fixes for B007

Author: AUTOMATIC1111

extensions-builtin/LDSR/ldsr_model_arch.py

@@ -88,7 +88,7 @@ def run(model, selected_path, custom_steps, eta):
 
         x_t = None
         logs = None
-        for n in range(n_runs):
+        for _ in range(n_runs):
             if custom_shape is not None:
                 x_t = torch.randn(1, custom_shape[1], custom_shape[2], custom_shape[3]).to(model.device)
                 x_t = repeat(x_t, '1 c h w -> b c h w', b=custom_shape[0])

extensions-builtin/Lora/lora.py

@@ -418,7 +418,7 @@ def infotext_pasted(infotext, params):
 
     added = []
 
-    for k, v in params.items():
+    for k in params:
         if not k.startswith("AddNet Model "):
             continue
 

extensions-builtin/ScuNET/scripts/scunet_model.py

@@ -132,7 +132,7 @@ def load_model(self, path: str):
         model = net(in_nc=3, config=[4, 4, 4, 4, 4, 4, 4], dim=64)
         model.load_state_dict(torch.load(filename), strict=True)
         model.eval()
-        for k, v in model.named_parameters():
+        for _, v in model.named_parameters():
             v.requires_grad = False
         model = model.to(device)
 

extensions-builtin/SwinIR/swinir_model_arch.py

@@ -848,7 +848,7 @@ def flops(self):
         H, W = self.patches_resolution
         flops += H * W * 3 * self.embed_dim * 9
         flops += self.patch_embed.flops()
-        for i, layer in enumerate(self.layers):
+        for layer in self.layers:
             flops += layer.flops()
         flops += H * W * 3 * self.embed_dim * self.embed_dim
         flops += self.upsample.flops()

extensions-builtin/SwinIR/swinir_model_arch_v2.py

@@ -1001,7 +1001,7 @@ def flops(self):
         H, W = self.patches_resolution
         flops += H * W * 3 * self.embed_dim * 9
         flops += self.patch_embed.flops()
-        for i, layer in enumerate(self.layers):
+        for layer in self.layers:
             flops += layer.flops()
         flops += H * W * 3 * self.embed_dim * self.embed_dim
         flops += self.upsample.flops()

modules/codeformer_model.py

@@ -94,7 +94,7 @@ def restore(self, np_image, w=None):
                 self.face_helper.get_face_landmarks_5(only_center_face=False, resize=640, eye_dist_threshold=5)
                 self.face_helper.align_warp_face()
 
-                for idx, cropped_face in enumerate(self.face_helper.cropped_faces):
+                for cropped_face in self.face_helper.cropped_faces:
                     cropped_face_t = img2tensor(cropped_face / 255., bgr2rgb=True, float32=True)
                     normalize(cropped_face_t, (0.5, 0.5, 0.5), (0.5, 0.5, 0.5), inplace=True)
                     cropped_face_t = cropped_face_t.unsqueeze(0).to(devices.device_codeformer)

modules/esrgan_model.py

@@ -16,9 +16,7 @@ def mod2normal(state_dict):
     # this code is copied from https://github.com/victorca25/iNNfer
     if 'conv_first.weight' in state_dict:
         crt_net = {}
-        items = []
-        for k, v in state_dict.items():
-            items.append(k)
+        items = list(state_dict)
 
         crt_net['model.0.weight'] = state_dict['conv_first.weight']
         crt_net['model.0.bias'] = state_dict['conv_first.bias']
@@ -52,9 +50,7 @@ def resrgan2normal(state_dict, nb=23):
     if "conv_first.weight" in state_dict and "body.0.rdb1.conv1.weight" in state_dict:
         re8x = 0
         crt_net = {}
-        items = []
-        for k, v in state_dict.items():
-            items.append(k)
+        items = list(state_dict)
 
         crt_net['model.0.weight'] = state_dict['conv_first.weight']
         crt_net['model.0.bias'] = state_dict['conv_first.bias']

modules/extra_networks.py

@@ -91,7 +91,7 @@ def deactivate(p, extra_network_data):
     """call deactivate for extra networks in extra_network_data in specified order, then call
     deactivate for all remaining registered networks"""
 
-    for extra_network_name, extra_network_args in extra_network_data.items():
+    for extra_network_name in extra_network_data:
         extra_network = extra_network_registry.get(extra_network_name, None)
         if extra_network is None:
             continue

modules/generation_parameters_copypaste.py

@@ -247,7 +247,7 @@ def parse_generation_parameters(x: str):
         lines.append(lastline)
         lastline = ''
 
-    for i, line in enumerate(lines):
+    for line in lines:
         line = line.strip()
         if line.startswith("Negative prompt:"):
             done_with_prompt = True

modules/hypernetworks/hypernetwork.py

@@ -177,34 +177,34 @@ def __init__(self, name=None, enable_sizes=None, layer_structure=None, activatio
 
     def weights(self):
         res = []
-        for k, layers in self.layers.items():
+        for layers in self.layers.values():
             for layer in layers:
                 res += layer.parameters()
         return res
 
     def train(self, mode=True):
-        for k, layers in self.layers.items():
+        for layers in self.layers.values():
             for layer in layers:
                 layer.train(mode=mode)
                 for param in layer.parameters():
                     param.requires_grad = mode
 
     def to(self, device):
-        for k, layers in self.layers.items():
+        for layers in self.layers.values():
             for layer in layers:
                 layer.to(device)
 
         return self
 
     def set_multiplier(self, multiplier):
-        for k, layers in self.layers.items():
+        for layers in self.layers.values():
             for layer in layers:
                 layer.multiplier = multiplier
 
         return self
 
     def eval(self):
-        for k, layers in self.layers.items():
+        for layers in self.layers.values():
             for layer in layers:
                 layer.eval()
                 for param in layer.parameters():
@@ -619,7 +619,7 @@ def train_hypernetwork(id_task, hypernetwork_name, learn_rate, batch_size, gradi
     try:
         sd_hijack_checkpoint.add()
 
-        for i in range((steps-initial_step) * gradient_step):
+        for _ in range((steps-initial_step) * gradient_step):
             if scheduler.finished:
                 break
             if shared.state.interrupted: