ruff linting fixes

CHANGELOG.md

@@ -2,6 +2,9 @@
 
 ## Update for 06/13/2023
 
+One bigger update...
+Both some **new functionality** as well as **massive merges** from upstream  
+
 - new cache for models/lora/lyco metadata: `metadata.json`  
   drastically reduces disk access on app startup  
 - allow saving of **ui default values**  
@@ -10,7 +13,8 @@
   default is to auto-load model on startup, can be changed in settings -> stable diffusion  
   if disabled, model will be loaded on first request, e.g. when you click generate  
 - updated `accelerate` and `xformers`
-- huge nubmer of changes ported from a1111 upstream  
+- huge nubmer of changes ported from **A1111** upstream  
+  this was a massive merge
   hopefully this does not cause any regressions  
 
 

cli/create-previews.py

@@ -163,8 +163,8 @@ async def lora(params):
     if not os.path.exists(folder):
         log.error({ 'lora directory not found': folder })
         return
-    models1 = [f for f in Path(folder).glob('*.safetensors')]
-    models2 = [f for f in Path(folder).glob('*.ckpt')]
+    models1 = list(Path(folder).glob('*.safetensors'))
+    models2 = list(Path(folder).glob('*.ckpt'))
     models = [f.stem for f in models1 + models2]
     log.info({ 'loras': len(models) })
     for model in models:
@@ -204,8 +204,8 @@ async def lyco(params):
     if not os.path.exists(folder):
         log.error({ 'lyco directory not found': folder })
         return
-    models1 = [f for f in Path(folder).glob('*.safetensors')]
-    models2 = [f for f in Path(folder).glob('*.ckpt')]
+    models1 = list(Path(folder).glob('*.safetensors'))
+    models2 = list(Path(folder).glob('*.ckpt'))
     models = [f.stem for f in models1 + models2]
     log.info({ 'lycos': len(models) })
     for model in models:

cli/generate.py

@@ -72,7 +72,7 @@ def exif(info, i = None, op = 'generate'):
         template += ' | grid {num}'.format(num = sd.generate.batch_size * sd.generate.n_iter) # pylint: disable=consider-using-f-string
     ifd = ImageFileDirectory_v2()
     exif_stream = io.BytesIO()
-    _TAGS = dict(((v, k) for k, v in TAGS.items())) # enumerate possible exif tags
+    _TAGS = {v: k for k, v in TAGS.items()} # enumerate possible exif tags
     ifd[_TAGS['ImageDescription']] = template
     ifd.save(exif_stream)
     val = b'Exif\x00\x00' + exif_stream.getvalue()
@@ -337,7 +337,7 @@ async def main():
         scheduler = sampler(params, options)
         t0 = time.perf_counter()
         data = await generate() # generate returns list of images
-        if not 'image' in data:
+        if 'image' not in data:
             break
         stats.images += len(data.image)
         t1 = time.perf_counter()

cli/image-exif.py

@@ -16,8 +16,8 @@ class Exif: # pylint: disable=single-string-used-for-slots
     def __init__(self, image = None):
         super(Exif, self).__setattr__('exif', Image.Exif())
         self.pnginfo = PngImagePlugin.PngInfo()
-        self.tags = {**dict(((k, v) for k, v in ExifTags.TAGS.items())), **dict(((k, v) for k, v in ExifTags.GPSTAGS.items()))}
-        self.ids = {**dict(((v, k) for k, v in ExifTags.TAGS.items())), **dict(((v, k) for k, v in ExifTags.GPSTAGS.items()))}
+        self.tags = {**dict(ExifTags.TAGS.items()), **dict(ExifTags.GPSTAGS.items())}
+        self.ids = {**{v: k for k, v in ExifTags.TAGS.items()}, **{v: k for k, v in ExifTags.GPSTAGS.items()}}
         if image is not None:
             self.load(image)
 
@@ -101,6 +101,6 @@ if __name__ == '__main__':
         if os.path.isfile(fn):
             read_exif(fn)
         elif os.path.isdir(fn):
-            for root, dirs, files in os.walk(fn):
+            for root, _dirs, files in os.walk(fn):
                 for file in files:
                     read_exif(os.path.join(root, file))

cli/image-watermark.py

@@ -39,7 +39,7 @@ def get_exif(image):
 
 def set_exif(d: dict):
     ifd = ImageFileDirectory_v2()
-    _TAGS = dict(((v, k) for k, v in TAGS.items())) # enumerate possible exif tags
+    _TAGS = {v: k for k, v in TAGS.items()} # enumerate possible exif tags
     for k, v in d.items():
         ifd[_TAGS[k]] = v
     exif_stream = io.BytesIO()

cli/process.py

@@ -21,7 +21,7 @@ all_images_by_type = {}
 
 
 class Result(object):
-    def __init__(self, typ: str, fn: str, tag: str = None, requested: list = []):
+    def __init__(self, typ: str, fn: str, tag: str = None, requested: list = []): # noqa: B006
         self.type = typ
         self.input = fn
         self.output = ''
@@ -139,7 +139,7 @@ def upscale_restore_image(res: Result, upscale: bool = False, restore: bool = Fa
         res.ops.append('upscale')
     if restore:
         kwargs.codeformer_visibility = 1.0
-        kwargs.codeformer_weight: 0.2
+        kwargs.codeformer_weight = 0.2
         res.ops.append('restore')
     if upscale or restore:
         result = sdapi.postsync('/sdapi/v1/extra-single-image', kwargs)
@@ -260,7 +260,7 @@ def save_image(res: Result, folder: str):
     return res
 
 
-def file(filename: str, folder: str, tag = None, requested = []):
+def file(filename: str, folder: str, tag = None, requested = []): # noqa: B006
     # initialize result dict
     res = Result(fn = filename, typ='unknown', tag=tag, requested = requested)
     # open image

cli/torch-compile.py

@@ -59,9 +59,9 @@ if __name__ == '__main__':
     results = {}
     times = []
     print('eager initial eval:', timed(lambda: evaluate(model, inp))[1])
-    for i in range(N_ITERS):
+    for _i in range(N_ITERS):
         inp = generate_data(16)[0]
-        _res, time = timed(lambda: evaluate(model, inp))
+        _res, time = timed(lambda: evaluate(model, inp)) # noqa: B023
         times.append(time)
     results['default'] = np.median(times)
 
@@ -71,11 +71,11 @@ if __name__ == '__main__':
             # required before changing backends
             torch._dynamo.reset() # pylint: disable=protected-access
             eval_dyn = dynamo.optimize(backend)(evaluate)
-            print('dynamo initial eval:', backend, timed(lambda: eval_dyn(model, inp))[1])
+            print('dynamo initial eval:', backend, timed(lambda: eval_dyn(model, inp))[1]) # noqa: B023
             times = []
-            for i in range(N_ITERS):
+            for _i in range(N_ITERS):
                 inp = generate_data(16)[0]
-                _res, time = timed(lambda: eval_dyn(model, inp))
+                _res, time = timed(lambda: eval_dyn(model, inp)) # noqa: B023
                 times.append(time)
             results[backend] = np.median(times)
         except Exception as err:

cli/train.py

@@ -399,7 +399,7 @@ if __name__ == '__main__':
             train_embedding()
         if args.type == 'lora' or args.type == 'lyco' or args.type == 'dreambooth':
             train_lora()
-    except KeyboardInterrupt as e:
+    except KeyboardInterrupt:
         log.error('interrupt requested')
         sdapi.interrupt()
     mem_stats()

installer.py

@@ -91,7 +91,7 @@ def print_profile(profile: cProfile.Profile, msg: str):
     ps.sort_stats(pstats.SortKey.CUMULATIVE).print_stats(15)
     profile = None
     lines = stream.getvalue().split('\n')
-    lines = [l for l in lines if '<frozen' not in l and '{built-in' not in l and '/logging' not in l and '/rich' not in l]
+    lines = [line for line in lines if '<frozen' not in line and '{built-in' not in line and '/logging' not in line and '/rich' not in line]
     print(f'Profile {msg}:', '\n'.join(lines))
 
 

modules/codeformer/codeformer_arch.py

@@ -165,7 +165,7 @@ class CodeFormer(VQAutoEncoder):
                 connect_list=('32', '64', '128', '256'),
                 fix_modules=('quantize', 'generator')):
         super(CodeFormer, self).__init__(512, 64, [1, 2, 2, 4, 4, 8], 'nearest',2, [16], codebook_size)
-      
+
         if fix_modules is not None:
             for module in fix_modules:
                 for param in getattr(self, module).parameters():
@@ -222,7 +222,7 @@ class CodeFormer(VQAutoEncoder):
         enc_feat_dict = {}
         out_list = [self.fuse_encoder_block[f_size] for f_size in self.connect_list]
         for i, block in enumerate(self.encoder.blocks):
-            x = block(x) 
+            x = block(x)
             if i in out_list:
                 enc_feat_dict[str(x.shape[-1])] = x.clone()
 
@@ -267,7 +267,7 @@ class CodeFormer(VQAutoEncoder):
         fuse_list = [self.fuse_generator_block[f_size] for f_size in self.connect_list]
 
         for i, block in enumerate(self.generator.blocks):
-            x = block(x) 
+            x = block(x)
             if i in fuse_list: # fuse after i-th block
                 f_size = str(x.shape[-1])
                 if w>0:

modules/codeformer/vqgan_arch.py

@@ -15,7 +15,7 @@ from basicsr.utils.registry import ARCH_REGISTRY
 
 def normalize(in_channels):
     return torch.nn.GroupNorm(num_groups=32, num_channels=in_channels, eps=1e-6, affine=True)
-    
+
 
 @torch.jit.script
 def swish(x):
@@ -212,15 +212,15 @@ class AttnBlock(nn.Module):
         # compute attention
         b, c, h, w = q.shape
         q = q.reshape(b, c, h*w)
-        q = q.permute(0, 2, 1)   
+        q = q.permute(0, 2, 1)
         k = k.reshape(b, c, h*w)
-        w_ = torch.bmm(q, k) 
+        w_ = torch.bmm(q, k)
         w_ = w_ * (int(c)**(-0.5))
         w_ = F.softmax(w_, dim=2)
 
         # attend to values
         v = v.reshape(b, c, h*w)
-        w_ = w_.permute(0, 2, 1) 
+        w_ = w_.permute(0, 2, 1)
         h_ = torch.bmm(v, w_)
         h_ = h_.reshape(b, c, h, w)
 
@@ -272,18 +272,18 @@ class Encoder(nn.Module):
     def forward(self, x):
         for block in self.blocks:
             x = block(x)
-            
+
         return x
 
 
 class Generator(nn.Module):
     def __init__(self, nf, emb_dim, ch_mult, res_blocks, img_size, attn_resolutions):
         super().__init__()
-        self.nf = nf 
-        self.ch_mult = ch_mult 
+        self.nf = nf
+        self.ch_mult = ch_mult
         self.num_resolutions = len(self.ch_mult)
         self.num_res_blocks = res_blocks
-        self.resolution = img_size 
+        self.resolution = img_size
         self.attn_resolutions = attn_resolutions
         self.in_channels = emb_dim
         self.out_channels = 3
@@ -317,24 +317,24 @@ class Generator(nn.Module):
         blocks.append(nn.Conv2d(block_in_ch, self.out_channels, kernel_size=3, stride=1, padding=1))
 
         self.blocks = nn.ModuleList(blocks)
-   
+
 
     def forward(self, x):
         for block in self.blocks:
             x = block(x)
-            
+
         return x
 
-  
+
 @ARCH_REGISTRY.register()
 class VQAutoEncoder(nn.Module):
     def __init__(self, img_size, nf, ch_mult, quantizer="nearest", res_blocks=2, attn_resolutions=None, codebook_size=1024, emb_dim=256,
                 beta=0.25, gumbel_straight_through=False, gumbel_kl_weight=1e-8, model_path=None):
         super().__init__()
         logger = get_root_logger()
-        self.in_channels = 3 
-        self.nf = nf 
-        self.n_blocks = res_blocks 
+        self.in_channels = 3
+        self.nf = nf
+        self.n_blocks = res_blocks
         self.codebook_size = codebook_size
         self.embed_dim = emb_dim
         self.ch_mult = ch_mult
@@ -365,11 +365,11 @@ class VQAutoEncoder(nn.Module):
                 self.kl_weight
             )
         self.generator = Generator(
-            self.nf, 
+            self.nf,
             self.embed_dim,
-            self.ch_mult, 
-            self.n_blocks, 
-            self.resolution, 
+            self.ch_mult,
+            self.n_blocks,
+            self.resolution,
             self.attn_resolutions
         )
 
@@ -434,4 +434,4 @@ class VQGANDiscriminator(nn.Module):
                 raise ValueError('Wrong params!')
 
     def forward(self, x):
-        return self.main(x)
+        return self.main(x)

modules/errors.py

@@ -19,7 +19,7 @@ traceback_install(console=console, extra_lines=1, width=console.width, word_wrap
 already_displayed = {}
 
 
-def install(suppress=[]):
+def install(suppress=[]): # noqa: B006
     warnings.filterwarnings("ignore", category=UserWarning)
     pretty_install(console=console)
     traceback_install(console=console, extra_lines=1, width=console.width, word_wrap=False, indent_guides=False, suppress=suppress)
@@ -34,7 +34,7 @@ def print_error_explanation(message):
         log.error(line)
 
 
-def display(e: Exception, task, suppress=[]):
+def display(e: Exception, task, suppress=[]): # noqa: B006
     log.error(f"{task or 'error'}: {type(e).__name__}")
     console.print_exception(show_locals=False, max_frames=5, extra_lines=1, suppress=suppress, theme="ansi_dark", word_wrap=False, width=min([console.width, 200]))
 
@@ -53,5 +53,5 @@ def run(code, task):
         display(e, task)
 
 
-def exception(suppress=[]):
+def exception(suppress=[]): # noqa: B006
     console.print_exception(show_locals=False, max_frames=10, extra_lines=2, suppress=suppress, theme="ansi_dark", word_wrap=False, width=min([console.width, 200]))

modules/esrgan_model_arch.py

@@ -104,7 +104,7 @@ class ResidualDenseBlock_5C(nn.Module):
     Modified options that can be used:
         - "Partial Convolution based Padding" arXiv:1811.11718
         - "Spectral normalization" arXiv:1802.05957
-        - "ICASSP 2020 - ESRGAN+ : Further Improving ESRGAN" N. C. 
+        - "ICASSP 2020 - ESRGAN+ : Further Improving ESRGAN" N. C.
             {Rakotonirina} and A. {Rasoanaivo}
     """
 

modules/images.py

@@ -368,7 +368,7 @@ class FilenameGenerator:
         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:

modules/img2img.py

@@ -42,7 +42,7 @@ 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/models/diffusion/ddpm_edit.py

@@ -195,7 +195,7 @@ class DDPM(pl.LightningModule):
                     print(f"{context}: Restored training weights")
 
     def init_from_ckpt(self, path, ignore_keys=None, only_model=False):
-        ignore_keys = ignore_keys or []        
+        ignore_keys = ignore_keys or []
         sd = torch.load(path, map_location="cpu")
         if "state_dict" in list(sd.keys()):
             sd = sd["state_dict"]
@@ -1212,8 +1212,10 @@ 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

modules/models/diffusion/uni_pc/sampler.py

@@ -29,10 +29,10 @@ class UniPCSampler(object):
         # first time we have all the info to get the real parameters from the ui
         # value from the hires steps slider:
         num_inference_steps = t[0] + 1
-        approx_denoise_strength = num_inference_steps / self.inflated_steps
+        num_inference_steps / self.inflated_steps
         self.denoise_steps = max(num_inference_steps, shared.opts.uni_pc_order)
 
-        init_timestep = max(self.inflated_steps - self.denoise_steps, 0)
+        max(self.inflated_steps - self.denoise_steps, 0)
 
         # actual number of steps we'll run
 

modules/models/diffusion/uni_pc/uni_pc.py

@@ -752,7 +752,7 @@ class UniPC:
         t_T = self.noise_schedule.T if t_start is None else t_start
         device = x.device
         if method == 'multistep':
-            if timesteps == None:
+            if timesteps is None:
                 timesteps = get_time_steps(self.noise_schedule, skip_type=skip_type, t_T=t_T, t_0=t_0, N=steps, device=device)
             #print(f"Running UniPC Sampling with {timesteps.shape[0]} timesteps, order {order}")
             assert steps >= order, "UniPC order must be < sampling steps"
@@ -773,7 +773,7 @@ class UniPC:
                         if self.after_update is not None:
                             self.after_update(x, model_x)
                         model_prev_list.append(model_x)
-                        t_prev_list.append(vec_t)                      
+                        t_prev_list.append(vec_t)
                         progress.update(task, advance=1, description=f"Progress {round(len(vec_t) * init_order / (time.time() - t), 2)}it/s")
                     # for step in trange(order, steps + 1):
                     for step in range(order, steps + 1):

modules/paths.py

@@ -36,7 +36,7 @@ path_dirs = [
 
 paths = {}
 
-for d, must_exist, what, options in path_dirs:
+for d, must_exist, what, _options in path_dirs:
     must_exist_path = os.path.abspath(os.path.join(script_path, d, must_exist))
     if not os.path.exists(must_exist_path):
         print(f"Warning: {what} not found at path {must_exist_path}", file=sys.stderr)

modules/processing.py

@@ -493,7 +493,7 @@ def print_profile(profile, msg: str):
     ps.sort_stats(pstats.SortKey.CUMULATIVE).print_stats(15)
     profile = None
     lines = stream.getvalue().split('\n')
-    lines = [l for l in lines if '<frozen' not in l and '{built-in' not in l and '/logging' not in l and '/rich' not in l]
+    lines = [line for line in lines if '<frozen' not in line and '{built-in' not in line and '/logging' not in line and '/rich' not in line]
     print(f'Profile {msg}:', '\n'.join(lines))
 
 

modules/scripts.py

@@ -325,7 +325,6 @@ class ScriptRunner:
             inputs_alwayson += [script.alwayson for _ in controls]
             script.args_to = len(inputs)
 
-        s = []
         with gr.Group(elem_id='scripts_alwayson_img2img' if self.is_img2img else 'scripts_alwayson_txt2img'):
             for script in self.alwayson_scripts:
                 t0 = time.time()
@@ -337,7 +336,6 @@ class ScriptRunner:
 
         dropdown = gr.Dropdown(label="Script", elem_id="script_list", choices=["None"] + self.titles, value="None", type="index")
         inputs[0] = dropdown
-        s = []
         for script in self.selectable_scripts:
             with gr.Group(visible=False) as group:
                 t0 = time.time()

modules/sd_hijack.py

@@ -34,7 +34,7 @@ def apply_optimizations():
     ldm.modules.diffusionmodules.model.nonlinearity = silu
     ldm.modules.diffusionmodules.openaimodel.th = sd_hijack_unet.th
     optimization_method = None
-    can_use_sdp = hasattr(torch.nn.functional, "scaled_dot_product_attention") and callable(getattr(torch.nn.functional, "scaled_dot_product_attention"))
+    can_use_sdp = hasattr(torch.nn.functional, "scaled_dot_product_attention") and callable(torch.nn.functional.scaled_dot_product_attention)
     if devices.device == torch.device("cpu"):
         if opts.cross_attention_optimization == "Scaled-Dot-Product":
             shared.log.warning("Scaled dot product cross attention is not available on CPU")

modules/sd_hijack_ip2p.py

@@ -6,4 +6,4 @@ 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

@@ -67,7 +67,7 @@ def split_cross_attention_forward_v1(self, x, context=None, mask=None): # pylint
     v_in = self.to_v(context_v)
     del context, context_k, context_v, x
 
-    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
+    q, k, v = (rearrange(t, 'b n (h d) -> (b h) n d', h=h) for t in (q_in, k_in, v_in))
     del q_in, k_in, v_in
 
     dtype = q.dtype
@@ -111,7 +111,7 @@ def split_cross_attention_forward(self, x, context=None, mask=None): # pylint: d
     with devices.without_autocast(disable=not shared.opts.upcast_attn):
         k_in = k_in * self.scale
         del context, x
-        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q_in, k_in, v_in))
+        q, k, v = (rearrange(t, 'b n (h d) -> (b h) n d', h=h) for t in (q_in, k_in, v_in))
         del q_in, k_in, v_in
         r1 = torch.zeros(q.shape[0], q.shape[1], v.shape[2], device=q.device, dtype=q.dtype)
         mem_free_total = get_available_vram()
@@ -252,7 +252,7 @@ def split_cross_attention_forward_invokeAI(self, x, context=None, mask=None): #
 
     with devices.without_autocast(disable=not shared.opts.upcast_attn):
         k = k * self.scale
-        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v))
+        q, k, v = (rearrange(t, 'b n (h d) -> (b h) n d', h=h) for t in (q, k, v))
         r = einsum_op(q, k, v)
     r = r.to(dtype)
     return self.to_out(rearrange(r, '(b h) n d -> b n (h d)', h=h))
@@ -357,7 +357,7 @@ def xformers_attention_forward(self, x, context=None, mask=None): # pylint: disa
     k_in = self.to_k(context_k)
     v_in = self.to_v(context_v)
 
-    q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b n h d', h=h), (q_in, k_in, v_in))
+    q, k, v = (rearrange(t, 'b n (h d) -> b n h d', h=h) for t in (q_in, k_in, v_in))
     del q_in, k_in, v_in
 
     dtype = q.dtype
@@ -485,7 +485,7 @@ def xformers_attnblock_forward(self, x):
         k = self.k(h_)
         v = self.v(h_)
         b, c, h, w = q.shape # pylint: disable=unused-variable
-        q, k, v = map(lambda t: rearrange(t, 'b c h w -> b (h w) c'), (q, k, v))
+        q, k, v = (rearrange(t, 'b c h w -> b (h w) c') for t in (q, k, v))
         dtype = q.dtype
         if shared.opts.upcast_attn:
             q, k = q.float(), k.float()
@@ -507,7 +507,7 @@ def sdp_attnblock_forward(self, x):
     k = self.k(h_)
     v = self.v(h_)
     b, c, h, w = q.shape # pylint: disable=unused-variable
-    q, k, v = map(lambda t: rearrange(t, 'b c h w -> b (h w) c'), (q, k, v))
+    q, k, v = (rearrange(t, 'b c h w -> b (h w) c') for t in (q, k, v))
     dtype = q.dtype
     if shared.opts.upcast_attn:
         q, k, v = q.float(), k.float(), v.float()
@@ -535,7 +535,7 @@ def sub_quad_attnblock_forward(self, x):
     k = self.k(h_)
     v = self.v(h_)
     b, c, h, w = q.shape # pylint: disable=unused-variable
-    q, k, v = map(lambda t: rearrange(t, 'b c h w -> b (h w) c'), (q, k, v))
+    q, k, v = (rearrange(t, 'b c h w -> b (h w) c') for t in (q, k, v))
     q = q.contiguous()
     k = k.contiguous()
     v = v.contiguous()

modules/sd_models.py

@@ -422,7 +422,7 @@ def enable_midas_autodownload():
 
 
 def repair_config(sd_config):
-    if not "use_ema" in sd_config.model.params:
+    if "use_ema" not in sd_config.model.params:
         sd_config.model.params.use_ema = False
     if shared.opts.no_half:
         sd_config.model.params.unet_config.params.use_fp16 = False

modules/sd_samplers_kdiffusion.py

@@ -331,7 +331,7 @@ class KDiffusionSampler:
                 return BrownianTreeNoiseSampler(x.to("cpu"), sigma_min, sigma_max, seed=current_iter_seeds, transform=lambda x: x.to("cpu"), transform_last=lambda x: x.to(shared.device)) # pylint: disable=E1123
             except Exception:
                 print("ERROR    Please apply this patch to repositories/k-diffusion/k_diffusion/sampling.py: https://github.com/crowsonkb/k-diffusion/pull/68/files")
-                return None    
+                return None
         else:
             return BrownianTreeNoiseSampler(x, sigma_min, sigma_max, seed=current_iter_seeds)
 

modules/shared.py

@@ -421,7 +421,7 @@ options_templates.update(options_section(('ui', "User interface"), {
     "keyedit_precision_extra": OptionInfo(0.05, "Ctrl+up/down precision when editing <extra networks:0.9>", gr.Slider, {"minimum": 0.01, "maximum": 0.2, "step": 0.001}),
     "keyedit_delimiters": OptionInfo(".,\/!?%^*;:{}=`~()", "Ctrl+up/down word delimiters"), # pylint: disable=anomalous-backslash-in-string
     "quicksettings_list": OptionInfo(["sd_model_checkpoint"], "Quicksettings list", ui_components.DropdownMulti, lambda: {"choices": list(opts.data_labels.keys())}),
-    "hidden_tabs": OptionInfo([], "Hidden UI tabs", ui_components.DropdownMulti, lambda: {"choices": [x for x in tab_names]}),
+    "hidden_tabs": OptionInfo([], "Hidden UI tabs", ui_components.DropdownMulti, lambda: {"choices": list(tab_names)}),
     "ui_tab_reorder": OptionInfo("From Text, From Image, Process Image", "UI tabs order"),
     "ui_scripts_reorder": OptionInfo("Enable Dynamic Thresholding, ControlNet", "UI scripts order"),
     "ui_reorder": OptionInfo(", ".join(ui_reorder_categories), "txt2img/img2img UI item order"),
@@ -501,7 +501,7 @@ options_templates.update(options_section(('upscaling', "Upscaling"), {
     "ESRGAN_tile": OptionInfo(192, "Tile size for ESRGAN upscalers (0 = no tiling)", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
     "ESRGAN_tile_overlap": OptionInfo(8, "Tile overlap in pixels for ESRGAN upscalers", gr.Slider, {"minimum": 0, "maximum": 48, "step": 1}),
     "SCUNET_tile": OptionInfo(256, "Tile size for SCUNET upscalers (0 = no tiling)", gr.Slider, {"minimum": 0, "maximum": 512, "step": 16}),
-    "SCUNET_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for SCUNET upscalers (low values = visible seam)", gr.Slider, {"minimum": 0, "maximum": 64, "step": 1}),    
+    "SCUNET_tile_overlap": OptionInfo(8, "Tile overlap, in pixels for SCUNET upscalers (low values = visible seam)", gr.Slider, {"minimum": 0, "maximum": 64, "step": 1}),
     "use_old_hires_fix_width_height": OptionInfo(False, "Hires fix uses width & height to set final resolution rather than first pass"),
     "dont_fix_second_order_samplers_schedule": OptionInfo(False, "Do not fix prompt schedule for second order samplers"),
 }))
@@ -524,7 +524,7 @@ options_templates.update(options_section(('extra_networks', "Extra Networks"), {
     "extra_networks_card_width": OptionInfo(0, "Card width for Extra Networks (px)"),
     "extra_networks_card_height": OptionInfo(0, "Card height for Extra Networks (px)"),
     "extra_networks_add_text_separator": OptionInfo(" ", "Extra text to add before <...> when adding extra network to prompt"),
-    "sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in hypernetworks.keys()]}, refresh=reload_hypernetworks),
+    "sd_hypernetwork": OptionInfo("None", "Add hypernetwork to prompt", gr.Dropdown, lambda: {"choices": ["None"] + list(hypernetworks.keys())}, refresh=reload_hypernetworks),
 }))
 
 options_templates.update(options_section(('token_merging', 'Token Merging'), {

modules/ui.py

@@ -315,7 +315,7 @@ def create_sampler_and_steps_selection(choices, tabname):
 
 def ordered_ui_categories():
     user_order = {x.strip(): i * 2 + 1 for i, x in enumerate(modules.shared.opts.ui_reorder.split(","))}
-    for i, category in sorted(enumerate(modules.shared.ui_reorder_categories), key=lambda x: user_order.get(x[1], x[0] * 2 + 0)):
+    for _i, category in sorted(enumerate(modules.shared.ui_reorder_categories), key=lambda x: user_order.get(x[1], x[0] * 2 + 0)):
         yield category
 
 
@@ -1452,7 +1452,7 @@ def create_ui():
 
     with gr.Blocks(theme=modules.shared.gradio_theme, analytics_enabled=False, title="SD.Next", allowed_paths=[cmd_opts.data_dir]) 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
 
@@ -1483,7 +1483,7 @@ def create_ui():
         restart_submit.click(fn=lambda x: modules.shared.restart_server(restart=True), _js="restart_reload")
         shutdown_submit.click(fn=lambda x: modules.shared.restart_server(restart=False), _js="restart_reload")
 
-        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_textual_inversion.py

@@ -14,7 +14,7 @@ class ExtraNetworksPageTextualInversion(ui_extra_networks.ExtraNetworksPage):
         sd_hijack.model_hijack.embedding_db.load_textual_inversion_embeddings(force_reload=True)
 
     def list_items(self):
-        embeddings = [emb for emb in sd_hijack.model_hijack.embedding_db.word_embeddings.values()]
+        embeddings = list(sd_hijack.model_hijack.embedding_db.word_embeddings.values())
         if len(embeddings) == 0: # maybe not loaded yet, so lets just look them up
             for root, _dirs, fns in os.walk(shared.opts.embeddings_dir, followlinks=True):
                 for fn in fns:

pyproject.toml

@@ -29,11 +29,7 @@ ignore = [
   "F401", # Imported but unused
 ]
 
-[tool.ruff.per-file-ignores]
-"webui.py" = ["E402"]  # Module level import not at top of file
-
 [tool.ruff.flake8-bugbear]
-# Allow default arguments like, e.g., `data: List[str] = fastapi.Query(None)`.
 extend-immutable-calls = ["fastapi.Depends", "fastapi.security.HTTPBasic"]
 
 [tool.pytest.ini_options]

scripts/outpainting_mk_2.py

@@ -69,7 +69,7 @@ def get_matched_noise(_np_src_image, np_mask_rgb, noise_q=1, color_variation=0.0
     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/sd_upscale.py

@@ -54,7 +54,7 @@ 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])
 
@@ -83,7 +83,7 @@ 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