make style

examples/community/masked_stable_diffusion_img2img.py

-from typing import Optional, Union, List, Callable, Dict, Any
+from typing import Any, Callable, Dict, List, Optional, Union
 
 import numpy as np
 import PIL
 import torch
+
 from diffusers import StableDiffusionImg2ImgPipeline
 from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
 
 
 class MaskedStableDiffusionImg2ImgPipeline(StableDiffusionImg2ImgPipeline):
-
     debug_save = False
 
     @torch.no_grad()
@@ -158,7 +158,8 @@ class MaskedStableDiffusionImg2ImgPipeline(StableDiffusionImg2ImgPipeline):
 
         # mean of the latent distribution
         init_latents = [
-            self.vae.encode(image.to(device=device, dtype=prompt_embeds.dtype)[i : i + 1]).latent_dist.mean for i in range(batch_size)
+            self.vae.encode(image.to(device=device, dtype=prompt_embeds.dtype)[i : i + 1]).latent_dist.mean
+            for i in range(batch_size)
         ]
         init_latents = torch.cat(init_latents, dim=0)
 
@@ -236,7 +237,7 @@ class MaskedStableDiffusionImg2ImgPipeline(StableDiffusionImg2ImgPipeline):
 
     def _make_latent_mask(self, latents, mask):
         if mask is not None:
-            latent_mask = list()
+            latent_mask = []
             if not isinstance(mask, list):
                 tmp_mask = [mask]
             else:
@@ -250,7 +251,7 @@ class MaskedStableDiffusionImg2ImgPipeline(StableDiffusionImg2ImgPipeline):
                         m = m / 255.0
                     m = self.image_processor.numpy_to_pil(m)[0]
                 if m.mode != "L":
-                    m = m.convert('L')
+                    m = m.convert("L")
                 resized = self.image_processor.resize(m, l_height, l_width)
                 if self.debug_save:
                     resized.save("latent_mask.png")