Autofix Ruff W (not W605) (mostly whitespace)

scripts/img2imgalt.py

@@ -149,9 +149,9 @@ class Script(scripts.Script):
         sigma_adjustment = gr.Checkbox(label="Sigma adjustment for finding noise for image", value=False, elem_id=self.elem_id("sigma_adjustment"))
 
         return [
-            info, 
+            info,
             override_sampler,
-            override_prompt, original_prompt, original_negative_prompt, 
+            override_prompt, original_prompt, original_negative_prompt,
             override_steps, st,
             override_strength,
             cfg, randomness, sigma_adjustment,
@@ -191,17 +191,17 @@ class Script(scripts.Script):
                 self.cache = Cached(rec_noise, cfg, st, lat, original_prompt, original_negative_prompt, sigma_adjustment)
 
             rand_noise = processing.create_random_tensors(p.init_latent.shape[1:], seeds=seeds, subseeds=subseeds, subseed_strength=p.subseed_strength, seed_resize_from_h=p.seed_resize_from_h, seed_resize_from_w=p.seed_resize_from_w, p=p)
-            
+
             combined_noise = ((1 - randomness) * rec_noise + randomness * rand_noise) / ((randomness**2 + (1-randomness)**2) ** 0.5)
-            
+
             sampler = sd_samplers.create_sampler(p.sampler_name, p.sd_model)
 
             sigmas = sampler.model_wrap.get_sigmas(p.steps)
-            
+
             noise_dt = combined_noise - (p.init_latent / sigmas[0])
-            
+
             p.seed = p.seed + 1
-            
+
             return sampler.sample_img2img(p, p.init_latent, noise_dt, conditioning, unconditional_conditioning, image_conditioning=p.image_conditioning)
 
         p.sample = sample_extra

scripts/loopback.py

@@ -14,7 +14,7 @@ class Script(scripts.Script):
     def show(self, is_img2img):
         return is_img2img
 
-    def ui(self, is_img2img):        
+    def ui(self, is_img2img):
         loops = gr.Slider(minimum=1, maximum=32, step=1, label='Loops', value=4, elem_id=self.elem_id("loops"))
         final_denoising_strength = gr.Slider(minimum=0, maximum=1, step=0.01, label='Final denoising strength', value=0.5, elem_id=self.elem_id("final_denoising_strength"))
         denoising_curve = gr.Dropdown(label="Denoising strength curve", choices=["Aggressive", "Linear", "Lazy"], value="Linear")
@@ -104,7 +104,7 @@ class Script(scripts.Script):
 
                 p.seed = processed.seed + 1
                 p.denoising_strength = calculate_denoising_strength(i + 1)
-                
+
                 if state.skipped:
                     break
 
@@ -121,7 +121,7 @@ class Script(scripts.Script):
                 all_images.append(last_image)
 
             p.inpainting_fill = original_inpainting_fill
-                
+
             if state.interrupted:
                     break
 
@@ -132,7 +132,7 @@ class Script(scripts.Script):
 
             if opts.return_grid:
                 grids.append(grid)
-                
+
         all_images = grids + all_images
 
         processed = Processed(p, all_images, initial_seed, initial_info)

scripts/poor_mans_outpainting.py

@@ -19,7 +19,7 @@ class Script(scripts.Script):
     def ui(self, is_img2img):
         if not is_img2img:
             return None
-        
+
         pixels = gr.Slider(label="Pixels to expand", minimum=8, maximum=256, step=8, value=128, elem_id=self.elem_id("pixels"))
         mask_blur = gr.Slider(label='Mask blur', minimum=0, maximum=64, step=1, value=4, elem_id=self.elem_id("mask_blur"))
         inpainting_fill = gr.Radio(label='Masked content', choices=['fill', 'original', 'latent noise', 'latent nothing'], value='fill', type="index", elem_id=self.elem_id("inpainting_fill"))

scripts/prompt_matrix.py

@@ -96,7 +96,7 @@ class Script(scripts.Script):
         p.prompt_for_display = positive_prompt
         processed = process_images(p)
 
-        grid = images.image_grid(processed.images, p.batch_size, rows=1 << ((len(prompt_matrix_parts) - 1) // 2)) 
+        grid = images.image_grid(processed.images, p.batch_size, rows=1 << ((len(prompt_matrix_parts) - 1) // 2))
         grid = images.draw_prompt_matrix(grid, processed.images[0].width, processed.images[0].height, prompt_matrix_parts, margin_size)
         processed.images.insert(0, grid)
         processed.index_of_first_image = 1

scripts/prompts_from_file.py

@@ -109,7 +109,7 @@ class Script(scripts.Script):
     def title(self):
         return "Prompts from file or textbox"
 
-    def ui(self, is_img2img):       
+    def ui(self, is_img2img):
         checkbox_iterate = gr.Checkbox(label="Iterate seed every line", value=False, elem_id=self.elem_id("checkbox_iterate"))
         checkbox_iterate_batch = gr.Checkbox(label="Use same random seed for all lines", value=False, elem_id=self.elem_id("checkbox_iterate_batch"))
 
@@ -166,7 +166,7 @@ class Script(scripts.Script):
 
             proc = process_images(copy_p)
             images += proc.images
-            
+
             if checkbox_iterate:
                 p.seed = p.seed + (p.batch_size * p.n_iter)
             all_prompts += proc.all_prompts

scripts/sd_upscale.py

@@ -16,7 +16,7 @@ class Script(scripts.Script):
     def show(self, is_img2img):
         return is_img2img
 
-    def ui(self, is_img2img):        
+    def ui(self, is_img2img):
         info = gr.HTML("<p style=\"margin-bottom:0.75em\">Will upscale the image by the selected scale factor; use width and height sliders to set tile size</p>")
         overlap = gr.Slider(minimum=0, maximum=256, step=16, label='Tile overlap', value=64, elem_id=self.elem_id("overlap"))
         scale_factor = gr.Slider(minimum=1.0, maximum=4.0, step=0.05, label='Scale Factor', value=2.0, elem_id=self.elem_id("scale_factor"))