ def reload_model_weights(sd_model=None, info=None):
 
     try:
         load_model_weights(sd_model, checkpoint_info, state_dict, timer)
-    except Exception as e:
+    except Exception:
         print("Failed to load checkpoint, restoring previous")
         load_model_weights(sd_model, current_checkpoint_info, None, timer)
         raise

modules/textual_inversion/textual_inversion.py

@@ -603,7 +603,7 @@ 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()

modules/ui.py

@@ -246,7 +246,7 @@ def copy_seed(gen_info_string: str, index):
                 all_seeds = gen_info.get('all_seeds', [-1])
                 res = all_seeds[index if 0 <= index < len(all_seeds) else 0]
 
-        except json.decoder.JSONDecodeError as e:
+        except json.decoder.JSONDecodeError:
             if gen_info_string != '':
                 print("Error parsing JSON generation info:", file=sys.stderr)
                 print(gen_info_string, file=sys.stderr)
@@ -736,8 +736,8 @@ def update_orig(image, state):
                     with gr.TabItem('Batch', id='batch', elem_id="img2img_batch_tab") as tab_batch:
                         hidden = '<br>Disabled when launched with --hide-ui-dir-config.' if shared.cmd_opts.hide_ui_dir_config else ''
                         gr.HTML(
-                            f"<p style='padding-bottom: 1em;' class=\"text-gray-500\">Process images in a directory on the same machine where the server is running." +
-                            f"<br>Use an empty output directory to save pictures normally instead of writing to the output directory." +
+                            "<p style='padding-bottom: 1em;' class=\"text-gray-500\">Process images in a directory on the same machine where the server is running." +
+                            "<br>Use an empty output directory to save pictures normally instead of writing to the output directory." +
                             f"<br>Add inpaint batch mask directory to enable inpaint batch processing."
                             f"{hidden}</p>"
                         )
@@ -746,7 +746,6 @@ def update_orig(image, state):
                         img2img_batch_inpaint_mask_dir = gr.Textbox(label="Inpaint batch mask directory (required for inpaint batch processing only)", **shared.hide_dirs, elem_id="img2img_batch_inpaint_mask_dir")
 
                     img2img_tabs = [tab_img2img, tab_sketch, tab_inpaint, tab_inpaint_color, tab_inpaint_upload, tab_batch]
-                    img2img_image_inputs = [init_img, sketch, init_img_with_mask, inpaint_color_sketch]
 
                     for i, tab in enumerate(img2img_tabs):
                         tab.select(fn=lambda tabnum=i: tabnum, inputs=[], outputs=[img2img_selected_tab])
@@ -1290,8 +1289,8 @@ def get_textual_inversion_template_names():
 
             with gr.Column(elem_id='ti_gallery_container'):
                 ti_output = gr.Text(elem_id="ti_output", value="", show_label=False)
-                ti_gallery = gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(columns=4)
-                ti_progress = gr.HTML(elem_id="ti_progress", value="")
+                gr.Gallery(label='Output', show_label=False, elem_id='ti_gallery').style(columns=4)
+                gr.HTML(elem_id="ti_progress", value="")
                 ti_outcome = gr.HTML(elem_id="ti_error", value="")
 
         create_embedding.click(
@@ -1668,7 +1667,7 @@ def request_restart():
                     interface.render()
 
         if os.path.exists(os.path.join(script_path, "notification.mp3")):
-            audio_notification = gr.Audio(interactive=False, value=os.path.join(script_path, "notification.mp3"), elem_id="audio_notification", visible=False)
+            gr.Audio(interactive=False, value=os.path.join(script_path, "notification.mp3"), elem_id="audio_notification", visible=False)
 
         footer = shared.html("footer.html")
         footer = footer.format(versions=versions_html())

modules/ui_extensions.py

@@ -490,7 +490,7 @@ def create_ui():
     config_states.list_config_states()
 
     with gr.Blocks(analytics_enabled=False) as ui:
-        with gr.Tabs(elem_id="tabs_extensions") as tabs:
+        with gr.Tabs(elem_id="tabs_extensions"):
             with gr.TabItem("Installed", id="installed"):
 
                 with gr.Row(elem_id="extensions_installed_top"):

modules/ui_extra_networks.py

@@ -263,7 +263,7 @@ def create_ui(container, button, tabname):
     ui.stored_extra_pages = pages_in_preferred_order(extra_pages.copy())
     ui.tabname = tabname
 
-    with gr.Tabs(elem_id=tabname+"_extra_tabs") as tabs:
+    with gr.Tabs(elem_id=tabname+"_extra_tabs"):
         for page in ui.stored_extra_pages:
             page_id = page.title.lower().replace(" ", "_")
 

pyproject.toml

@@ -2,7 +2,9 @@
 
 ignore = [
 	"E501",
-	"E731"
+	"E731",
+	"E402",   # Module level import not at top of file
+	"F401"    # Module imported but unused
 ]
 
 exclude = ["extensions"]

scripts/outpainting_mk_2.py

@@ -72,7 +72,7 @@ def _get_masked_window_rgb(np_mask_grey, hardness=1.):
     height = _np_src_image.shape[1]
     num_channels = _np_src_image.shape[2]
 
-    np_src_image = _np_src_image[:] * (1. - np_mask_rgb)
+    _np_src_image[:] * (1. - np_mask_rgb)
     np_mask_grey = (np.sum(np_mask_rgb, axis=2) / 3.)
     img_mask = np_mask_grey > 1e-6
     ref_mask = np_mask_grey < 1e-3

scripts/postprocessing_upscale.py

@@ -98,13 +98,13 @@ def process(self, pp: scripts_postprocessing.PostprocessedImage, upscale_mode=1,
         assert upscaler2 or (upscaler_2_name is None), f'could not find upscaler named {upscaler_2_name}'
 
         upscaled_image = self.upscale(pp.image, pp.info, upscaler1, upscale_mode, upscale_by, upscale_to_width, upscale_to_height, upscale_crop)
-        pp.info[f"Postprocess upscaler"] = upscaler1.name
+        pp.info["Postprocess upscaler"] = upscaler1.name
 
         if upscaler2 and upscaler_2_visibility > 0:
             second_upscale = self.upscale(pp.image, pp.info, upscaler2, upscale_mode, upscale_by, upscale_to_width, upscale_to_height, upscale_crop)
             upscaled_image = Image.blend(upscaled_image, second_upscale, upscaler_2_visibility)
 
-            pp.info[f"Postprocess upscaler 2"] = upscaler2.name
+            pp.info["Postprocess upscaler 2"] = upscaler2.name
 
         pp.image = upscaled_image
 
@@ -134,4 +134,4 @@ def process(self, pp: scripts_postprocessing.PostprocessedImage, upscale_by=2.0,
         assert upscaler1, f'could not find upscaler named {upscaler_name}'
 
         pp.image = self.upscale(pp.image, pp.info, upscaler1, 0, upscale_by, 0, 0, False)
-        pp.info[f"Postprocess upscaler"] = upscaler1.name
+        pp.info["Postprocess upscaler"] = upscaler1.name

scripts/xyz_grid.py

@@ -316,7 +316,7 @@ def index(ix, iy, iz):
         return Processed(p, [])
 
     z_count = len(zs)
-    sub_grids = [None] * z_count
+
     for i in range(z_count):
         start_index = (i * len(xs) * len(ys)) + i
         end_index = start_index + len(xs) * len(ys)

webui.py

@@ -360,7 +360,7 @@ def fastapi_setup(self):
         if cmd_opts.subpath:
             redirector = FastAPI()
             redirector.get("/")
-            mounted_app = gradio.mount_gradio_app(redirector, shared.demo, path=f"/{cmd_opts.subpath}")
+            gradio.mount_gradio_app(redirector, shared.demo, path=f"/{cmd_opts.subpath}")
 
         wait_on_server(shared.demo)
         print('Restarting UI...')