ruff auto fixes

4 files changed, 15 insertions, 15 deletions

diff --git a/extensions-builtin/LDSR/sd_hijack_autoencoder.py b/extensions-builtin/LDSR/sd_hijack_autoencoder.py
index 6303fed5..f457ca93 100644
--- a/extensions-builtin/LDSR/sd_hijack_autoencoder.py
+++ b/extensions-builtin/LDSR/sd_hijack_autoencoder.py
@@ -288,5 +288,5 @@ class VQModelInterface(VQModel):
         dec = self.decoder(quant)
         return dec
 
-setattr(ldm.models.autoencoder, "VQModel", VQModel)
-setattr(ldm.models.autoencoder, "VQModelInterface", VQModelInterface)
+ldm.models.autoencoder.VQModel = VQModel
+ldm.models.autoencoder.VQModelInterface = VQModelInterface
diff --git a/extensions-builtin/LDSR/sd_hijack_ddpm_v1.py b/extensions-builtin/LDSR/sd_hijack_ddpm_v1.py
index 4d3f6c56..d8fc30e3 100644
--- a/extensions-builtin/LDSR/sd_hijack_ddpm_v1.py
+++ b/extensions-builtin/LDSR/sd_hijack_ddpm_v1.py
@@ -1116,7 +1116,7 @@ class LatentDiffusionV1(DDPMV1):
         if cond is not None:
             if isinstance(cond, dict):
                 cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
-                list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
+                [x[:batch_size] for x in cond[key]] for key in cond}
             else:
                 cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
 
@@ -1215,7 +1215,7 @@ class LatentDiffusionV1(DDPMV1):
         if cond is not None:
             if isinstance(cond, dict):
                 cond = {key: cond[key][:batch_size] if not isinstance(cond[key], list) else
-                list(map(lambda x: x[:batch_size], cond[key])) for key in cond}
+                [x[:batch_size] for x in cond[key]] for key in cond}
             else:
                 cond = [c[:batch_size] for c in cond] if isinstance(cond, list) else cond[:batch_size]
         return self.p_sample_loop(cond,
@@ -1437,7 +1437,7 @@ class Layout2ImgDiffusionV1(LatentDiffusionV1):
         logs['bbox_image'] = cond_img
         return logs
 
-setattr(ldm.models.diffusion.ddpm, "DDPMV1", DDPMV1)
-setattr(ldm.models.diffusion.ddpm, "LatentDiffusionV1", LatentDiffusionV1)
-setattr(ldm.models.diffusion.ddpm, "DiffusionWrapperV1", DiffusionWrapperV1)
-setattr(ldm.models.diffusion.ddpm, "Layout2ImgDiffusionV1", Layout2ImgDiffusionV1)
+ldm.models.diffusion.ddpm.DDPMV1 = DDPMV1
+ldm.models.diffusion.ddpm.LatentDiffusionV1 = LatentDiffusionV1
+ldm.models.diffusion.ddpm.DiffusionWrapperV1 = DiffusionWrapperV1
+ldm.models.diffusion.ddpm.Layout2ImgDiffusionV1 = Layout2ImgDiffusionV1
diff --git a/extensions-builtin/Lora/lora.py b/extensions-builtin/Lora/lora.py
index 0ab43229..9795540f 100644
--- a/extensions-builtin/Lora/lora.py
+++ b/extensions-builtin/Lora/lora.py
@@ -172,7 +172,7 @@ def load_lora(name, filename):
         else:

             print(f'Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}')

             continue

-            assert False, f'Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}'

+            raise AssertionError(f"Lora layer {key_diffusers} matched a layer with unsupported type: {type(sd_module).__name__}")

 

         with torch.no_grad():

             module.weight.copy_(weight)

@@ -184,7 +184,7 @@ def load_lora(name, filename):
         elif lora_key == "lora_down.weight":

             lora_module.down = module

         else:

-            assert False, f'Bad Lora layer name: {key_diffusers} - must end in lora_up.weight, lora_down.weight or alpha'

+            raise AssertionError(f"Bad Lora layer name: {key_diffusers} - must end in lora_up.weight, lora_down.weight or alpha")

 

     if len(keys_failed_to_match) > 0:

         print(f"Failed to match keys when loading Lora {filename}: {keys_failed_to_match}")

@@ -202,7 +202,7 @@ def load_loras(names, multipliers=None):
     loaded_loras.clear()

 

     loras_on_disk = [available_lora_aliases.get(name, None) for name in names]

-    if any([x is None for x in loras_on_disk]):

+    if any(x is None for x in loras_on_disk):

         list_available_loras()

 

         loras_on_disk = [available_lora_aliases.get(name, None) for name in names]

@@ -309,7 +309,7 @@ def lora_apply_weights(self: Union[torch.nn.Conv2d, torch.nn.Linear, torch.nn.Mu
 

             print(f'failed to calculate lora weights for layer {lora_layer_name}')

 

-        setattr(self, "lora_current_names", wanted_names)

+        self.lora_current_names = wanted_names

 

 

 def lora_forward(module, input, original_forward):

@@ -343,8 +343,8 @@ def lora_forward(module, input, original_forward):
 

 

 def lora_reset_cached_weight(self: Union[torch.nn.Conv2d, torch.nn.Linear]):

-    setattr(self, "lora_current_names", ())

-    setattr(self, "lora_weights_backup", None)

+    self.lora_current_names = ()

+    self.lora_weights_backup = None

 

 

 def lora_Linear_forward(self, input):

diff --git a/extensions-builtin/Lora/scripts/lora_script.py b/extensions-builtin/Lora/scripts/lora_script.py
index 7db971fd..b70e2de7 100644
--- a/extensions-builtin/Lora/scripts/lora_script.py
+++ b/extensions-builtin/Lora/scripts/lora_script.py
@@ -53,7 +53,7 @@ script_callbacks.on_infotext_pasted(lora.infotext_pasted)
 

 

 shared.options_templates.update(shared.options_section(('extra_networks', "Extra Networks"), {

-    "sd_lora": shared.OptionInfo("None", "Add Lora to prompt", gr.Dropdown, lambda: {"choices": ["None"] + [x for x in lora.available_loras]}, refresh=lora.list_available_loras),

+    "sd_lora": shared.OptionInfo("None", "Add Lora to prompt", gr.Dropdown, lambda: {"choices": ["None"] + list(lora.available_loras)}, refresh=lora.list_available_loras),

 }))