make style

examples/community/latent_consistency_img2img.py

@@ -20,22 +20,19 @@ from dataclasses import dataclass
 from typing import Any, Dict, List, Optional, Tuple, Union
 
 import numpy as np
+import PIL.Image
 import torch
 from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
 
 from diffusers import AutoencoderKL, ConfigMixin, DiffusionPipeline, SchedulerMixin, UNet2DConditionModel, logging
 from diffusers.configuration_utils import register_to_config
-from diffusers.image_processor import VaeImageProcessor, PipelineImageInput
+from diffusers.image_processor import PipelineImageInput, VaeImageProcessor
 from diffusers.pipelines.stable_diffusion import StableDiffusionPipelineOutput
 from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
 from diffusers.utils import BaseOutput
-
 from diffusers.utils.torch_utils import randn_tensor
 
 
-import PIL.Image
-
-
 logger = logging.get_logger(__name__)  # pylint: disable=invalid-name
 
 
@@ -167,7 +164,19 @@ class LatentConsistencyModelImg2ImgPipeline(DiffusionPipeline):
             )
         return image, has_nsfw_concept
 
-    def prepare_latents(self, image, timestep, batch_size, num_channels_latents, height, width, dtype, device, latents=None, generator=None):
+    def prepare_latents(
+        self,
+        image,
+        timestep,
+        batch_size,
+        num_channels_latents,
+        height,
+        width,
+        dtype,
+        device,
+        latents=None,
+        generator=None,
+    ):
         shape = (batch_size, num_channels_latents, height // self.vae_scale_factor, width // self.vae_scale_factor)
 
         if not isinstance(image, (torch.Tensor, PIL.Image.Image, list)):
@@ -201,7 +210,7 @@ class LatentConsistencyModelImg2ImgPipeline(DiffusionPipeline):
 
         if batch_size > init_latents.shape[0] and batch_size % init_latents.shape[0] == 0:
             # expand init_latents for batch_size
-            deprecation_message = (
+            (
                 f"You have passed {batch_size} text prompts (`prompt`), but only {init_latents.shape[0]} initial"
                 " images (`image`). Initial images are now duplicating to match the number of text prompts. Note"
                 " that this behavior is deprecated and will be removed in a version 1.0.0. Please make sure to update"
@@ -643,7 +652,9 @@ class LCMSchedulerWithTimestamp(SchedulerMixin, ConfigMixin):
 
         return sample
 
-    def set_timesteps(self, stength, num_inference_steps: int, lcm_origin_steps: int, device: Union[str, torch.device] = None):
+    def set_timesteps(
+        self, stength, num_inference_steps: int, lcm_origin_steps: int, device: Union[str, torch.device] = None
+    ):
         """
         Sets the discrete timesteps used for the diffusion chain (to be run before inference).
         Args:
@@ -662,7 +673,9 @@ class LCMSchedulerWithTimestamp(SchedulerMixin, ConfigMixin):
 
         # LCM Timesteps Setting:  # Linear Spacing
         c = self.config.num_train_timesteps // lcm_origin_steps
-        lcm_origin_timesteps = np.asarray(list(range(1, int(lcm_origin_steps * stength) + 1))) * c - 1  # LCM Training  Steps Schedule
+        lcm_origin_timesteps = (
+            np.asarray(list(range(1, int(lcm_origin_steps * stength) + 1))) * c - 1
+        )  # LCM Training  Steps Schedule
         skipping_step = len(lcm_origin_timesteps) // num_inference_steps
         timesteps = lcm_origin_timesteps[::-skipping_step][:num_inference_steps]  # LCM Inference Steps Schedule