manual fixes for ruff

extensions-builtin/LDSR/ldsr_model_arch.py

@@ -243,7 +243,7 @@ def make_convolutional_sample(batch, model, custom_steps=None, eta=1.0, quantize
         x_sample_noquant = model.decode_first_stage(sample, force_not_quantize=True)
         log["sample_noquant"] = x_sample_noquant
         log["sample_diff"] = torch.abs(x_sample_noquant - x_sample)
-    except:
+    except Exception:
         pass
 
     log["sample"] = x_sample

extensions-builtin/LDSR/scripts/ldsr_model.py

@@ -7,7 +7,8 @@ from basicsr.utils.download_util import load_file_from_url
 from modules.upscaler import Upscaler, UpscalerData
 from ldsr_model_arch import LDSR
 from modules import shared, script_callbacks
-import sd_hijack_autoencoder, sd_hijack_ddpm_v1
+import sd_hijack_autoencoder
+import sd_hijack_ddpm_v1
 
 
 class UpscalerLDSR(Upscaler):

extensions-builtin/LDSR/sd_hijack_autoencoder.py

 # The content of this file comes from the ldm/models/autoencoder.py file of the compvis/stable-diffusion repo
 # The VQModel & VQModelInterface were subsequently removed from ldm/models/autoencoder.py when we moved to the stability-ai/stablediffusion repo
 # As the LDSR upscaler relies on VQModel & VQModelInterface, the hijack aims to put them back into the ldm.models.autoencoder
-
+import numpy as np
 import torch
 import pytorch_lightning as pl
 import torch.nn.functional as F
 from contextlib import contextmanager
+
+from torch.optim.lr_scheduler import LambdaLR
+
+from ldm.modules.ema import LitEma
 from taming.modules.vqvae.quantize import VectorQuantizer2 as VectorQuantizer
 from ldm.modules.diffusionmodules.model import Encoder, Decoder
 from ldm.util import instantiate_from_config
 
 import ldm.models.autoencoder
+from packaging import version
 
 class VQModel(pl.LightningModule):
     def __init__(self,
@@ -249,7 +254,8 @@ class VQModel(pl.LightningModule):
         if plot_ema:
             with self.ema_scope():
                 xrec_ema, _ = self(x)
-                if x.shape[1] > 3: xrec_ema = self.to_rgb(xrec_ema)
+                if x.shape[1] > 3:
+                    xrec_ema = self.to_rgb(xrec_ema)
                 log["reconstructions_ema"] = xrec_ema
         return log
 

extensions-builtin/LDSR/sd_hijack_ddpm_v1.py

@@ -450,7 +450,7 @@ class LatentDiffusionV1(DDPMV1):
         self.cond_stage_key = cond_stage_key
         try:
             self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
-        except:
+        except Exception:
             self.num_downs = 0
         if not scale_by_std:
             self.scale_factor = scale_factor
@@ -877,16 +877,6 @@ class LatentDiffusionV1(DDPMV1):
                 c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float()))
         return self.p_losses(x, c, t, *args, **kwargs)
 
-    def _rescale_annotations(self, bboxes, crop_coordinates):  # TODO: move to dataset
-        def rescale_bbox(bbox):
-            x0 = clamp((bbox[0] - crop_coordinates[0]) / crop_coordinates[2])
-            y0 = clamp((bbox[1] - crop_coordinates[1]) / crop_coordinates[3])
-            w = min(bbox[2] / crop_coordinates[2], 1 - x0)
-            h = min(bbox[3] / crop_coordinates[3], 1 - y0)
-            return x0, y0, w, h
-
-        return [rescale_bbox(b) for b in bboxes]
-
     def apply_model(self, x_noisy, t, cond, return_ids=False):
 
         if isinstance(cond, dict):
@@ -1157,8 +1147,10 @@ class LatentDiffusionV1(DDPMV1):
 
             if i % log_every_t == 0 or i == timesteps - 1:
                 intermediates.append(x0_partial)
-            if callback: callback(i)
-            if img_callback: img_callback(img, i)
+            if callback:
+                callback(i)
+            if img_callback:
+                img_callback(img, i)
         return img, intermediates
 
     @torch.no_grad()
@@ -1205,8 +1197,10 @@ class LatentDiffusionV1(DDPMV1):
 
             if i % log_every_t == 0 or i == timesteps - 1:
                 intermediates.append(img)
-            if callback: callback(i)
-            if img_callback: img_callback(img, i)
+            if callback:
+                callback(i)
+            if img_callback:
+                img_callback(img, i)
 
         if return_intermediates:
             return img, intermediates
@@ -1322,7 +1316,7 @@ class LatentDiffusionV1(DDPMV1):
 
             if inpaint:
                 # make a simple center square
-                b, h, w = z.shape[0], z.shape[2], z.shape[3]
+                h, w = z.shape[2], z.shape[3]
                 mask = torch.ones(N, h, w).to(self.device)
                 # zeros will be filled in
                 mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0.

extensions-builtin/ScuNET/scunet_model_arch.py

@@ -61,7 +61,9 @@ class WMSA(nn.Module):
         Returns:
             output: tensor shape [b h w c]
         """
-        if self.type != 'W': x = torch.roll(x, shifts=(-(self.window_size // 2), -(self.window_size // 2)), dims=(1, 2))
+        if self.type != 'W':
+            x = torch.roll(x, shifts=(-(self.window_size // 2), -(self.window_size // 2)), dims=(1, 2))
+
         x = rearrange(x, 'b (w1 p1) (w2 p2) c -> b w1 w2 p1 p2 c', p1=self.window_size, p2=self.window_size)
         h_windows = x.size(1)
         w_windows = x.size(2)
@@ -85,8 +87,9 @@ class WMSA(nn.Module):
         output = self.linear(output)
         output = rearrange(output, 'b (w1 w2) (p1 p2) c -> b (w1 p1) (w2 p2) c', w1=h_windows, p1=self.window_size)
 
-        if self.type != 'W': output = torch.roll(output, shifts=(self.window_size // 2, self.window_size // 2),
-                                                 dims=(1, 2))
+        if self.type != 'W':
+            output = torch.roll(output, shifts=(self.window_size // 2, self.window_size // 2), dims=(1, 2))
+
         return output
 
     def relative_embedding(self):

extensions-builtin/SwinIR/scripts/swinir_model.py

@@ -45,7 +45,7 @@ class UpscalerSwinIR(Upscaler):
         img = upscale(img, model)
         try:
             torch.cuda.empty_cache()
-        except:
+        except Exception:
             pass
         return img
 

modules/api/models.py

@@ -223,8 +223,9 @@ for key in _options:
     if(_options[key].dest != 'help'):
         flag = _options[key]
         _type = str
-        if _options[key].default is not None: _type = type(_options[key].default)
-        flags.update({flag.dest: (_type,Field(default=flag.default, description=flag.help))})
+        if _options[key].default is not None:
+            _type = type(_options[key].default)
+        flags.update({flag.dest: (_type, Field(default=flag.default, description=flag.help))})
 
 FlagsModel = create_model("Flags", **flags)
 

modules/codeformer/codeformer_arch.py

@@ -7,7 +7,7 @@ from torch import nn, Tensor
 import torch.nn.functional as F
 from typing import Optional, List
 
-from modules.codeformer.vqgan_arch import *
+from modules.codeformer.vqgan_arch import VQAutoEncoder, ResBlock
 from basicsr.utils import get_root_logger
 from basicsr.utils.registry import ARCH_REGISTRY
 

modules/esrgan_model_arch.py

@@ -438,9 +438,11 @@ def conv_block(in_nc, out_nc, kernel_size, stride=1, dilation=1, groups=1, bias=
     padding = padding if pad_type == 'zero' else 0
 
     if convtype=='PartialConv2D':
+        from torchvision.ops import PartialConv2d  # this is definitely not going to work, but PartialConv2d doesn't work anyway and this shuts up static analyzer
         c = PartialConv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,
                dilation=dilation, bias=bias, groups=groups)
     elif convtype=='DeformConv2D':
+        from torchvision.ops import DeformConv2d  # not tested
         c = DeformConv2d(in_nc, out_nc, kernel_size=kernel_size, stride=stride, padding=padding,
                dilation=dilation, bias=bias, groups=groups)
     elif convtype=='Conv3D':

modules/extra_networks_hypernet.py

-from modules import extra_networks, shared, extra_networks
+from modules import extra_networks, shared
 from modules.hypernetworks import hypernetwork
 
 

modules/images.py

@@ -472,9 +472,9 @@ def get_next_sequence_number(path, basename):
     prefix_length = len(basename)
     for p in os.listdir(path):
         if p.startswith(basename):
-            l = os.path.splitext(p[prefix_length:])[0].split('-')  # splits the filename (removing the basename first if one is defined, so the sequence number is always the first element)
+            parts = os.path.splitext(p[prefix_length:])[0].split('-')  # splits the filename (removing the basename first if one is defined, so the sequence number is always the first element)
             try:
-                result = max(int(l[0]), result)
+                result = max(int(parts[0]), result)
             except ValueError:
                 pass
 

modules/img2img.py

@@ -13,7 +13,6 @@ from modules.shared import opts, state
 import modules.shared as shared
 import modules.processing as processing
 from modules.ui import plaintext_to_html
-import modules.images as images
 import modules.scripts
 
 

modules/interrogate.py

@@ -11,7 +11,6 @@ import torch.hub
 from torchvision import transforms
 from torchvision.transforms.functional import InterpolationMode
 
-import modules.shared as shared
 from modules import devices, paths, shared, lowvram, modelloader, errors
 
 blip_image_eval_size = 384

modules/modelloader.py

@@ -108,12 +108,12 @@ def move_files(src_path: str, dest_path: str, ext_filter: str = None):
                     print(f"Moving {file} from {src_path} to {dest_path}.")
                     try:
                         shutil.move(fullpath, dest_path)
-                    except:
+                    except Exception:
                         pass
             if len(os.listdir(src_path)) == 0:
                 print(f"Removing empty folder: {src_path}")
                 shutil.rmtree(src_path, True)
-    except:
+    except Exception:
         pass
 
 
@@ -141,7 +141,7 @@ def load_upscalers():
             full_model = f"modules.{model_name}_model"
             try:
                 importlib.import_module(full_model)
-            except:
+            except Exception:
                 pass
 
     datas = []

modules/models/diffusion/ddpm_edit.py

@@ -479,7 +479,7 @@ class LatentDiffusion(DDPM):
         self.cond_stage_key = cond_stage_key
         try:
             self.num_downs = len(first_stage_config.params.ddconfig.ch_mult) - 1
-        except:
+        except Exception:
             self.num_downs = 0
         if not scale_by_std:
             self.scale_factor = scale_factor
@@ -891,16 +891,6 @@ class LatentDiffusion(DDPM):
                 c = self.q_sample(x_start=c, t=tc, noise=torch.randn_like(c.float()))
         return self.p_losses(x, c, t, *args, **kwargs)
 
-    def _rescale_annotations(self, bboxes, crop_coordinates):  # TODO: move to dataset
-        def rescale_bbox(bbox):
-            x0 = clamp((bbox[0] - crop_coordinates[0]) / crop_coordinates[2])
-            y0 = clamp((bbox[1] - crop_coordinates[1]) / crop_coordinates[3])
-            w = min(bbox[2] / crop_coordinates[2], 1 - x0)
-            h = min(bbox[3] / crop_coordinates[3], 1 - y0)
-            return x0, y0, w, h
-
-        return [rescale_bbox(b) for b in bboxes]
-
     def apply_model(self, x_noisy, t, cond, return_ids=False):
 
         if isinstance(cond, dict):
@@ -1171,8 +1161,10 @@ class LatentDiffusion(DDPM):
 
             if i % log_every_t == 0 or i == timesteps - 1:
                 intermediates.append(x0_partial)
-            if callback: callback(i)
-            if img_callback: img_callback(img, i)
+            if callback:
+                callback(i)
+            if img_callback:
+                img_callback(img, i)
         return img, intermediates
 
     @torch.no_grad()
@@ -1219,8 +1211,10 @@ class LatentDiffusion(DDPM):
 
             if i % log_every_t == 0 or i == timesteps - 1:
                 intermediates.append(img)
-            if callback: callback(i)
-            if img_callback: img_callback(img, i)
+            if callback:
+                callback(i)
+            if img_callback:
+                img_callback(img, i)
 
         if return_intermediates:
             return img, intermediates
@@ -1337,7 +1331,7 @@ class LatentDiffusion(DDPM):
 
             if inpaint:
                 # make a simple center square
-                b, h, w = z.shape[0], z.shape[2], z.shape[3]
+                h, w = z.shape[2], z.shape[3]
                 mask = torch.ones(N, h, w).to(self.device)
                 # zeros will be filled in
                 mask[:, h // 4:3 * h // 4, w // 4:3 * w // 4] = 0.

modules/models/diffusion/uni_pc/sampler.py

@@ -54,7 +54,8 @@ class UniPCSampler(object):
         if conditioning is not None:
             if isinstance(conditioning, dict):
                 ctmp = conditioning[list(conditioning.keys())[0]]
-                while isinstance(ctmp, list): ctmp = ctmp[0]
+                while isinstance(ctmp, list):
+                    ctmp = ctmp[0]
                 cbs = ctmp.shape[0]
                 if cbs != batch_size:
                     print(f"Warning: Got {cbs} conditionings but batch-size is {batch_size}")

modules/processing.py

@@ -664,7 +664,7 @@ def process_images_inner(p: StableDiffusionProcessing) -> Processed:
             if not shared.opts.dont_fix_second_order_samplers_schedule:
                 try:
                     step_multiplier = 2 if sd_samplers.all_samplers_map.get(p.sampler_name).aliases[0] in ['k_dpmpp_2s_a', 'k_dpmpp_2s_a_ka', 'k_dpmpp_sde', 'k_dpmpp_sde_ka', 'k_dpm_2', 'k_dpm_2_a', 'k_heun'] else 1
-                except:
+                except Exception:
                     pass
             uc = get_conds_with_caching(prompt_parser.get_learned_conditioning, negative_prompts, p.steps * step_multiplier, cached_uc)
             c = get_conds_with_caching(prompt_parser.get_multicond_learned_conditioning, prompts, p.steps * step_multiplier, cached_c)

modules/prompt_parser.py

@@ -54,18 +54,21 @@ def get_learned_conditioning_prompt_schedules(prompts, steps):
     """
 
     def collect_steps(steps, tree):
-        l = [steps]
+        res = [steps]
+
         class CollectSteps(lark.Visitor):
             def scheduled(self, tree):
                 tree.children[-1] = float(tree.children[-1])
                 if tree.children[-1] < 1:
                     tree.children[-1] *= steps
                 tree.children[-1] = min(steps, int(tree.children[-1]))
-                l.append(tree.children[-1])
+                res.append(tree.children[-1])
+
             def alternate(self, tree):
-                l.extend(range(1, steps+1))
+                res.extend(range(1, steps+1))
+
         CollectSteps().visit(tree)
-        return sorted(set(l))
+        return sorted(set(res))
 
     def at_step(step, tree):
         class AtStep(lark.Transformer):

modules/textual_inversion/autocrop.py

@@ -185,7 +185,7 @@ 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:

modules/ui.py

-import html
 import json
-import math
 import mimetypes
 import os
-import platform
-import random
 import sys
-import tempfile
-import time
 import traceback
-from functools import partial, reduce
+from functools import reduce
 import warnings
 
 import gradio as gr

modules/upscaler.py

@@ -45,7 +45,7 @@ class Upscaler:
         try:
             import cv2
             self.can_tile = True
-        except:
+        except Exception:
             pass
 
     @abstractmethod