Revert "much stricter ruff linting"

This reverts commit 310dbf1574.

scripts/consistory/consistory_unet_sdxl.py

@@ -789,10 +789,10 @@ class ConsistorySDXLUNet2DConditionModel(ModelMixin, ConfigMixin, UNet2DConditio
             b2 (`float`): Scaling factor for stage 2 to amplify the contributions of backbone features.
         """
         for i, upsample_block in enumerate(self.up_blocks):
-            upsample_block.s1 = s1
-            upsample_block.s2 = s2
-            upsample_block.b1 = b1
-            upsample_block.b2 = b2
+            setattr(upsample_block, "s1", s1)
+            setattr(upsample_block, "s2", s2)
+            setattr(upsample_block, "b1", b1)
+            setattr(upsample_block, "b2", b2)
 
     def disable_freeu(self):
         """Disables the FreeU mechanism."""

scripts/consistory_ext.py

@@ -196,7 +196,7 @@ class ConsiStoryScript(scripts_manager.Script):
             log.warning(f'ConsiStory: class={shared.sd_model.__class__.__name__} model={shared.sd_model_type} required={supported_model_list}')
             return None
 
-        _subject, concepts, prompts, dropout, _sampler, steps, same, queries, sdsa, _freeu, _freeu_preset, alpha, injection = args # pylint: disable=unused-variable
+        subject, concepts, prompts, dropout, sampler, steps, same, queries, sdsa, freeu, _freeu_preset, alpha, injection = args # pylint: disable=unused-variable
 
         self.create_model() # create model if not already done
         concepts, anchors, prompts, alpha, steps, seed = self.set_args(p, *args) # set arguments

scripts/custom_code.py

@@ -3,7 +3,7 @@ import ast
 import gradio as gr
 from modules import scripts_manager
 from modules.processing import Processed, get_processed
-from modules.shared import cmd_opts # pylint: disable=unused-import
+from modules.shared import opts, cmd_opts, state # pylint: disable=unused-import
 
 
 def convertExpr2Expression(expr):

scripts/differential_diffusion.py

@@ -1627,7 +1627,7 @@ class StableDiffusionDiffImg2ImgPipeline(DiffusionPipeline):
 
         if isinstance(image[0], PIL.Image.Image):
             w, h = image[0].size
-            w, h = map(lambda x: x - x % 8, (w, h))  # resize to integer multiple of 8
+            w, h = map(lambda x: x - x % 8, (w, h))  # resize to integer multiple of 8 # noqa: C417
 
             image = [np.array(i.resize((w, h), resample=PIL_INTERPOLATION["lanczos"]))[None, :] for i in image]
             image = np.concatenate(image, axis=0)

scripts/example.py

@@ -1,5 +1,5 @@
 import gradio as gr
-from diffusers.pipelines import StableDiffusionPipeline # pylint: disable=unused-import
+from diffusers.pipelines import StableDiffusionPipeline, StableDiffusionXLPipeline # pylint: disable=unused-import
 from modules import shared, scripts_manager, processing, sd_models, devices
 from modules.logger import log
 

scripts/freescale/free_lunch_utils.py

@@ -137,10 +137,10 @@ def register_free_upblock2d(model, b1=1.2, b2=1.4, s1=0.9, s2=0.2):
     for i, upsample_block in enumerate(model.unet.up_blocks):
         if isinstance_str(upsample_block, "UpBlock2D"):
             upsample_block.forward = up_forward(upsample_block)
-            upsample_block.b1 = b1
-            upsample_block.b2 = b2
-            upsample_block.s1 = s1
-            upsample_block.s2 = s2
+            setattr(upsample_block, 'b1', b1)
+            setattr(upsample_block, 'b2', b2)
+            setattr(upsample_block, 's1', s1)
+            setattr(upsample_block, 's2', s2)
 
 
 def register_crossattn_upblock2d(model):
@@ -300,7 +300,7 @@ def register_free_crossattn_upblock2d(model, b1=1.2, b2=1.4, s1=0.9, s2=0.2):
     for i, upsample_block in enumerate(model.unet.up_blocks):
         if isinstance_str(upsample_block, "CrossAttnUpBlock2D"):
             upsample_block.forward = up_forward(upsample_block)
-            upsample_block.b1 = b1
-            upsample_block.b2 = b2
-            upsample_block.s1 = s1
-            upsample_block.s2 = s2
+            setattr(upsample_block, 'b1', b1)
+            setattr(upsample_block, 'b2', b2)
+            setattr(upsample_block, 's1', s1)
+            setattr(upsample_block, 's2', s2)

scripts/infiniteyou/resampler.py

@@ -18,7 +18,7 @@ def FeedForward(dim, mult=4):
 
 
 def reshape_tensor(x, heads):
-    bs, length, _width = x.shape
+    bs, length, width = x.shape
     #(bs, length, width) --> (bs, length, n_heads, dim_per_head)
     x = x.view(bs, length, heads, -1)
     # (bs, length, n_heads, dim_per_head) --> (bs, n_heads, length, dim_per_head)

scripts/instantir/sdxl_instantir.py

@@ -166,11 +166,11 @@ PREVIEWER_LORA_MODULES = [
 
 def remove_attn2(model):
     def recursive_find_module(name, module):
-        if "up_blocks" not in name and "down_blocks" not in name and "mid_block" not in name: return
+        if not "up_blocks" in name and not "down_blocks" in name and not "mid_block" in name: return
         elif "resnets" in name: return
         if hasattr(module, "attn2"):
-            module.attn2 = None
-            module.norm2 = None
+            setattr(module, "attn2", None)
+            setattr(module, "norm2", None)
             return
         for sub_name, sub_module in module.named_children():
             recursive_find_module(f"{name}.{sub_name}", sub_module)
@@ -834,8 +834,8 @@ class InstantIRPipeline(
         )
         if (
             isinstance(self.aggregator, Aggregator)
-            or (is_compiled
-            and isinstance(self.aggregator._orig_mod, Aggregator))
+            or is_compiled
+            and isinstance(self.aggregator._orig_mod, Aggregator)
         ):
             self.check_image(image, prompt, prompt_embeds)
         else:

scripts/mixture_of_diffusers.py

@@ -71,6 +71,7 @@ class MoDScript(scripts_manager.Script):
         from installer import install
         install('ligo-segments')
         try:
+            from ligo.segments import segment # pylint: disable=unused-import
             return True
         except Exception as e:
             log.error(f'MoD: {e}')

scripts/mixture_tiling.py

@@ -17,6 +17,7 @@ def check_dependencies():
         if not installed(pkg[1], quiet=True):
             install(pkg[0], pkg[1], ignore=False)
     try:
+        from ligo.segments import segment # pylint: disable=unused-import
         checked_ok = True
         return True
     except Exception as e:

scripts/prompt_enhance.py

@@ -798,7 +798,7 @@ class PromptEnhanceScript(scripts_manager.Script):
             if debug_enabled:
                 errors.display(e, 'Prompt enhance')
             self.busy = False
-            response = f'Error: {e!s}'
+            response = f'Error: {str(e)}'
         finally:
             offload_aux('prompt_enhance')
             devices.torch_gc(force=False, reason='prompt-enhance')

scripts/pulid/eva_clip/factory.py

@@ -18,7 +18,7 @@ from .tokenizer import HFTokenizer, tokenize
 from .utils import resize_clip_pos_embed, resize_evaclip_pos_embed, resize_visual_pos_embed, resize_eva_pos_embed
 
 
-_MODEL_CONFIG_PATHS = [Path(__file__).parent / "model_configs/"]
+_MODEL_CONFIG_PATHS = [Path(__file__).parent / f"model_configs/"]
 _MODEL_CONFIGS = {}  # directory (model_name: config) of model architecture configs
 
 

scripts/pulid/eva_clip/hf_model.py

@@ -14,7 +14,7 @@ try:
     from transformers import AutoModel, AutoModelForMaskedLM, AutoTokenizer, AutoConfig, PretrainedConfig
     from transformers.modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling, \
         BaseModelOutputWithPoolingAndCrossAttentions
-except ImportError:
+except ImportError as e:
     transformers = None
 
 

scripts/pulid/eva_clip/model.py

@@ -385,7 +385,7 @@ def build_model_from_openai_state_dict(
     vocab_size = state_dict["token_embedding.weight"].shape[0]
     transformer_width = state_dict["ln_final.weight"].shape[0]
     transformer_heads = transformer_width // 64
-    transformer_layers = len(set(k.split(".")[2] for k in state_dict if k.startswith("transformer.resblocks")))
+    transformer_layers = len(set(k.split(".")[2] for k in state_dict if k.startswith(f"transformer.resblocks")))
 
     vision_cfg = CLIPVisionCfg(
         layers=vision_layers,

scripts/pulid/eva_clip/timm_model.py

@@ -110,7 +110,7 @@ class TimmModel(nn.Module):
     def set_grad_checkpointing(self, enable=True):
         try:
             self.trunk.set_grad_checkpointing(enable)
-        except Exception:
+        except Exception as e:
             logging.warning('grad checkpointing not supported for this timm image tower, continuing without...')
 
     def forward(self, x):

scripts/pulid/eva_clip/tokenizer.py

@@ -12,6 +12,7 @@ import regex as re
 import torch
 
 # https://stackoverflow.com/q/62691279
+import os
 os.environ["TOKENIZERS_PARALLELISM"] = "false"
 
 

scripts/pulid/eva_clip/utils.py

@@ -149,7 +149,7 @@ def resize_rel_pos_embed(state_dict, model, interpolation: str = 'bicubic', seq_
             dst_num_pos, _ = model.visual.state_dict()[key].size()
             dst_patch_shape = model.visual.patch_embed.patch_shape
             if dst_patch_shape[0] != dst_patch_shape[1]:
-                raise NotImplementedError
+                raise NotImplementedError()
             num_extra_tokens = dst_num_pos - (dst_patch_shape[0] * 2 - 1) * (dst_patch_shape[1] * 2 - 1)
             src_size = int((src_num_pos - num_extra_tokens) ** 0.5)
             dst_size = int((dst_num_pos - num_extra_tokens) ** 0.5)

scripts/pulid/pulid_flux.py

@@ -6,7 +6,7 @@ from modules.logger import log
 
 
 def apply_flux(pipe: FluxPipeline):
-    if not hasattr(pipe, 'transformer') or 'Nunchaku' not in pipe.transformer.__class__.__name__:
+    if not hasattr(pipe, 'transformer') or not 'Nunchaku' in pipe.transformer.__class__.__name__:
         log.error('PuLID: flux support requires nunchaku')
         return pipe
 

scripts/stablevideodiffusion.py

@@ -87,6 +87,7 @@ class SVDScript(scripts_manager.Script):
         c = shared.sd_model.__class__.__name__
         model_loaded = shared.sd_model.sd_checkpoint_info.model_name if shared.sd_loaded else None
         if model_name != model_loaded or c != 'StableVideoDiffusionPipeline':
+            from diffusers import StableVideoDiffusionPipeline # pylint: disable=unused-import
             shared.opts.sd_model_checkpoint = model_path
             sd_models.reload_model_weights()
             shared.sd_model._encode_vae_image = self._encode_image # pylint: disable=protected-access

scripts/xyz/xyz_grid_classes.py

@@ -34,6 +34,7 @@ from scripts.xyz.xyz_grid_shared import ( # pylint: disable=no-name-in-module, u
     apply_control,
     format_value_add_label,
     format_bool,
+    format_value,
     format_value_join_list,
     do_nothing,
     format_nothing,

scripts/xyz_grid.py

@@ -11,6 +11,7 @@ import gradio as gr
 from scripts.xyz.xyz_grid_shared import str_permutations, list_to_csv_string, restore_comma, re_range, re_plain_comma # pylint: disable=no-name-in-module
 from scripts.xyz.xyz_grid_classes import axis_options, AxisOption, SharedSettingsStackHelper # pylint: disable=no-name-in-module
 from scripts.xyz.xyz_grid_draw import draw_xyz_grid # pylint: disable=no-name-in-module
+from scripts.xyz.xyz_grid_shared import apply_field, apply_task_args, apply_setting, apply_prompt, apply_order, apply_sampler, apply_hr_sampler_name, confirm_samplers, apply_checkpoint, apply_refiner, apply_unet, apply_clip_skip, apply_vae, list_lora, apply_lora, apply_lora_strength, apply_te, apply_styles, apply_upscaler, apply_context, apply_detailer, apply_override, apply_processing, apply_options, apply_seed, format_value_add_label, format_value, format_value_join_list, do_nothing, format_nothing # pylint: disable=no-name-in-module, unused-import
 from modules import shared, errors, scripts_manager, images, video, processing
 from modules.ui_components import ToolButton
 from modules.ui_sections import create_video_inputs