From d25219b7e889cf34bccae9cb88497708796efda2 Mon Sep 17 00:00:00 2001
From: AUTOMATIC <16777216c@gmail.com>
Date: Wed, 10 May 2023 11:55:09 +0300
Subject: manual fixes for some C408

---
 modules/models/diffusion/ddpm_edit.py     | 8 ++++----
 modules/models/diffusion/uni_pc/uni_pc.py | 4 ++--
 2 files changed, 6 insertions(+), 6 deletions(-)

(limited to 'modules/models')

diff --git a/modules/models/diffusion/ddpm_edit.py b/modules/models/diffusion/ddpm_edit.py
index af4dea15..3fb76b65 100644
--- a/modules/models/diffusion/ddpm_edit.py
+++ b/modules/models/diffusion/ddpm_edit.py
@@ -405,7 +405,7 @@ class DDPM(pl.LightningModule):
 
     @torch.no_grad()
     def log_images(self, batch, N=8, n_row=2, sample=True, return_keys=None, **kwargs):
-        log = dict()
+        log = {}
         x = self.get_input(batch, self.first_stage_key)
         N = min(x.shape[0], N)
         n_row = min(x.shape[0], n_row)
@@ -413,7 +413,7 @@ class DDPM(pl.LightningModule):
         log["inputs"] = x
 
         # get diffusion row
-        diffusion_row = list()
+        diffusion_row = []
         x_start = x[:n_row]
 
         for t in range(self.num_timesteps):
@@ -1263,7 +1263,7 @@ class LatentDiffusion(DDPM):
 
         use_ddim = False
 
-        log = dict()
+        log = {}
         z, c, x, xrec, xc = self.get_input(batch, self.first_stage_key,
                                            return_first_stage_outputs=True,
                                            force_c_encode=True,
@@ -1291,7 +1291,7 @@ class LatentDiffusion(DDPM):
 
         if plot_diffusion_rows:
             # get diffusion row
-            diffusion_row = list()
+            diffusion_row = []
             z_start = z[:n_row]
             for t in range(self.num_timesteps):
                 if t % self.log_every_t == 0 or t == self.num_timesteps - 1:
diff --git a/modules/models/diffusion/uni_pc/uni_pc.py b/modules/models/diffusion/uni_pc/uni_pc.py
index 6f8ad631..f6c49f87 100644
--- a/modules/models/diffusion/uni_pc/uni_pc.py
+++ b/modules/models/diffusion/uni_pc/uni_pc.py
@@ -344,7 +344,7 @@ def model_wrapper(
                 t_in = torch.cat([t_continuous] * 2)
                 if isinstance(condition, dict):
                     assert isinstance(unconditional_condition, dict)
-                    c_in = dict()
+                    c_in = {}
                     for k in condition:
                         if isinstance(condition[k], list):
                             c_in[k] = [torch.cat([
@@ -355,7 +355,7 @@ def model_wrapper(
                                 unconditional_condition[k],
                                 condition[k]])
                 elif isinstance(condition, list):
-                    c_in = list()
+                    c_in = []
                     assert isinstance(unconditional_condition, list)
                     for i in range(len(condition)):
                         c_in.append(torch.cat([unconditional_condition[i], condition[i]]))
-- 
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