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
@@ -75,7 +75,7 @@ def load_control_net(v2, unet, model):
     zero_conv_sd = {}
     for key in list(ctrl_sd_sd.keys()):
         if key.startswith("control_"):
-      unet_key = "model.diffusion_" + key[len("control_"):]
+            unet_key = "model.diffusion_" + key[len("control_") :]
             if unet_key not in ctrl_unet_sd_sd:  # zero conv
                 zero_conv_sd[key] = ctrl_sd_sd[key]
                 continue
@@ -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
 
@@ -285,13 +305,13 @@ def unet_forward(is_control_net, control_net: ControlNet, unet: UNet2DConditionM
     for i, upsample_block in enumerate(unet.up_blocks):
         is_final_block = i == len(unet.up_blocks) - 1
 
-    res_samples = down_block_res_samples[-len(upsample_block.resnets):]
+        res_samples = down_block_res_samples[-len(upsample_block.resnets) :]
         down_block_res_samples = down_block_res_samples[: -len(upsample_block.resnets)]
 
         if not is_control_net and len(ctrl_outs) > 0:
             res_samples = list(res_samples)
-      apply_ctrl_outs = ctrl_outs[-len(res_samples):]
+            apply_ctrl_outs = ctrl_outs[-len(res_samples) :]
-      ctrl_outs = ctrl_outs[:-len(res_samples)]
+            ctrl_outs = ctrl_outs[: -len(res_samples)]
             for j in range(len(res_samples)):
                 res_samples[j] = res_samples[j] + apply_ctrl_outs[j]
             res_samples = tuple(res_samples)