From 550256db1ce18778a9d56ff343d844c61b9f9b83 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 10 May 2023 11:19:16 +0300
Subject: ruff manual fixes

---
 modules/models/diffusion/ddpm_edit.py     | 14 ++++++++------
 modules/models/diffusion/uni_pc/uni_pc.py |  7 +++++--
 2 files changed, 13 insertions(+), 8 deletions(-)

(limited to 'modules/models')

diff --git a/modules/models/diffusion/ddpm_edit.py b/modules/models/diffusion/ddpm_edit.py
index 09432117..af4dea15 100644
--- a/modules/models/diffusion/ddpm_edit.py
+++ b/modules/models/diffusion/ddpm_edit.py
@@ -52,7 +52,7 @@ class DDPM(pl.LightningModule):
                  beta_schedule="linear",
                  loss_type="l2",
                  ckpt_path=None,
-                 ignore_keys=[],
+                 ignore_keys=None,
                  load_only_unet=False,
                  monitor="val/loss",
                  use_ema=True,
@@ -107,7 +107,7 @@ class DDPM(pl.LightningModule):
             print(f"Keeping EMAs of {len(list(self.model_ema.buffers()))}.")
 
         if ckpt_path is not None:
-            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys, only_model=load_only_unet)
+            self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys or [], only_model=load_only_unet)
 
             # If initialing from EMA-only checkpoint, create EMA model after loading.
             if self.use_ema and not load_ema:
@@ -194,7 +194,9 @@ class DDPM(pl.LightningModule):
                 if context is not None:
                     print(f"{context}: Restored training weights")
 
-    def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
+    def init_from_ckpt(self, path, ignore_keys=None, only_model=False):
+        ignore_keys = ignore_keys or []
+
         sd = torch.load(path, map_location="cpu")
         if "state_dict" in list(sd.keys()):
             sd = sd["state_dict"]
@@ -473,7 +475,7 @@ class LatentDiffusion(DDPM):
             conditioning_key = None
         ckpt_path = kwargs.pop("ckpt_path", None)
         ignore_keys = kwargs.pop("ignore_keys", [])
-        super().__init__(conditioning_key=conditioning_key, *args, load_ema=load_ema, **kwargs)
+        super().__init__(*args, conditioning_key=conditioning_key, load_ema=load_ema, **kwargs)
         self.concat_mode = concat_mode
         self.cond_stage_trainable = cond_stage_trainable
         self.cond_stage_key = cond_stage_key
@@ -1433,10 +1435,10 @@ class Layout2ImgDiffusion(LatentDiffusion):
     # TODO: move all layout-specific hacks to this class
     def __init__(self, cond_stage_key, *args, **kwargs):
         assert cond_stage_key == 'coordinates_bbox', 'Layout2ImgDiffusion only for cond_stage_key="coordinates_bbox"'
-        super().__init__(cond_stage_key=cond_stage_key, *args, **kwargs)
+        super().__init__(*args, cond_stage_key=cond_stage_key, **kwargs)
 
     def log_images(self, batch, N=8, *args, **kwargs):
-        logs = super().log_images(batch=batch, N=N, *args, **kwargs)
+        logs = super().log_images(*args, batch=batch, N=N, **kwargs)
 
         key = 'train' if self.training else 'validation'
         dset = self.trainer.datamodule.datasets[key]
diff --git a/modules/models/diffusion/uni_pc/uni_pc.py b/modules/models/diffusion/uni_pc/uni_pc.py
index a4c4ef4e..6f8ad631 100644
--- a/modules/models/diffusion/uni_pc/uni_pc.py
+++ b/modules/models/diffusion/uni_pc/uni_pc.py
@@ -178,13 +178,13 @@ def model_wrapper(
     model,
     noise_schedule,
     model_type="noise",
-    model_kwargs={},
+    model_kwargs=None,
     guidance_type="uncond",
     #condition=None,
     #unconditional_condition=None,
     guidance_scale=1.,
     classifier_fn=None,
-    classifier_kwargs={},
+    classifier_kwargs=None,
 ):
     """Create a wrapper function for the noise prediction model.
 
@@ -275,6 +275,9 @@ def model_wrapper(
         A noise prediction model that accepts the noised data and the continuous time as the inputs.
     """
 
+    model_kwargs = model_kwargs or []
+    classifier_kwargs = classifier_kwargs or []
+
     def get_model_input_time(t_continuous):
         """
         Convert the continuous-time `t_continuous` (in [epsilon, T]) to the model input time.
-- 
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