lint

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

.ruff.toml

@@ -85,6 +85,7 @@ ignore = [
   "RUF013", # PEP 484 prohibits implicit `Optional`
   "RUF015", # Prefer `next(...)` over single element slice
   "RUF046", # Value being cast to `int` is already an integer
+  "RUF059", # Unpacked variables are not used
   "RUF051", # Prefer pop over del
 ]
 fixable = ["ALL"]

modules/face/instantid_model.py

@@ -58,7 +58,7 @@ 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)

modules/processing_vae.py

@@ -276,7 +276,7 @@ def vae_decode(latents, model, output_type='np', vae_type='Full', width=None, he
         shared.state.end(jobid)
         if tensors is not None and len(tensors) > 0:
             return vae_postprocess(tensors, model, output_type)
-    
+
     jobid = shared.state.begin('VAE Decode')
     if latents.shape[0] == 0:
         shared.log.error(f'VAE nothing to decode: {latents.shape}')

modules/sdnq/__init__.py

@@ -541,10 +541,10 @@ class SDNQConfig(QuantizationConfigMixin):
             self.modules_dtype_dict = {}
 
 
-import diffusers.quantizers.auto # noqa: E402,RUF100
+import diffusers.quantizers.auto # noqa: E402,RUF100 # pylint: disable=wrong-import-order
 diffusers.quantizers.auto.AUTO_QUANTIZER_MAPPING["sdnq"] = SDNQQuantizer
 diffusers.quantizers.auto.AUTO_QUANTIZATION_CONFIG_MAPPING["sdnq"] = SDNQConfig
 
-import transformers.quantizers.auto # noqa: E402,RUF100
+import transformers.quantizers.auto # noqa: E402,RUF100 # pylint: disable=wrong-import-order
 transformers.quantizers.auto.AUTO_QUANTIZER_MAPPING["sdnq"] = SDNQQuantizer
 transformers.quantizers.auto.AUTO_QUANTIZATION_CONFIG_MAPPING["sdnq"] = SDNQConfig

modules/upscaler.py

@@ -1,5 +1,4 @@
 import os
-import copy
 from abc import abstractmethod
 from PIL import Image
 from modules import modelloader, shared

scripts/consistory_ext.py

@@ -195,7 +195,7 @@ class Script(scripts_manager.Script):
             shared.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/instantir/ip_adapter/resampler.py

@@ -21,7 +21,7 @@ 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/layerdiffuse/layerdiffuse_model.py

@@ -270,7 +270,7 @@ class TransparentVAEDecoder(AutoencoderKL):
             alpha = y[..., :1]
             fg = y[..., 1:]
 
-            B, H, W, C = fg.shape
+            _B, H, W, _C = fg.shape
             cb = checkerboard(shape=(H // 64, W // 64))
             cb = cv2.resize(cb, (W, H), interpolation=cv2.INTER_LANCZOS4)
             cb = (0.5 + (cb - 0.5) * 0.1)[None, ..., None]

scripts/loopback.py

@@ -56,7 +56,7 @@ class Script(scripts_manager.Script):
             change = (final_denoising_strength - initial_denoising_strength) * strength
             return initial_denoising_strength + change
 
-        for n in range(initial_batch_count):
+        for _n in range(initial_batch_count):
             p.init_images = initial_init_images
             p.denoising_strength = initial_denoising_strength
             last_image = None

scripts/outpainting_mk_2.py

@@ -206,7 +206,7 @@ class Script(scripts_manager.Script):
         p.n_iter = 1
         state.job_count = batch_count * ((1 if left > 0 else 0) + (1 if right > 0 else 0) + (1 if up > 0 else 0) + (1 if down > 0 else 0))
         all_processed_images = []
-        for i in range(batch_count):
+        for _i in range(batch_count):
             imgs = [init_img] * batch_size
             if left > 0:
                 imgs = expand(imgs, batch_size, left, is_left=True)

scripts/style_aligned/inversion.py

@@ -49,7 +49,7 @@ def _get_text_embeddings(prompt: str, tokenizer, text_encoder, device):
 
 def _encode_text_sdxl(model: StableDiffusionXLPipeline, prompt: str) -> tuple[dict[str, T], T]:
     device = model._execution_device # pylint: disable=protected-access
-    prompt_embeds, pooled_prompt_embeds, = _get_text_embeddings(prompt, model.tokenizer, model.text_encoder, device) # pylint: disable=unused-variable
+    prompt_embeds, _pooled_prompt_embeds, = _get_text_embeddings(prompt, model.tokenizer, model.text_encoder, device) # pylint: disable=unused-variable
     prompt_embeds_2, pooled_prompt_embeds2, = _get_text_embeddings( prompt, model.tokenizer_2, model.text_encoder_2, device)
     prompt_embeds = torch.cat((prompt_embeds, prompt_embeds_2), dim=-1)
     text_encoder_projection_dim = model.text_encoder_2.config.projection_dim