ruff updates

Signed-off-by: Vladimir Mandic <mandic00@live.com>

extensions-builtin/Lora/network_lora.py

@@ -31,7 +31,7 @@ class NetworkModuleLora(network.NetworkModule):
         if is_linear:
             weight = weight.reshape(weight.shape[0], -1)
             module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
-        elif is_conv and key == "lora_down.weight" or key == "dyn_up":
+        elif is_conv and (key == "lora_down.weight" or key == "dyn_up"):
             if len(weight.shape) == 2:
                 weight = weight.reshape(weight.shape[0], -1, 1, 1)
             if weight.shape[2] != 1 or weight.shape[3] != 1:
@@ -40,7 +40,7 @@ class NetworkModuleLora(network.NetworkModule):
                 module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False)
         elif is_conv and key == "lora_mid.weight":
             module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], self.sd_module.kernel_size, self.sd_module.stride, self.sd_module.padding, bias=False)
-        elif is_conv and key == "lora_up.weight" or key == "dyn_down":
+        elif is_conv and (key == "lora_up.weight" or key == "dyn_down"):
             module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False)
         else:
             raise AssertionError(f'Lora unsupported: layer={self.network_key} type={type(self.sd_module).__name__}')

modules/devices_mac.py

@@ -24,7 +24,7 @@ def cumsum_fix(input, cumsum_func, *args, **kwargs): # pylint: disable=redefined
         output_dtype = kwargs.get('dtype', input.dtype)
         if output_dtype == torch.int64:
             return cumsum_func(input.cpu(), *args, **kwargs).to(input.device)
-        elif output_dtype == torch.bool or cumsum_needs_int_fix and (output_dtype == torch.int8 or output_dtype == torch.int16):
+        elif output_dtype == torch.bool or (cumsum_needs_int_fix and (output_dtype == torch.int8 or output_dtype == torch.int16)):
             return cumsum_func(input.to(torch.int32), *args, **kwargs).to(torch.int64)
     return cumsum_func(input, *args, **kwargs)
 
@@ -42,7 +42,7 @@ if has_mps:
 
         # MPS workaround for https://github.com/pytorch/pytorch/issues/79383
         CondFunc('torch.Tensor.to', lambda orig_func, self, *args, **kwargs: orig_func(self.contiguous(), *args, **kwargs),
-                                                          lambda _, self, *args, **kwargs: self.device.type != 'mps' and (args and isinstance(args[0], torch.device) and args[0].type == 'mps' or isinstance(kwargs.get('device'), torch.device) and kwargs['device'].type == 'mps'))
+                                                          lambda _, self, *args, **kwargs: self.device.type != 'mps' and ((args and isinstance(args[0], torch.device) and args[0].type == 'mps') or (isinstance(kwargs.get('device'), torch.device) and kwargs['device'].type == 'mps')))
         # MPS workaround for https://github.com/pytorch/pytorch/issues/80800
         CondFunc('torch.nn.functional.layer_norm', lambda orig_func, *args, **kwargs: orig_func(*([args[0].contiguous()] + list(args[1:])), **kwargs),
                                                                                         lambda _, *args, **kwargs: args and isinstance(args[0], torch.Tensor) and args[0].device.type == 'mps')

modules/images_namegen.py

@@ -45,12 +45,12 @@ class FilenameGenerator:
         'prompt_hash': lambda self: hashlib.sha256(self.prompt.encode()).hexdigest()[0:8],
 
         'sampler': lambda self: self.p and self.p.sampler_name,
-        'seed': lambda self: self.seed and str(self.seed) or '',
+        'seed': lambda self: (self.seed and str(self.seed)) or '',
         'steps': lambda self: self.p and getattr(self.p, 'steps', 0),
         'cfg': lambda self: self.p and getattr(self.p, 'cfg_scale', 0),
         'clip_skip': lambda self: self.p and getattr(self.p, 'clip_skip', 0),
         'denoising': lambda self: self.p and getattr(self.p, 'denoising_strength', 0),
-        'styles': lambda self: self.p and ", ".join([style for style in self.p.styles if not style == "None"]) or "None",
+        'styles': lambda self: (self.p and ", ".join([style for style in self.p.styles if not style == "None"])) or "None",
         'uuid': lambda self: str(uuid.uuid4()),
     }
     default_time_format = '%Y%m%d%H%M%S'

modules/lora/network_lora.py

@@ -31,7 +31,7 @@ class NetworkModuleLora(network.NetworkModule):
         if is_linear:
             weight = weight.reshape(weight.shape[0], -1)
             module = torch.nn.Linear(weight.shape[1], weight.shape[0], bias=False)
-        elif is_conv and key == "lora_down.weight" or key == "dyn_up":
+        elif is_conv and (key == "lora_down.weight" or key == "dyn_up"):
             if len(weight.shape) == 2:
                 weight = weight.reshape(weight.shape[0], -1, 1, 1)
             if weight.shape[2] != 1 or weight.shape[3] != 1:
@@ -40,7 +40,7 @@ class NetworkModuleLora(network.NetworkModule):
                 module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False)
         elif is_conv and key == "lora_mid.weight":
             module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], self.sd_module.kernel_size, self.sd_module.stride, self.sd_module.padding, bias=False)
-        elif is_conv and key == "lora_up.weight" or key == "dyn_down":
+        elif is_conv and (key == "lora_up.weight" or key == "dyn_down"):
             module = torch.nn.Conv2d(weight.shape[1], weight.shape[0], (1, 1), bias=False)
         else:
             raise AssertionError(f'Lora unsupported: layer={self.network_key} type={type(self.sd_module).__name__}')

modules/merging/merge_methods.py

@@ -4,7 +4,7 @@ from typing import Tuple
 import torch
 from torch import Tensor
 
-__all__ = [
+__all__ = [ # noqa: RUF022
     "weighted_sum",
     "weighted_subtraction",
     "tensor_sum",

modules/processing_args.py

@@ -23,7 +23,7 @@ def task_specific_kwargs(p, model):
         if isinstance(p.init_images[0], str):
             p.init_images = [helpers.decode_base64_to_image(i, quiet=True) for i in p.init_images]
         p.init_images = [i.convert('RGB') if i.mode != 'RGB' else i for i in p.init_images]
-    if sd_models.get_diffusers_task(model) == sd_models.DiffusersTaskType.TEXT_2_IMAGE or len(getattr(p, 'init_images', [])) == 0 and not is_img2img_model:
+    if (sd_models.get_diffusers_task(model) == sd_models.DiffusersTaskType.TEXT_2_IMAGE or len(getattr(p, 'init_images', [])) == 0) and not is_img2img_model:
         p.ops.append('txt2img')
         if hasattr(p, 'width') and hasattr(p, 'height'):
             task_args = {

modules/processing_helpers.py

@@ -159,7 +159,7 @@ def create_random_tensors(shape, seeds, subseeds=None, subseed_strength=0.0, see
     # enables the generation of additional tensors with noise that the sampler will use during its processing.
     # Using those pre-generated tensors instead of simple torch.randn allows a batch with seeds [100, 101] to
     # produce the same images as with two batches [100], [101].
-    if p is not None and p.sampler is not None and (len(seeds) > 1 and shared.opts.enable_batch_seeds or eta_noise_seed_delta > 0):
+    if p is not None and p.sampler is not None and ((len(seeds) > 1 and shared.opts.enable_batch_seeds) or (eta_noise_seed_delta > 0)):
         sampler_noises = [[] for _ in range(p.sampler.number_of_needed_noises(p))]
     else:
         sampler_noises = None
@@ -414,7 +414,7 @@ def resize_hires(p, latents): # input=latents output=pil if not latent_upscaler
     if latent_upscaler is not None:
         return torch.nn.functional.interpolate(latents, size=(p.hr_upscale_to_y // 8, p.hr_upscale_to_x // 8), mode=latent_upscaler["mode"], antialias=latent_upscaler["antialias"])
     first_pass_images = processing_vae.vae_decode(latents=latents, model=shared.sd_model, full_quality=p.full_quality, output_type='pil', width=p.width, height=p.height)
-    if p.hr_upscale_to_x == 0 or p.hr_upscale_to_y == 0 and hasattr(p, 'init_hr'):
+    if p.hr_upscale_to_x == 0 or (p.hr_upscale_to_y == 0 and hasattr(p, 'init_hr')):
         shared.log.error('Hires: missing upscaling dimensions')
         return first_pass_images
     resized_images = []

modules/sd_disable_initialization.py

@@ -53,7 +53,7 @@ class DisableInitialization:
         def transformers_utils_hub_get_file_from_cache(original, url, *args, **kwargs):
 
             # this file is always 404, prevent making request
-            if url == 'https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/added_tokens.json' or url == 'openai/clip-vit-large-patch14' and args[0] == 'added_tokens.json':
+            if (url == 'https://huggingface.co/openai/clip-vit-large-patch14/resolve/main/added_tokens.json' or url == 'openai/clip-vit-large-patch14') and args[0] == 'added_tokens.json':
                 return None
 
             try:

modules/sd_hijack_unet.py

@@ -70,7 +70,7 @@ CondFunc('ldm.modules.diffusionmodules.openaimodel.timestep_embedding', lambda o
 if version.parse(torch.__version__) <= version.parse("1.13.2") or torch.cuda.is_available():
     CondFunc('ldm.modules.diffusionmodules.util.GroupNorm32.forward', lambda orig_func, self, *args, **kwargs: orig_func(self.float(), *args, **kwargs), unet_needs_upcast)
     CondFunc('ldm.modules.attention.GEGLU.forward', lambda orig_func, self, x: orig_func(self.float(), x.float()).to(devices.dtype_unet), unet_needs_upcast)
-    CondFunc('open_clip.transformer.ResidualAttentionBlock.__init__', lambda orig_func, *args, **kwargs: kwargs.update({'act_layer': GELUHijack}) and False or orig_func(*args, **kwargs), lambda _, *args, **kwargs: kwargs.get('act_layer') is None or kwargs['act_layer'] == torch.nn.GELU)
+    CondFunc('open_clip.transformer.ResidualAttentionBlock.__init__', lambda orig_func, *args, **kwargs: (kwargs.update({'act_layer': GELUHijack}) and False) or orig_func(*args, **kwargs), lambda _, *args, **kwargs: kwargs.get('act_layer') is None or kwargs['act_layer'] == torch.nn.GELU)
 
 first_stage_cond = lambda _, self, *args, **kwargs: devices.unet_needs_upcast and self.model.diffusion_model.dtype == torch.float16 # pylint: disable=unnecessary-lambda-assignment
 first_stage_sub = lambda orig_func, self, x, **kwargs: orig_func(self, x.to(devices.dtype_vae), **kwargs) # pylint: disable=unnecessary-lambda-assignment

scripts/ltxvideo.py

@@ -43,7 +43,7 @@ class Script(scripts.Script):
         # set params
         image = getattr(p, 'init_images', None)
         image = None if image is None or len(image) == 0 else image[0]
-        if p.width == 0 or p.height == 0 and image is not None:
+        if (p.width == 0 or p.height == 0) and image is not None:
             p.width = image.width
             p.height = image.height
         num_frames = 8 * int(num_frames // 8) + 1

scripts/regional_prompting.py

@@ -9,7 +9,7 @@ from modules import shared, devices, scripts, processing, sd_models, prompt_pars
 def hijack_register_modules(self, **kwargs):
     for name, module in kwargs.items():
         register_dict = None
-        if module is None or isinstance(module, (tuple, list)) and module[0] is None:
+        if module is None or (isinstance(module, (tuple, list)) and module[0] is None):
             register_dict = {name: (None, None)}
         elif isinstance(module, bool):
             pass