ruff linting fixes

modules/models/diffusion/ddpm_edit.py

@@ -195,7 +195,7 @@ class DDPM(pl.LightningModule):
                     print(f"{context}: Restored training weights")
 
     def init_from_ckpt(self, path, ignore_keys=None, only_model=False):
-        ignore_keys = ignore_keys or []        
+        ignore_keys = ignore_keys or []
         sd = torch.load(path, map_location="cpu")
         if "state_dict" in list(sd.keys()):
             sd = sd["state_dict"]
@@ -1212,8 +1212,10 @@ class LatentDiffusion(DDPM):
 
             if i % log_every_t == 0 or i == timesteps - 1:
                 intermediates.append(img)
-            if callback: callback(i)
-            if img_callback: img_callback(img, i)
+            if callback:
+                callback(i)
+            if img_callback:
+                img_callback(img, i)
 
         if return_intermediates:
             return img, intermediates

modules/models/diffusion/uni_pc/sampler.py

@@ -29,10 +29,10 @@ class UniPCSampler(object):
         # first time we have all the info to get the real parameters from the ui
         # value from the hires steps slider:
         num_inference_steps = t[0] + 1
-        approx_denoise_strength = num_inference_steps / self.inflated_steps
+        num_inference_steps / self.inflated_steps
         self.denoise_steps = max(num_inference_steps, shared.opts.uni_pc_order)
 
-        init_timestep = max(self.inflated_steps - self.denoise_steps, 0)
+        max(self.inflated_steps - self.denoise_steps, 0)
 
         # actual number of steps we'll run
 

modules/models/diffusion/uni_pc/uni_pc.py

@@ -752,7 +752,7 @@ class UniPC:
         t_T = self.noise_schedule.T if t_start is None else t_start
         device = x.device
         if method == 'multistep':
-            if timesteps == None:
+            if timesteps is None:
                 timesteps = get_time_steps(self.noise_schedule, skip_type=skip_type, t_T=t_T, t_0=t_0, N=steps, device=device)
             #print(f"Running UniPC Sampling with {timesteps.shape[0]} timesteps, order {order}")
             assert steps >= order, "UniPC order must be < sampling steps"
@@ -773,7 +773,7 @@ class UniPC:
                         if self.after_update is not None:
                             self.after_update(x, model_x)
                         model_prev_list.append(model_x)
-                        t_prev_list.append(vec_t)                      
+                        t_prev_list.append(vec_t)
                         progress.update(task, advance=1, description=f"Progress {round(len(vec_t) * init_order / (time.time() - t), 2)}it/s")
                     # for step in trange(order, steps + 1):
                     for step in range(order, steps + 1):