format by black

tools/original_control_net.py

@@ -57,7 +57,7 @@ def load_control_net(v2, unet, model):
     if model_util.is_safetensors(model):
         ctrl_sd_sd = load_file(model)
     else:
-    ctrl_sd_sd = torch.load(model, map_location='cpu')
+        ctrl_sd_sd = torch.load(model, map_location="cpu")
         ctrl_sd_sd = ctrl_sd_sd.pop("state_dict", ctrl_sd_sd)
 
     # 重みをU-Netに読み込めるようにする。ControlNetはSD版のstate dictなので、それを読み込む
@@ -115,6 +115,7 @@ def load_preprocess(prep_type: str):
         def canny(img):
             img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
             return cv2.Canny(img, th1, th2)
+
         return canny
 
     print("Unsupported prep type:", prep_type)
@@ -156,7 +157,17 @@ def get_guided_hints(control_nets: List[ControlNetInfo], num_latent_input, b_siz
     return guided_hints
 
 
-def call_unet_and_control_net(step, num_latent_input, original_unet, control_nets: List[ControlNetInfo], guided_hints, current_ratio, sample, timestep, encoder_hidden_states):
+def call_unet_and_control_net(
+    step,
+    num_latent_input,
+    original_unet,
+    control_nets: List[ControlNetInfo],
+    guided_hints,
+    current_ratio,
+    sample,
+    timestep,
+    encoder_hidden_states,
+):
     # ControlNet
     # 複数のControlNetの場合は、出力をマージするのではなく交互に適用する
     cnet_cnt = len(control_nets)
@@ -204,7 +215,16 @@ def call_unet_and_control_net(step, num_latent_input, original_unet, control_net
 """
 
 
-def unet_forward(is_control_net, control_net: ControlNet, unet: UNet2DConditionModel, guided_hint, ctrl_outs, sample, timestep, encoder_hidden_states):
+def unet_forward(
+    is_control_net,
+    control_net: ControlNet,
+    unet: UNet2DConditionModel,
+    guided_hint,
+    ctrl_outs,
+    sample,
+    timestep,
+    encoder_hidden_states,
+):
     # copy from UNet2DConditionModel
     default_overall_up_factor = 2**unet.num_upsamplers